thesis ahmad farid abu bakar.pdf
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EROSION AND SEDIMENTATION
AT
PUTRAJAYA WETLAND
AHMAD FARID ABU BAKAR
FACULTY OF SCIENCE
UNIVERSITY OF MALAYA
KUALA LUMPUR
2009
EROSION AND SEDIMENTATION
AT
PUTRAJAYA WETLAND
AHMAD FARID ABU BAKAR
THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE
SUPERVISORS:
Assoc. Prof. Dr. ISMAIL YUSOFF
Assoc. Prof. Dr. ROSLAN HASHIM
FACULTY OF SCIENCE
UNIVERSITY OF MALAYA
KUALA LUMPUR
2009
ABSTRACT
Erosion and sedimentation process study was carried out at Putrajaya
wetland area. The sheet and rill erosion was estimated using USLE approach while
the observed bank erosion throughout study area was documented entirely. The
sedimentation process (in term of sediment yield) was estimated and compared
using USLE-SDR approach, TSS rating curve technique (for suspended sediment
yield) and wetland cell reservoir sediment yield (from wetland sedimentation
survey).
The USLE gross erosion and specific erosion value show a relatively high
variability in term of spatial and temporal characteristic together with the effect of
using different grid cell size. Sensitivity analysis was performed in GIS environment
using grid regression analysis extension show that for almost all analysis year, the
LS factor is the most sensitive parameter in comparison to the other factors by
using 20m, 30m and 40m grid cell size. Throughout 2003 to 2004, moderate to
major bank erosion had been observed and documented around Putrajaya wetland
area. Bank scour and mass failure had also been observed respectively.
The catchment TSS yield (suspended sediment yield) using TSS rating
curve show a variability of TSS yield with 2004 value recorded higher average TSS
yields in comparison to 2003. The USLE-SDR catchment sediment yield estimation
using Vanoni (1975) SDR equation show a slightly lower USLE sediment yield in
comparison with USLE sediment yield result using USDA-SCS (1972) SDR
equation. The highest average annual reservoir sediment yield was estimated at
UE1 wetland cell (536 t/ha/yr) while the lowest at UW7 (9.75 t/ha/yr).
Comparative analysis between three catchment sediment yield estimation
method show that the catchment sediment yields estimation using both USLE-SDR
(Vanoni, 1975) and USLE-SDR (USDA-SCS, 1972) approach had overestimate
and underestimate the specific TSS yield and wetland reservoir sediment yield
value showing that USLE-SDR Vanoni (1975) and USDA-SCS (1972) is consider a
fair to poor sediment yield estimator. The linkages between wetland TSS yield,
ii
wetland reservoir sediment yield and USLE total gross erosion could be analyzed
in term of sediment delivery ratio (SDR). The increasing (UW, UN, UE and UB
wetland subcatchment area) and reduction (LE wetland subcatchment area) trends
of SDR value toward downstream stations was observed from the result, indicate
that there was an increasing amount of sediment transported (increasing transport
capacity) from upstream to downstream wetland at UW, UN, UE and UB wetland
subcatchment area while declining trend of SDR values in LE wetland
subcatchment area implied the effectiveness of wetland filtration processes within
LE’s wetland cells.
In term of sediment mitigation measure, the management should consider
culvert redesigned using permanent bank reinforced structure or heavy stone to
provide armor protection together with effective culvert enlargement for critical and
major bank erosion inlet. Wetland geotechnical monitoring or wetland slope and
embankment monitoring are also needed. Rehabilitation on wetland storage
capacity is needed by using the siphon dredging technique while replanting and
desilting exercise for wetland cells with dead storage (almost zero storage
capacity).
iii
ABSTRAK
Kajian hakisan dan pemendapan sedimen telah dijalankan di kawasan
tanah lembab, Putrajaya. Hakisan lembar telah dianggarkan menggunakan kaedah
USLE manakala hakisan tebing di kawasan kajian telah didokumentasikan. Proses
pemendapan sedimen (dari segi hasilan sedimen) telah dianggarkan dan
dibandingkan dengan 3 kaedah penganggaran hasilan iaitu menggunakan kaedah
USLE-SDR, teknik TSS “rating curve” (luahan sedimen terampai) dan luahan
sedimen tadahan bagi sel tanah lembap (dari survei sedimentasi tanah lembap).
Kadar hakisan keseluruhan dan hakisan spesifik yang dianggarkan
menggunakan USLE menunjukkan kepelbagaian dari segi lokasi dan tempoh
kajian, juga kesan dengan penggunaan saiz sel grid yang berbeza. Analisa
kesensitifan telah dijalankan di dalam sekitaran GIS dengan menggunakan analisa
regresi grid menunjukkan bahawa, bagi keseluruhan tahun kajian, faktor LS
didapati faktor yang paling sensitif dibandingkan faktor-faktor yang lain dengan
menggunakan saiz sel grid 20m, 30m dan 40m. Daripada tahun 2002 hingga 2006,
hakisan tebing dari skala minor hingga major telah diperhatikan dan didokumenkan
bagi keseluruhan kawasan tanah lembap, Putrajaya. Kerukan tebing dan
kegagalan jisim (mass failure) juga telah diperhatikan.
Anggaran hasilan TSS (hasilan sedimen terampai) lembangan dengan
menggunakan “TSS rating curve” menunjukkan kepelbagaian dari segi lokasi dan
tempoh, dengan hasilan TSS pada tahun 2004 adalah paling tinggi berbanding
hasilan pada tahun 2003 dan Januari hingga Mei 2006. Hasilan
sedimentlembangan dengan menggunakan persamaan USLE-SDR yang
dicadangkan oleh Vanoni (1975) adalah lebih rendah berbanding hasil sedimen
yang dianggarkan dengan menggunakan persamaan SDR yang dicadangkan oleh
USDA-SCS (1972). Purata hasilan tahunan sedimen tadahan yang tertinggi telah
dianggarkan di sel UE1 (536 t/ha/yr) manakala yang terendah di UW7 (9.75
t/ha/yr).
iv
Analisa perbandingan antara ketiga-tiga kaedah penganggaran hasilan
sedimen lembangan menunjukkan bahawa hasilan sedimen dengan menggunakan
kedua-dua persamaan USLE-SDR (Vanoni, 1975) dan USLE-SDR (USDA-SCS,
1972) telah terlebih anggar dan terkurang anggar hasilan TSS dan hasilan
sedimen tadahan menunjukkan bahawa kaedah penganggaran hasilan sedimen
dengan menggunakan kedua-dua persamaan USLE-SDR (Vanoni, 1975) dan
USLE-SDR (USDA-SCS, 1972) boleh dianggap sebagai kaedah penganggaran
yang sederhana kepada lemah. Perkaitan antara hasilan TSS, hasilan sedimen
tadahan dan keseluruhan hakisan USLE bolah dianalisakan dari segi nisbah
hantaran sedimen (sediment delivery ratio, SDR). Trend peningkatan nilai SDR di
sublembangan UW, UN, UE dan UB dan trend penurunan nilai SDR di
sublembangan LE ke arah hiliran lembagan telah diperhatikan menunjukkan di
sublembangan UW, UN, UE dan UB, terdapat peningkatan amaun sedimen yang
diangkut daripada hulu ke hiliran sublembangan tersebut dan penurunan amaun
sedimen yang diangkut di sublembangan LE.
Dari segi mitigasi sedimen, pihak pengurusan perlu mempertimbangkan
rekabentruk semula perparitan menggunakan struktur sokongan kekal atau batuan
berat bagi menghasilkan perlindungan serta pembesaran perparitan bagi kawasan
yang mengalami hakisan tebing yang utama dan kritikal. Pemantauan geoteknikal
atau pemantauan tebing dan cerun juga diperlukan. Pemulihan kapasiti simpanan
tanah lembab diperlukan menggunakan teknik “siphon dredging” dan aktiviti
pembuangan sediment dan penanaman semula diperlukan bagi kawasan sel tanah
lembap yang dipenuhi sedimen.
v
Acknowledgement
First of all, the author would like to express thanks to Allah Rabbul Jalil for
all the strength, ideas and guidance. A very much appreciation and gratitude to my
supervisor, Assoc. Prof. Dr. Ismail Yusoff, Department of Geology, University
Malaya, for his patience, guidance, help and support throughout the duration of this
research (also to his wife, Assoc. Prof. Dr. Yatimah Alias). Gratitude is also
extended to my co-supervisor, Assoc. Prof Dr. Roslan Hashim, Civil Engineering
Department, Faculty of Engineering, University Malaya for his kindly guidance and
support. Special thanks to Mr. Zorkeflee Abu Hassan (REDAC, USM) and Mr.
Omar Al Kouri (UPM) for the guidance and expose to GIS and Remote Sensing
matters.
To all Geology Department staff, especially to Prof. Dr. Azman Abdul Ghani,
Prof. Dr. Wan Hasiah Abdullah, Prof. Dr. John Kuna Raj, Prof. Dr. Lee Chai Peng,
Assoc. Prof. Dr. Samsudin Hj. Taib, Assoc. Prof. Dr. Ng Tham Fatt, Dr. Ros
Fatihah Muhammad, Mr. Khairul Azlan, Mr. Jasmi Hafiz, Mr. Aizad, Mr. Zamri, Mr.
Sahlan, Mr. Yusri, the late arwah Hj. Mokhtar, Mr. Bahaa eldin and other Geology
Department staff for all the helping hands and support throughout this research
undertaken. Special thanks for Prof. Dr. Mohd Jamil Ma’ah and Dr. Abdul Hadi for
all the advices and guidance, to IPPP for the grant provided.
Finally, the author wish to thanks all parties including staff of Perbadanan
Putrajaya (Mr Akashah, Mr. Faizal), Unit Perunding Universiti Malaya (UPUM) (Mrs
Hafiza, Mrs Maznolita, Mrs wan Zuraini and Mrs Haniza), KLCC urusharta
(Putrajaya branch, Mr. Ramzi, Mr. Shaharani, Mr. Fasron), staff of DID Jalan
Ampang Branch, MACRES (Mr. Khairul Anam), Department of Agricultural (DOA)
Malaysia (Soil Management & Conservation division, Mr. Mustafa Kamal) Alam
Sekitar Malaysia (ASMA) (Mr. Aidil, Mr. Anuar, Mr. Wan Affendi), Spatialworks
Sdn. Bhd. (Mr. Nazari, Miss Tsu Fei) and friends (Mr. Japareng, Mr. Lukman, Mr.
Lutfi, Cik Aiza, Mr. Daicus and etc.) and others that are involved directly or
indirectly in making this thesis a success.
vi
Dedication
Dedicated to My dearest wife, Juliana Safina
My lovely daughter, Nur Umairah Syahmina My Beloved Abah and Mak
And Family members
vii
CONTENTS PAGE
ABSTRAK ii
ABSTRACT iv
ACKNOWLEDGEMENTS vi
DEDICATION vii
LIST OF FIGURES xiv
LIST OF TABLES xviii
LIST OF SYMBOLS AND ABBREVIATIONS xxii
CHAPTER 1: INTRODUCTION
1.1 GENERAL INTRODUCTION 1
1.2 PROBLEM STATEMENT 4
1.3 THE OBJECTIVES 5
1.4 DESCRIPTION OF STUDY AREA
1.4.1 Location
1.4.2 Topography
1.4.3 Stream, River and Catchment Characteristics
1.4.4 Meteorology and Climate
1.4.5 Geology and Soil Formation
1.4.5.1 Geology
1.4.5.1.1 Alluvium
1.4.5.1.2 Kenny Hill Formation
1.4.5.1.3 Hawthornden Schist
1.4.5.1.4 Lineaments and Geological Structure
1.4.5.2 Soil Formation and Classification
1.4.5.2.1 The Munchong Series
1.4.5.2.2 The Serdang Series
1.4.5.2.3 The Prang Series
6
6
6
9
13
15
15
15
17
17
18
18
21
21
22
viii
1.5 PUTRAJAYA LAKE AND WETLAND SYSTEM
1.5.1 The Wetlands Component
1.5.2 The Primary lake
22
23
30
1.6 SEDIMENTATION, BASELINE CONDITION AND EXPECTED
PROBLEM
31
1.7 THESIS ORGANIZATION
32
CHAPTER 2: LITERATURE REVIEW AND THEORETICAL BACKGROUND
2.1 SOIL EROSION PROCESS
2.1.1 Energy Factors
2.1.1.1 Rainfall Erosivity
2.1.1.2 The Slope Steepness, Length and Curvature
2.1.2 Resistance Factor (Soil Erodibility)
2.1.3 Protection Factors (Plant Covers)
34
35
35
38
39
41
2.2 SEDIMENTATION PROCESS (SEDIMENT DELIVERY AND
SEDIMENT YIELD)
2.2.1 Sediment Delivery Ratio
2.2.2 Sediment Yield
41
42
45
2.3 EROSION AND SEDIMENTATION MEASUREMENT 46
2.4 EROSION AND SEDIMENTATION MODEL
2.4.1 Empirical Model
2.4.2 Conceptual Model
2.4.3 Physically Based Model
2.4.4 Grid Cell Size Effect to USLE Calculation in GIS
Environment
48
49
49
50
51
2.5 EFFECT OF EROSION AND SEDIMENTATION PROCESS TO
WETLAND FUNCTIONING
53
2.6 EROSION AND SEDIMENTATION STUDIES IN MALAYSIA 53
2.7 SUMMARY 56
ix
CHAPTER 3: MATERIALS AND METHOD
3.1 INTRODUCTION 58
3.2 SUMMARY OF RESEARCH METHODOLOGY 58
3.3 METHOD OF QUANTIFICATION AND ESTIMATION ON
WETLAND WATER DISCHARGE (m3/s), TSS
CONCENTRATION (mg/l), TSS LOADING (t/yr) and TSS YIELD
(t/ha/yr)
3.3.1 Water Discharge Quantification
3.3.1.1 Weir Method
3.3.1.2 Bucket Method
3.3.2 Total Suspended Solid Quantification
3.3.3 Generation of TSS Rating Curve and TSS Loading
Estimation
3.3.4 Sediment Yield Estimation from Total Suspended Solid
Data
62
62
62
66
66
67
69
3.4 DETERMINATION OF CATCHMENT EROSION AND
SEDIMENT YIELD (t/ha/yr) USING USLE AND SDR IN GIS
3.4.1 Rainfall-Runoff Erosivity Factor, R, Determination
3.4.2 Soil Erodibility Factor, K, Determination
3.4.3 Slope Length and Steepness Factor, LS, Determination
3.4.4 Land Use and Management Factor, CP, Determination
3.4.5 USLE Soil Loss (Erosion) rate and USLE Catchment
Sediment Yield Result
70
72
74
79
81
81
3.5 METHOD FOR DETERMINATION OF WETLAND RESERVOIR
SEDIMENT YIELD FROM SEDIMENTATION SURVEY
3.5.1 Method for Sedimentation Survey
3.5.1.1 Static Station Sedimentation Survey
3.5.1.2 Moveable Station Sedimentation Survey
3.5.2 Conversion of Sediment Volume Unit (m3) to Mass Unit
(t, tonnes)
3.5.3 Wetland Reservoir Sediment Yield Estimation
83
84
84
84
84
85
x
CHAPTER 4: RESULT AND DISCUSSION
4.1 INTRODUCTION 86
4.2 EROSION ESTIMATION USING USLE
4.2.1 Introduction
4.2.2 Soil Erodibility Factor (K Factor) Determination Result
4.2.3 Rainfall-Runoff Erosivity Factor (R Factor) Determination
Result
4.2.4 Slope Length and Steepness (LS Factor) Determination
Result
4.2.5 Land Cover and Management (CP Factor) Determination
Result
4.2.6 Results of Spatial and Temporal USLE Erosion
Calculations for Different Grid Resolution Size
4.2.7 Analysis of USLE Total Gross Erosion and Specific Erosion
at Putrajaya Wetland Catchment Area
4.2.8 Sensitivity Analysis of USLE Factors to USLE Result
86
86
86
93
98
102
104
108
111
4.3 BANK EROSION AT PUTRAJAYA WETLAND AREA
4.3.1 Introduction
4.3.2 Severity and Location of Bank Erosion Within Putrajaya
Wetland Area
4.3.3 Estimation of Bank Erosion Within Putrajaya Wetland Area
113
113
114
118
4.4 WETLAND ANNUAL TSS LOADING AND TSS YIELD
ESTIMATION FROM TSS RATING CURVE
4.4.1 Introduction
4.4.2 Upper West Wetland TSS Rating Curve
4.4.3 Upper North Wetland TSS Rating Curve
4.4.4 Upper East Wetland TSS Rating Curve
4.4.5 Lower East Wetland TSS Rating Curve
4.4.6 Upper Bisa Wetland TSS Rating Curve
4.4.7 Central Wetland TSS Rating Curve
120
120
120
123
125
127
129
129
xi
4.4.8 Annual TSS Loading and TSS Yield Estimation Based on
TSS Rating Curve
131
4.5 CATCHMENT SEDIMENT YIELD ESTIMATION FROM USLE-
SDR APPROACH
4.5.1 Introduction
4.5.2 Calculated SDR Value from Vanoni (1975) and USDA-SCS
(1979) Equation
4.5.3 Result of Catchment Sediment Yield Estimation using
USLE-SDR Approach
136
136
137
138
4.6 WETLAND RESERVOIR SEDIMENT YIELD ESTIMATION
FROM SEDIMENTATION SURVEY DATA
4.6.1 Introduction
4.6.2 Spatial and Temporal Variability of Wetland Sediment
Accumulation
4.6.3 Wetland Specific reservoir Sediment Yield
140
140
140
145
4.7 COMPARATIVE ANALYSIS BETWEEN CATCHMENT TSS
YIELD, WETLAND RESERVOIR SEDIMENT YIELD AND USLE-
SDR SEDIMENT YIELD RESULT
4.7.1 Introduction
4.7.2 Comparative Analysis for 2003 and 2004
4.7.3 Linkages Between Wetland Specific TSS Yield, Wetland
Specific Reservoir Sediment Yield and USLE Total Gross
Erosion
148
148
148
152
4.8 PROPOSED SPECIFIC SEDIMENT CONTROL MEASURE
FOR PUTRAJAYA WETLAND AREA
156
CHAPTER 5: CONCLUSION AND RECOMMENDATION 159
REFERENCES 163
xii
APPENDICES
APPENDIX 1: METHOD OF APHA 2540-D
APPENDIX 2: PARTICLE SIZE ANALYSIS RESULT & MONTHLY
RAINFALL DATA
APPENDIX 3: WETLAND WATER DISCHARGE (m3/s), TSS
CONCENTRATION (mg/l) AND SEDIMENTATION SURVEY DATA
APPENDIX 4: SATELLITE IMAGES OF PUTRAJAYA AREA FOR
YEAR 2003 (SPOT 4), 2004 (SPOT 5) AND 2006 (SPOT 4)
APPENDIX 5: REGRESSION RESULTS AND ANOVA TABLE OF
SENSITIVITY ANALYSIS (USLE PARAMETERS VERSUS
USLE RESULTS)
xiii
LIST OF FIGURES PAGE Figure 1.1 Process and type of erosion and sedimentation in a
define catchment
2
Figure 1.2 Location of Putrajaya Lake and Wetland, Putrajaya, Malaysia
7
Figure 1.3 Topographic elevation at Putrajaya catchment area 8
Figure 1.4 River system within Putrajaya catchment area
10
Figure 1.5 Extent of Putrajaya catchment and subcatchment area
11
Figure 1.6 Mean annual rainfall distribution surrounding Putrajaya area (After NAHRIM, 1999)
14
Figure 1.7 General Pattern of Long Term Monthly Rainfall Distribution (1951-1990) around Putrajaya area.
14
Figure 1.8 Lithology and geological formation around Putrajaya catchment area
16
Figure 1.9 Distribution of soil series association around Putrajaya catchment area
19
Figure 1.10 Putrajaya Lake and wetland system
24
Figure 1.11 Schematic Layout of Longitudinal Profile of the Wetland Cell of UW, UN and UE
26
Figure 1.12 Schematic Layout of Longitudinal Profile of the Wetland Cell of LE, UB and Central Wetland
27
Figure 1.13 Typical wetland cell design at Putrajaya wetland complex
28
Figure 1.14 Panoramic view of selected wetland cell at Putrajaya wetland complex
29
Figure 1.15 Panoramic view of Putrajaya lake area
30
Figure 3.1 Research flow of erosion and sedimentation process study
61
Figure 3.2 Example of 90o V-notch weir
63
Figure 3.3 Simplified sharp-crested rectangular weir, showing the parameters measured for the measurements of water discharge (modified from Chin, 2000)
64
xiv
Figure 3.4 Example of constructed wetland weir structure at Putrajaya wetland (photo taken at LE1 wetland cell)
65
Figure 3.5 Typical example of free fall water over the weir crest (picture taken at UE2 wetland cell)
65
Figure 3.6 Flow chart for application of GIS capabilities in USLE sheet erosion and catchment sediment yield assessment
71
Figure 3.7 Location of rainfall station around Putrajaya area
73
Figure 3.8 The location and distribution of soil sampling station around Putrajaya lake and wetland area
76
Figure 3.9 Classification of soil structure adopted for Parameter b evaluation in K factor determination
77
Figure 3.10 Estimated permeability value based on soil texture (Bazzofi, 2006)
78
Figure 3.11 Combine Slope Length-Steepness Factor, LS (Wischmeier & Smith, 1978)
80
Figure 4.1 Triangular plot of particle size results on soil USDA classification system
88
Figure 4.2 Histogram of calculated K factor result for Putrajaya lake and respective wetland subcatchment area
91
Figure 4.3 Soil erodibility factor maps (K factor map) for Putrajaya catchment area in 10m (a), 20m (b), 30m (c) and 40m (d) grid cell size
92
Figure 4.4 Histogram of total annual rainfall recorded within Putrajaya area for year 2003 and 2004
94
Figure 4.5 Histogram of average annual maximum 30 minute rainfall intensity (i30) within Putrajaya area for year 2003 and 2004
95
Figure 4.6 Histogram of calculated rainfall-runoff erosivity factor (R factor) within Putrajaya area for year 2003 and 2004
97
Figure 4.7 Rainfall-runoff erosivity factor (R factor) map for the Putrajaya catchment area for 2003 (a) and 2004 (b)
97
Figure 4.8 Slope Length and Steepness (LS) factor maps using 10m (a), 20m (b), 30m (c) and 40m (d) grid cell sizes for Putrajaya catchment area
99
xv
Figure 4.9 Plot of maximum LS factor value versus grid cell size
100
Figure 4.10 Plot of mean LS factor value versus grid cell size
100
Figure 4.11 Plot of Standard deviation of LS factor value versus grid cell size
101
Figure 4.12 Percentage of land use at the Putrajaya catchment area for 2003 and 2004
103
Figure 4.13 CP factor raster grid maps for 2003 (a) and 2004 (b)
103
Figure 4.14 USLE erosion maps of Putrajaya catchment area for 2003 in 10m (a), 20m (b), 30m (c) and 40m (d) grid cell sizes
105
Figure 4.15 USLE erosion maps of Putrajaya catchment area for 2004 in 10m (a), 20m (b), 30m (c) and 40m (d) grid cell sizes
106
Figure 4.16 Variation of Total gross erosion in Putrajaya catchment area with cell size for 2003 and 2004
107
Figure 4.17 Plot of R2 value from grid regression analysis of USLE factors for year 2003, 2004 and 2006 using different grid size
112
Figure 4.18 Location map of severity of bank erosion at Putrajaya Wetland Area
116
Figure 4.19 Plot of TSS rating curve in log-log axis for UW1, UW2, UW3, UW7 and UW8 sampling stations
121-122
Figure 4.20 Plot of TSS rating curve fitted on log-log axis for UN1, UN2, UN4, and UN6 sampling stations
124-125
Figure 4.21 Plot of TSS rating curve fitted on log-log axis for UE1, UE2 and UE3 station
126
Figure 4.22 Plot of TSS rating curve fitted on log-log axis for LE1 and LE2 sampling station
128
Figure 4.23 Plot of TSS rating curve fitted on log-log axis for UB1 and UB2 sampling station
130
Figure 4.24 Plot of TSS rating curve fitted on log-log axis for CW sampling station
131
xvi
Figure 4.25 Catchment specific TSS yields (t/ha/yr) for 2003 and 2004
134
Figure 4.26 Sediment accumulations from 1998 to 2001 (2001 sedimentation survey), 2001 to 2002 (2002 sedimentation survey) and 2002 to 2004 (2004 sedimentation survey) in volume (a) and weight (b)
142
Figure 4.27 Wetland annual sedimentation rate (in volume, m3/yr and weight, tonnes/yr) from 1998 to 2004
143
Figure 4.28 Spatial variability of wetland specific reservoir sediment yield for 2001, 2002 and 2004
147
Figure 4.29 Average annual wetland specific reservoir sediment yield for selected wetland cells
147
Figure 4.30 Trends of SDR values for UW subcatchment wetland (a), UN subcatchment wetland (b), UE subcatchment wetland (c), LE subcatchment wetland (d) and UB subcatchment wetland (e)
154
Figure 4.31 Location of Proposed Sediment Mitigation Measure around Putrajaya Wetland Area
158
xvii
LIST OF TABLES
PAGE
Table 1.1 Distribution and area with respect to topographic elevation at Putrajaya area
6
Table 1.2 Putrajaya subcatchment area, landowners and their current landuse
12
Table 1.3 Characteristics of soil within study area
20
Table 1.4 Design characteristic of Putrajaya wetland system
25
Table 2.1 Classification of Rainfall Intensity
36
Table 2.2 Soil loss from various slope segments caused by runoff
39
Table 2.3 Erodibility of five common Malaysian soil series
40
Table 2.4 Erosion and sediment transport models (modified from Merritt, 2002)
52
Table 3.1 Summary of parameter considered in erosion and sedimentation process
60
Table 3.2 Parameter and analysis undertaken for the estimation of wetland TSS yield using TSS rating curve method
63
Table 3.3 Correction factor, K [b,T ]
68
Table 3.4 Daily rainfall and i30 rainfall intensity per rainfall event data gathered from rainfall station around Putrajaya area.
74
Table 3.5
Classification of permeability value adopted in K factor determination
77
Table 3.6 CP factor value as given by Roslan and Tew (1996) 82
Table 3.7 Summary of the data source and material, data generation process and the scenario undertaken for land use analysis
82
Table 4.1 Particle size analysis result and USDA soil classification system for the sample collected around Putrajaya catchment area
87
Table 4.2 Calculated soil erodibility factor result for 42 samples collected within study area
90
xviii
Table 4.3 Statistic of K factor result calculated for each Putrajaya subcatchment area
91
Table 4.4 Total annual rainfall recorded at selected rainfall station located within Putrajaya area for 2003 and 2004
94
Table 4.5 Average annual maximum 30 minute rainfall intensity (i30) recorded at selected rainfall station located within Putrajaya area for 2003 and 2004
95
Table 4.6 Calculated rainfall-runoff erosivity factor (R factor) for 2003 and 2004 at selected station within Putrajaya catchment area
96
Table 4.7 Statistical characteristic of the LS factor maps for Putrajaya area
100
Table 4.8 Statistics of potential erosion map calculated for 2003 and 2004 using different grid cell size
107
Table 4.9 Result of USLE total gross erosion (t/yr) estimated for selected wetland subcatchment areas in Putrajaya Wetland
109
Table 4.10 Result of USLE specific erosion (t/ha/yr) estimated for selected wetland subcatchment areas in Putrajaya Wetland
110
Table 4.11 Sensitivity analysis for USLE factors to USLE erosion results
112
Table 4.12 Summary of bank erosion at the Putrajaya wetland area
115
Table 4.13 Historical photo of bank erosion for selected wetland cell from year 2003 to 2004
117
Table 4.14 Estimated volumes of bank erosion based measurement of scour length (m), width (m) and depth (m)
119
Table 4.15 Regression coefficients of TSS rating curves fitted for selected sampling stations
122
Table 4.16 Regression coefficients value of TSS rating curves fitted for selected sampling stations
125
Table 4.17 Regression coefficients of rating curves fitted for selected sampling stations at UE subcatchment
127
Table 4.18 Regression coefficients of rating curves fitted for selected sampling stations at LE subcatchment
128
Table 4.19 Regression coefficients of rating curves fitted for selected sampling stations at UB subcatchment
130
xix
Table 4.20 Regression coefficients of rating curves fitted for selected sampling stations at CW subcatchment
131
Table 4.21 TSS loading (t/yr) and specific catchment TSS yield (t/ha/yr) for selected sampling stations at Putrajaya wetland
132
Table 4.22 Actual / corrected TSS yields (after multiplication with correction factor, K [b,T])
133
Table 4.23 Calculated SDR value from vanoni (1975) and USDA-SCS (1972) equation
137
Table 4.24 Gross sediment yields determined using Vanoni (1975) SDR equation
139
Table 4.25 Gross sediment yields determined using USDA-SCS (1972) SDR equation
139
Table 4.26 Sediment accumulations (in volume, m3, and weight, tonnes) and annual sedimentation rates for selected Putrajaya wetland cells from 1998 to 2004
141
Table 4.27 Wetland specific reservoir sediment yields for 2001, 2002 and 2004
146
Table 4.28 Comparison between wetland specific TSS yield, wetland specific reservoir sediment yield and USLE-SDR specific sediment yield for 2004 (accumulation of year 2003 and 2004 value)
149
Table 4.29 SDR values for Putrajaya wetland subcatchment areas
153
xx
xxi
LIST OF SYMBOLS AND ABBREVIATIONS
A Soil Loss
C Crop and Management Factor
GIS Geographical Information System
ha Hectare
K Soil erodibility
kg Kilogram
l Litre
LS Slope length and steepness factor
mg Miligram
mm Milimetre
i30 30 minute maximum rainfall intensity
OM Organic Matter Content
P Conservation Practise Factor
R Rainfall Erosivity
SDR Sediment Delivery Ratio
Sg. Sungai (River)
t Tonnes
TOC Total Organic Carbon
TSS Total Suspended Solid
USLE Universal Soil Loss Equation
yr Year
Introduction
1.1 General Introduction
Erosion and sedimentation problems are becoming major threats and
hazards for the lifespan of man-made surface water reservoirs as well as for the
natural water bodies. In Malaysia, due to rapid urbanization and agricultural
necessity, land clearing activities and human intervention to natural ecosystem
are unavoidable. These land clearing activities may accelerate erosional
processes and thus introduce water derived sediment to adjacent water bodies
and may affect water quality subsequently. The fact that soil erosion and
sedimentation continue to be an environmental problem of significant
proportions in the country suggests that additional and more definitive
guidelines, and more stringent monitoring and enforcement are required. In
addition, proper mitigation measures need to be in place and maintained from
time to time.
Erosion and sedimentation are linked to each other and embody the
processes of erosion, transportation and deposition mechanism of sediments
(Julien, 1995; Foster et al., 1995). It reflects the circulation of sediment, from
eroded material, transportation along their pathway to depositional processes
downstream. As the erosion and sedimentation are linked to each other, various
factor contribute to such processes need to be considered and understood
mainly to minimize the on-site and off-site effect of erosion and sedimentation
and thus, enhance catchment management effectiveness respectively.
In general, soil erosion is a two-phase process consisting of the
detachment of individual particles from the soil mass and their transport by
erosive agents such as running water and wind. When sufficient energy is no
1
Introduction
longer available to transport the particles, deposition occurs (Morgan, 1986).
Because water acts as a transport mechanism of sediment, the hydrological
process (such as water discharge trends) of the particular catchment had to be
identified and understood mainly to determine the extent and characterizations
of sediment deposition. Figure 1.1 shows the process and type of erosion and
sedimentation that may occur in a define catchment.
Figure 1.1: Process and type of erosion and sedimentation in a define catchment.
Source: (http://muextension.missouri.edu/explore/images/g01509art01.jpg)
The effect of erosion can be derived both at on-site as well as off-site
effects (Lal, 1981; Dregne, 1992). At on-site, the implications of soil erosion
extend beyond the removal of valuable topsoil that directly affect the loss of
natural nutrients, the soil quality, structure, stability and texture. The breakdown
of aggregates and the removal of smaller particles or entire layers of soil or
organic matter can weaken the structure and even change the texture. Textural
changes can in turn affect the water-holding capacity of the soil, making it more
2
Introduction
susceptible to extreme conditions. Off-site effects of soil erosion may cause a
reduction in soil productivity (NSE-SPRPC, 1981) and in term of sedimentation
problems, disruption of water supply and the damage of freshwater resources
may be significant (Murtedza and Chuan, 1993).
Sediments which reach streams or watercourses can accelerate bank
erosion, clog drainage ditches and stream channels, reduce the depth and
capacity of the channels and silt reservoirs. This may cause hydrological
deterioration and can lead to severe flooding. Sedimentation of lakes and
reservoirs reduces their capacity, value, and life expectancy (Frederick et al.,
1991). Furthermore, pesticides and fertilizers, frequently transported along with
the eroding soil can contaminate or pollute downstream water sources and
recreational areas and reduce downstream water quality (Cook et al., 1994).
The prevention of soil erosion, which means reducing the rate of soil
erosion and sediment yield to approximately that which would occur under
natural conditions, relies on selecting appropriate strategies for soil
conservation (Morgan, 1979). Although it is impossible to stop soil erosion
completely under natural conditions, there is a great need to control erosion for
proper land and water use planning. This requires awareness of sediment yield
and foreseeing changes such as in land use. Controlling soil erosion keeps
streams, wetlands, and lakes from filling rapidly with sediment. Reservoir
capacities are thus maintained for recreation, flood control, and irrigation.
3
Introduction
1.2 Problem Statement
Sedimentation at Putrajaya wetland is become more problematic through
out the years. According to sediment monitoring reports (Ismail et al., 2003,
2004, 2005 and 2006) the effects of sedimentation can be clearly observed at
effected wetland cells as loss of wetland cell storage volume with high recorded
total suspended sediment loading during rainfall events. Since Putrajaya is now
still under development, there are significant changes of land use and land
being cleared for construction purposes and thus, may contribute to soil loss
and produce water derived sediments particularly during monsoonal rainfall
(high rainfall period).
The problem occurs when the sediments enter the wetland can
eventually destroy habitat and fill up water bodies, thus minimizing the
performance and life span (Whigham et al., 1988) of the wetland. The most
severe impact occurs when wetlands get filled up with so much sediment that
they lost storage capacity and fail to perform most wetland designated functions
(Luo et al., 1997).
Therefore, the measurement of potential catchment erosional rates and
sediment yields as well as wetland sedimentation rates are needed to
understand the relationship between such parameters affecting the erosion and
sedimentation processes spatially and temporally. The appropriate control
measures to minimize sediment yield to the wetland could then be proposed
accordingly to minimize the high cost of dredging and desilting activities.
4
Introduction
1.3 The Objectives
The specific objectives of this research project are:
• To identify, investigate and estimate potential high risk erosional area
and the amount of soil loss within the Putrajaya catchment area.
• To estimate the TSS yield for selected wetland cells at Putrajaya wetland
area using the TSS rating curve method and spatially and temporally
analyze the variability of the estimated the TSS yield.
• To spatially and temporally analyze the catchment sediment yield
estimated using the USLE and SDR approach.
• To spatially and temporally analyze wetland reservoir sediment yield
generated from the sedimentation survey data.
• To compare, evaluate and discuss sediment yield values calculated from
the total suspended sediment data, USLE modeling and sedimentation
survey data.
• To analyze the linkages between the total gross erosion calculated from
USLE, TSS yield derived from the TSS rating curve and the wetland
reservoir sediment yield gathered from wetland sedimentation survey in
terms of the SDR value.
• To propose suitable and appropriate mitigation measures to control
erosion and sedimentation at Putrajaya wetland.
5
Introduction
6
1.4 Description of Study Area
1.4.1 Location
The Putrajaya Wetland and lake system is located between latitudes 2
53’ 30” N to 2 58” 30”N and longitudes 101 40’ 30”E to 101 43’ 30”E. Situated
approximately 35 km south of Kuala Lumpur, the complex is separated into two
components of distinct water bodies – the Wetlands and the Primary Lake
(Figure 1.2). These two water-bodies were artificially constructed and are
closely linked; however they are different in their geographical or
geomorphological locations, their hydrological functions and they are governed
by distinctly different water-sediment transport systems.
1.4.2 Topography
Generally, 95% of the study area is below 80m above sea level.
However, there were several undulating hilly terrains which above 100m in
height surrounding the Putrajaya area. Table 1.1 and Figure 1.3 indicate the
distribution and area with respect to topographic elevations at Putrajaya area.
Generally, the existing terrain is undulating with levels that vary from 80m to
152m. Steep upland is found at to the upper northwest, east and hills in
northeast, west and central areas.
Table 1.1: Distribution and area with respect to topographic elevation at
Putrajaya area. Topographic classification Area (ha) Percentage Area with elevation < 20m 3432 39.0 Area with elevation >20m and <80m 4928 56.0 Area with elevation >80m and <100m 352 4.0 Area with elevation >100m 88 1.0 Total 8,800 100.0 Source: (UPM,1995)
Introduction
7
Figure 1.2: Location of Putrajaya Lake and Wetland, Putrajaya, Malaysia.
Introduction
Figure 1.3: Topographic elevation at Putrajaya catchment area.
8
Introduction
1.4.3 Stream, River and Catchment Characteristics
A stream is any body of flowing water confined within a channel,
regardless of size whereas the region from which a stream draws water is
define as its drainage basin (Montgomery, 1989). The drainage system was
formed by Sg. Chuau (14.8 km length) and its tributaries form the main drainage
system for a catchment of about 53.7 km2 in the study area with its complex
topographic and geological features. The water from Sg. Chuau flows from
north to south across the Putrajaya area. Sg. Bisa, Sg. kuyoh and Sg. Limau
Manis are the main tributaries of Sg. Chuau. Figure 1.4 shows the river
network around the Putrajaya area.
According to Strahler (1957) river classification, Sg. Chuau is classified
as a 4th order river while Sg. Bisa and Sg. Limau Manis are classified as 3rd
order river systems. It means that Sg. Chuau is a confluence of two 3rd order
rivers which are the Sg. Bisa and Sg. Limau Manis. Sg. Chuau is then joined to
Sg. Langat at the southern in the outer part of study area. The total catchment
area of Putrajaya is 53.7 km2, extending about 12 kilometres from north to south
and about 4.5km from east to west. The catchment is then divided into the
Upper North (UN), Upper West (UW), Upper East (UE), Lower East (LE), Upper
Bisa (UB), Central Wetland (CW) and Lake sub-catchments respectively.
Figure 1.5 show the extent of the Putrajaya catchment and subcatchment areas
while Table 1.2 show the Putrajaya sub-catchment area, landowners and their
current land use.
9
Introduction
Figure 1.4: River system within Putrajaya catchment area.
10
Introduction
UW
UN
UE
LE
UB
CW
Figure 1.5: Extent of Putrajaya catchment and subcatchment area.
(Note: CW = Central Wetland, UW = Upper West, UN = Upper North, UE = Upper East, LE = Lower East and UB = Upper Bisa)
11
Introduction
Table 1.2: Putrajaya subcatchment area, landowners and their current landuse.
Sub-catchment Area, km2 % of Area Landowners Current Landuse
Upper North (UN) River catchment: Sg. Chuau
12.4 23.1 UPM Mardi PPJ IOI
Agriculture, Institutional, Residential, Parks, Golf course, Commercial
Upper West (UW) River catchment: Sg. Kuyoh
6.2 11.5 Mardi, PPJ, TNB
Agriculture, Power Station, Parks,
Upper East (UE)
4.2 7.8 PPJ, Uniten, West Country
Parks, Government, Institutional, Commercial, Golf course
Lower East (LE)
1.7 3.2 PPJ Residential, Government
Central River catchment: Sg. Chuau
7.1 13.2 PPJ Residential, Parks
Upper Bisa (UB) River catchment: Sg. Bisa
5.9 11 PPJ Parks, Government, Commercial
Lower Sg. Chuau river catchment
14.7 27.4 PPJ, Cyberjaya
Residential, Commercial, Government
Total Sg. Chuau 52.2 97.2 Limau Manis 1.5 2.8 PPJ Residential,
Government Total Lake 53.7 100
Source: (NAHRIM, 1999)
12
Introduction
1.4.4 Metereology and Climate
The climate of the study area is tropical with relatively high temperature and
rainfall and governed by the moist monsoonal equatorial air-streams equatorial
(Af type) according to the Koppen-Geiger system of climate classification
(Strahler, 1960). The average temperature recorded was 27°C with an average
2500mm of rainfall. It has no distinct dry season and consists of two wetter
periods followed by two less wet ones (Wong, 1966). Four distinct monsoonal
“seasons” can be discerned;
1. Northeast monsoon (December to March)
2. Inter-monsoon (April to May)
3. Southwest monsoon (June to October)
4. Inter-monsoon (October to November)
Storms and high rainfall are always found during the inter-monsoon and
northeast-monsoon while relatively dry spells happen primarily during the
southwest monsoon. However, due to the “Sumatra phenomenon”, several rain-
storms can occur during the early part of the day during the southwest
monsoon. The average humidity is relatively high at about 70% during the day
and 90% during the night time. The average wind velocity recorded was below
2 m/s. Figure 1.6 shows mean annual rainfall distribution through out study
area while Figure 1.7 shows general pattern of long term monthly rainfall
distribution.
13
Introduction
Figure 1.6: Mean annual rainfall distribution surrounding Putrajaya area. Source: (NAHRIM, 1999)
Mean Monthly Rainfall
0.0
50.0
100.0
150.0
200.0
250.0
300.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
mm
Prang Besar (1981-2006)
Galloway
Ldg West CountryStor JPS Kajang (1975-1990)
Ldg Sedgeley (1930-2000)
Figure 1.7: General pattern of long term monthly rainfall distribution around Putrajaya area.
14
Introduction
15
1.4.5 Geology and Soil Formation 1.4.5.1 Geology
There are three geological formations that can be found in Putrajaya
catchment area. Figure 1.8 shows the general geology of the area. The
Quaternary river alluvium named the Simpang Formation is the youngest
deposit and it overlies the older Carboniferous-Permian Kenny Hill Formation
and the Silurian Hawthornden Schist in study area.
1.4.5.1.1 Alluvium
The alluvium is found in the flat and low-lying area in the central and
southern part of the catchment. The Simpang Formation of sand and gravel
layer is the target aquifer of the present study. This river alluvium, Quaternary
deposits consist of the uncemented layers of fine gravel, sand, silt and peat that
overlie the metasedimentary bedrock (UPM, 1995). The thickness of this
formation varies from 3 to 12m regionally.
Generally, the alluvium of the Sg. Chuau catchment area is thicker than
that the Bisa catchment area, whereas the drilling bits penetrate almost 9 m
without reaching any bedrock, while at the Bisa catchment area, the maximum
penetration is only 5m respectively. Sand and gravel layers are thicker closer to
the river, particularly along the flood plains and near the lower reaches of Sg.
Chuau.
Introduction
Figure 1.8: Lithology and geological formation around Putrajaya catchment area.
16
Introduction
1.4.5.1.2 Kenny Hill Formation
The Carboniferous to Permian Kenny Hill Formation (Lee et al., 2004) is
found in the west and northwest of Putrajaya area and consists of a
monotonous sequence of interbedded phyllitic shale, mudstone and thick -
bedded fine to medium grained sandstone (Lee et al., 2004) that has undergone
some degree of regional metamorphism. This formation can be identified from
the layers of clastic meta-sedimentary rocks, meta-argilite and meta-arenite.
The fine grained rocks had been metamorphosed to phyllite and schist (Choy,
1973).
Generally, the layers of meta-arenite are thicker than meta-argilite. The
thickness of meta-arenite is more than 0.5m and less than 10cm for meta-
argilite. The general strike of the bedding for the meta-sedimentary rock is
northeast-southwest with dip of 30°-50° and parallel with the lineaments in study
area (Zaiton and Tjia, 1984). Tjia (1980) suggested that recumbent folds in
rocks of Kenny Hill Formation indicate a sense of tectonic transport to the west.
Choy (1973) and Tan and Yeap (1977) proposed that the probable paleo-
environment deposition site for Kenny Hill formation is at the outer portion of
delta, probably on the upper portion of submarine slope.
1.4.5.1.3 Hawthornden Schist
The Ordovician - Lower Silurian Hawthornden Schist (Gobbett, 1965)
occupies about 70% of the area and is the oldest rock unit outcropping in the
study area. The formation is also known as the Kuala Lumpur Schist and can be
found in the east and northeast of Putrajaya. The lower boundary of the
Hawthornden Schist is gradational with Dinding schist while the upper boundary
17
Introduction
18
is conformable with the Kuala Lumpur Limestone (Lee et al., 2004). This
formation is made up of low grade metamorphosed rocks (Hamzah et al., 1986)
consisting of moderate to fine-grained quartz-mica-schist, quartz schist,
graphitic schist and phyllite. The soil formed by weathering of this unit is more
than 20m in thickness.
1.4.5.1.4 Lineaments and Geological Structure
A study published by the Geological Survey of Malaysia in 1994 show
that the rocks in and around the Putrajaya catchment are regionally folded
along a NE-SW axis resulting in the development of broad anticlines and
synclines. There are three sets of lineaments (major fractures and joints)
trending NNE-SSW, NW-SE and NE-SW have been recognised in study area.
1.4.5.2 Soil Formation and Classification
The climate has affected the distribution of soil formation greatly in the
state of Selangor (Wong, 1966) and Malaysia. The effect of high temperature,
intense rainfall and high humidity throughout the year has resulted in intense
weathering of the rocks. Nevertheless, the parent rock material has also strong
influence on the composition of the soil. In the study area, the soils were
classified as Munchong, Serdang and Prang series respectively. Figure 1.9
show the distribution of soils in the Putrajaya area while Table 1.3 summarized
the characteristic of soils found in study area.
Introduction
Figure 1.9: Distribution of soil series association around Putrajaya catchment area.
19
Introduction
20
Table 1.3: Characteristics of soil series within study area.
Characteristic Soil series
Soil Profile Photo Colour Texture Structure Parent Material
Munchong Series
Yellowish brown to strong brown
Fine sandy clay to heavy clay texture
Granular to fine subangular blocky structures
quartz-mica schists intermixed with phyllites and quartz vein
Serdang Series
Dark greyish brown
Fine sandy clay to fine sandy clay loam
moderately fine subangular blocky to medium crumb
quartzite, sandstone or conglomerate
Prang Series
Dark brown to dark red
clay loam to clay with clay content often over 65%
weak to medium weak to fine subangular blocky and consistence very friable
Schist
Source: (Jabatan Pertanian Malaysia, 1993)
Introduction
1.4.5.2.1 The Munchong Series
The Munchong Series is most likely found in areas overlying
metamorphosed sediments such as quartz-mica schists intermixed with
phyllites and quartz vein. They also have been observed on steeper terrain over
the contact between the underlying schist and intrusive granite (Wong, 1966).
The soil can be recognized by its fine sandy clay to heavy clay texture, granular
to fine subangular blocky structures and yellowish brown to strong brown
colour.
The Munchong Series typically occurs on undulating, rolling to hilly
terrain at elevations of less than 200 m. The soil drainages are generally well to
excessively drained with a rapid permeability (Param, 2000). The Munchong
Series is classified as a member of the very fine, kaolinitic, isohyperthermic,
red-yellow family of Tipik Tempalemoks according to the Malaysian Soil
Taxonomy-Second Approximation, (Param, 1998) and as Haplic Hapludox (Soil
Survey Staff, 1998).
1.4.5.2.2 The Serdang Series
The Serdang Series is characterized by the presence of quartz gravels
and angular pebbles in the subsoil with the dark greyish brown fine sandy clay
to fine sandy clay. It is loamy in texture with a moderately fine subangular
blocky to medium crumb structure and was classified as a member of the fine
loamy, siliceous, isohyperthermic, red-yellow family of Tipik Lutualemkuts
according to the Malaysian Soil Taxonomy-Second Approximation (Param,
1998) and Typic Kandiudults (Soil Survey Staff, 1998).
21
Introduction
The Serdang Series is developed on undulating hilly terrain over mixed
sedimentary rocks such as quartzite, sandstone or conglomerate parent
material. In terms of drainage and permeability, the soil is well drained to over
100 cm depth with good permeability.
1.4.5.2.3 The Prang Series
The Prang Series is developed over amphibolite and schist parent
material and is typically dark brown to dark red in colour. Textures are clay loam
to clay with clay content often over 65%. Structures are weak to medium weak
to fine subangular blocky and consistence is very friable (Param, 2000). The
Prang Series occur on undulating to rolling terrain with excessive drainage
features and good permeability.
The Series is classified as a member of very fine, oxidic,
isohyperthermic, red family of Tipik Akrolemoks according to the Malaysian Soil
Taxonomy-Second Approximation, (Param, 1998) and Typic Hapludox (Soil
Survey Staff, 1998) as the soils have a deep oxic horizon, heavy clay textures,
weak structures and a low (<1.5 cmol (+) kg-1 ) clay cation retention capacity.
1.5 Putrajaya Lake and Wetland System
The Putrajaya Lake and Wetlands complex is the main and biggest
component of Putrajaya, the new Malaysian Government Administrative Centre
and the first constructed wetlands in Malaysia. The Wetland Park is managed
by Putrajaya Corporation, and was opened to the public in October 1999. These
wetlands are believed to be the world’s largest fully constructed freshwater
22
Introduction
23
wetlands using cutting edge technology and have been designed to enhance
the water quality and play a role in wetland education and eco tourism in the
country.
The Putrajaya man-made lake and wetland is formed by constructing
wetland cells following the terrain of Sg. Chuau and Sg. Bisa with a dam at
downstream to create the impoundment. The overall water body comprises of
multi-celled wetlands and the primary lake. The multi-celled wetland comprises
of the Upper West, Upper North, Upper East, Lower East, Upper Bisa and
Central wetlands. The Putrajaya Lake is the water body the from Central
wetland weir to the Putrajaya dam as shown in Figure 1.10.
1.5.1 The Wetlands Component
The wetlands component is a network of cellular and segmented water-
bodies. The network comprises of five wetland arms; these are the Upper West
arm (UW), the Upper North arm (UN), the Upper East arm (UE), the Lower East
arm (LE) and the Upper Bisa wetland arm (UB). They are mainly located in the
northern upstream region of Putrajaya (with the exception of UB) and are
directly connected at their upstream end to the natural streams and rivers that
flow into the Putrajaya district. These wetlands receive water and sediment
mainly from the natural streams and rivers, the wetlands drainage network and
rainfall; however, slope incursions of water and sediment, especially near areas
undergoing active construction work, are known to occur during periods of
heavy rainfall (stormwater flows).
Introduction
Putrajaya Dam
Central Wetland Weir
Figure 1.10: Putrajaya Lake and wetland system.
(Note: CW = Central Wetland, UW = Upper West, UN = Upper North, UE = Upper East, LE = Lower East and UB = Upper Bisa)
24
Introduction
The Putrajaya wetland had been constructed with the primary objective
of catchment runoff treatments before it drains into the Putrajaya Lake to ensure
the lake’s water quality meets the standard required for body contact recreation.
Generally, the Putrajaya wetland had been designed using multi-cell and multi-
stage approach for better hydraulic performance and retention of pollutant that
involves a total area of 197 hectares and 12.3 million wetland plants
strategically located to act as buffer for the Putrajaya lake. Figure 1.11 and
Figure 1.12 shows the schematic layout of the longitudinal profiles of the
wetland cells respectively while Table 1.4 show the design characteristics of the
Putrajaya wetland system.
Table 1.4: Design characteristics of Putrajaya wetland system.
Wetland System Upper West
Upper North
Upper East
Lower East
Upper Bisa
Central Wetland
Catchment Area (km2)
5.53 11.54 3.34 1.73 4.03 24.7
Wetland Area (ha) 38.5 54.1 15.8 14.3 23.6 50.9
Normal Pool Area (ha)
27.0 38.3 10.8 9.5 20.6 48.3
Normal Pool Volume (ML)
230 310 130 150 430 1200
Design Inflow Rate (ML/d)
18.8 37.6 11.4 5.9 13.7 79.5
Mean Residence Time (d)
12.2 8.2 11.4 25.4 31.4 15.1
Hydraulic Loading Rate (cm/d)
7.3 11.1 8.9 6.2 6.7 15.1
Source: (Perbadanan Putrajaya and Putrajaya Holdings Sdn Bhd, 1999)
25
Introduction
Figure 1.11: Schematic Layout of Longitudinal Profile of the Wetland Cell of UW, UN and UE.
Source: (Perbadanan Putrajaya and Putrajaya Holdings Sdn Bhd, 1999)
26
Introduction
Figure 1.12: Schematic Layout of Longitudinal Profile of the Wetland Cell
of LE, UB and Central Wetland Source: (Perbadanan Putrajaya and Putrajaya Holdings Sdn Bhd, 1999)
27
Introduction
28
Each wetland arm is segmented into small wetland cells along the
downstream direction. These cells are connected to one another by drains with
overflow weirs. The cells were designed to operate as water-retention and
water-filtration ponds, filtering and improving the quality of water that flows
through them and downstream into the primary lake. The typical wetland design
is shown in Figure 1.13 while Figure 1.14 shows the panoramic view of
selected wetland cells at the Putrajaya wetland complex.
Water Flow
Figure 1.13: Typical wetland cell design at Putrajaya wetland complex. Source: (Perbadanan Putrajaya and Putrajaya Holdings Sdn Bhd, 1999)
Introduction
(a) (b)
(c) (d)
29 Figure 1.14: Panoramic view of UN2 (a), UW2 (b), LE1 (c) and UB1 (d) wetland cell at the Putrajaya wetland
complex.
Introduction
1.5.2 The Primary Lake
The lake is an open water-body complex designed to become the
aesthetic centre of Putrajaya. This open lake borders and is in contact with the
city offices and residential areas. Like other open lake complexes, the primary
lake is a recipient of inputs of water and sediment from fluvial (& drains),
colluvial (lake banks flows), organic matter, biogenic silica, lake bank erosion
and rain and airborne dust. The catchment-derived clastic materials which
enter the lake via the wetlands, rivers and drains are expected to be the main
contributors of clastic inputs. By design, two water-sediment delivery systems
becomes the main transportation route of catchment-derived water and
sediment into the Lake – the Wetlands system and the Putrajaya Drainage
system. Figure 1.15 shows the panoramic view of Putrajaya lake area.
(b) (a) Figure 1.15: Panoramic view of Putrajaya lake area nearby Perdana bridge
(a) and view of Putrajaya Main Dam and Putrajaya Convention Centre (b)
30
Introduction
1.6 Sedimentation, Baseline Conditions and Expected Problems
The natural rate of erosion can be accelerated by any human activity that
increases the rate of water movement or decreases the physical stability of
stream beds and shorelines. Obstructions placed in streams-boat docks, piers,
and dams-speed up water flowing around them and increase the energy that
can erode the stream bed or bank. Removing trees, shrubs, and rocks from
stream banks and shorelines and tilling fields or gardens close to the water's
edge can also increase the risk of erosion.
The potential for sedimentation to degrade wetlands is great and the
most obvious research need is to evaluate land-use practices that reduce
surface runoff and erosion of valuable topsoil. It is very important to identify and
map the locations of soil loss and sediment sources around the wetland and
lake, to choose an effective solution for the problems.
The soil erosion rate in the Putrajaya area is dependent on various
factors such as soil cover, slope gradient and etc. The Environmetal Impact
Assessment report written by a team from University Pertanian Malaysia (UPM),
had estimated that the erosional rate around the Putrajaya catchment, during
the construction phase without any treatment and mitigation measures can be
as high as 2476 t/ha/year while the erosional rate for construction activity with
good measures ranges between 13 to 77 t/ha/year (UPM, 1995).
31
Introduction
1.7 Thesis Organization
The work presented in this thesis consists of Five (5) chapters. Chapter 1
introduces on general information about soil erosion and sedimentation, its on-
site and off-site problems and the important of research in this problem
accordingly. Objectives of this study and the description of study area are also
part of this chapter.
Chapter 2 reviews the literature regarding erosion and sedimentation
process, their measurement and estimation procedures. Background
information about predicting soil erosion and sediment yield, using GIS as a
platform for soil erosion and sediment yield prediction and review on other
models for soil erosion and sediment yield analysis is discussed.
Chapter 3 explains the methodology of research that describes how the
methodology of field sampling and laboratory analyse for determination of
Universal Soil Loss Equation (USLE) parameters and catchment sediment yield
(t/ha/yr), water discharge (m3/s), TSS concentration (mg/l) and TSS yield
(t/ha/yr) estimation and sedimentation survey and reservoir sediment yield
(t/ha/yr) undertaken. The methodology on GIS analysis for soil erosion and
sediment yield using USLE is also described.
Chapter 4 present results and discussion on sheet erosion estimation
using USLE and bank erosion documentation observed within the Putrajaya
wetland area. The results of spatial and temporal TSS yield (suspended
sediment yield) using the TSS rating curve method, the results on wetland
sedimentation rates and wetland reservoir sediment yields estimated from
32
Introduction
33
wetland sedimentation survey data and catchment sediment yield estimations
using USLE and sediment delivery ratio (SDR) method will be discussed and
compared accordingly. Finally, the comparison and linkages for the results
gathered from the analyse above will be presented. Based on the findings,
applicable and suitable sediment mitigation measures are proposed.
Chapter 5 concludes on analyse and interpretation of data undertaken
and further recommendations on mitigation measures.
Literature Review and Theoretical Background
2.1 Soil Erosion Process
Erosion is a natural process by which soil and rock material is loosened
and removed. Natural erosion occurs primarily on a geologic time scale, but
when man’s activities alter the landscape, the erosion process can be greatly
accelerated and these increases are critical environmental problems in many
parts of the world (Walling, 1997). Accelerated soil erosion is either seen as the
result of logging activities, the introduction of rubber plantations, tin mining
activities or deforestation associated with land conversion for agricultural,
industrial or urbanization purposes (Brooks et al., 1993; Wan Ruslan Ismail,
1996).
Nevertheless, water erosion can be a consequence of degradation of the
soil structure, especially the functional attributes of soil pores to transmit and
retain water, and to facilitate root growth. Climate, soil and topographic
characteristics determine runoff and erosion potential from agricultural and
disturbed lands. The main factor causing soil erosion can be divided into three
groups;
• Energy factors: rainfall erosivity, runoff volume, slope steepness, slope length.
• Protection factors: population density, plant cover, and land management.
• Resistance factors: soil erodibility, infiltration capacity and soil management.
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Literature Review and Theoretical Background
2.1.1 Energy Factors
The energy available for erosion may form from potential and kinetic
energy. When a drop of rain falls on the soil surface, its momentum (potential
energy) is converted into kinetic energy (KE, the energy of motion) that is
related to the mass and the velocity (v) of the eroding agent (Morgan, 1986).
2.1.1.1 Rainfall Erosivity
The characteristics of rainfall intensity, duration and total rainfall should
be considered to facilitate the erosivity factors due to rainfall. Furthermore, the
size, velocity and shape of the rain drop and the kinetic energy of the rain may
have a very important influence on erosion. Morgan (1986) presumed that the
most suitable expression of the erosivity of rainfall as an index based on the
kinetic energy of the rain which is a function of its intensity and duration, the
mass, diameter and velocity of the raindrops.
The strike of rain drop on top of the soil surface supply the energy for the
soil detachment in form of kinetic energy (KE). Data from the United States
show that rainfall energy alone is not a good indicator of erosive potential
(Wischemier and Smith, 1978). Thus, both intensity and duration must be taken
into consideration to analyze the effect of erosion by rainfall. The rainfall
intensity, duration and total rainfall then contribute on the resultant runoff.
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Literature Review and Theoretical Background
Basically, Rainfall intensity, I, is expressed as mm per minute, can be obtained
from the formula:
I = A/N ….. (2.1) (Morgan, 1974)
Where,
I = intensity of rainfall
A = total depth of rainfall (mm)
N = time of accumulation of above rainfall depth (minute or hour)
Roslan (1995) classified the rainfall intensity as shown in Table 2.1.
Table 2.1: Classification of Rainfall Intensity.
Rainfall Intensity (cm/h) Remarks
< 0.65 Low
0.65 - 1.3 Medium
1.3 - 5.0 High
> 5.0 Severe
Source: (Roslan, 1995)
The proportion of falling rainwater drops down in variable diameter size
ranging from 1 to 4 mm provide the kinetic energy for soil detachment and
transportation of soil particles. Morgan (1974) found that the raindrop diameters
vary from slightly over 1 mm for a rainfall intensity of 127 mm per hour to
approximately 3.25 mm for a rainfall intensity of 212 mm per hour. This
exponential relationship was also found by Laws and Parsons (1949) in
temperate regions. However, Hudson (1965) has shown that in tropical areas,
median drop-size increases up to a maximum intensity of 100 mm per hour, and
after that period decreases with increasing intensities.
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Literature Review and Theoretical Background
The erosivity index proposed by Hudson (1965) is found to be more
efficient than that used by Wischemier and Smith (1978) in describing the
rainfall erosivity in tropical regions. Hudson (1965) found that an intensity value
of 25 cm per hour is the critical value necessary to initiate soil erosion. The
relationship is as follows;
EV = ΣEK > 25 ….. (2.2) (Hudson, 1965)
The equation above describe that the erosivity value (EV in
Joules/meter2) of an individual rain event is the sum of kinetic energy (EK in
Joules/meter2) of all rain falling at intensities (I) equal or greater than 25 cm per
hour. Morgan (1974) was then applied the daily rainfall data for computing daily
Hudson’s erosivity index to produced total annual erosivity. The following
equation shows the relationship between annual erosivity annual precipitation
as proposed by Morgan (1974) for Malaysian condition.
Eva = 28P-8838.15 ….. (2.3) (Morgan, 1974)
Where,
Eva = annual erosivity (J/m2)
P = annual precipitation (mm)
According to FRIM (1999), the rainfall erosivity factor, R (t.m/ha.hr) could be
estimated using expression below:
R = Ei30 / 170.2 ….. (2.4) (FRIM, 1999)
Where,
E = 9.28P-8838.15
P = Annual rainfall (mm)
i30 = Maximum 30 minute rainfall intensity (mm/hr)
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Literature Review and Theoretical Background
2.1.1.2 Slope Steepness, Length and Curvature
Erosion is expected to increase with increases in slope steepness and
slope length as a result of respective increases in velocity and volume of
surface runoff (Morgan, 1986). There make the water a better transporting
agent. The slope steepness is usually expressed in percent or degree.
Wischemier and Smith (1958) show that the erosion varies with the percent of
slope steepness according to the equation:
Xc = 0.65S1.49 ….. (2.5) ( Wischemier and Smith, 1958)
Where,
Xc = the coded total soil loss
S = Slope steepness in percent
The slope length is defined as the distance from the point of origin of
overland flow to the point where either the slope decreases (deposition begin)
or enters a defined channel (Wischemier and Smith, 1978). The data from the
United States Soil Conservation Services show that the average soil loss per
unit area is proportional to the power of slope length. The accumulation of water
in the downslope direction with higher velocity results in more soil loss from the
lower parts than the upper parts of a slope. Table 2.2 show the soil loss from
various slope segments caused by runoff (Wischemier and Smith, 1958).
In term of slope curvature, sheet erosion is usually more severe on a
convex slope than on a concave slope. A laboratory study by Rieke and
Nearing (2005) found that the slope shape had significant impact on rill
patterns, sediment yield and runoff production. The uniform and convex slope
has higher sediment yields at the toe slope compared to the concave slope
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Literature Review and Theoretical Background
indicating higher erosion for uniform and convex slopes over concave slopes
respectively.
Table 2.2: Soil loss from various slope segments caused by runoff
Segment Length of
Slope (m)
Relative soil loss per unit
area (tonnes)
Average soil loss for the
whole slope (tonnes)
0-23 0.91 0.91
23-46 1.65 1.28
46-69 2.13 1.56
69-92 2.52 1.80
Source: (Wischemier and Smith, 1958)
2.1.2 Resistance Factors (Soil Erodibility)
Soil erodibility defines the resistance of the soil to both detachment and
transport. Generally, the erodibility of soil varies with soil texture, aggregate
stability, shear strength, infiltration capacity, organic matter and chemical
content (Morgan, 1986). In terms of texture, the soil with large particles is
resistant to erosion due to greater force required for entrainment whereas fine
particles are resistant because of their cohesiveness but they are easily
transported. Soil with high silt content is the most erodible (Morgan, 1986).
Richter and Negendank (1977) found that soils with 40 to 60 percent of silt
content are the most erodible (soil with low clay and sand content).
The infiltration capacity of soil has also major influenced on soil erosion.
Typically, soil with low infiltration capacity is more erodible than the soil with
high infiltration. This is because the low infiltration capacity of soil tends to
initiate runoff compared to soils with high infiltration capacity. The infiltration
39
Literature Review and Theoretical Background
capacity of soil is influenced by pore size, pore stability and the form of soil
profile (Morgan, 1986).
In term of aggregates stability, stable soil aggregates are more resistant
to detachment. The stability of aggregates is generally influenced by the organic
and chemical constituents of the soil. Soil with less than 3.5 % of organic matter
content can be considered erodible (Evans, 1980) while Voroney et al. (1981)
suggested that soil erodibility decreases linearly with increasing of organic
matter content. This suggestion however, is commonly untrue for peat soils as
they are highly erodible by water. (Morgan, 1986)
The erodibility of Malaysian soils varies considerably. Table 2.3 shows
the erodibility of five common Malaysian soil series (Prang soil not included due
to unavailable record found in literature with regards to erodibility for Prang soil).
Table 2.3: Erodibility of five common Malaysian soil series
Soil Series Texture % organic
carbon % aggregate > 0.25 mm
Soil Loss (t/ha)
Munchong Clay 1.87 83.1 100
Rengam Sandy clay
loam 1.69 59.0 212
Serdang Fine sandy
loam 1.10 55.9 339
Holyrood Loamy sand 1.35 73.5 252
Sg. Buloh Loamy
coarse sand 2.02 64.9 220
Source: (RRIM, 1975)
40
Literature Review and Theoretical Background
2.1.3 Protection Factors (Plant Covers)
The effectiveness of a plant cover in reducing erosion depends upon the
height and continuity of the canopy, the density of ground cover and the root
density (Morgan, 1986). Many researchers agreed that plant covers reduce soil
erosion to approximately due to the ability of plant to intercept raindrop impact
to reduce the amount of kinetic energy upon impact with the soil surface
(Maene and Wan Sulaiman, 1980). Furthermore, the plant roots bind soil
particles together physically into stable aggregates.
2.2 Sedimentation Process (Sediment Delivery Ratio and Sediment Yield)
Although sedimentation in ponds and wetlands is important, for removing
the sediment, nutrients and contaminants which are readily attached to fine
particles (Fennessy et al., 1994; Raisin et al., 1997), excess sedimentation can
lower wetlands lifespan and thus degrade wetland function which generally
reduce wetland retention time, lower groundwater level and bring suspended
sediments and sediment-associated nutrients into the wetlands (Whigham et al.,
1988). As sediment is a major pollutant and also a transporter of pollutants, the
need for assessments and estimations on catchment’s surface runoff, sediment
delivery and sediment yield are vital through water resources analyses,
modeling, and engineering methodology.
41
Literature Review and Theoretical Background
2.2.1 Sediment Delivery Ratio
The sediment delivery ratio (SDR) is defined as the fraction of gross
erosion that is transported from a catchment in a specific time interval. The SDR
is a dimensionless scalar and in terms of the definition, SDR can be expressed
as:
SDR = Y / E ….. (2.6) (Ouyang and Bartholic, 1997)
Where,
SDR = sediment delivery ratio
Y = average annual sediment yield per unit area
E = total gross annual erosion for the same area
Factors influencing SDR include hydrological inputs (mainly rainfall),
landscape properties (e.g., vegetation, topography, and soil properties) and
their complex interactions (Walling, 1983; Richards, 1993). The multitude of
such interactions makes it difficult to identify the dominant controls on
catchment sediment response and on catchment-to-catchment variability. As a
result, work on SDR regionalization remains largely empirical. The SDR often
has a value between 0 and 1 due to sediment deposition caused by change of
flow regime and reservoir storage. However, values larger than 1 were also
found at event basis or when bank or gully erosion predominates (Lu et al.,
2005).
According to the upland theory of Boyce (1975) SDR generally
decreases with increasing catchment size area because average slope
decreases with increasing catchment size, and large catchment also have more
sediment storage sites located between sediment source areas and the basin
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Literature Review and Theoretical Background
outlet. At catchment scale, the most widely used method to estimate SDR is
through an SDR-area power function given as:
SDR= αAβ ….. (2.7) (Maner, 1958; Roehl, 1962)
Where,
A = catchment area (km2)
α = constant empirical parameters β = scaling exponent
Field measurements using the statistical regression technique suggest
that β is in the range –0.01 to –0.025 (Walling 1983; Richards 1993), which
means that SDR decreases with increasing catchment area. The relationship for
SDR and catchment size is known as the SDR curve (USDA, 1972). The SDR
curve based on watershed size is widely used because of its simplicity (Lim et
al. 2005). USDA (1972), Boyce (1975) and Vanoni (1975) also developed SDR
curves expressed as:
SDR = 0.4724 A -0.125 ….. (2.8) (Vanoni,1975)
SDR = 0.3750 A -0.2382 ….. (2.9) (Boyce, 1975)
SDR = 0.5656 A -0.11 ….. (2.10) (USDA,1979)
where,
A = Catchment area (km2). The differences in SDR equation above is because of the amount of data
used to derive such equation, the locality and process involved. The Vanoni
(1975) used data from 300 watersheds throughout the world to develop a model
by the drainage area power function. The USDA SCS (1979) developed a SDR
model based on the data from the Blackland Prairie, Texas. A power function
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Literature Review and Theoretical Background
derived from the graphed data points. Boyce (1975) developed an equation
based on relationship between sediment delivery ratio and catchment area
using data from five experimental catchment.
There were also many researchers who tried to relate the SDR value to
catchment area, sediment particle size, sediment travel time, sediment transport
capacity and topography (relief-length ratio) of the catchment. For example,
Maner (1958) suggested that SDR was better correlated with relief-watershed
length ratio (R/L) than with other factors. Renfro (1975) modified the model (with
regression coefficient, R2, value of 0.97) as follows:
log (SDR) = 2.94259 + 0.82362 log (R/L) ….. (2.11) (Renfro, 1975) where,
R = relief of a watershed (the difference in elevation between the
average elevation of the watershed divide and the watershed outlet)
L = maximum length of a watershed (measured parallel to mainstream
drainage)
Balamurugan (1989) found the relationship between catchment area size
and relief – length ratio from study at Sg. Klang catchment area as expression
below:
SDR= 77.683 x A-0.065 (R/L)0.213 ….. (2.12) (Balamurugan, 1989)
where, A = Catchment area (km2)
R = relief of a watershed (the difference in elevation between the
average elevation of the watershed divide and the watershed outlet)
L = maximum length of a watershed (measured parallel to mainstream
drainage)
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Literature Review and Theoretical Background
All the proposed SDR equations above were developed for river basin
geomorphology without any consideration of wetland or pond filtration effects.
Furthermore, almost all the equations above disregarded the effects of other
types of erosion (gully, bank erosion, etc.) assuming that there was minimal and
negligible gullying process and almost no bank erosion.
Thus, the author notes that the application of these equations in different
environmental settings such as constructed wetland and the interpretation of
results calculated with these equations should be applied with caution as these
equations are only valid for sheet erosion sources and need further
consideration if it applied in different environmental settings. For example, the
effect of storage capacity should be considered in application of such formulas
in catchment with lake dominated area.
2.2.2 Sediment Yield
Sediment yield is defined as the amount of eroded material that moves
from a source to a downstream control point, such as a reservoir or to the edge
of catchment outlet, per unit time (Chow, 1964). The fate of eroded material
within a watershed is influenced by hydrologic, topographic, vegetative and
groundcover characteristics. Eroded particles may be transported to the
watershed outlet, or they may be deposited and stored within the watershed.
Lane et al. (1997) defined the sediment discharge from a watershed as the total
quantity of sediment moving out of the watershed in a given time interval
(mass/time). This sediment discharge is often termed sediment yield (ASCE,
1970). The total sediment discharge from a watershed relative to the watershed
area is also called sediment yield (mass/area/time) (ASCE, 1982).
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Literature Review and Theoretical Background
Observations show that sediment yields from catchments are often about
an order of magnitude lower than the soil erosion rates measured from hillslope
plots (Edwards, 1993; Wasson et al., 1996). This implies that most of the
sediment travels only a short distance (Parsons and Stromberg, 1998) and is
deposited. The SDR concept as discussed before was suggested by many
researchers based on this assumption.
2.3 Erosion and Sedimentation Measurement
Measuring the amount of erosion and sedimentation within the
catchment is difficult due to catchment complexities and large uncertainties on
quantification. Study on catchment erosion started a few decades ago and
advanced with progress in measurement and model capabilities. Hudson (1957)
had initiated runoff measurements under field conditions using a series of
experiments in Rhodesia. He successfully recorded the annual soil losses as
high as 225 tons per acre. However, varying amounts and division of runoff flow
required special apparatus and large number of plots and considerable errors in
measurement arose because of the effects of plot boundaries, silting of
collecting apparatus and problems of emptying large amounts of water and
sediment from the tanks (Morgan 1986).
Erosion measurement over a larger area (catchment area ranges from
20 to 640 km2) had been shown by Rapp (1975) by determining sediment
concentration in rivers and the rates of sediment accumulation in reservoirs.
Dunne (1977) stated that there are two basic approaches to erosion study using
sampling at the outlet of the drainage basin and direct measurement of soil
46
Literature Review and Theoretical Background
removal at a number of locations within catchments. However no comparison
has been made between results from measured sampling outlets and data from
erosion plot due a portion of the soil mobilized from hillsides comes to rest in
swales, floodplains and other storage sites.
Zarris (2002) attempted to reconstruct sediment yield records of a
drainage basin using the hydrographic survey procedure resulting a quite
satisfactory result due to lack of temporal evolution of the measurement. An
apparent weakness of the method is that it gives only an over-year average of
the sediment yield and not its temporal evolution. However, if frequent
hydrographic surveying of the reservoir is permitted (e.g. every 5 years) then
sediment yield can be computed in finer time scales.
Recent advancements on erosion and sedimentation focus on
understanding of catchment processes as a whole integrated and spatially
distributed system. This approach takes into consideration the complexities in
catchment processes in which the dynamics are likely to be best understood by
examining cross - system organisation rather than concentrating on parts from
which a whole system is constructed. Consideration of the many factors
contributing to erosion and sedimentation is vital to maximize the understanding
of sedimentation triggered by erosion processes.
Over the past decade numerous significant advancements have been
made in the linkage of geographic information systems (GIS) and various
research into catchment hydrology, erosion and sedimentation processes that
provide better understanding of the spatial and distribution processes involved
(Hoyos, 2005; Shen et al., 2005; USACE, 2003; He et al., 2001; Pullar and
47
Literature Review and Theoretical Background
Springer, 2000; Arnold et al., 1998). These GIS-based systems have greatly
enhanced the capacity for research scientists to develop and apply models due
to improved data management and apply rapid parameter estimation tools that
can be built into a GIS system.
The availability of GIS tools and more powerful computing facilities
makes it possible to overcome many difficulties and limitations in developing
distributed continuous time basin-scale models, based on available regional
information. The use of a distributed approach permits both the spatial
heterogeneity of catchment land-use, soil properties, topography and the spatial
variability and interaction of erosion and sediment delivery processes to be
represented, and can therefore provide spatially distributed predictions of soil
erosion and sediment redistribution for complex three-dimensional terrains
(Moore et al., 1993; Kothyari and Jain, 1997; De Roo, 1998; Parson et al.,
1998).
2.4 Erosion and Sedimentation Model
With the increased computing power and efficiency, there has been a
rapid increase in the exploration of catchment erosion and sediment transport
through the use of computer models in simulating sediment transport and
associated pollutant transport. In general there is no ‘best’ model for all
applications. The most appropriate model will depend on the intended use and
the characteristics of the catchment being considered (Merrit, 2003).
Erosion and sedimentation models can be classified into three main
categories, depending on the physical processes simulated by the model, the
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Literature Review and Theoretical Background
model algorithms describing these processes and the data dependence of the
model:
• Empirical or statistical/metric
• Conceptual
• Physically based
2.4.1 Empirical Model
Empirical models are generally the simplest of all three model types.
They are based primarily on the analysis of observations and seek to
characterize responses from these data (Wheater et al., 1993). Parameter
values in empirical models may be obtained by calibration, but are more often
transferred from calibration at experimental sites. They are particularly useful as
a first step in identifying sources of sediment and nutrient generation (Merrit,
2002). Prosser et al. (2001) noted that, on a regional scale, patterns of
sediment delivery and sediment residence time remain poorly understood.
Hence, prediction of sediment delivery at these scales is commonly based on
empirical methods that are applied uniformly in a region.
2.4.2 Conceptual Model
Conceptual models of erosion sedimentation models are typically based
on the representation of a catchment as a series of internal storages. They
usually incorporate the underlying transfer mechanisms of sediment and runoff
generation in their structure, representing flow paths in the catchment as a
series of storages, each requiring some characterization of its dynamic
behaviour (Merrit, 2002). Parameter values for conceptual models have typically
been obtained through calibration against observed data, such as stream
discharge and concentration measurements (Abbott et al., 1986). Beck (1987)
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Literature Review and Theoretical Background
noted that conceptual models play an intermediary role between empirical and
physically-based models. Due to the requirement that parameter values are
determined through calibration against observed data, conceptual models tend
to suffer from problems associated with the identifiability of their parameter
values (Jakeman and Hornberger, 1993).
2.4.3 Physically Based Model
Physically based models are based on the solution of fundamental
physical equations describing stream flow and sediment and associated nutrient
generation in a catchment. Standard equations used in such models are the
equations of conservation of mass and momentum for flow and the equation of
conservation of mass for sediment (e.g. Bennett, 1974). The derivation of
mathematical expressions describing individual processes in physics-based
models is subject to numerous assumptions that may not be relevant in many
real world situations (Dunin, 1975). In general, the equations governing the
processes in physics-based models are derived at the small scale and under
very specific physical conditions (Beven, 1989).
Among these models, the Universal Soil Loss Equation (USLE) empirical
model has remained the most practical method of estimating soil erosion
potential in fields and to estimate the effects of different control management
practices on soil erosion for nearly 40 years (Dennis and Rorke, 1999; Kinnell,
2000) while other process-based erosion models have intensive data and
computation requirements. The new version of the USLE, called the Revised
Universal Soil Loss Equation (RUSLE), was developed by modifying the USLE
to more accurately estimate USLE factors, and thus the soil erosion estimates
(Renard et al., 1991).
50
Literature Review and Theoretical Background
51
The USLE has been integrated with Geographic Information Systems
(GIS) to estimate soil erosion to enable users to manipulate and analyze the
spatial data more easily, and helps users identify the spatial locations
vulnerable to soil erosion (Yitayew et al., 1999 and Lufafa et al., 2002). The list
of available erosion and sedimentation models is summarized in Table 2.4.
2.4.4 Grid Cell Size Effect to USLE Calculation in GIS Environment
Although the estimation of erosion using Universal Soil Loss Equation
(USLE) in GIS enhance users efficiency and favors many researchers, some
researchers also question the effect of grid cell size on estimation of LS factor
(slope length and steepness factor). Molnar and Julien (1998) designed a study
to compare USLE calculations in a GIS environment at grid sizes ranging from
30X30 m up to 6X6 km. They found that large grid cell sizes apparently tend to
underestimate soil losses.
Wu et al. (2005) found that the selection of the DEM grid size has
considerable influence on the soil loss estimation with the empirical models.
The estimate of total soil loss from the watershed decreases significantly with
the increasing DEM cell size as the spatial variability is reduced by the cell
aggregation. They also suggested applications of discretion process for
quantitative estimation of soil loss concerning the sensitivity to the grid size
selection. Lee and Lee (2006) had used the Revised Universal Soil Loss
Equation (RUSLE) together with GIS spatial analysis to quantify soil loss in a
small basin. They found it difficult to select a suitable grid size in a subjective
and intuitive way. The results of their study show that the LS factors are
sensitive to the grid size while the optimal resolution to quantify soil loss in the
RUSLE model for the study site is 125 m.
Literature Review and Theoretical Background
Table 2.4: Erosion and sediment transport models
Model Type Scale Input output References
USLE / RUSLE
Empirical Hillslope / catchment
Low Erosion, using SDR to estimate catchment sediment yield
Wischmeier and Smith (1978)
IHACRES-WQ
Empirical Catchment Low runoff, sediment and Conceptual nutrients
Jakeman et al. (1990)
SEDNET Empirical / Conceptual
Catchment Moderatesediment, relative contributions from overland flow, gully and bank erosion processes
Prosser et al. (2001)
AGNPS Conceptual Small catchment High runoff volume; peak rate, SS, N, P, and COD concentrations
Young et al. (1987)
ANSWERS Physical Small catchment High sediment, nutrients Beasley et al. (1980)
CREAMS Physical field 40–400 ha High erosion; deposition Knisel (1980)
GUEST Physical Plot High runoff; sediment concentration Yu et al. (1997)
WEPP Physical Hillslope/
catchment High
runoff; sediment characteristics; form of sediment loss
Laflen et al. (1991)
Source: (modified from Merritt, 2002)
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Literature Review and Theoretical Background
2.5 Effect of Erosion and Sedimentation Process to Wetland Functioning
One management solution that has been widely reported to reduce diffuse
source pollution is the use of wetlands along the river corridor (Brunet, 1994;
Chambers, 1993 and Lowrance, 1985). These vegetated riparian zones appear to
act as a natural buffer for nitrogen, phosphorus and suspended sediment, thus
controlling nutrient movement from the drainage area into the stream.
The role of rainfall and water discharge in controlling suspended sediment
concentrations at the upstream end of the wetland is evident. Prior and Johnes
(2002), found that suspended sediment concentrations were often higher in
upstream areas in comparison to downstream areas, indicating that the wetland
may perform a nutrient and sediment retention function.
The efficacy of wetlands in removing pollutants from the upslope surface
and groundwater is highly dependent upon hydrology (Correll, 1997). Results
indicate that suspended sediment concentrations are driven by variations in rainfall
and flow and again with concentrations significantly higher upstream of the
wetland. Wetlands also provide an interface between the upslope drainage areas
and the stream channel.
2.6 Erosion and Sedimentation Studies in Malaysia
Many attempts had been made by Malaysian researchers to quantify
erosion and sediment yield in small plots as well as large catchments using
available models. Presented here is only some of the work done by them. Douglas
et al. (1993) analyzed the impact of selective logging on stream hydrology,
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Literature Review and Theoretical Background
chemistry and sediment loads in the Ulu Segama rain forest, Sabah. They found
that road construction activities and logging mobilization had a marked effect on
the sediment yield, leading to a significant increase of sediment yield in
comparison to the initial stage (pre-logging). The sediment yield of 16 t/ha/yr had
been estimated for 56 ha of catchment area.
Hashim et al. (1995) conducted a soil erosion study on steep slopes (10” to
20”) at the MARDI (Malaysian Agricultural Research and Development Institute)
research station in Kemaman on the east coast of Peninsular Malaysia using
experimental plots of 1000 m2 (or 0.1 ha) and one small bare plot of 20 m2. The
results show that maximum soil loss for the bare plot was 181.06 t/ha. Before that,
Hashim and Erh (1978) had tried to determine the relationship between single
event rainfall intensity soil loss on plot (10 x 1 m plot) of bare soil and covered by
mulch. They found that mulching significantly reduced the total soil loss in
comparison to bare soil plot with soil loss on the bare plot as high as 5.143 kg per
plot compared to 1.093 kg per plot in mulched plot.
Leong and Abustan (2000) try to quantify the extent of rainfall that
contributes runoff in the Sg. Kayu Ara river catchment with the development of
rainfall-runoff statistical relationship and baseflow hydrograph separation analysis.
The initial results indicate that temporal and spatial distribution of precipitation over
the catchment had some effects on the degree of runoff. Gregersen et al. (2003)
using USLE as erosion risk assessment method together with river discharge and
turbidity monitoring at Tikolod Sabah found that the erosion in study area ranged
from 68.6 t/ha/yr to 669.5 t/ha/yr and was mainly determined by high slope length
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Literature Review and Theoretical Background
and steepness factor values due to the high steepness of slope (> 30 % slope
steepness).
Mohd Kamil et al. (2003) had investigated soil erosion within a logged-over
tropical forest at Pasoh, Negeri Sembilan using erosion pins at five unbounded
research grids in a 100 m x 100 m plot for 154 days. From the investigation, they
found that the maximum soil erosion recorded was 14.6 mm in the form of sheet
and splash erosion while the maximum soil deposition was 7.2 mm. They also
concluded that the soil erosion occuring at their research plot falls within the high
erosion class. Raj (2003) analyzed the linkages and the impact between
sedimentation rates in the Ringlet Reservoir and land use changes as potential
sources of sediment. He suggested that increasing annual discharge of study area
is caused by the increasing trend of overland flow, as a result of increasing erosion
rates due to land use changes.
Ruslan (2004) had applied the AGNPS (Agricultural Non Point Source)
model to quantify sediment yields in a small catchment at Waterfall River (4.98
km2) located in Penang, Malaysia. He reported that in some events the AGNPS
model overestimated the actual sediment yield, while in some other events it
underestimated the yield. However, some of the AGNPS model results were
reasonable (within 20% deviation). The AGNPS model uses many empirical and
quasi-physically based algorithms that might not be appropriate for a tropical
country like Malaysia. Therefore, one possible future effort is to modify the various
equations used in the AGNPS model to suit local conditions.
55
Literature Review and Theoretical Background
River sediment transport study had been exercised by Chang et al. (2005),
for the Kulim River using mathematical model, FLUVIAL-12 that was formulated
and developed for water and sediment routing analysis in man-made and natural
channels. The study shows that the simulation result was able to predict sediment
transport in comparison with the observed river geometry and channel processes.
Mohammad Firuz et al. (2005) attempted to quantify erosion potential at Langkawi
Island using GRASS GIS capability. The result demonstrates the applicability of
open source GIS for soil erosion studies as one of the GIS software. They also
found that improper land management may contribute to high erosion potential.
The range of erosion value in the study area ranges between 0 to 122,470 t/ha/yr
with 58% of study area classified as of low to moderate erosion risk. These
extreme values are likely because of the extreme values in LS factor which are
probably due to the problems in DEM interpolation.
Ariffin and Abu Talib (2006) performed a sediment monitoring and sampling
exercise along three river systems namely Sungai Selangor, Sungai Gerachi and
Sungai Luit to estimate and quantify the rate of sediment deposition in the Sg.
Selangor Dam. The observed incoming sediment load or discharge is in the range
of 0.05 kg/s to 21 kg/s on average that gives an annual sediment yield in the range
of 1580 t/yr to 660,000 t/yr.
2.7 Summary Many factors could affect the erosion and sedimentation processes in a
particular catchment. The SDR concept is proposed as a measurement of
transporting mechanism or process from upland or upstream sediment source to
56
Literature Review and Theoretical Background
57
downstream effluent area. Various methods of erosion and sedimentation
measurements could be applied to measure erosion and sedimentation. However,
these methods should be use with care taking into consideration the suitability and
data dependant factors. The studies of erosion and sedimentation in Malaysia were
done in a variety of environments using different methods and approachs resulting
in various erosion and sediment yields respectively. The estimated erosion value
ranges from as low as 68.6 t/ha/yr (Gregersen et al., 2003) to 122,470 t/ha/yr
(Mohammad Firuz et al., 2005).
Material and Method
3.1 Introduction
On site erosion and sedimentation assessments depend on many
parameters evaluating both at point and non point sources. To deal with these
parameters contributing to erosion and sedimentation processes, an integrated
approach is applied as describe below. This material and method chapter will
explain the methodologies applied for this research project.
3.2 Summary of Research Methodology
The research had been conducted using an integrated approach to
determine the parameters influencing erosion and sedimentation processes
affecting the study area. Generally, six steps of analysis and investigation had
been done. Table 3.1 show the summary of parameters considered while Figure
3.1 show the research on the flow of erosion and sedimentation process
characterization.
1. USLE, Universal Soil Loss Equation (Wischemier and Smith, 1978) have
been applied to estimate soil erosion risk as a source of sedimentation for
the 2003 and 2004.
2. Bank erosion had been identified and documented accordingly. This bank
erosion documentation will be used for further comparison with the gathered
result.
58
Material and Method
3. Seventeen wetland cells had been chosen to determine their total
suspended solid yield (TSS yield) using the TSS rating curve method. The
TSS rating curves were constructed using instantaneous water discharge
and TSS loading data respectively. The spatial and temporal (annual) TSS
yields were then analyzed, chart and characterized accordingly.
4. Wetland reservoir sediment yields and wetland sedimentation rates for
seventeen wetland cells had been determined using wetland sedimentation
survey data (from Putrajaya Corporation) for year 2001, 2002 and 2004.
5. Catchment sediment yields for seventeen wetland subcatchment areas had
been determined using the USLE-SDR approach. The SDR equations
proposed by Vanoni (1975) and USDA-SCS (1972) had been utilized to
calculate the catchment sediment yield respectively.
6. Comparisons and linkages between sediment yield determination resulting
from TSS yields using TSS rating curves, catchment sediment yield
determinations using USLE-SDR and wetland reservoir sediment yields
derived from sedimentation survey data are evaluated. Finally, specific
erosion and sediment mitigation measures are proposed accordingly.
59
Material and Method
60
Table 3.1: Summary of parameter considered in erosion and sedimentation process (PJC = Putrajaya Corporation,
DID = Drainage and Irrigation Department, Malaysia).
No. Subject Parameter
Sampling / Laboratory
methodology Collected material
R = Rainfall /runoff erosivity
Acquired rainfall data from PJC
and DID
Rainfall and rainfall intensity data
K = Soil erodibility
Soil sampling / Soil particle size
analysis, organic matter
content
Soil sample
LS = Hillslope length and steepness
Surface elevation digitizing
procedure
Digitized topograhic map
1
Application of USLE-SDR erosion and catchment sediment yield estimation using GIS
CP = Land Cover and Support practice
Satellite image analysis
Satellite image land use classification and support practice map
Water discharge Weir / bucket Water discharge data
2 Wetland cell TSS rating curve generation and TSS yield estimation
TSS concentration APHA 2540-D Water-sediment sample
3 Wetland Reservoir Sediment yield from sedimentation survey data
Wetland annual sedimentation volume and rate
Wetland cell sediment bulk
density analysis
Wetland annual sediment accumulation data (in volume and weight, tonnes)
Material and Method
Annual Wetland Sediment
Accumulation
Erosion and sedimentation process at Putrajaya wetland
Sediment Delivery Ratio A= RKLSCP
Annual TSS Loading
TSS Discharge Data
Water Discharge Data
USLE GIS (Sediment Source spatial Analysis)
TSS Concentration Data
TSS Rating Curve
Annual Wetland
Sedimentation Rate
Wetland Sedimentation survey Data
Sub-Catchment / weir by weir Sediment Yield
Figure 3.1: Research flow of erosion and sedimentation process study.
61
Material and Method
3.3 Method of Quantification and Estimation on Wetland Water Discharge
(m3/s), TSS Concentration (mg/l), TSS Loading (t/yr) and TSS Yield
(t/ha/yr)
Seventeen wetland cells had been chosen for characterization and
estimation of their water discharge (m3/s), total suspended solid (mg/l) and total
suspended solid loading (t/yr) for the determination of wetland TSS yield (t/ha/yr)
using the TSS rating curve method. Table 3.2 summarizes the parameters and
analyse undertaken for the estimation of wetland TSS yield using the TSS rating
curve method.
3.3.1 Water Discharge Quantification
Bucket method and weir method had been applied for the measurements of
water flow and water discharge throughout this research. Generally, water
discharge measurements for sampling station with weir structure had been
estimated using the weir method while bucket method has been employed for
elevated weir (difference in elevation between weir crest and outflow culvert is
above 1.5 m). The general description for each method and the field procedures
are outlined below.
3.3.1.1 Weir Method
The wide variety of weir types can provide the measurement of water
discharge ranging from a few litres per second to many hundreds of cubic meters
per second. A weir is basically a small dam with a spillway opening of specified
shape for flow control purposes. The upstream head is uniquely related to the
discharge over the crest of the structure, where the flow passes through critical
conditions (the relationship between the inertial forces and the gravitational forces
62
Material and Method
of the flow is equal to 1.0; therefore, the velocity of the flow, V, is equal to the
velocity of the wave (or celerity)). The most common sharp-crested weir type is a
90o V-notch (Figure 3.2) and rectangular cut out.
Figure 3.2: Example of 90o V-notch weir.
Source: (www.fao.org/docrep/T0231E/t0231e05.htm)
Table 3.2: Parameter and analysis undertaken for the estimation of wetland
TSS yield using TSS rating curve method.
Measurement method
No Stations Water Discharge (m3/s)
TSS concentration (mg/l)
1 UW1 Weir method APHA 2540-D 2 UW2 Weir method APHA 2540-D 3 UW3 Weir method APHA 2540-D 4 UW7 Weir method APHA 2540-D 5 UW8 Bucket method APHA 2540-D 6 UN1 Weir method APHA 2540-D 7 UN2 Weir method APHA 2540-D 8 UN4 Weir method APHA 2540-D 9 UN6 Weir method APHA 2540-D 10 UE1 Weir method APHA 2540-D 11 UE2 Weir method APHA 2540-D 12 UE3 Weir method APHA 2540-D 13 LE1 Bucket method APHA 2540-D 14 LE2 Bucket method APHA 2540-D 15 UB1 Weir method APHA 2540-D 16 UB2 Weir method APHA 2540-D 17 CW B Weir method APHA 2540-D
63
Material and Method
In the Putrajaya wetland area, rectangular sharp–crested weirs (Figure 3.3)
had been designed and constructed (Figure 3.4) to connect wetland cells, to
impound a required amount of water that should free-fall over the weir crest
(Figure 3.5). With this weir type, the elevation of the backwater above the weir
crest, H, and the length of weir crest is measured. Thus, the discharge (Q) over the
weir is calculated from the following equation;
Q=1.83 bH3/2 …. (3.1) (Chin, 2000)
Where;
Q = Water discharge (m3/s)
b = the length of the weir crest (m)
H = the head of the backwater above the weir crest (m)
b
H
Hw: weir crest level
Hw
Flowing water
Figure 3.3: Simplified sharp-crested rectangular weir, showing the
parameters used for the measurement of water discharge
(modified from Chin, 2000)
64
Material and Method
Figure 3.4: Example of constructed wetland weir structure at Putrajaya
wetland (photo taken at LE1 wetland cell).
Water Jump
Weir Crest
Figure 3.5: Typical example of free fall water over the weir crest (photo taken
at UE2 wetland cell).
65
Material and Method
Three steps below had been followed to determine water discharge for a sharp-
crested rectangular weir:
1) The length of the weir crest (L) had been measured, using a measuring tape.
2) The head of backwater (H) above the weir crest was recorded.
3) The discharge was calculated using Equation (3.1) (refer to Figure 3.3).
3.3.1.2 Bucket Method
The quantification of water discharge using the bucket method was applied
for sampling stations with elevated culverts. Generally the bucket method is done
using a known volume of bucket to retain water for a particular time. The time is
taken until the bucket filled with water and thus, the discharge can be quantified
using the equation below:
Q = Vb / t …. (3.2)
Where,
Q = Water discharge (m3/s)
Vb = Volume of water retain (m3)
t = Time for bucket to be filled with water (s)
The quantification of water discharge using the bucket method was repeated for at
least three times for each sampling exercise in order to determine the average
water discharge accordingly.
3.3.2 Total Suspended Solid Quantification
Suspended sediment is the concentration of sediment particles held in
suspension in a particular flow. The units commonly used in the measurement of
sediment concentration vary with the range of concentrations and the standard
66
Material and Method
measurement techniques utilized in different countries (Julien, 1995). The unit
milligram per litre (mg/l), which describes the ratio of the mass of sediment
particles to the volume of the water-sediment mixture, is used in this study.
One litre of water-sediment sample was collected at each sampling station,
at free fall water after the weir crest. The measurements of the TSS concentration
were carried out laboratory at Geology Department, University Malaya using APHA
2540-D method. Details on the APHA 2540-D method can be found in Appendix 1.
3.3.3 Generation of TSS Rating Curve and TSS Loading Estimation
The determination of TSS fluxes or loads requires data on both water
discharge and total suspended solid discharge. A total suspended solid discharge
rating curve is a plot of instantaneous total suspended solid discharge, Qs, against
instantaneous water discharge, Q, for a measurement site. The total suspended
solid discharge rating curve in this study was produced based on the modified
method proposed by US Army Corps of Engineers (1989). They constructed a
sediment rating curve based on flow duration curve (using average continuous
daily water discharge) and suspended sediment discharge data.
However, in this study, because of the inavailability of continuous daily
water discharge data, the total suspended solid rating curve was built on
instantaneous water discharge data. This instantaneous total suspended solid
discharge had been extrapolated into daily basis in order to determine the gross
annual total suspended solid yield. Usually, extrapolation process may introduce
certain errors to the calculated results. Thus, correction factor (K[b,T ]) proposed
67
Material and Method
by Balamurugan (1989) was multiplied to the computed loading in order to obtain
actual TSS loading.
Table 3.3: Correction factor, K[b,T ]
Discharge Data b
Daily Weekly Monthly Annual 1.5 1.078 1.132 1.176 1.261 2.0 1.185 1.335 1.476 1.802 2.5 1.300 1.582 1.895 2.871 3.0 1.395 1.840 2.416 5.226 Source: (Balamurugan, 1989)
All instantaneous water discharge and total suspended solid discharge from
2002 to May 2006 (one to two sampling and measurement per month with variable
weather conditions) were used to produce the total suspended solid discharge
rating curve fitted as a log-log linear graph. The power regression method is
adopted to generate a unique rating curve for each selected sampling stations.
Regression methods, and their resulting rating curves, define the empirical
relationships between water discharges and total suspended solid discharges. The
power regression equation is expressed as follow:
log10(C)=a+b·log10(Q) …. (3.3) (Asselman, 2000)
Where,
C = total suspended solid discharge (t / day)
Q = instantaneous water discharge (m3 / s)
The expression above is then back-transformed to obtain:
C = aQb …. (3.4) (Asselman, 2000)
68
Material and Method
Where “a” and “b” are regression coefficients. The equation above covers both the
effect of increased stream power at higher discharges and the extent to which new
sources of sediment become available in weather conditions that cause high
discharge (Asselman, 2000).
The interpretation of the a and b regression coefficients is based on Walling
(1974) and Asselman (2000) with the use of the parameter of total suspended solid
discharge instead of the suspended sediment concentration (mg/l) used by both
researchers. The annual TSS loading was calculated from the input of the total
annual water discharge into the power regression equation of the produced TSS
rating curves respectively.
3.3.4 Sediment Yield Estimation from Total Suspended Solid Data
Sediment yield is defined as the total sediment outflow from a watershed
measurable at a point of reference during a specified period of time. The sediment
outflow from the watershed is induced by processes of detachment, transportation,
and deposition of soil materials by rainfall and runoff (Cigizoglu, 2003). In this
study, the annual TSS loading for 2003 and 2004 calculated from TSS rating
curves were divided with respective catchment areas to produce specific TSS
yields (t/ha/yr) for the respective wetland cell subcatchments. These data will be
used to further analyze and compare with data from the USLE catchment sediment
yield and wetland reservoir sediment yields respectively.
69
Material and Method
3.4 Determination of Catchment Erosion and Sediment Yield (t/ha/yr) using
USLE and SDR in GIS
Universal Soil Loss Equation (USLE) is an empirical relationship of
parameters effecting soil erosion loss that has been adopted to estimate annual
soil loss rate and hence the catchment sediment yield for the Putrajaya wetland
subcatchment area. Generally, USLE consist of six factors as below:
A = RKLSCP …. (3.5)
Where,
A = Soil loss per unit area (t/ha/yr)
R = Rainfall-Runoff erositivity (Mj.mm/ha.h.yr)
K = Soil erodibility (t.ha.h/ha.Mj.mm)
L = Slope length factor
S = Slope steepness factor
C = Crop (land cover) factor
P = Land management factor
The method for calibration and determination of Universal Soil Loss
Equation (USLE) factors from laboratory and raw data source and GIS application
on USLE equation will be explained below. The calculation of USLE annual soil
loss and erosion potential in GIS was done on a subcatchment basis using “spatial
analyst extension” (an extension in Arcview GIS for raster grid map calculation) .
Figure 3.6 shows the flow chart for the application of GIS capabilities in erosion
and sediment yield assesments in this study.
70
Material and Method
Figure 3.6: Flow chart for application of GIS in USLE erosion estimation and
USLE-SDR catchment sediment yield assesment.
Raster Grid Conversion
K (Soil Erodibility)
R (Soil Erosivity)
LS (Slope Length)
CP (Cover and Management)
Rainfall data Soil data
Digital Elevation Model
(Raster grid)
2003, 2004 and 2006 Spot 4 Satellite Image
Kriging surface data interpolation
(Raster grid)
Kriging surface data interpolation
(Raster grid)
Surface elevation from
Topographic map
Arcview Raster Calculator R*K*LS*CP
A (annual soil loss)
Sediment Delivery Ratio estimation by catchment area proposed by Vanoni (1975) and USDA (1972)
Catchment Sediment Yield
71
Material and Method
72
3.4.1 Determination of Rainfall-Runoff Erosivity factor, R.
The R factor is an expression of the erosivity of rainfall and runoff at a
particular location. As the erosivity factor in USLE equation measures the effect of
raindrop impact (rainfall intensity) and total storm energy in contributing to soil
erosion, the value of "R" will increase as the amount and intensity of rainfall
increase. The rainfall data for this study was gathered from Department of Irrigation
and Drainage, Malaysia (DID), (Prang Besar rainfall station, ID: 2916001) and
Putrajaya Corporation (PJC) (telemetric rainfall station W01, R01, R02, R03, R04
and K01 manual rainfall station). Table 3.4 shows the longitudes and latitudes of
rainfall station while Figure 3.7 shows the map location of rainfall station around
study area. The USLE erosivity, R factor calibrated for this study is based on the
equation published by FRIM 1999 as stated below;
R = (E*i30) / 170.2 …. (3.6) (FRIM, 1999)
E = 9.28P – 8838.15 …. (3.7) (Morgan, 1986)
Where;
i30 = the maximum 30-minute rainfall intensity (mm/hr)
E = annual erosivity (J/m2)
P = annual rainfall (mm)
Data generated from the above equation were entered into an Arcview GIS
database for spatial distribution analysis of rainfall erosivity around the study area.
Using the Kriging interpolation extension written by Yi Tang (1998) downloaded
from ESRI website, the surface interpolation for erosivity, E and i30, rainfall
intensity (raster grid data) in the Putrajaya area was generated for further USLE
calculation.
Material and Method
W 01
R 01
K 01
R 04
R 03
R 02 DID PB
Figure 3.7: Location of rainfall stations around Putrajaya area.
73
Material and Method
Table 3.4: Daily rainfall and i30 rainfall intensity per rainfall event data gathered from rainfall station around Putrajaya area.
Rainfall Station Latitude Longitude Data gathered
DID’s Prang
Besar
(ID: 2916001)
2 55’ 40” N 101 41’ 50”E
W01 2 56’ 36” N 101 42’ 02”E
R01 2 58’ 05” N 101 41’ 40”E
R02 2 55’ 43” N 101 41’ 33”E
R03 2 57’ 05” N 101 40’ 44”E
R04 2 55’ 46” N 101 40’ 34”E
Daily Rainfall, i30 rainfall intensity per rainfall event
3.4.2 Determination of Soil Erodibility Factor, K.
The soil erodibility factor, K, is an expression of the inherent erodibility of the
soil or surface material at a particular site under standard experimental conditions.
The value of K is a function of the particle-size distribution, organic-matter content,
structure, and permeability of the soil or surface material. For this study, the soil
erodibility equation and nomograph modified by Tew (1999) for Malaysia condition
from Wischemier and Smith (1978) had been applied to quantify soil erodibility
factor as stated below.
100K = 2.1M 1.14(10-4)(12-a) + 4.5(b-3) + 8.0(c-2) …. (3.8) (Tew, 1999 )
Where;
K= Soil erodibility factor (t.ha.h/ha.Mj.mm)
M= (% silt + % very fine sand) x (100-%clay)
a= % organic matter
b= soil structure code
c= permeability class
74
Material and Method
75
Forty two soil samples of 10 cm depth (surface soil sample) had been
collected in consideration of soil type and accessibility permission, using a one inch
hand auger to quantify and estimate the soil erodibility characteristics around study
area. Figure 3.8 shows the location and distribution of soil sampling station around
Putrajaya lake and wetland area.
The M ((% silt + % very fine sand) x (100-%clay)) parameter was
determined from soil particle size distribution analysis (dry sieve and Mastersizer S
particle size analysis). The % organic matter, a, is obtained from TOC (total
organic carbon) analysis using the AJ2000 Carbon analyzer at the Geology
Department, University of Malaya. The result from the TOC (total organic carbon)
analysis is multiplied by 1.72 for organic matter contain determination in the soil
sample according to the Walkley and Black method (organic matter [OM] = TOC%
x 1.72) (Varvaeke et al., 2004). The soil structure parameter, b, is determined from
the soil profile structure according to Figure 3.9.
The permeability value was estimated based on soil texture triangular as
proposed by Bazzofi (2006) and is classified into six classes as shown in Table 3.5
and Figure 3.10. Results from K factor calculations are transferred to a GIS USLE
database to create point data value in shape file format. Kriging’s interpolation
extension (Yi Tang, 1998), from the ESRI website was used for the surface
interpolation of the K factor (raster grid data) in the Putrajaya area for further USLE
calculations.
Material and Method
Figure 3.8: Location and distribution of soil sampling station around Putrajaya lake and wetland area.
76
Material and Method
Figure 3.9: Classification of soil structure adopted for Parameter b evaluation
in K factor determination.
Granular Blocky
Prismatic Columnar
Platy
Note: Parameter b (soil structure) value for K factor determination; 1 : Very fine granular 2 : fine granular 3 : Medium to coarse granular 4 : Blocky, platy or coarse
Source: (http://soil.gsfc.nasa.gov/pvg/prop1.htm)
Table 3.5: Classification of permeability value adopted in K factor
determination.
Value for parameter c
Permeability Permeability value (cm/hr)
1 Rapid 20.0 – 30.0 2 Moderate to Rapid 5.4 - 20.0 3 Moderate 2.0 - 5.4 4 Slow to Moderate 1.0 - 2.0 5 Slow 0.1 – 1.0 6 Very Slow <0.10
77
Material and Method
Permeability
1 = Rapid
2 = Moderate to rapid
3 = Moderate
4 = Slow to Moderate
5 = Slow
6 = Very slow
Figure 3.10: Estimated permeability value based on soil texture.
Source: (Bazzofi, 2006)
78
Material and Method
79
3.4.3 Determination of Slope Length and Steepness Factor, LS.
The slope and slope length factor, LS, had been calibrated from a digitized
topographic map of the Putrajaya area. The digitized topographic map in line
format (GIS shapefile) was then converted to digital elevation model (DEM) using
“contour gridder extension” written by Stuckens (2003).
The LS factor was originally calculated using the equation below;
LS = (λ/22.13)m(0.065 + 0.046S + 0.0065S2)…. (3.9)(Wischemier and Smith, 1978)
Where,
λ = slope length (m)
S = slope in percent
m = 0.2 for S<1%, 0.3 for 1%<S<3%, 0.4 for 3%<S<5%,
0.5 for 5%<S<12% and 0.6 for S>12%
Figure 3.11 shows the graph that combines the L and S factors. The LS factor
equation is modified to better express the influence of complex terrain for
computation in GIS environment as below:
LS = (Lhill/22.1)m (65.41 sin2 a + 4.56 sina + 0.065) …. (3.10) (Wischemier and
Smith, 1978)
Where,
Lhill = slope length in meters,
a = angle of slope,
m = 0.5 if % S >= 4.51, 0.4 if % S is = 3.01 to 4.5, 0.3 if %S = 1 to 3, 0.2 if
% S < 1,
ArcView Spatial Analyst was then been applied to calculate the LS Factor using
expression:
([Flow Length Grid] / 22.1).Pow( [M Value Grid] )* [Slope Radians Grid].Sin.Pow(2 ) * 65.41+ [Slope Radians Grid].Sin * 4.56 + 0.065
Source: (Nadine, 2003)
Material and Method
Figure 3.11: Combine Slope Length-Steepness Factor, LS, chart.
Source: (Wischemier & Smith, 1978)
80
Material and Method
3.4.4 Determination of Land Cover and Management Factor, CP.
Generally, the C factor is a factor in USLE that is defined as the ratio of soil
loss from land cropped under specific conditions while P factor is the expression of
land management factor (Wischemier and Smith, 1958). The land use and land
management factor, CP, for this study was calibrated based on land use
classification for the years 2003 and 2004 spot 4 satellite images, classified using
PCI Geomatica satellite image analysis software.
The land cover and management factor (CP) value based on research
carried by Roslan and Tew (1996) for Malaysian conditions was adopted for this
study. Table 3.6 show the CP factor value as given by Roslan and Tew (1996).
Table 3.7 summarizes the data source and material and data generation
processes for the respective analysis.
3.4.5 Determination of USLE Soil Loss (Erosion) rate and USLE Catchment
Sediment Yield
The generated USLE factor raster grid maps are then combined with each
other using the “map calculator” function in Arcview GIS to generate spatial annual
soil loss rate in t/ha/yr for the years 2003 and 2004. Furthermore, the sensitivity of
USLE factors contributing to the USLE erosion results had been assessed in GIS
using the grid regression analysis extension developed by Jenness (2006).
81
Material and Method
Table 3.6: CP factor value used in USLE.
Land Cover CP
factor
Water body 0.000
Bareland (mining areas, newly cleared land, construction area) 1.000 Horticultural, agricultural, Palm oil 0.250
Permanent Cropland 0.150
Cropland 0.200
Rangeland / Shrubland 0.229
Commercial 0.008
Impervious 0.005
Residential 0.003
Swamps 0.001
Forest 0.010
Grassland 0.015
Source: (Roslan and Tew, 1996)
Table 3.7: Summary of the data source and material, data generation
process and the scenario undertaken for land use analysis.
Selected Scenario Data source and material Data Generation Process
Year 2003
land use Spot 4 satellite image
Year 2004
land use Spot 5 satellite image
Year 2006
land use Spot 4 satellite image
Remote sensing
image classification
procedure to land use
polygon shape file format
82
Material and Method
For determination of the catchment sediment yield from USLE, the sediment
delivery ratio (SDR) for each subcatchment was calculated based on the Vanoni
(1975) and USDA (1972) equations:
SDR = 0.4724 A -0.125 …. (3.11) (Vanoni, 1975)
SDR = 0.5656 A -0.11 …. (3.12) (USDA, 1972)
Where,
A = watershed area (km2).
These SDR equations express the effect of catchment area size to the
sediment deposition downstream where the greater catchment size results in lower
sediment yields in the catchment outlet downstream. Although there are numerous
SDR equations and calculation methods these two methods are chosen because
of their simplicity, the applicability and their general reception by other researchers
(Lim et al., 2005). Other methods may be applied for further studies. The USLE
gross erosion results were multiplied to the value calculated using SDR equations
above.
3.5 Method for Determination of Wetland Reservoir Sediment Yield (t/ha/yr)
from Sedimentation Survey Exercise
The sedimentation survey data was used primarily to determine wetland
reservoir sediment yield. Initially, all the data from the sedimentation survey was by
volume (m3 of sediment). The volume was converted to mass (tonnes, t) in this
study by multiplying it with the bulk density (g/cm3) of the wetland bed sediment.
Details on this exercise will be explained further in subsection 3.5.2.
83
Material and Method
3.5.1 Method for Sedimentation Survey
3.5.1.1 Static station sedimentation survey
Static station sedimentation survey was done for the wetland with emerging
sediment accumulation or in area with low water depths. A ranging pole was used
to get the levels of the sedimentation surface at every +/- 20m (grid point). The
Theodolite Total Station was used to collect all the data of height and coordinates
(X,Y,Z) of the point, the instrument was located at the control points in the weir.
Lines at 20m interval were laid out several directions to get the levels of the
sedimentation surface.
3.5.1.2 Moveable station sedimentation survey
In order to quantify the sedimentation for an area with deeper water depth,
the boat equipped with GPS and echo sounder was used. The path of the boat
followed the direction of the 20m interval grid across the water surface.
The whole area of sedimentation and underwater topography was charted
continuously by an Echo Sounder. The boat was located using a Global Positioning
System (GPS) with ‘Static Survey L1 & L2’ with an accuracy not less than (5mm +1
mm). The depth and the location were recorded in the GPS Trimble 400Ssi L1 &
L2, Bathy 500 AECH0 Sounder & Transducer, Radio, Data Recorder and Note
Book automatically.
3.5.2 Conversion of Sediment Volume Unit (m3) to Mass Unit (t, tonnes)
Generally, the measured sediment volume (m3) from the sedimentation
survey exercise was converted to the sediment mass (tonnes) using the
representative bulk density (Verstraeten and Poesen, 2001) acquired from the
84
Material and Method
85
wetland bed sediment. Three to four wetland bed sediment samples were collected
randomly close to the middle of each wetland cell area to quantify their
representative bulk density. The collected samples were brought back to the
Geology Department to analyze their bulk density according to British Standard
method (BS 1137: 1990, Part 2, Method 7.2).
The results gathered from the bulk density analysis for the wetland bed
sediment samples were then been averaged and multiplied with their respective
wetland sediment volumes (m3) to obtain unit in tones.
3.5.3 Wetland Reservoir Sediment Yield Estimation
The wetland reservoir sediment yield (t/ha/yr) was calculated using the formula
below:
SYres = Mres / A …. (3.13)
Where,
SYres = Reservoir sediment yield for particular year (t/ha/yr)
Mres = Sediment accumulation in reservoir for respective year (t)
A = Catchment area (ha)
These data will be used to further analyze and compare with other data from USLE
catchment sediment yields and TSS yields respectively.
4.1 Introduction
This chapter will present and discuss the results and findings obtained
from analyses undertaken during the study of erosion and sedimentation
process within the Putrajaya wetland area. Spatial and temporal sheet and rill
erosion at the Putrajaya area was determined using the Universal Soil Loss
Equation (USLE) while the observed bank erosion within the wetland area was
documented respectively. Three different sediment yield data approaches had
been used (the determination of the TSS yield from the TSS rating curve, the
determination of the catchment sediment yield from the USLE-SDR approach
and the wetland reservoir sediment yield from sedimentation survey data). The
results from the above were compared and the linkages of the results were
further assessed. Finally, specific and suitable erosion and sediment control
measures are proposed at the end of the chapter.
4.2 Erosion determination using USLE 4.2.1 Introduction
The USLE was adopted to obtain measurements of sheet and rill erosion
in the study area using 10m, 20m, 30m and 40m grids. Further analyses of
USLE for total gross and specific erosion for selected wetland cell
subcatchment areas were carried out. Sensitivity analysis for all factors that
contribute to a particular USLE result was also performed accordingly.
4.2.2 Soil Erodibility Factor (K Factor) Determination Results
The samples collected from the surrounding Putrajaya area show a
variety of soil types (Table 4.1, Figure 4.1) ranging from clay, sandy clay to
sandy clay loam. Result of the detail particle size analyses are found in
Appendix 2.
86
Table 4.1: Particle size analyses results and USDA soil classification system for samples collected from the Putrajaya catchment area.
No. Sample name
Subcatchment Area
Gravel Sand silt clay USDA Soil Classification
System 1 PL1 4.05 25.96 17.93 52.06 Clay 2 PL2 2.69 14.07 15.32 67.92 Clay 3 PL3 8.89 59.09 5.38 26.65 Sandy Clay Loam 4 PL4 4.86 38.00 13.47 43.68 Clay
5 PL5 8.45 41.63 10.71 39.20 Sandy Clay
6 PL6 1.24 37.13 21.41 40.21 Clay
7 PL7 9.87 42.56 7.75 39.82 Sandy Clay
8 PL8 31.30 28.00 10.94 29.76 Gravelly Clay
9 PL9 4.26 27.11 19.86 48.77 Clay 10 PL10 1.47 48.83 12.52 37.17 Sandy Clay 11 PL11 2.53 23.13 18.95 55.39 Clay 12 PL12 9.72 30.53 11.64 48.11 Clay 13 PL13 3.40 20.29 19.88 56.43 Clay 14 PL14 3.25 36.18 14.92 45.65 Clay 15 PL15 20.14 47.45 7.07 25.34 Gravelly Sandy Clay Loam 16 PL16 6.78 44.44 12.30 36.48 Sandy Clay 17 PL17 9.11 43.38 9.51 38.00 Sandy Clay 18 PL18 15.50 27.55 13.54 43.41 Clay 19 PL19 1.00 68.12 6.65 24.23 Sandy Clay Loam 20 PL20
Lake
10.41 44.98 10.43 34.18 Sandy Clay 21 UB1(1) 0.30 49.62 9.06 41.02 Sandy Clay 22 UB1(2) 29.60 56.36 2.24 11.58 Gravelly Sandy Loam 23 UB2(1) 16.87 71.76 2.16 8.80 Gravelly Loamy Sand 24 UB2(2) 6.25 47.89 10.73 35.13 Sandy Clay 25 UB2(3)
Upper Bisa
3.42 33.79 8.30 54.50 Clay 26 LE1(1) 2.36 44.57 20.23 32.84 Sandy Clay Loam 27 LE1(2) 17.69 38.41 9.42 34.48 Gravelly Sandy Clay 28 LE2
Lower East 5.61 61.51 10.53 22.35 Sandy Clay Loam
29 UW1 3.63 26.79 9.41 60.16 Clay 30 UW2-8 0.26 4.36 24.52 70.86 Clay 31 UW4 8.49 51.36 9.30 30.85 Sandy Clay Loam 32 UW6 9.85 37.92 9.91 42.32 Clay 33 UW8
Upper West
7.88 52.00 8.74 31.38 Sandy Clay Loam 34 UE1 2.04 26.65 13.01 58.31 Clay 35 UE1(ioi) 18.40 36.48 5.90 39.22 Gravelly Clay 36 UE3
Upper East 6.08 30.98 12.48 50.46 Clay
37 UN1 3.53 48.96 9.66 37.85 Sandy Clay 38 UN3 0.07 43.41 16.58 39.94 Clay 39 UN5 5.95 47.60 11.33 35.12 Sandy Clay 40 UN7 1.83 37.02 9.38 51.60 Clay 41 UN8
Upper North
0.16 30.45 15.88 53.06 Clay 42 CWA Central Wetland 3.90 33.56 17.99 44.55 Clay
87
88
CWA
LE1(1) LE1(2) LE2 PL1 PL10 PL11 PL12 PL13 PL14 PL15 PL16PL17PL18PL19 PL2 PL20 PL3 PL4 PL5 PL6
PL7 PL8 PL9 UB1(1) UB1(2) UB2(1) UB2(2) UB2(3) UE1 UE1(ioi) UE3 UN1 UN3 UN5 UN7 UN8 UW1 UW2-8 UW4 UW6 UW8
100
90
80
70
60
50
40
30
20
10
100
90
80
70
60
50
40
30
20
10
100 90 80 70 60 50 40 30 20
CLAY
CLAY
SILTY CLAY
SANDY CLAY
10SAND SILT
SILTY CLAY LOAMCLAY LOAM
SANDY CLAY LOAM
LOAMSANDY LOAMSILT LOAM
LOAMY SAND
SANDSILT
Figure 4.1: Triangular plot of particle size results on soil USDA
classification system.
The samples collected from the Upper West subcatchment area have
clay to sandy clay loam soils type. The soil types range from clay to sandy clay
in Upper North area., All samples from the Upper East and Central
subcatchment area have a classification of clay while samples collected at
Lower East subcatchment area are classified as sandy clay loam to sandy clay.
The Upper Bisa subcatchment area shows a variety of soil types ranging from
sandy clay, sandy loam, loamy sand to clay with organic matter content ranging
from 0.14 to 8.70 % respectively.
The calculated soil erodibility factor (K factor) from (Table 4.2, Figure
4.2) around the Putrajaya area ranges from as low as 0.05 (at UB1(2)) to as
high as 0.38 (at PL6). The statistics of the K factor (Table 4.3) show that the
lake subcatchment area has the highest mean average K factor (0.24) while the
lowest mean average K factor (0.16) was observed at the Upper Bisa
subcatchment area. The lowest standard deviation K factor was from the Upper
North subcatchment area (0.02).
Raster grid maps for surface interpolated K factor (soil erodibility factor
maps) for the Putrajaya catchment area in different grid sizes (10m, 20m, 30m
and 40m grid size) are shown in Figure 4.3. The interpolated K factor maps
show relatively similar values for almost all the different grid resolution sizes
with only slight differences in terms of maximum and mean K factor. The
generated K factor maps in different grid sizes will be used for further USLE
erosion raster calculations. The K factor for Putrajaya area is assumed to be
constant throughout the analysis for each year (2003 and 2004).
89
Table 4.2: Calculated soil erodibility factor results for 42 samples within study area.
No. Sample name
Subcatchment Area
M a b c K
1 PL1 1562.04 2.14 4 6 0.23 2 PL2 730.38 0.61 4 6 0.18 3 PL3 2052.43 0.31 4 5 0.26 4 PL4 1865.24 0.88 4 6 0.26
5 PL5 1765.65 0.49 4 6 0.26
6 PL6 3096.21 0.19 4 6 0.38
7 PL7 1560.06 0.69 3 6 0.21
8 PL8 1500.53 0.28 3 6 0.21
9 PL9 1626.62 0.95 2 6 0.18 10 PL10 2787.08 0.26 4 6 0.35 11 PL11 1429.01 0.80 4 6 0.23 12 PL12 1522.73 0.86 2 6 0.17 13 PL13 1328.35 0.83 3 6 0.19 14 PL14 1888.46 0.46 4 6 0.27 15 PL15 1726.97 0.50 3 5 0.20 16 PL16 2005.38 0.62 4 5 0.25 17 PL17 1912.81 1.53 2 5 0.17 18 PL18 1397.11 0.34 4 6 0.23 19 PL19 4168.66 2.11 3 5 0.36 20 PL20
Lake
2400.89 2.22 2 5 0.20 21 UB1(1) 2198.72 0.19 4 5 0.28 22 UB1(2) 692.30 0.36 3 2 0.05 23 UB2(1) 2319.05 0.20 2 2 0.15 24 UB2(2) 2479.16 5.58 3 5 0.18 25 UB2(3)
Upper Bisa
1581.09 0.22 1 6 0.15 26 LE1(1) 3230.10 1.68 3 4 0.27 27 LE1(2) 1952.32 8.70 3 4 0.10 28 LE2
Lower East 2999.53 1.49 3 4 0.26
29 UW1 983.19 1.01 4 6 0.20 30 UW2-8 812.30 0.14 4 5 0.17 31 UW4 2840.28 2.85 4 3 0.23 32 UW6 1618.09 1.37 3 6 0.21 33 UW8
Upper West
2878.07 2.92 4 5 0.28 34 UE1 1129.34 0.16 2 6 0.15 35 UE1(ioi) 1302.71 0.66 4 6 0.22 36 UE3
Upper East 1390.31 5.79 2 6 0.12
37 UN1 2485.62 2.21 4 5 0.27 38 UN3 2557.82 0.17 3 5 0.27 39 UN5 2504.49 1.88 4 5 0.27 40 UN7 1486.86 2.88 4 6 0.22 41 UN8
Upper North
2021.36 2.52 4 6 0.26 42 CWA Central Wetland 2136.90 1.22 4 4 0.23
90
Figure 4.2: Histogram of calculated K factor for the Putrajaya lake and respective wetland subcatchment areas. Table 4.3: Statistics of K factor results for each Putrajaya subcatchment area.
Putrajaya Sub Catchment
N Mean Min Max St Dev
Lake 20 0.24 0.17 0.38 0.06 Upper Bisa 5 0.16 0.05 0.28 0.08 Lower East 3 0.21 0.10 0.26 0.10 Upper West 5 0.22 0.17 0.28 0.04 Upper East 3 0.17 0.12 0.22 0.05 Upper North 5 0.26 0.22 0.27 0.02
The calculated K factor result for sample collected around Putrajaya area
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40P
L1
PL2
PL3
PL4
PL5
PL6
PL7
PL8
PL9
PL1
0
PL1
1
PL1
2
PL1
3
PL1
4
PL1
5
PL1
6
PL1
7
PL1
8
PL1
9
PL2
0
UB
1(1)
UB
1(2)
UB
2(1)
UB
2(2)
UB
2(3)
LE1(
1)
LE1(
2)
LE2
UW
1
UW
2-8
UW
4
UW
6
UW
8
UE
1
UE
1(io
i)
UE
3
UN
1
UN
3
UN
5
UN
7
UN
8
CW
A
Sampling Station
K fa
ctor
K (TKH, 1999)
Lake Upper Bisa Low er East Upper West Upper East Upper North
91
2 0 2 4 Kilometers
kfac_10m0.08 - 0.1520.152 - 0.2240.224 - 0.2960.296 - 0.3680.368 - 0.440.44 - 0.5120.512 - 0.5850.585 - 0.6570.657 - 0.729
N
2 0 2 4 Kilometers
kfac_20m0.081 - 0.1530.153 - 0.2250.225 - 0.2970.297 - 0.3690.369 - 0.4410.441 - 0.5130.513 - 0.5850.585 - 0.6570.657 - 0.729
N
( )
(b) (a)
2 0 2 4 Kilometers
kfac_30m0.081 - 0.1530.153 - 0.2250.225 - 0.2970.297 - 0.3690.369 - 0.4410.441 - 0.5120.512 - 0.5840.584 - 0.6560.656 - 0.728
N
2 0 2 4 Kilometers
kfac_40m0.084 - 0.1560.156 - 0.2270.227 - 0.2990.299 - 0.370.37 - 0.4420.442 - 0.5130.513 - 0.5850.585 - 0.6560.656 - 0.728
N
(d) (c) Figure 4.3: Soil erodibility factor maps (K factor maps) for the Putrajaya
catchment area in 10m (a), 20m (b), 30m (c) and 40m (d) grid
sizes.
92
4.2.3 Rainfall-Runoff Erosivity Factor (R Factor) Determination Results
The erosivity of rainfall is a major input variable in USLE. Indices of rain
erosivity are parameters derived from rainfall characteristics that are sufficiently
correlated with surface erosion (splash, sheet and rill erosion) resulting from
rainfall to be used in soil loss prediction.
The total annual rainfall and average annual rainfall intensity data from
six rainfall stations maintained by the Putrajaya Corporation (PJC) and one
Drainage of Irrigation Department (DID) rainfall station located in the Putrajaya
area for 2003 and 2004 are used for rainfall-runoff erosivity (R) factor
computation. Table 4.4 and Figure 4.4 show the total annual rainfall recorded
from different rainfall stations located in the Putrajaya area for 2003 and 2004
while Table 4.5 and Figure 4.5 show the recorded average annual maximum
30 minute rainfall intensity (i30) respectively. Details of the total annual rainfall
and average annual rainfall intensity data are found in Appendix 2.
In general, 2004 has lower total annual rainfall and average annual
maximum 30 minute rainfall intensity (i30) in compared to 2003. The total
annual rainfall recorded for 2003 ranges from 1272.9 (DID PB rainfall station)
mm to 3297.3 mm (K-01 rainfall station) and for 2004, the recorded total annual
rainfall ranged from 1147.8 mm (W-01 rainfall station) to 2779.6 mm (K-01
rainfall station). The recorded average annual maximum 30 minute rainfall
intensity (i30) for 2003 ranged from 16.3 mm (W-01 rainfall station) to 50.4 mm
(R-01 and K-01 rainfall station) while for 2004, the average annual maximum 30
minute rainfall intensity (i30) ranged from 17.6 mm (W-01 rainfall station) to 47.2
mm (R-01 and K-01 rainfall station).
93
Table 4.4: Total annual rainfall recorded at selected rainfall station located
within Putrajaya area for year 2003 and 2004.
Rainfall Station
2003 2004
K-01 3297.3 2779.6 W-01 1535.7 1147.8 R-01 3166.9 2578.2 R-02 1943.3 2389.7 R-03 2577.8 2395.9 R-04 2196.6 2178.2
DIDPB 1272.9 1898.9
Putrajaya Total Annual Rainfall
0
500
1000
1500
2000
2500
3000
3500
K-01 W-01 R-01 R-02 R-03 R-04 DIDPB
Rainfall Station
Ra
infa
ll (
mm
)
2003
2004
Figure 4.4: Histogram of total annual rainfall recorded within Putrajaya
area for year 2003 and 2004.
94
Table 4.5: Average annual 30 minute maximum rainfall intensity (i30)
recorded at selected rainfall station located within Putrajaya area
for year 2003 and 2004.
Rainfall Station
2003 2004
K-01* 50.4 47.2 W-01 16.3 17.6 R-01 50.4 47.2 R-02 38.4 30.1 R-03 18.9 24.9 R-04 25.3 32.3
DIDPB 32.7 34.4 * Assume same i30 value with R-01 rainfall station.
Putrajaya Average Annual Maximum 30 Minute
Rainfall Intensity (i30)
0
10
20
30
40
50
60
K-01 W-01 R-01 R-02 R-03 R-04 DIDPB
Rainfall Station
Rai
nfa
ll I
nte
nsi
ty (
mm
/hr)
2003
2004
Figure 4.5: Histogram of average annual maximum 30 minute rainfall
intensity (i30) within Putrajaya area for year 2003 and 2004.
95
The results show that the calculated rainfall-runoff erosivity factor (Table
4.6, Figure 4.6) are highly varied in terms of spatial and temporal aspects
where almost all rainfall stations in year 2004 show lower R factor value
compared to 2003. In 2003, the lowest R factor value recorded was at the W-01
rainfall station (518.4) while the highest (6443.9) was recorded at the K-01
rainfall station. The lowest R factor value for 2004 was recorded also at the W-
01 rainfall station (187.6) while the highest was from the K-01 rainfall station
(4702.4).
These total annual rainfall and average annual rainfall intensity data for
each rainfall station were then inserted into the USLE GIS database and the
Thiesen area-precipitation calculation extension of Petras (2001) was utilized to
calculate the Thiesen rainfall area poligon for Putrajaya area. The rainfall
erosivity map for USLE calculations was produced using raster grid. (Figure
4.7).
Table 4.6: Calculated rainfall-runoff erosivity factor (R factor) for year 2003
and 2004 at selected station within Putrajaya catchment area.
Rainfall Station
2003 2004
K-01 6443.85 4702.40W-01 518.44 187.55 R-01 6085.53 4184.16R-02 2071.91 2361.45R-03 1674.99 1957.19R-04 1716.39 2161.04
DIDPB 571.70 1773.01
96
Putrajaya Rainfall-Runoff Erosivity Factor
0
1000
2000
3000
4000
5000
6000
7000
K-01 W-01 R-01 R-02 R-03 R-04 DIDPB
Rainfall Station
Rai
nfa
ll-R
un
off
Ero
sivi
ty
Fac
tor
(mm
)
2003
2004
Figure 4.6: Histogram of calculated rainfall-runoff erosivity factor (R
factor) within Putrajaya area for year 2003 and 2004.
1 0 1 2 Kilometers
2003 Rainfall Erosivity (Mj.mm/ha.h.yr)0 - 10001000 - 20002000 - 30003000 - 40004000 - 50005000 - 60006000 - 7000
N
1 0 1 2 Kilometers
2004 Rainfall Erosivity (Mj.mm/ha.h.yr)0 - 10001000 - 20002000 - 30003000 - 40004000 - 50005000 - 60006000 - 7000
N
(a) (b)
Figure 4.7: Rainfall-runoff erosivity factor (R factor) map for the Putrajaya
catchment area for 2003 (a) and 2004 (b).
97
4.2.4 Slope Length and Steepness (LS Factor) Determination Result
The LS factor is an expression of the effects of topography, specifically
hillslope length and steepness, on rates of soil loss at a particular site. In
general, the value of the LS factor increases as hillslope length and steepness
increase, under the assumption that runoff accumulates and accelerates in the
downslope direction. A visual examination of the Slope Length and Steepness
factor map (Figure 4.8) indicates that the grid cell size does have profound a
effect on the spatial pattern of the LS factor (scale effect). The LS factor map for
the 10m grid (the finest grid cell size) shows a smoother texture in comparison
to higher grid resolution size.
In term of statistical characteristics (Table 4.7), the LS factor for 10m grid
resolution size ranges from 0 to 0.330, for 20m grid from 0 to 0.274, for 30m
grid from 0 to 0.259 and for 40m grid from 0 to 0.263. This results indicate that
the maximum value of the LS factor decreases with the increase of grid cell
resolution sizes (Figure 4.9). The mean values (Figure 4.10) for the LS factor
also show a similar trend with maximum value decreasing with the increasing
grid sizes (mean LS factor for 10m grid size is 0.053, for 20m grid size is 0.048,
for 30m grid size is 0.042 and for 40m grid size is 0.039).
However, in terms of standard deviation (SD) (Figure 4.11), the SD value
of the LS factor shows an increasing trend with the increase of grid resolution
sizes. The SD value of 0.051 have been calculated for 10m grid resolution size
LS factor and is 0.054 for 20m grid, 0.056 for the 30m grid and 0.057 for the
40m grid. This result indicates that using a higher grid resolution, wider LS
factor values are dispersed from the average value and consequently, may
affect the USLE result accordingly.
98
1 0 1 2 Kilometers
LSfac_10m0 - 0.0370.037 - 0.0730.073 - 0.110.11 - 0.1470.147 - 0.1830.183 - 0.220.22 - 0.2560.256 - 0.2930.293 - 0.33
N
1 0 1 2 Kilometers
LSfac_20m0 - 0.030.03 - 0.0610.061 - 0.0910.091 - 0.1220.122 - 0.1520.152 - 0.1830.183 - 0.2130.213 - 0.2430.243 - 0.274
N
(b) (a)
1 0 1 2 Kilometers
LSfac_30m0 - 0.0290.029 - 0.0570.057 - 0.0860.086 - 0.1150.115 - 0.1440.144 - 0.1720.172 - 0.2010.201 - 0.230.23 - 0.259
N
1 0 1 2 Kilometers
LSfac_40m0 - 0.0290.029 - 0.0580.058 - 0.0880.088 - 0.1170.117 - 0.1460.146 - 0.1750.175 - 0.2040.204 - 0.2340.234 - 0.263
N
(c) (d)
Figure 4.8: Slope Length and Steepness (LS) factor maps using 10m (a),
20m (b), 30m (c) and 40m (d) grid cell sizes for Putrajaya
catchment area.
99
Table 4.7: Statistical characteristics of the LS factor maps for the
Putrajaya area.
Grid Size 10m 20m 30m 40m
N 500344 125051 55604 31264 Mean 0.053 0.048 0.042 0.039
SD 0.051 0.054 0.056 0.057 Min 0.000 0.000 0.000 0.000 Max 0.330 0.274 0.259 0.263
Range 0.330 0.274 0.259 0.263
Maximum LS Factor Value for Different Grid Cell Size Resolution
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
10m 20m 30m 40m
Grid Cell Size
Max
imu
m L
S F
acto
r
max
Figure 4.9: Plot of maximum LS factor value versus grid cell size.
Mean LS Factor Value for Different Grid Cell Size Resolution
0
0.01
0.02
0.03
0.04
0.05
0.06
10m 20m 30m 40m
Grid Cell Size
Mea
n L
S F
acto
r
mean
Figure 4.10: Plot of mean LS factor value versus grid cell size.
100
Standard Deviation of LS Factor Value for Different Grid Cell Size Resolution
0.048
0.049
0.05
0.051
0.052
0.053
0.054
0.055
0.056
0.057
0.058
10m 20m 30m 40m
Grid Cell Size
Sta
nd
ard
Dev
iati
on
SD
Figure 4.11: Plot of LS factor Standard Deviation value versus grid cell
size. Since the use of different grid resolution sizes greatly affect the LS factor
value, the use of suitable and correct grid resolution for USLE calculation is
needed. Many researchers had emphasized the effects of grid cell sizes on
USLE calculations using GIS. Molnar and Julien (1998) used cell sizes of 30 m
to 690 m for USLE calculation within the GRASS GIS platform and found that
the LS factor is clearly affected by changes in scale. As cell size is increased
from 30 m to 690 m, the distribution of slopes is smoothed out and tends toward
a lower mean slope for the entire watershed. The coarser resolution will
underestimate the calculation result of erosion losses for the entire basin.
Lee and Lee (2006) also found that the spatial resolution is very sensitive
to the determination of soil loss in the RUSLE model (revised USLE) and
suggested that caution needs to be taken in selecting the grid size for
estimating soil loss using the numerical modeling approach. Wu et. al (2005)
found that selection of the DEM grid size has considerable influence on soil loss
calculation with the empirical models. The determination of total soil loss from
the watershed decreases significantly with increasing DEM cell size as the
101
spatial variability is reduced by cell aggregation. Furthermore, Ramli et. al
(2006) who studied the effect of digital elevation model on soil erosion studies
at Cameron Highlands, Malaysia, found that the different DEM resolutions
produced different slope angles, slope aspects and especially USLE LS factor
and emphasize the importance of suitable DEM scale in soil erosion studies.
4.2.5 Land Cover and Management Factor (CP Factor) Determination
Result
The land cover and management factor expressed the ratio of soil loss
under specified field conditions to the corresponding loss from the standard soil
plot. The scenario for 2003 and 2004 had been analyzed in terms of their land
use. These temporal scenarios have been classified from Spot 4 satellite
images that have been discussed in Subsection 3.3.4. The satellite images that
have been utilized in this study are found in Appendix 4.
From Figure 4.12, it has been observed that there is a slight decline in
percentage of bare area, impervious areas or pavements like parking lots and
water bodies from 2003 to 2004. The grassland or scrubland show the highest
percentage in 2004 compared to 2003 indicating that major land clearing
activities took place during 2004. This land clearing activities tally with the clear
percentages by reduction of trees or secondary forest or oil palm in 2004. The
slight decrease water bodies percentages from 2003 to 2004 suggested the
sedimentation have reduced respective water bodies. Figure 4.13 show the CP
factor raster grid maps produced for 2003 and 2004 respectively. These CP
factor raster grid maps will be used for further USLE factor calculations.
102
Percentage of Land Use at Putrajaya for 2003 and 2004
05
101520253035404550
Bare area /construction
site
Grassland /Scrubland
Mixedresidential /
Govt Institution
Trees /secondary
forest/ Oil Palm
Road /Impervious /Pavement
Water bodies
Land Use
Pe
rce
nta
ge
(%
)
2003
2004
Figure 4.12: Percentage of land use at the Putrajaya catchment area for
2003 and 2004.
1 0 1 2 Kilometers
CP factor 20030 - 0.10.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.60.6 - 0.70.7 - 0.80.8 - 0.90.9 - 1
N
1 0 1 2 Kilometers
CP Factor 20040 - 0.10.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.60.6 - 0.70.7 - 0.80.8 - 0.90.9 - 1
N
(b) (a)
Figure 4.13: CP factor raster grid maps for 2003 (a) and 2004 (b)
103
4.2.6 Results of Spatial and Temporal USLE erosion calculations for
different grid resolution size
The USLE potential erosion maps (Figure 4.14 and Figure 4.15) and
statistics of erosion for 2003 and 2004 (Table 4.8) clearly show the declining
trend of total gross erosion, average mean and maximum erosion with the
increase of grid cell sizes. During 2003, using the 10m grid cell size, the total
gross erosion for the Putrajaya catchment area was 11,281,090 t/yr while the
total gross erosion was 2,505,608 t/yr for the 20m grid, 992,408 t/yr for the 30m
grid and 507,837.4 t/yr for the 40m grid.
The declining trend of total gross erosion with increase in grid cell size
has also been observed in the 2004 total gross erosion result. The total gross
erosion calculated for year 2004 in 10 m grid cell size (7,812,817 t/yr) had
declined to 1,772,180 t/yr when using the 20 m grid cell size, to 713,329.2 t/yr
with the use of the 30 m grid cell size and 356,791 t/yr for 40 m grid cell size.
In terms of temporal characteristic (Figure 4.16), a 30% decrease of total
gross erosion is observed from 2003 to 2004. This large decrease in total gross
erosion corresponded to a decrease in total annual rainfall that affected the
rainfall-runoff erosivity factor (refer to Figure 4.4) even with a slight increase of
bare area (refer to Figure 4.12). These results indicate that the rainfall-runoff
erosivity factor was one of the major parameters effecting the erosion
processes in study area instead of just land use factor.
104
( )
1 0 1 2 Kilometers
Soil erosion 2003 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
NN
1 0 1 2 Kilometers
Soil erosion 2003 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
(b) (a)
1 0 1 2 Kilometers
Soil erosion 2003 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
N
1 0 1 2 Kilometers
Soil erosion 2003 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
N
Soil erosion (t/ha/yr)
Figure 4.14: USLE erosion maps of Putrajaya catchment area for 2003 in
10m (a), 20m (b), 30m (c) and 40m (d) grid cell sizes.
0 - 1 (very low) 1 - 5 (low)
5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
(c) (d)
105
1 0 1 2 Kilometers
Soil erosion 2004 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)> 100 (extreme)
N
1 0 1 2 Kilometers
Soil erosion 2004 (Tons/ha0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
N
(b) (a)
1 0 1 2 Kilometers
Soil erosion 2004 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
N
1 0 1 2 Kilometers
Soil erosion 2004 (Tons/ha/yr)0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)>100 (extreme)
N
106
Figure 4.15: USLE erosion maps of Putrajaya catchment area for 2004 in
10m (a), 20m (b), 30m (c) and 40m (d) grid cell sizes.
0 - 1 (very low)1 - 5 (low)5 - 10 (moderate)10 - 20 (high)20 - 50 (very high)50 - 100 (severe)
Soil erosion (t/ha/yr)
)>100 (extreme
(c) (d)
Table 4.8: Statistics of potential erosion maps calculated for 2003 and
2004 using different grid cell sizes.
Analysis Year 2003 Grid Size (m) 10 20 30 40 Average Mean 22.60 20.08 17.86 16.37 Total gross erosion (t/yr) 11,281,090.00 2,505,608.00 992,408.00 507,837.40Standard Deviation 51.65 50.81 49.65 49.52 Min 0 0 0 0 Max 1357.30 888.27 918.01 825.59
Analysis Year 2004 Grid Size (m) 10 20 30 40 Average Mean 15.65 14.21 12.86 11.50 Total gross erosion (t/yr) 7,812,817.00 1,772,180.00 713,329.20 356,791.00Standard Deviation 39.10 39.66 39.07 37.60 Min 0 0 0 0 Max 707.53 573.15 602.86 584.86
Total Gross Erosion in Putrajaya Catchment Area
0.00E+00
2.00E+06
4.00E+06
6.00E+06
8.00E+06
1.00E+07
1.20E+07
10 20 30 40
Grid Cell Size
To
tal
Gro
ss E
rosi
on
(t/h
a/yr
)
2003
2004
Figure 4.16: Variation of total gross erosion in Putrajaya catchment area
with cell size for 2003 and 2004.
107
108
4.2.7 Analysis of USLE Total Gross Erosion and Specific Erosion at
Putrajaya Wetland Catchment Area
Further detailed analysis of total gross and specific erosion at the
Putrajaya wetland area was conducted in order to investigate the variability of
specific erosion for particular wetland cell subcatchment areas. The Putrajaya
wetland catchment area had been divided into several wetland subcatchment
areas. Generally, the total gross (Table 4.9) and specific erosion (Table 4.10)
for selected wetland subcatchment results show a clear decreasing amount of
specific erosion with increasing grid cell size. The average total gross erosion
and specific erosion calculated for 2003 clearly decreased from 404,418.33
t/ha/yr and 2479.22 t/ha/yr for the 10m grid cell size to 89,940.67 t/ha/yr and
557.89 t/ha/yr for the 20m grid cell size and 35,336.31 t/ha/yr and 2214.45
t/ha/yr for the 30m grid cell size until 18,061.49 t/ha/yr and 114.19 t/ha/yr for the
40 m grid cell size.
The minimum total gross erosion was observed at the UN8
subcatchment area (10m; 25,710.46 t/ha/yr, 20m; 6011.45 t/ha/yr, 30m;
1881.24 t/ha/yr and 40m; 939.89 t/ha/yr) while the maximum total gross erosion
was at UB1 (10m; 3,921,418.00 t/ha/yr, 20m; 868,008.40 t/ha/yr, 30m;
81,050.60 t/ha/yr and 40m; 41981.75 t/ha/yr). The minimum specific erosion
was at LE2 subcatchment area (10m; 205.16 t/ha/yr, 20m; 44.35 t/ha/yr, 30m;
17.58 t/ha/yr and 40m; 7.85 t/ha/yr) while the maximum specific erosion was at
the UW2 subcatchment area (10m; 4624.66 t/ha/yr, 20m; 1040.28 t/ha/yr, 30m;
402.83 t/ha/yr and 40m; 206.07 t/ha/yr).
Table 4.9: Result of USLE total gross erosion (t/yr) for selected wetland subcatchment areas in Putrajaya Wetland.
2003 2004 Wetland Cell
Wetland Cell subcatchment
Area (ha) 10m 20m 30m 40m 10m 20m 30m 40m
UW1 19.94 72384.72 15277.49 6481.22 4108.99 55126.86 9978.97 5911.33 2026.68
UW2 21.84 101002.64 22719.70 8797.88 3930.00 94706.49 16956.07 8878.07 2984.36
UW3 20.17 42473.31 9033.80 4047.25 1917.38 29547.51 6127.19 2362.63 1215.94
UW7 333.93 75361.24 16901.13 6804.26 3498.64 33310.85 7113.28 3378.06 1560.26
UW8 78.84 55349.66 10624.84 3833.21 2177.52 28287.53 7744.07 2451.32 1782.15
UN1 72.80 27606.21 6027.52 1881.24 939.89 23424.73 5857.59 2539.33 893.71
UN2 13.85 825689.40 185676.32 69812.03 35703.34 530052.70 117462.51 42918.71 22145.24
UN4 32.12 240050.08 55951.84 23675.73 11392.12 273638.80 60689.53 25586.11 11751.80
UN6 239.23 180052.13 37630.37 16550.05 7012.00 190320.20 46950.56 19645.95 8352.15
UN8 924.93 25710.46 6011.45 2307.60 1537.04 24068.51 6960.25 2287.73 1483.26
UE1 31.23 63746.18 13259.08 6301.14 3106.23 27334.63 9580.73 2634.48 1333.81
UE2 41.13 109221.93 26106.43 11838.63 5344.12 114890.82 33527.77 12859.78 5809.42
UE3 285.62 149290.64 33666.52 12878.22 6561.45 123326.70 18807.77 9196.18 5529.54
LE1 75.27 746864.30 167787.20 66740.00 32363.64 510142.80 114088.95 45926.08 23224.54
LE2 78.51 57051.18 12064.64 5529.51 2460.18 20987.81 6379.86 3289.19 1178.70
UB1 116.35 3921418.00 868008.40 339716.26 176130.66 2486266.80 572262.20 222747.05 116652.31
UB2 277.95 181839.48 42244.69 13523.09 8862.19 229730.63 51491.63 21223.75 9211.93
central 197.46 127158.40 27941.73 10244.19 5209.04 122114.39 30843.37 11117.66 5713.41
Min 13.85 25710.46 6011.45 1881.24 939.89 20987.81 5857.59 2287.73 893.71
Max 924.93 3921418.00 868008.40 339716.26 176130.66 2486266.80 572262.20 222747.05 116652.31 Average (Mean)
158.95 404418.33 89940.67 35336.31 18061.49 282068.49 64234.05 25519.75 12772.69
109
2003 2004 Wetland Cell
Wetland Cell subcatchment
Area (ha) 10m 20m 30m 40m 10m 20m 30m 40m
UW1 19.94 3630.13 766.17 325.04 206.07 2764.64 500.45 296.46 101.64
UW2 21.84 4624.66 1040.28 402.83 179.95 4336.38 776.38 406.51 136.65
UW3 20.17 2105.77 447.88 200.66 95.06 1464.92 303.78 117.14 60.28
UW7 333.93 2472.64 556.03 209.06 106.92 1587.32 351.76 128.53 66.32
UW8 78.84 3044.78 709.69 300.30 144.50 3470.81 769.78 324.53 149.06
UN1 72.80 2473.24 516.90 227.34 96.32 2614.29 644.93 269.86 114.73
UN2 13.85 1856.35 434.04 166.61 110.98 1737.80 502.55 165.18 107.09
UN4 32.12 3400.43 812.78 368.58 166.38 3576.92 1043.83 400.37 180.87
UN6 239.23 3121.95 701.36 278.98 135.28 2132.44 476.90 191.97 97.08
UN8 924.93 4436.29 984.13 381.91 200.01 2936.44 674.38 263.77 136.08
UE1 31.23 4071.67 894.71 328.02 166.80 3910.16 987.62 355.99 182.95
UE2 41.13 3233.18 804.39 305.20 168.02 4909.27 1012.96 461.28 170.19
UE3 285.62 2277.75 510.78 199.94 99.11 1455.04 330.65 134.40 65.21
LE1 75.27 589.67 129.32 51.89 28.07 370.90 82.08 29.53 16.99
LE2 78.51 205.16 44.35 17.58 7.85 98.25 19.85 7.84 3.46
UB1 116.35 290.75 64.70 25.95 16.93 74.07 23.55 10.52 5.89
UB2 277.95 312.37 66.69 25.75 12.98 127.97 28.48 10.76 5.68
central 197.46 1955.31 424.34 172.10 77.85 463.89 196.01 63.62 29.71
Min 13.85 205.16 44.35 17.58 7.85 74.07 19.85 7.84 3.46
Max 924.93 4624.66 1040.28 402.83 206.07 4909.27 1043.83 461.28 182.95 Average (mean)
158.95 2479.22 557.89 224.45 114.19 2209.86 501.76 210.27 94.13
110
Table 4.10: Result of USLE specific erosion (t/ha/yr) for selected wetland subcatchment areas in Putrajaya Wetland.
The average mean of total gross erosion and specific erosion for 2004
decreased from 282,068.49 t/ha/yr and 2209.86 t/ha/yr (10m grid cell size) to
64,234.05 t/ha/yr and 501.76 t/ha/yr (20m grid cell size), 25,519.75 t/ha/yr and
210.27 t/ha/yr (30m grid cell size) and 12,772.68 t/ha/yr and 94.13 t/ha/yr (40m
grid cell size). The minimum total gross erosion was observed at the LE2
subcatchment area (10m; 20,987.81 t/ha/yr, 20m; 5857.51 t/ha/yr, 30m;
2287.73 t/ha/yr and 40m; 893.71 t/ha/yr) while the maximum total gross erosion
is identified at UB1 (10m; 2,486,266.80 t/ha/yr, 20m; 572,262.20 t/ha/yr, 30m;
222,747.20 t/ha/yr and 40m; 116,652.31 t/ha/yr).
The minimum specific erosion for 2004 was observed at the UB1
subcatchment area for 10m (74.07 t/ha/yr) and at LE2 for 20m (19.85 t/ha/yr),
30m (7.84 t/ha/yr) and 40m (3.46 t/ha/yr) while the maximum specific erosion
was from the UE2 subcatchment area for 10m (4909.27 t/ha/yr), at UN4 for the
20m grid size (1043.83 t/ha/yr), at UE2 for the 30m (461.28 t/ha/yr) and UE1 for
the 40m grid size (182.95 t/ha/yr).
The variability of locations for the calculated average means, minimum
and maximum of total gross erosions and specific erosions for different grid cell
sizes indicate a clear effect of the grid cell sizes to the resulting USLE
calculations. Instead of topographic effect (differences grid cell sizes) the
heterogeneity of rainfall erosivity, differences in the soil distribution
characteristics and land use in the Putrajaya catchment area also contributes to
these USLE results.
4.2.8 Sensitivity Analysis of USLE Factors on USLE Results
Sensitivity analysis was performed in a GIS environment using grid
regression analysis extension as mentioned in subsection 3.4.5 to evaluate the
111
factors that most affected the USLE calculations. This analysis was also
performed in a temporal manner for year 2003 and 2004 using different grid cell
sizes (10 m, 20 m, 30 m and 40 m).
Generally, the factors with the highest R2 value were regarded as the
most influential and sensitive factors on the results (Balamurugan, 1990). The
regression analysis results and ANOVA tables can be found in Appendix 5.
From the results (Table 4.11, Figure 4.17), it is clearly observed that for almost
all the years analysed, using 20m, 30m and 40m grid cell sizes, the LS factor is
the most sensitive parameter compared to the other factors, except for 2004.
The CP factor was noted as the most sensitive factor for the 10m grid cell size,
in all years followed by the LS factor, R factor and K factor.
Table 4.11: Sensitivity analysis for USLE factors to USLE erosion results.
R2 value Analysis Year
Factors 10m 20m 30m 40m
R 0.057 0.047 0.038 0.030 K 0.043 0.033 0.027 0.023 LS 0.172 0.198 0.220 0.226
2003
CP 0.178 0.141 0.119 0.103 R 0.048 0.040 0.034 0.026 K 0.038 0.032 0.025 0.022 LS 0.146 0.167 0.191 0.197
2004
CP 0.244 0.202 0.172 0.145 * Note: Value in red marks the highest R2 value.
R2 values from grid regression analysis
0
0.05
0.1
0.15
0.2
0.25
0.3
R K LS CP R K LS CP
2003 2004
USLE Factors
R2 v
alu
e 10m
20m
30m
40m
Figure 4.17: Plot of R2 value from grid regression analysis of USLE factors for 2003 and 2004 using different grid sizes.
112
It is also observed that the LS factor for the 10m grid cell size have the
lowest R2 value in comparison with the values from using 20m, 30m and 40m
grid cell sizes. Furthermore, the R2 values for LS factor also show an increasing
trend with the increase of grid cell sizes for all the years analysed. This result
indicates that the LS factor plays the main role in affecting the USLE results for
grid cell sizes from 20m and above.
The relatively low USLE result by using 20m, 30m and 40m grid cell
sizes in comparison to the USLE 10m grid cell size result was related to the
gross underestimation of the LS factor whereas a relatively high USLE result for
the 10m grid cell size is related to the relatively high CP factor. The application
of finer grid cell sizes may contributes to overestimation in the USLE results.
Thus, further determinations on sediment yield should be based on USLE using
10m grid size USLE results.
4.3 Bank Erosion at Putrajaya Wetland Area
4.3.1 Introduction
Bank erosion can be considered as one of major sources of
sedimentation at the Putrajaya wetland area. From 2002 to 2006, certain areas
within the Putrajaya wetland area were affected by bank erosion, from severe to
minimal erosion, based on site observations. Detail records on bank erosion
had been extracted from reports done by Abd Hadi et al. (2002), Yusoff et al.
(2003 to 2006) and the author’s on-site observation respectively.
113
4.3.2 Severity and Location of Bank Erosion Within Putrajaya Wetland
Area
In general, bank erosion within Putrajaya Wetland Area can be classified
as moderate to critical depending on the failure itself. Furthermore, both bank
scour and mass failure had been identified as the major group of bank erosion
observed at the Putrajaya Wetland Area. Bank scour is defined as the direct
removal of bank materials by the physical action of flowing water and the
sediment that it carries while mass failure describes the various mechanisms of
bank erosion that result in sections of the bank sliding or toppling into the
wetland.
Table 4.12 summarizes the bank erosion in Putrajaya Wetland Area
while Figure 4.18 shows the location and severity of bank erosion respectively.
From year 2002 to 2003, moderate to major bank erosion had been observed at
the UW2, UW6 and UW8 wetland cells (Upper West (UW) wetland area) while
at the Upper North (UN) wetland area, moderate to major bank erosion had
been observed at the UN1, UN7 and UN8 wetland cells.
At the Upper East (UE) wetland area, major bank erosion had been
observed at UE1 while minor bank erosion had been observed at LE2 in the
Lower East (LE) wetland area. Major bank erosion had also been observed at
the UB1 and UB2 wetland cell in the Upper Bisa (UB) wetland area as bank
scour and mass failure. Table 4.13 shows the photographic history of bank
erosion for selected wetland cells for 2003 and 2004.
114
115
Table 4.12: Summary of bank erosion at the Putrajaya wetland area.
Location Specific Name Type of bank erosion Erosion
Severity
UB2 UB2-03 Bank scour and mass failure Major
UB2 UB2-04 Bank scour and mass failure Major
UB2 UB2-05 Bank scour Minor
UB1 UB1-05 Bank scour and mass failure Major
UB1 UB1-01 Bank scour Moderate
UW2 UW2-GPT5 Bank scour Moderate
UW6 UW6-C3 Bank scour Moderate
UW8 UW8-06 Bank scour and mass failure Major
UW8 UW8-C2 Bank scour Moderate
UN8 UN8-17 Bank scour and mass failure Major
UN8 UN8-C4 Bank scour and mass failure Major
UN7 UN7-C1 Bank scour and mass failure Major
UN1 UN1A Bank scour Moderate
UE3 UE3-GPT2 Bank scour and
mass failure Major
UE1 UE1-01 Bank scour and mass failure Major
LE2 LE2-05 Bank scour Minor
CW CWA Bank scour and mass failure Major
Figure 4.18: Location map of severity of bank erosion at Putrajaya Wetland Area.
116
Table 4.13: Historical photo evident of bank erosion for selected wetland cell from 2003 to 2004.
Location 2003 2004 Location 2003 2004
UB2-03
Major bank erosion (bank scour and mass failure)
Major bank erosion (bank scour and mass failure)
UE1
Major bank erosion (bank scour and mass failure)
Major bank erosion (bank scour and mass failure)
UB1-05
Major bank erosion (bank scour)
Major bank erosion (bank scour and mass failure)
UN8-17
Major bank erosion (bank scour)
Moderate bank erosion (bank scour)
UE3
Major bank erosion (bank scour and mass failure)
Major bank erosion (bank scour and mass failure)
UW8-06
Moderate bank erosion (bank scour)
Major bank erosion (bank scour and mass failure)
117
Bank scour and mass failure had been observed at UW8-06, UN8-17,
UN8-C4, UN7-C1, UE1-01, UE3-GPT2, UB1-05, UB2-03, UB2-04 and CWA
while bank scour was observed at UW2-GPT5, UW6-C3, UW8-C2, UN1A, LE2-
05, UB1-01 and UB2-05. Attempts by the management (Putrajaya Corporation)
to mitigate bank erosion, were successful at UN7-C1, UE3-GPT2, UN8-17,
UN1A, UB1-01and LE2-05 and only partially successful at UW8-06, UB2-03,
UW2-GPT5 and UW6-C3. All the unsuccessful attempts were probably caused
by under-designing of the inlet culverts and lack of water buffer areas in the
waterways before inlet culverts. Waterway blockage by tree trunks, trash and
sediments could possibly have influenced bank stability and the occurrences of
bank erosion.
4.3.3 Estimation of bank erosion within the Putrajaya Wetland Area
It is not easy to determine the amount of soil eroded from bank erosion
due to the nature of bank erosion that was actively changing all the times.
Furthermore, because of the bank erosion varies in terms of their types (bank
scour and mass failure), different approaches and models used of
measurement could lead to misinterpretation of the process itself with large
systematic errors (De Rose et al., 2005). Furthermore, the study of bank
erosion needs a huge amount of data (remote sensing, soil strength, long term
water discharge data and etc.) and detailed measurement and monitoring
techniques. The irregularity nature of eroded banks is commonly attributed to
the complex interplay between bank material erosion, resuspension, and bed
load. Further studies are needed to understand this process particularly in
wetland dominated area.
118
The estimation of the volume of bank erosion (Table 4.14) in this study is
based on the observation and measurement of scour length, width and depth of
the eroded bank. This determination is considered as a gross or rough
determination only as the amount of eroded sediment was determined without
taking any consideration the time factor. The determination value is not included
in the sediment delivery ratio (SDR) calculation due to the lack of this time
variable parameter and the roughness of measurement itself. The calculated
value is only meant to be as a rough reference only and should be further
correlated with the calculated SDR value.
Table 4.14: Estimated volumes of bank erosion based measurement of
scour length (m), width (m) and depth (m).
Location Specific
Name Erosion Severity
Maximum Length
(m)
Maximum Width
(m)
Maximum Depth
(m)
Estimated volume
(m3)
UB2 UB2-03 Major 5 4 3 60.0
UB2 UB2-04 Major 3 2 2 12.0
UB2 UB2-05 Minor 1 0.5 0.2 0.1
UB1 UB1-05 Major 4 3.2 2.5 32.0
UB1 UB1-01 Moderate 1 0.5 1.5 0.8
UW2 UW2- Moderate 1.5 0.5 0.5 0.4
UW6 UW6-C3 Moderate 0.5 1 0.5 0.3
UW8 UW8-06 Major 2 1 1 2.0
UW8 UW8-C2 Moderate 1 1 0.5 0.5
UN8 UN8-17 Major 3.5 2 2 14.0
UN8 UN8-C4 Major 2 1.5 2 6.0
UN7 UN7-C1 Major 4 3 3 36.0
UN1 UN1A Moderate 3 1.5 0.5 2.3
UE3 UE3-GPT2 Major 5 5 2.5 62.5
UE1 UE1-01 Major 3 4 3 36.0
LE2 LE2-05 Minor 1.5 0.5 1 0.8
CW CWA Major 2 3 2 12.0
119
4.4 Wetland Annual TSS Loading and TSS Yield determination from TSS Rating Curve
4.4.1 Introduction
TSS rating curve was generated based on instantaneous water
discharge and instantaneous TSS discharge (TSS loading) data from January
2002 to May 2006. From the power regression equation generated for each
TSS rating curve, the erosion and the availability of sediment supply nearby
each respective sampling station can also been interpreted. Finally, wetland
annual TSS loading and TSS yield are determined using the generated power
equation from the respective TSS rating curve based the on extrapolated total
annual water discharge for the analysis in the year undertaken.
4.4.2 Upper West Wetland TSS Rating Curve
Generally, the plotted sediment rating curves (Figure 4.19 (a), (b), (c),
(d) and (e)) for selected stations within the Upper West wetland show R2
values above 0.65 with the sediment rating curve for UW3 records the highest
R2 value (0.85) compared to the other stations. However, in terms of the
regression coefficient value (Table 4.15), the produced TSS rating curve for
almost all the Upper West sampling stations show a general higher value of the
“a” regression coefficient in comparison with the “b” regression coefficient value
except for the UW1 TSS rating curve that has low “a” value and high “b” value.
Low “a” value and high “b” value in the UW1 TSS rating curve is due to
generally high water discharge value at UW1 and indicates the availability of
eroded material during high discharge event.
120
UW1 TSS Rating Curve
y = 1.043x1.2818
R2 = 0.6598
0.001
0.01
0.1
1
10
0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(a)
UW2 TSS Rating Curve
y = 2.3361x1.2423
R2 = 0.6724
0.001
0.01
0.1
1
10
100
0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(b)
UW3 TSS Rating Curve
y = 3.7496x1.6339
R2 = 0.8322
0.001
0.01
0.1
1
10
100
0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(c)
121
UW7 TSS Rating Curve
y = 2.7234x1.4122
R2 = 0.8134
0.0001
0.001
0.01
0.1
1
10
100
0.0010 0.0100 0.1000 1.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay) Series1
Power (Series1)
(d)
UW8 TSS Rating Curve
y = 10.945x1.627
R2 = 0.6035
0.00001
0.0001
0.001
0.01
0.1
1
10
0.0010 0.0100 0.1000 1.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(e)
Figure 4.19: Plot of TSS rating curve in log-log axis for UW1 (a), UW2 (b),
UW3 (c), UW7 (d) and UW8 (e) sampling stations.
Table 4.15: Regression coefficients of TSS rating curves fitted for selected
sampling stations.
Sampling Station
Power Function Equation
a b R2 N
UW1 y = 1.043x1.2818 1.04 1.28 0.66 73 UW2 y = 2.3361x1.2423 2.34 1.24 0.67 73 UW3 y = 3.7496x1.6339 3.75 1.63 0.83 73 UW7 y = 2.7234x1.4122 2.72 1.41 0.81 73 UW8 y = 10.945x1.627 10.95 1.63 0.60 73
122
The UW8 TSS rating curve record the highest “a” regression coefficient
value (10.95) while UW3 record the lowest (1.04). Meanwhile, for the regression
coefficient “b” value, UW3 recorded the highest value (1.63) and the lowest was
observed at UW2 (1.24). The results could indicate that the UW8 surrounding
area was characterized by the highest availability of intensively weathered
material which could easily be eroded and transported (highest “a” value) while
the highest “b” value at UW3 station can be interpreted as an indicative of a
strong increase in erosive power and in sediment transport capacity when
discharge increased (Morgan, 1986; Asselman, 2000).
4.4.3 Upper North Wetland TSS Rating Curve
The TSS rating curve (Figure 4.20 (a), (b), (c) and (d)) for selected
stations at the Upper North (UN) wetland subcatchment show a relatively good
model efficiency criterion (R2) with the calculated R2 value above 0.71. The TSS
rating curve for UN1 recorded the highest R2 value (0.85) in comparison to the
other stations (UN2, 0.78; UN4, 0.77 and UN6, 0.71).
Generally, the TSS rating curve for the UN1, UN4 and UN6 sampling
stations show a higher value of “a” regression coefficient (Table 4.16) in
comparison to “b” regression coefficient while UN2 sampling station record
lower “a” regression coefficient in comparison to “b” regression coefficient. This
results indicate that the surrounding area of UN1, UN4 and UN6 sampling
stations can be characterized as the area with the greater availability of
weathered material which can easily be eroded (general high erodibility area)
and transported while at the UN2 station, with higher “b” regression coefficient
value, indicates that this wetland cell suffered increase in sediment transport
capacity when discharge increased.
123
UN1 TSS Rating Curve
y = 1.8137x1.4141
R2 = 0.8486
0.001
0.01
0.1
1
10
100
0.0010 0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(a)
UN2 TSS Rating Curve
y = 1.2141x1.4254
R2 = 0.7754
0.001
0.01
0.1
1
10
100
0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(b)
UN4 TSS Rating Curve
y = 1.5715x1.3506
R2 = 0.7733
0.001
0.01
0.1
1
10
100
0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(c)
124
UN6 TSS Rating Curve
y = 2.7135x1.3587
R2 = 0.7053
0.001
0.01
0.1
1
10
100
0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(d)
Figure 4.20: Plot of TSS rating curve fitted on log-log axis for UN1 (a), UN2
(b), UN4 (c), and UN6 (d) sampling stations. Table 4.16: Regression coefficients value of TSS rating curves fitted for
selected sampling stations.
Sampling Station
Power Function Equation
a b R2 N
UN1 y = 1.8137x1.4141 1.82 1.41 0.85 73 UN2 y = 1.2141x1.4254 1.21 1.43 0.76 73 UN4 y = 1.5715x1.3506 1.57 1.35 0.77 73 UN6 y = 2.7135x1.3587 2.71 1.36 0.71 71
4.4.4 Upper East Wetland TSS Rating Curve
Figure 4.21 (a), (b) and (c) show the plot of the TSS rating curve in log-
log axis for the UE1, UE2 and UE3 stations located at the Upper East (UE)
wetland subcatchment. Table 4.17 summarizes the regression coefficients of
rating curves fitted for selected sampling stations accordingly. A moderate to
strong model efficiency criterion (R2) had been observed (0.65 to 0.75) for all
TSS rating curve fitted at selected Upper East wetland sampling stations. The
TSS rating curve for the UE2 records the highest R2 value (0.77) in comparison
to the other stations (UE1, 0.75; UE3, 0.65).
125
UE1 TSS Rating Curve
y = 1.8474x1.4236
R2 = 0.7542
0.0001
0.001
0.01
0.1
1
10
100
0.0010 0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(a)
UE2 TSS Rating Curve
y = 1.6868x1.3129
R2 = 0.7726
0.0001
0.001
0.01
0.1
1
10
0.0010 0.0100 0.1000 1.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
/Day
)
Series1
Power (Series1)
(b)
UE3 TSS Rating Curve
y = 1.6609x1.154
R2 = 0.6522
0.0001
0.001
0.01
0.1
1
10
0.0010 0.0100 0.1000 1.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(c)
Figure 4.21: Plots of TSS rating curve fitted on log-log axis for UE1 (a), UE
(2) and UE3 (c) station.
126
Table 4.17: Regression coefficients of rating curves fitted for selected sampling stations at the UE subcatchment.
Sampling Station
Power Function Equation
a b R2 N
UE1 y = 1.8474x1.4236 1.85 1.42 0.75 73 UE2 y = 1.6868x1.3129 1.67 1.31 0.77 73 UE3 y = 1.6609x1.154 1.66 1.15 0.65 73
The TSS rating curve for UE1 shows the highest “a” and “b” regression
coefficient values (1.85 and 1.42) while the TSS rating curve for UE3 shows the
lowest “a” and “b” regression coefficient value (1.66 and 1.15) respectively. The
result also shows an increase of “a” and “b” regression coefficient values from
UE3 to UE1 indicating that there were increasing available amounts of
sediments together with higher TSS transport capacity from upstream to
downstream wetland cells.
An increase of curve steepness had been observed from the upstream to
downstream rating curves indicating that there was an increase of bed sediment
deposition toward downstream direction within the wetland cells with the highest
sediment deposition at the UE1 wetland cell. An increase of discharge will result
in a large increment of suspended sediment respectively (Asselman, 2000).
4.4.5 Lower East Wetland TSS Rating Curve
In general, the TSS rating curves for both LE1 and LE2 sampling station
(Figure 4.22a and 4.22b) show a moderate model efficiency criterion (R2)
where the R2 values for LE1 and LE2 were 0.55 and 0.63 respectively. The
decreasing value of “a” and “b” regression coefficients (Table 4.18) towards the
downstream direction (LE2 to LE1) had been observed at Lower East wetland
subcatchment suggesting that there was a reduction of fine sediment supply in
the downstream direction.
127
LE1 TSS Rating Curve
y = 0.5496x1.2508
R2 = 0.5454
0.0001
0.001
0.01
0.1
1
0.0010 0.0100 0.1000 1.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(a)
LE2 TSS Rating Curve
y = 4.2227x1.4758
R2 = 0.6252
0.0001
0.001
0.01
0.1
1
0.0010 0.0100 0.1000 1.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(b)
Figure 4.22: Plots of TSS rating curves fitted on log-log axis for LE1 (a)
and LE2 (b).
Table 4.18: Regression coefficients of rating curves fitted for selected
sampling stations at the LE subcatchment.
Sampling Station
Power Function Equation
A b R2 N
LE1 y = 0.5496x1.2508 0.55 1.25 0.55 73 LE2 y = 4.2227x1.4758 4.22 1.48 0.63 73
128
The relatively high “a” regression coefficient value recorded at LE2 could
indicate the higher availability of weathered material or sediment near the LE2
area. This result was confirmed by field observations and sediment monitoring
reports (Yusoff et al., 2004) in terms of the availability of high sediment supply
near the LE2 sampling station during the year analysed.
4.4.6 Upper Bisa Wetland TSS Rating Curve
The TSS rating curves (Figure 4.23 (a) and (b)) for the UB1 and UB2
wetland show a moderate (0.59) and relatively good (0.70) model efficiency
criterion (R2) value. Decreasing value of “a” regression coefficient and
increasing “b” regression coefficient values (Table 4.19) from upstream (UB2)
to downstream (UB1) stations had been observed at the Upper Bisa wetland
subcatchment.
The decreasing “a” regression coefficient value toward downstream
(UB2 to UB1) station indicates that there was a reduction of fine sediment
supply towards the downstream direction (high availability of sediment supply at
upstream area), while the increase of “b” regression coefficient value could
indicate higher fine sediment transport capacity towards downstream direction.
4.4.7 Central Wetland TSS rating curve
The TSS rating curve for the Central Wetland (CW) sampling station
(Figure 4.24) show a moderate (0.56) model efficiency criterion (R2) value. In
terms of “a” regression coefficient and “b” regression coefficient value (Table
4.20), a relatively low “a” regression coefficient (0.34) and high “b” regression
coefficient value (1.62) had been observed from the CW TSS rating curve. This
indicate that a high amount of fine sediment had been deposited behind the CW
weir. During high discharge, this fine sediment could be flushed out resulting in
a high amount of fine sediment concentration respectively.
129
UB1 TSS Rating Curve
y = 0.8582x1.3756
R2 = 0.5867
0.0001
0.001
0.01
0.1
1
10
0.0010 0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(a)
UB2 TSS Rating Curve
y = 1.9577x1.3363
R2 = 0.7002
0.00001
0.0001
0.001
0.01
0.1
1
10
0.0001 0.0010 0.0100 0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
(b)
Figure 4.23: Plot of TSS rating curve fitted on log-log axis for UB1 (a) and UB2 (b) sampling station.
Table 4.19: Regression coefficients of rating curves fitted for selected sampling stations at UB subcatchment.
Sampling Station
Power Function Equation
A b R2 N
UB1 y = 0.8582x1.3756 0.86 1.38 0.59 72 UB2 y = 1.9577x1.3363 1.96 1.34 0.70 70
130
Central Wetland TSS Rating Curve
y = 0.3419x1.6235
R2 = 0.5614
0.01
0.1
1
10
0.1000 1.0000 10.0000
Q (m3/s)
TS
S L
oad
ing
(T
on
nes
/ D
ay)
Series1
Power (Series1)
Figure 4.24: The plot of TSS rating curve fitted on log-log axis for CW
sampling station.
Table 4.20: The regression coefficients of rating curves fitted for selected
sampling stations at CW subcatchment.
Sampling Station
Power Function Equation
A b R2 N
CW y = 0.3419x1.6235 0.34 1.62 0.56 72 4.4.8 Annual TSS loading and TSS yield determination based on the TSS
rating curve
The annual TSS loading (t/yr) had been determined from the TSS rating
curve fitted for selected wetland cells using the average annual water discharge
while the specific TSS yield (t/ha/yr) had been estimated after dividing the
annual TSS loading value with respect to the wetland catchment area (Table
4.21). The TSS yield is then multiplied with the correction factor (as discussed
in Material and Method chapter) to obtain actual TSS yield (Table 4.22). Figure
4.25 show the histogram of TSS yield for the selected wetland cell respectively.
131
Table 4.21: TSS loading (t/yr) and specific catchment TSS yield (t/ha/yr) for selected sampling stations at Putrajaya
wetland.
Total annual Discharge (m3/year)
TSS Loading (t/year)
TSS yield (t/ha/year) Sampling
Stations
Wetland Sub catchment Area
(Ha)
Wetland Cell Area
(Ha)
Power Regression Equation 2003 2004 2003 2004 2003 2004
UW 1 19.94 6.39 y = 1.043x1.2818 65.26 52.03 80643.40 60481.57 4044.30 3033.18
UW 2 21.84 5.27 y = 2.3361x1.2423 70.10 64.26 167372.35 150663.52 7663.57 6898.51
UW 3 20.17 3.73 y = 3.7496x1.6339 51.88 39.53 867816.29 558085.70 43025.10 27669.10
UW 7 333.93 2.98 y = 2.7234x1.4122 33.39 22.70 140965.72 81947.65 422.14 245.40
UW 8 78.84 2.10 y = 10.945x1.627 3.62 4.28 32369.57 42688.42 410.57 541.46
UN 1 72.8 8.21 y = 1.8137x1.4141 167.76 111.53 926410.95 521508.12 12725.43 7163.57
UN 2 13.85 3.96 y = 1.2141x1.4254 119.80 226.31 406609.72 1009571.71 29358.10 72893.26
UN 4 32.12 2.99 y = 1.5715x1.3506 171.78 160.44 598623.51 547402.16 18637.10 17042.41
UN 6 239.23 9.81 y = 2.7135x1.3587 202.57 130.50 1348254.26 743858.69 5635.81 3109.39
UE 1 31.23 3.65 y = 1.8474x1.4236 44.78 29.38 151140.22 83179.71 4839.58 2663.46
UE 2 41.13 2.51 y = 1.6868x1.3129 31.92 27.55 58082.65 48014.47 1412.17 1167.38
UE 3 285.62 4.95 y = 1.6609x1.154 21.40 17.00 20795.82 15985.97 72.81 55.97
LE 1 75.27 6.00 y = 0.5496x1.2508 7.09 7.62 2323.06 2552.85 30.86 33.92
LE 2 78.51 1.64 y = 4.2227x1.4758 3.68 3.73 10528.13 10777.55 134.10 137.28
UB 1 116.35 9.17 y = 0.8582x1.3756 76.55 75.56 122292.55 120470.99 1051.07 1035.42
UB 2 277.95 8.36 y = 1.9577x1.3363 33.12 32.57 76798.91 75302.76 276.30 270.92
CW 197.46 37.74 y = 0.3419x1.6235 450.17 333.70 2534794.57 1563319.01 12837.00 7917.14
132
Table 4.22: Actual / corrected TSS yield (after multiplication with correction factor, K [b,T])
Estimated TSS yield (t/ha/year)
Actual TSS yield (t/ha/year) Sampling
Stations
Wetland Cell Sub catchment Area
(Ha)
Wetland Cell Area
(Ha)
Power Regression Equation
b value
K [b,T]
2003 2004 2003 2004
UW 1 19.94 6.39 y = 1.043x1.2818 1.28 1.415 4044.30 3033.18 5722.29 4291.65
UW 2 21.84 5.27 y = 2.3361x1.2423 1.24 1.455 7663.57 6898.51 11149.41 10036.36
UW 3 20.17 3.73 y = 3.7496x1.6339 1.63 1.313 43025.10 27669.10 56487.25 36326.50
UW 7 333.93 2.98 y = 2.7234x1.4122 1.41 1.325 422.14 245.40 559.39 325.19
UW 8 78.84 2.10 y = 10.945x1.627 1.63 1.313 410.57 541.46 539.04 710.87
UN 1 72.8 8.21 y = 1.8137x1.4141 1.41 1.325 12725.43 7163.57 16862.86 9492.68
UN 2 13.85 3.96 y = 1.2141x1.4254 1.43 1.317 29358.10 72893.26 38657.65 95983.12
UN 4 32.12 2.99 y = 1.5715x1.3506 1.35 1.359 18637.10 17042.41 25326.79 23159.70
UN 6 239.23 9.81 y = 2.7135x1.3587 1.36 1.352 5635.81 3109.39 7621.88 4205.14
UE 1 31.23 3.65 y = 1.8474x1.4236 1.42 1.321 4839.58 2663.46 6391.96 3517.80
UE 2 41.13 2.51 y = 1.6868x1.3129 1.31 1.389 1412.17 1167.38 1961.14 1621.20
UE 3 285.62 4.95 y = 1.6609x1.154 1.15 1.566 72.81 55.97 114.02 87.65
LE 1 75.27 6.00 y = 0.5496x1.2508 1.25 1.444 30.86 33.92 44.58 48.99
LE 2 78.51 1.64 y = 4.2227x1.4758 1.48 1.302 134.10 137.28 174.62 178.76
UB 1 116.35 9.17 y = 0.8582x1.3756 1.38 1.340 1051.07 1035.42 1408.87 1387.88
UB 2 277.95 8.36 y = 1.9577x1.3363 1.34 1.366 276.30 270.92 377.39 370.04
CW 197.46 37.74 y = 0.3419x1.6235 1.62 1.310 12837.00 7917.14 16811.82 10368.59
Average 1.39 1.365 8386.82 8933.99 11188.88 11888.95
Maximum 1.63 1.566 43025.10 72893.26 56487.25 95983.12
Minimum 1.15 1.302 30.86 33.92 44.58 48.99
133
134
TSS yield (tonnes/ha/yr)
1
10
100
1,000
10,000
100,000
UW1 UW2 UW3 UW7 UW8 UN1 UN2 UN4 UN6 UE1 UE2 UE3 LE1 LE2 UB1 UB2 CW
Sampling Stations
TS
S y
ield
(to
nn
es
/ha
/yr)
2003
2004
UW wetland
UNwetland
UEwetland
LEwetland
UB wetland
CW
Figure 4.25: Catchment specific TSS yields (t/ha/yr) for 2003 and 2004.
At the UW catchment, the UW3 sampling station record the highest TSS
loading while the lowest at UW8 station for 2003 and 2004. In terms of actual
TSS yield UW3 again recorded the highest value for all the years analysed
while the lowest value was observed at UW8 in 2003 and UW7 for 2004. The
highest annual TSS loading value for the UN wetland subcatchment for 2003
was recorded at the UN6 station and for 2004 at UN2. The lowest annual TSS
loading was recorded at UN2 for 2003 and UN1 for 2004. In terms of TSS yield,
UN2 recorded the highest TSS yield in comparison to the other stations while
the lowest was recorded at UN6 station.
At the UE subcatchment, the UE1 station recorded the highest TSS
loading for 2003 and 2004 while the lowest was recorded at UE3. The highest
actual TSS yield was also recorded at UE1 while the lowest was recorded at
UE3. Site observations confirmed that the high TSS loading and TSS yields
recorded at these stations was because of the presence of bank erosion and
active land clearing activities near to this wetland cell. For the LE subcatchment
area, the upstream station, LE2, recorded the highest TSS loading and TSS
yields for 2003 and 2004 compared with the LE1 station downstream. The
location of LE2 (upstream area) and the availability of sediment supply from
active construction activities nearby were responsible these high yield
throughout the years analysed.
The highest TSS loading and TSS yields for the UB subcatchment
wetland was recorded at UB1 (downstream) and the lowest at UB2 (upstream).
Although active construction sites and bank erosion were located near to UB2
station, TSS loading and TSS yields recorded higher at the UB1 indicates that
the design of the UB2 wetland cell which is the highest and the deepest water
135
ponding area in comparison to the UW, UN, UE and LE wetland cells, was
successful in reducing the effect of sedimentation in the UB2 wetland from bank
erosion near to the water source (Ismail et al., 2004).
In terms of temporal characteristics, average TSS yield calculated in
2004 was slight higher compared to 2003. The highest average TSS yield in
2003 was recorded at the UW and UB wetland cells while at LE and UN wetland
cell for year 2004. The actual TSS yield data for selected wetland sampling
stations obtained from their respective TSS rating curve will be used to compare
or possibly link them with the catchment erosion and sediment yield results
gathered by the USLE – SDR method and wetland reservoir sediment yield
estimated from the sedimentation survey accordingly and will be discussed
further in this chapter.
4.5 Catchment sediment yield estimation using the USLE-SDR approach
4.5.1 Introduction
The catchment sediment yield can be derived from the USLE erosion
result by multiplying it with the SDR (sediment delivery ratio). Generally, the
sediment delivery ratio is a factor that defines how much sediment is delivered
from source by erosion to the catchment outlet or reservoir. The SDR value
usually ranges from 0 to 1 while a value above 1 indicates excess
sedimentation or the availability of other sources of erosion in that particular
catchment. The method proposed by Vanoni (1975) and USDA-SCS (1972)
based on catchment area, had been applied to determine the catchment
sediment yield from the USLE 10 m grid cell size in this study for reasons
mentioned in subsection 4.2.8.
136
4.5.2 Calculated SDR values from Vanoni (1975) and USDA-SCS (1972)
Equations
From Table 4.23, it was observed that using the equation proposed by
Vanoni (1975), the calculated SDR value had a lower value in compared to the
SDR value proposed by USDA-SCS (1972). The SDR values calculated using
Vanoni’s (1975) equation ranged from 0.41 (UE3 and UW7) to 0.60 (UN2) while
the calculated SDR values from the equation proposed by the USDA SCS
(1972) ranged from 0.50 (UE3 and UW7) to 0.70 (UN2). The mean average
SDR value using Vanoni’s (1975) equation was 0.49 and 0.58 using the USDA-
SCS equation. The calculated SDR value is then been multiplied with the USLE
erosion to determine the catchment sediment yield.
Table 4.23: Calculated SDR values from Vanoni (1975) and USDA-SCS (1972) equations.
wetland cell
subcatchment Area (Ha)
Area (km2)
SDR (Vanoni, 1975)
SDR (USDA SCS, 1972)
UW1 19.94 0.1994 0.58 0.68 UW2 21.84 0.2184 0.57 0.67 UW3 20.17 0.2017 0.58 0.67 UW7 333.93 3.3393 0.41 0.50 UW8 78.84 0.7884 0.49 0.58 UN1 72.8 0.728 0.49 0.59 UN2 13.85 0.1385 0.60 0.70 UN4 32.12 0.3212 0.54 0.64 UN6 239.23 2.3923 0.42 0.51 UE1 31.23 0.3123 0.55 0.64 UE2 41.13 0.4113 0.53 0.62 UE3 285.62 2.8562 0.41 0.50 LE1 75.27 0.7527 0.49 0.58 LE2 78.51 0.7851 0.49 0.58 UB1 116.35 1.1635 0.46 0.56 UB2 277.95 2.7795 0.42 0.51 CW 197.46 1.9746 0.43 0.52
137
138
4.5.3 Result of catchment sediment yield determination using the USLE-
SDR approach.
Generally, the USLE gross and specific sediment yield determination
using the Vanoni (1975) SDR equation have a slightly lower value compared to
the USLE sediment yield determination using USDA-SCS (1972) SDR equation.
Table 4.24 shows the USLE gross catchment sediment yield determined from
the Vanoni (1975) and USDA-SCS (1972) SDR equation while Table 4.25
shows the USLE specific catchment sediment yield. The highest gross
catchment sediment yield using Vanoni (1975) SDR equation in 2003, was from
the UW7 subcatchment area (335,482 t/yr) while the lowest (7,843 t/yr) was
from LE2. In 2004, the highest gross sediment yield value was from the UN6
wetland subcatchment area (216,097 t/yr) while the lowest was again from LE2
(3,756 t/yr). The highest gross catchment sediment yield, using the USDA-SCS
(1972) SDR equation in 2003 was from the UW7 subcatchment area (409,000
t/yr) while the lowest (9,356 t/yr) was from LE2. In 2004, the highest gross
sediment yield value was from the UN6 wetland subcatchment area (262,559
t/yr) while the lowest was from LE2 (4,480 t/yr).
The highest specific catchment sediment yield using the Vanoni (1975)
SDR equation in 2003 was from the UE3 subcatchment area (6,553 t/ha/yr)
while the lowest (76 t/ha/yr) was from LE1. In 2004, the highest specific
sediment yield was from the UN6 wetland subcatchment area (4,186 t/ha/yr)
while the lowest was again from the LE1 subcatchment (48 t/ha/yr). The highest
specific catchment sediment yield using the USDA-SCS (1972) SDR equation in
2003 was from the UW7 subcatchment area (7,971 t/ha/yr) while the lowest (88
t/ha/yr) was from UE1. In 2004, the highest gross sediment yield value was from
UN6 (5,092 t/ha/yr) while the lowest was from the LE1 (57 t/ha/yr).
Table 4.24: Gross sediment yields determined using Vanoni (1975) and USDA-SCS (1972) SDR equation.
Gross Sediment Yield
(t/yr)
Vanoni (1975) equation USDA-SCS (1972) equation Wetland
cell
Wetland Cell Sub
catchment area (ha)
Wetland Cell area (ha)
2003 2004 2003 2004
UW 1 19.94 6.39 41830.32 31857.19 48886.23 37230.84
UW 2 21.84 5.27 57708.00 54110.69 67534.29 63324.44
UW 3 20.17 3.73 24509.69 17050.71 28648.89 19930.25
UW 7 333.93 2.98 335482.34 215363.46 409000.66 262558.66
UW 8 78.84 2.10 116820.33 133166.27 139369.94 158871.12
UN 1 72.8 8.21 88499.69 93546.67 105456.47 111470.47
UN 2 13.85 3.96 15550.29 14557.20 18074.23 16919.95
UN 4 32.12 2.99 59466.56 62553.02 69996.12 73629.09
UN 6 239.23 9.81 316373.16 216097.48 383779.15 262138.88
UE 1 31.23 3.65 69475.78 66719.88 81743.18 78500.66
UE 2 41.13 2.51 70198.64 106589.76 82935.52 125929.47
UE 3 285.62 4.95 269544.83 172186.46 327844.09 209428.29
LE 1 75.27 6.00 21725.12 13665.01 25900.67 16291.42
LE 2 78.51 1.64 7842.65 3755.77 9355.91 4480.46
UB 1 116.35 9.17 15680.79 3994.94 18817.16 4793.99
UB 2 277.95 8.36 36095.40 14787.32 43884.47 17978.29
CW 197.46 37.74 167521.36 39744.07 202629.13 48073.31
Table 4.25: Specific sediment yields determined using Vanoni (1975) and
USDA-SCS (1972) SDR equation.
Specific Sediment Yield (t/yr)
Vanoni (1975) equation USDA-SCS (1972) equation Wetland
cell
Wetland Cell Sub
catchment area (ha)
Wetland Cell area (ha)
2003 2004 2003 2004
UW 1 19.94 6.39 2097.81 1597.65 2451.67 1867.14 UW 2 21.84 5.27 2642.31 2477.60 3092.23 2899.47 UW 3 20.17 3.73 1215.16 845.35 1420.37 988.11 UW 7 333.93 2.98 1004.65 644.94 1224.81 786.27 UW 8 78.84 2.10 1481.74 1689.07 1767.76 2015.11 UN 1 72.8 8.21 1215.66 1284.98 1448.58 1531.19 UN 2 13.85 3.96 1122.76 1051.06 1305.00 1221.66 UN 4 32.12 2.99 1851.39 1947.48 2179.21 2292.31 UN 6 239.23 9.81 1322.46 903.30 1604.23 1095.76 UE 1 31.23 3.65 75.11 72.14 88.38 84.87 UE 2 41.13 2.51 2247.80 3413.06 2655.64 4032.32 UE 3 285.62 4.95 6553.48 4186.40 7970.92 5091.86 LE 1 75.27 6.00 76.06 47.84 90.68 57.04 LE 2 78.51 1.64 104.19 49.90 124.30 59.53 UB 1 116.35 9.17 199.73 50.88 239.68 61.06 UB 2 277.95 8.36 310.23 127.09 377.18 154.52 CW 197.46 37.74 602.70 142.99 729.01 172.96
139
4.6 Wetland Reservoir Sediment Yield Estimation from Sedimentation
Survey Data
4.6.1 Introduction
In general, reservoir sedimentation survey exercise give valuable data on
how much sediment was deposited in the particular water bodies. Since the
amounts of deposited sediment may differ from year to year, the availability of
temporal sedimentation data can give an understanding on the annual amounts
of sediment deposited and thus, the rate of sedimentation can be assessed.
The reservoir sediment yield is then estimated from the particular data
respectively.
4.6.2 Spatial and temporal variability of wetland sediment accumulation
Table 4.26 summarizes the sediment accumulation (in volume, m3, and
weight, tonnes) and annual sedimentation rate calculated for selected Putrajaya
wetland cell from 1998 to 2004 while Figure 4.26 shows the comparison of
sediment accumulations 1998 to 2001 (2001 sedimentation survey), 2001 to
2002 (2002 sedimentation survey) and 2002 to 2004 (2004 sedimentation
survey). Figure 4.27 shows the annual sedimentation rate in volume, m3, and
weight, tonnes, in selected Putrajaya Wetland cells. In 2001 sedimentation
survey (3 years of sediment accumulation), the highest sediment accumulation
was detected at the CW wetland cell (52,284 m3) while the lowest sediment
accumulation had been observed at LE1 (2,150 m3) with an average sediment
accumulation of 12,692 m3. The highest sediment accumulation in 2002
sedimentation survey (1 year sediment accumulation) was recorded at the UE3
wetland cell (54,738 m3) while the lowest was detected at the UW7 cell (-2,690
m3) with the average sediment accumulation value of 11,354 m3.
140
Table 4.26: Sediment accumulations (in volume, m3, and weight, tonnes) and annual sedimentation rates for selected Putrajaya
wetland cells from 1998 to 2004.
Sediment Accumulation in volume (m3)
Sediment Accumulation in weight (Tonnes)
Annual Sedimentation rate Wetland cell
Wetland Sub
catchment area (ha)
Wetland Cell area (ha)
Average Bulk
Density (g/cm3)* 1998-2001 2001-2002 2002-2004 1998-2001 2001-2002 2002-2004 (m3/yr) (Tonnes/yr)
UW 1 19.94 6.39 1.57 6,920.98 3,204.61 13,581.58 10,890.48 5,042.59 21,371.25 3951.19 6217.39
UW 2 21.84 5.27 1.38 6,462.82 3,745.87 8,811.27 8,918.95 5,169.45 12,159.90 3169.99 4374.72
UW 3 20.17 3.73 1.35 12,135.80 1,226.14 1,738.12 16,369.52 1,653.89 2,344.48 2516.68 3394.65
UW 7 333.93 2.98 1.43 12,197.76 -2,690.13 9,566.18 17,451.59 -3,848.82 13,686.54 3178.97 4548.22
UW 8 78.84 2.10 1.59 5,630.07 829.10 6,553.77 8,933.07 1,315.51 10,398.68 2168.82 3441.21
UN 1 72.8 8.21 1.49 21,386.75 21,325.14 19,195.14 31,814.47 31,722.82 28,554.28 10317.84 15348.59
UN 2 13.85 3.96 1.50 2,861.44 2,847.37 1,524.31 4,285.16 4,264.09 2,282.73 1205.52 1805.33
UN 4 32.12 2.99 1.68 1,100.96 3,410.63 761.33 1,853.75 5,742.67 1,281.89 878.82 1479.72
UN 6 239.23 9.81 1.47 24,963.26 9,839.42 55,481.21 36,781.42 14,497.61 81,747.24 15047.31 22171.04
UE 1 31.23 3.65 2.13 5,349.36 12,676.06 20,839.00 11,420.82 27,063.23 44,491.01 6477.40 13829.18
UE 2 41.13 2.51 1.38 2,841.12 15,759.51 2,213.02 3,912.30 21,701.26 3,047.39 3468.94 4776.82
UE 3 285.62 4.95 1.40 20,963.38 54,737.68 33,535.22 29,319.38 76,556.12 46,902.36 18206.05 25462.98
LE 1 75.27 6.00 1.44 2,149.62 9,308.29 7,047.28 3,098.75 13,418.22 10,158.90 3084.20 4445.98
LE 2 78.51 1.64 1.42 7,166.48 1,107.50 5,180.17 10,143.40 1,567.55 7,331.98 2242.36 3173.82
UB 1 116.35 9.17 1.41 17,107.24 11,484.33 14,295.79 24,153.07 16,214.30 20,183.69 10721.84 10091.84
UB 2 277.95 8.36 1.55 14,241.86 26,337.44 20,289.65 22,011.32 40,705.49 31,358.41 15217.24 15679.20
CW 197.46 37.74 1.69 52,284.35 17,875.13 25,636.66 88,360.55 30,208.97 43,325.96 15966.02 26982.58
141
Wetland Sediment Accumulation (m3)
-10,000.00
0.00
10,000.00
20,000.00
30,000.00
40,000.00
50,000.00
60,000.00
UW 1 UW 2 UW 3 UW 7 UW 8 UN 1 UN 2 UN 4 UN 6 UE 1 UE 2 UE 3 LE 1 LE 2 UB 1 UB 2 CW
Wetland Cell
m3
1998-2001
2001-2002
2002-2004
(a)
Wetland Sediment Accumulation (tonnes)
-20,000.00
0.00
20,000.00
40,000.00
60,000.00
80,000.00
100,000.00
UW 1 UW 2 UW 3 UW 7 UW 8 UN 1 UN 2 UN 4 UN 6 UE 1 UE 2 UE 3 LE 1 LE 2 UB 1 UB 2
Wetland Cell
ton
ne
s
1998-2001
2001-2002
2002-2004
(b) Figure 4.26: Sediment accumulations from 1998 to 2001 (2001
sedimentation survey), 2001 to 2002 (2002 sedimentation survey) and 2002 to 2004 (2004 sedimentation survey) in volume (a) and weight (b).
142
Wetland Annual Sedimentation rate (in volume, m3/yr, and weight, tonnes/yr)
0.00
2000.00
4000.00
6000.00
8000.00
10000.00
12000.00
14000.00
16000.00
18000.00
20000.00
UW 1 UW 2 UW 3 UW 7 UW 8 UN 1 UN 2 UN 4 UN 6 UE 1 UE 2 UE 3 LE 1 LE 2 UB 1 UB 2 CW
Wetland Cell
m3/y
r
0.00
5000.00
10000.00
15000.00
20000.00
25000.00
30000.00
ton
nes
/yr
Sedimentation rate (m3/yr)
Sedimentation rate (tonnes/yr)
Figure 4.27: Wetland annual sedimentation rate (in volume, m3/yr and weight, tonnes/yr) from 1998 to 2004.
143
The negative value of sediment volume is due to desilting exercise
carried out in the particular wetland cell before the sedimentation survey
exercise and was confirmed by field observation and in the sediment monitoring
record (Yusoff et al., 2004). The highest sediment accumulation in the 2004
sedimentation survey (2 years sediment accumulation) was observed at the
UN6 wetland cell (55,481m3) while the lowest was observed at the UN4
(761m3). The average sediment accumulation for the selected wetland cells was
14,485m3.
Bulk density measurement had been conducted for sediment samples
collected within the wetland cells. It had been observed that the bulk densities
of the samples ranged from 1.35 g/cm3 (UW3 cell) to 2.13 g/cm3 (UE1 cell) with
an average bulk density of 1.52 g/cm3. These bulk density values were then
multiplied with the measured sedimentation volume to derive the sediment
accumulations in tonnes (t). From this calculation, it has been observed that
during the2001 sedimentation survey, the highest sediment accumulation (in t)
was recorded at the CW wetland cell (88,361 t) while the lowest was recorded
at the UN4 (1,854 t). In the 2002 sedimentation survey, the highest sediment
accumulation was recorded at the UE3 wetland cell (76,556 t) while the lowest
at the UW7 (-3,849 t). During 2004 sedimentation survey, the highest sediment
accumulation was recorded at the UN6 (81,747 t) while the lowest sediment
accumulation was at the UN4 (1,282 t).
In terms of annual sedimentation rate (rate of sediment accumulation
from 1998 to 2004), the highest sedimentation rate was observed at the UE3
wetland cell (18,206 m3/yr or 25,463 t/yr) while the lowest was from the UN4
wetland cell (879 m3/yr or 1480 t/yr).
144
145
4.6.3 Wetland Specific Reservoir Sediment Yield
Table 4.27 shows the calculated wetland specific reservoir sediment
yield for 2001, 2002 and 2004 sedimentation survey data. Figure 4.28 shows
the spatial variability of the calculated wetland specific reservoir sediment yield
and Figure 4.29 shows the average annual wetland specific reservoir sediment
yield for selected wetland cells. In 2001, the highest reservoir sediment yield
was from the UW3 wetland cell (271 t/ha/yr) while the lowest was from the LE1
(14 t/ha/yr). The highest wetland sediment yield for 2002 was observed from the
UE1 wetland cell (867 t/ha/yr) while the lowest was from the UW7 (-12 t/ha/yr).
Again, the negative value of sediment volume is due to the desilting exercise
that took place in the UW7 wetland cell.
In the 2004 sedimentation survey, the highest wetland sediment yield
was also for the UE1 wetland cell (712 t/ha/yr) and the lowest wetland reservoir
sediment yield was from the UW7 and UN4 wetland cell (20 t/ha/yr). The
highest average annual reservoir sediment yield was calculated at UE1 wetland
cell (536 t/ha/yr) while the lowest at UW7 (9.75 t/ha/yr). These results indicate
that UE1 cell was continuously receiving high amounts of sediment from
surrounding area from 1998 to 2004 and this had been confirmed with on-site
field observations and sediment monitoring report that there was aggressive
land clearing activities and major embankment failures near the UE1 wetland
cell. The lowest reservoir sediment yield value at UW7 corresponded to the
desilting exercise done at UW7 and the relatively good sediment filtering
process in the UW7’s subcatchment area. The result also shows the areal effect
as UW7 has the largest wetland subcatchment area compared to the other
wetland cells.
Table 4.27: Wetland specific reservoir sediment yields for 2001, 2002 and 2004.
Wetland cell
Wetland Cell Sub
catchment area (ha)
Wetland Cell area (ha)
Average Bulk
Density (g/cm3)*
2001 Sediment
yield (t/ha/yr)
2002 Sediment
yield (t/ha/yr)
2004 Sediment
yield (t/ha/yr)
Average Annual
Sediment yield
(t/ha/yr)
UW 1 19.94 6.39 1.57 182 253 536 320.75 UW 2 21.84 5.27 1.38 136 237 278 212.75 UW 3 20.17 3.73 1.35 271 82 58 144.75 UW 7 333.93 2.98 1.43 17 -12 20 9.75 UW 8 78.84 2.10 1.59 38 17 66 41.25 UN 1 72.8 8.21 1.49 146 436 196 247.25 UN 2 13.85 3.96 1.50 103 308 82 155.75 UN 4 32.12 2.99 1.68 19 179 20 66.00 UN 6 239.23 9.81 1.47 51 61 171 94.00 UE 1 31.23 3.65 2.13 122 867 712 536.00 UE 2 41.13 2.51 1.38 32 528 37 178.25 UE 3 285.62 4.95 1.40 34 268 82 118.25 LE 1 75.27 6.00 1.44 14 178 67 79.50 LE 2 78.51 1.64 1.42 43 20 47 37.50 UB 1 116.35 9.17 1.41 69 139 87 106.25 UB 2 277.95 8.36 1.55 26 146 56 78.25 CW 197.46 37.74 1.69 149 153 110 137.25
146
Wetland Specific Reservoir Sediment Yield (t/ha/yr)
-200
0
200
400
600
800
1,000
UW 1 UW 2 UW 3 UW 7 UW 8 UN 1 UN 2 UN 4 UN 6 UE 1 UE 2 UE 3 LE 1 LE 2 UB 1 UB 2
Wetland Cells
t/h
a/yr
2001
2002
2004
Figure 4.28: Spatial variability of wetland specific reservoir sediment yield for 2001, 2002 and 2004.
Average Annual Wetland Reservoir Sediment Yield (t/ha/yr)
0
100
200
300
400
500
600
UW 1 UW 2 UW 3 UW 7 UW 8 UN 1 UN 2 UN 4 UN 6 UE 1 UE 2 UE 3 LE 1 LE 2 UB 1 UB 2 CW
Wetland Cells
t/h
a/yr
Average AnnualSediment yield(t/ha/yr)
Figure 4.29: Average annual wetland specific reservoir sediment yield for selected wetland cells.
147
148
The results also show the variability of wetland sediment yields in terms
of spatial and temporal behaviour due to the processes contributing to sediment
accumulation. The results of the reservoir specific sediment yield from this
analysis will then be compared to the wetland subcatchment TSS yields, USLE
specific erosion and USLE catchment sediment yields to further analyze the
erosion and sedimentation processes that took place in study area.
4.7 Comparative analysis between wetland catchment TSS yield, wetland
reservoir sediment yield and USLE-SDR sediment yield result
4.7.1 Introduction
Comparative analysis was performed between the three different
sediment yield values obtained from the estimation of TSS yield using the TSS
rating curve, the catchment sediment yield from USLE-SDR and wetland
reservoir sediment yield from sedimentation survey data. The values of
catchment sediment yield using USLE-SDR approach from the 10m grid cell
size results were used for reasons as mentioned in subsection 4.2.8. Since only
sedimentation survey data for 2001, 2002 and 2004 were available, the value of
TSS yields 2003 have to be combined with the 2004 estimated value and this
procedure was also repeated for the catchment sediment yield value in the
USLE-SDR results.
4.7.2 Comparative analysis for 2003 and 2004
The results of the catchment sediment yield (Table 4.28) using both the
USLE-SDR (Vanoni, 1975) and USLE-SDR (USDA-SCS, 1972) approaches
underestimated the specific TSS yield values at almost all the selected
sampling stations in this study (except for UW7, UW8, UE2, UE3 and LE1
station). The % underestimates for the comparison between USLE-SDR
149
Table 4.28: Comparison between wetland specific TSS yield, wetland specific reservoir sediment yield and USLE-SDR specific sediment yield for 2004 (accumulation of year 2003 and 2004 value).
Note: -ve % value= % underestimate, +ve % value= % overestimate
t/ha/year % overestimate or underestimate
Wetland cell
Wetland Cell Sub
catchment area (ha)
Wetland Cell area
(ha) TSS yield
Wetland reservoir sediment
yield
USLE-SDR Vanoni
sediment yield
USLE-SDR USDA-SCS sediment
yield
USLE-SDR Vanoni
vs. TSS yield
USLE-SDR USDA-SCS
vs. TSS yield
USLE-SDR Vanoni vs.
wetland reservoir sediment yield
USLE-SDR USDA-SCS vs.
wetland reservoir sediment yield
UW 1 19.94 6.39 10013.94 535.89 3695.46 4318.81 -63.10 -56.87 589.59 705.91
UW 2 21.84 5.27 21185.77 278.39 5119.90 5991.70 -75.83 -71.72 1739.11 2052.27
UW 3 20.17 3.73 92813.75 58.12 2060.51 2408.48 -97.78 -97.41 3445.27 4043.98
UW 7 333.93 2.98 884.58 20.49 1649.58 2011.08 86.48 127.35 7950.66 9714.93
UW 8 78.84 2.10 1249.91 65.95 3170.81 3782.86 153.68 202.65 4707.90 5635.95
UN 1 72.8 8.21 26355.54 196.11 2500.64 2979.77 -90.51 -88.69 1175.12 1419.44
UN 2 13.85 3.96 134640.77 82.41 2173.83 2526.66 -98.39 -98.12 2537.82 2965.96
UN 4 32.12 2.99 48486.49 19.95 3798.87 4471.52 -92.17 -90.78 18941.95 22313.63
UN 6 239.23 9.81 11827.02 170.85 2225.77 2699.99 -81.18 -77.17 1202.76 1480.33
UE 1 31.23 3.65 9909.76 712.31 147.25 173.25 -98.51 -98.25 -79.33 -75.68
UE 2 41.13 2.51 3582.34 37.05 5660.85 6687.96 58.02 86.69 15178.95 17951.17
UE 3 285.62 4.95 201.67 82.11 10739.88 13062.79 5225.47 6377.31 12979.87 15808.89
LE 1 75.27 6.00 93.57 67.48 123.91 147.72 32.42 57.87 83.62 118.91
LE 2 78.51 1.64 353.38 46.69 154.09 183.82 -56.40 -47.98 230.03 293.70
UB 1 116.35 9.17 2796.75 86.74 250.61 300.74 -91.04 -89.25 188.92 246.71
UB 2 277.95 8.36 747.43 56.41 437.32 531.70 -41.49 -28.86 675.25 842.56
CW 197.46 37.74 27180.41 109.71 745.69 901.97 -97.26 -96.68 579.69 722.14
(Vanoni, 1975) catchment sediment yields and TSS yields range from -98.51 %
(UE1) to -41.49 % (UB2) while % overestimates range from 32.42 % (LE1) to
5225.47 % (UE3). The % underestimates between USLE-SDR (USDA SCS,
1972) catchment sediment yield and TSS yield range from -98.25 % (UE1) to -
28.86 % (UB2) while % overestimates ranges from 57.87 % (LE1) to 6377.31 %
(UE3).
Comparisons between the wetland catchment specific sediment yields
using USLE-SDR (Vanoni, 1975) and USLE-SDR (USDA-SCS, 1972) with
wetland reservoir specific sediment yields show that both catchment specific
sediment yields determined using the USLE-SDR approach overestimated the
wetland reservoir specific sediment yield values at almost all stations except for
UE1 station (-79.33 % underestimate for Vanoni (1975) USLE SDR and -75.68
% underestimate for USDA SCS (1972)). The % overestimates for the
comparison between USLE-SDR (Vanoni, 1975) catchment sediment yield and
wetland reservoir sediment yields ranges from 83.62 % (LE1) to 18941.95 %
(UN4) while the comparison between USLE-SDR (USDA SCS, 1972)
catchment sediment yields and wetland reservoir sediment yields ranges from
118.91 % (LE1) to 22313.63 % (UN4).
The clearly overestimate USLE-SDR (Vanoni, 1975) and USLE-SDR
(USDA-SCS, 1972) to wetland reservoir sediment yield value for all station is
expected due to the differences of these two measurement approach and
definition whereas the USLE-SDR approach measure the portion of sediment
leaving their respective watershed (catchment) while the reservoir sediment
yield measure the sediment trapping within their respective reservoir
accumulated from the entire watershed.
150
The overestimated and underestimated values using USLE-SDR
(Vanoni, 1975) and USLE-SDR (USDA-SCS, 1972) to TSS yield could be
related to the overestimation and underestimation of the LS factor in the USLE
calculation as mentioned in the sensitivity analysis (subsection 4.2.8) where
USLE calculations in GIS tend to underestimate USLE value when using larger
grid cell sizes. The availability of wetlands and their respective weirs that act as
a filtration system requires a reduction factor to be applied for calculating the
resulting USLE-SDR (Vanoni, 1975) and USLE-SDR (USDA-SCS, 1972)
catchment sediment yields. Comparative study between USLE-SDR approach
and suspended sediment yield values done by Boomer et. al (2008) at 78
catchments of the Chesapeake Bay watershed and 23 catchments monitored
by the USGS (United States Geological Survey) found that the models
prediction exceeded observed sediment yields by more than 100% and failed to
identify catchments with higher yields (r range, -0.28 – 0.00; p > 0.14). Lack of
calibration parameters of runoff and water discharge in USLE-SDR also
contributes to large discrepancies from estimated values (Bhattarai and Dutta,
2008).
The overestimated and underestimated results for the USLE-SDR
(Vanoni, 1975) and USLE-SDR (USDA-SCS, 1972) for TSS yield and wetland
reservoir sediment yields value have also indicated that there was a lack of
SDR parameter estimations when using the SDR equations proposed by
Vanoni (1975) and USDA-SCS (1972). The results suggest that this SDR value
not be suitable for wetland sub-environment with sediment trapping facilities
and the availability of bank erosion with the observed general increase
catchment sediment yield with the decrease of catchment area. Thus, the new
151
152
SDR value specifically for the study area is needed as a linkage between TSS
yields, reservoir sediment yields and erosion estimation from USLE and will be
discussed further in subsection 4.7.3.
4.7.3 Linkages between wetland specific TSS yield, wetland specific
reservoir sediment yield and USLE total gross erosion
The linkages between wetland TSS yield, wetland reservoir sediment
yield and USLE total gross erosion could be analyzed in terms of sediment
delivery ratio (SDR) that is defined as the portion of sediment transported from
the source area to the catchment outlet or particular reservoir.
Generally, the calculated SDR values (Table 4.29) show an increasing
trend (Figure 4.30) of SDR values towards the downstream catchment wetland
cells except for the LE wetland subcatchment. The SDR values for stations
within the UW subcatchment range from 0.001 (UW7) to 1.290 (UW3) for 2003
and 2004 while at the UN subcatchment, the SDR values range between 0.01
(UN6) to 2.706 (UN2). The SDR values range between 0.0003 (UE3) to 0.043
(UE1) for stations within the UE subcatchment and the SDR values range from
0.002 (LE1) to 0.017 (LE2) for the stations within the LE subcatchment. The
UB1 station has a higher SDR value (0.068) than UB2 (0.007) while the SDR
value was 0.057 for the CW wetland. The highest SDR value was from the UN2
wetland subcatchment area at 2.706.
The reduction of SDR values towards the downstream stations indicates
a decreasing amount of sediment transported from upstream to downstream or
decreasing transport capacity of sediment towards the downstream direction
and vice-versa for the increase of SDR values. Other factors that could
influence the calculated SDR values include hydrological inputs (mainly rainfall),
Table 4.29: SDR values for Putrajaya wetland subcatchment areas.
Wetland cell
Wetland Cell Sub
catchment area (ha)
Wetland Cell area (ha)
TSS yield
(t/ha/year)
Wetland reservoir sediment
yield (t/ha/year)
TSS yield + wetland reservoir sediment
yield (t/ha/year)
USLE total gross
erosion (t/ha/year)
SDR value
UW 1 19.94 6.39 10013.94 535.89 10549.83 127512 0.083
UW 2 21.84 5.27 21185.77 278.39 21464.15 195709 0.11
UW 3 20.17 3.73 92813.75 58.12 92871.87 72020.8 1.29
UW 7 333.93 2.98 884.59 20.49 905.08 1355742 0.001
UW 8 78.84 2.10 1249.91 65.95 1315.86 513689 0.003
UN 1 72.8 8.21 26355.54 196.11 26551.65 370372 0.072
UN 2 13.85 3.96 134640.78 82.41 134723.19 49779 2.706
UN 4 32.12 2.99 48486.48 19.95 48506.44 224113 0.216
UN 6 239.23 9.81 11827.02 170.85 11997.88 1257007 0.01
UE 1 31.23 3.65 9909.76 712.31 10622.07 249273 0.043
UE 2 41.13 2.51 3582.34 37.05 3619.39 334899 0.011
UE 3 285.62 4.95 201.67 82.11 283.77 1066159 0.0003
LE 1 75.27 6.00 93.56 67.48 161.04 72302 0.002
LE 2 78.51 1.64 353.38 46.69 400.08 23820.7 0.017
UB 1 116.35 9.17 2796.75 86.74 2883.48 42446.5 0.068
UB 2 277.95 8.36 747.43 56.41 803.84 122393 0.007
CW 197.46 37.74 27180.41 109.71 27290.12 477696 0.057
153
154
The SDR value calculated at UW subcatchment area
0
0.2
0.4
0.6
0.8
1
1.2
1.4
UW 1 UW 2 UW 3 UW 7 UW 8
Wetland
SD
R v
alu
e
The SDR value calculated at UN subcatchment area
0
0.5
1
1.5
2
2.5
3
UN 1 UN 2 UN 4 UN 6
Wetland
SD
R v
alu
e
The SDR value calculated at UE subcatchment area
-0.01
0
0.01
0.02
0.03
0.04
0.05
UE 1 UE 2 UE 3
Wetland
SD
R v
alu
e
Figure 4.30: Trends of SDR values for UW subcatchment wetland (a), UN subcatchment wetland (b), UE subcatchment wetland (c), LE subcatchment wetland (d) and UB subcatchment wetland (e).
(c)
The SDR value calculated at LE subcatchment area
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
LE 1 LE 2
Wetland
SD
R v
alu
e
The SDR value calculated at UB subcatchment area
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
UB 1 UB 2
Wetland
SD
R v
alu
e
(e)
(b)
(d)
(a)
landscape properties (e.g., vegetation, topography, and soil properties) and
their complex interactions (Walling, 1983; Richards, 1993).
The decreasing trend towards the downstream direction at the LE
subcatchment wetland indicates a relatively good sediment filtering capability
for wetland station within their respective subcatchment. The increasing SDR
values toward the downstream direction for UW, UE and UB subcatchment
wetland cell could be related to the availability of bank erosion at the UW2, UE1
and UB1 downstream stations respectively.
The larger than 1 SDR value calculated primarily at UW3 and UN2 for
year 2003 plus 2004 could indicate the availability of bank or gully erosion near
the respective stations. However, the combination of calculated SDR values
with site observations and previous sediment monitoring reports (Yusoff et al.,
2004, 2006) show that only UW2, UB1, UB2 and CW suffered from bank
erosion (for UW3, bank erosion at UW6) while no clear bank erosion was
observed for the rest of stations.
This indicates that other factors such as sediment re-suspension and
flushing effect could contribute to the higher SDR values at respective stations.
Furthermore, the uncertainties of the USLE gross erosion results (overestimate
at lower and underestimate for higher grid size) could possibly have contributed
to this anomaly where the result clearly shows the increase of SDR values with
the increase grid cell sizes with lower USLE gross erosion value for higher grid
cell size.
155
4.8 Proposed specific sediment control measures for Putrajaya Wetland
area
The results indicate that the sedimentation processes within wetland cells
together with the availability of bank erosion nearby clearly affect the TSS yield,
wetland reservoir sediment yield and hence, SDR value for the respective
wetland. For the wetland without any bank erosion, high sediment and TSS
yield are suspected to be due to the effect of sediment re-suspension and
sediment flushing. Furthermore, the availability of sediment input from spilled-
over water during storm events could worsen the scenario itself. Thus, these
two factors (bank erosion and sediment re-suspension or flushing effect)
together with sediment input during storm events should be controlled to
minimize the damage due to sedimentation at the respective wetland cells and
downstream areas (Putrajaya lake).
Sedimentation due to bank erosion could be control by installing
sediment fences near to the source areas with bank erosion. However, this is
only considered as a temporary measure and need to be monitored frequently
because sediment fences tend to collapse or fail after heavy storms. The best
solution is to remove the sediment and repair the failure itself. The heavily
affected of bank erosion for example at UB1-05, UN8-C5, UN8-17 and etc.
should be redesigned using permanent bank reinforced structure or heavy
stone to provide armor protection together with effective culvert enlargement
program. The management also needs to consider wetland geotechnical
monitoring or wetland slope and embankment monitoring to monitor potential
slope embankment failure surrounding wetland area.
156
157
Further site observations revealed that during storm events, excessive
runoff tend to spilled-over from drainage culvert and GPT (gross pollutant trap)
when the culvert and GPT were filled with sediments. This phenomenon
weakens the nearby soil structure as the soil becomes oversaturated resulting
bank erosion. Furthermore, the spilled-over phenomenon could transport more
sediment due to high discharge and sediment supply (refer to Table 4.13).
Thus, the culverts and GPT should regularly be monitored, maintained and
freed from infilling sediment as one of the bank erosion control strategies to
avoid any diversion of runoff. The locations of different sediment mitigation
measures are shown in Figure 4.31.
It is not an easy task to control sediment re-suspension and sediment
flushing during high discharge and to minimize their effects due to the source of
sediment coming from the accumulated sediments within the wetland cell itself
particularly for sediment accumulation with highly clay content. Rehabilitation on
wetland storage capacity is needed by using the siphon dredging technique
instead of removing all sediments, that easily implemented and very effective
for small to medium reservoirs together with controlled sediment flushing and
emptying through under-sluice method. The management should also consider
replanting and desilting exercise for wetland cells with dead storage (almost
zero storage capacity).
Figure 4.31: Location of sediment mitigation measure around Putrajaya Wetland Area.
158
Conclusion and Recommendations
5.0 Conclusion and recommendations
Erosion and sedimentation process studies were carried out at the
Putrajaya wetland area. The sheet and rill erosion was determined using the
USLE approach while the observed bank erosion throughout study area was
documented completely. The sedimentation process in terms of sediment yields
was determined and correlated using the USLE-SDR, TSS rating curve
technique (for suspended sediment yields) and wetland reservoir sediment
yields (from wetland sedimentation survey).
The estimated USLE gross erosion and specific erosion values show
relatively high variability in terms of spatial and temporal characteristics together
with the effect of using different grid cell sizes. The USLE results using different
grid cell sizes of 10m, 20m, 30m and 40m show a declining trend of total gross
erosion, average mean and maximum erosion with increase of grid cell sizes. In
term of temporal characteristics, a decrease of total gross erosion is observed
from 2003 to 2004 with an approximately 30 % decrease of total gross erosion.
Sensitivity analysis performed in the GIS environment using grid
regression analysis extension shows that for almost all the years analysed the
LS factor is the most sensitive parameter compared to the other factors using
20m, 30m and 40m grid cell sizes. However, by using the 10m grid cell size, the
CP factor was noted as the most sensitive factor for the years analysed
followed by the LS factor, R factor and K factor. Thus, the relatively low USLE
values calculated using 20m, 30m and 40m grid cell sizes can be related to the
perdominant underestimation of the LS factor whereas the relatively high USLE
results for the relative contribution of the high CP factor for the 10m grid cell
size. From this findings, the result of USLE 10m grid size were used for further
159
Conclusion and Recommendations
analysis and comparison regarding USLE erosion (e.g USLE SDR sediment
yield). Thus, it is recommended that further calculation in GIS using DEM as
major input, should consider the effect of grid cell size to the results.
Throughout 2002 to 2004, moderate to major bank erosion had been
observed at UW2, UW6 and UW8 wetland cell (Upper West (UW) wetland area)
while at Upper North (UN) wetland area, bank erosion had been observed at
UN1, UN7 and UN8 wetland (also moderate to major bank erosion). At Upper
East (UE) wetland, major bank erosion was observed at UE1 wetland cell while
at Lower East (LE) wetland, minor bank erosion had been observed at LE2.
Major bank erosion had also been observed at UB1 and UB2 wetland (for
Upper Bisa (UB) wetland area). In terms of the types of bank erosion, bank
scour and mass failure had been observed at UW8-06, UN8-17, UN8-C4, UN7-
C1, UE1-01, UE3-GPT2, UB1-05, UB2-03, UB2-04 and CWA while bank scour
alone was observed at UW2-GPT5, UW6-C3, UW8-C2, UN1A, LE2-05, UB1-01
and UB2-05.
The TSS suspended sediment yields from the TSS rating curves show a
variability of TSS yields in terms of spatial and temporal characteristics. In term
of temporal characteristics, 2004 recorded higher average TSS yields in
comparison to 2003. The highest average TSS yields in 2003 was recorded at
the UW and UB wetland cells and at the LE and UN wetland cell for 2004.
The USLE-SDR catchment sediment yields determined using the Vanoni
(1975) SDR equation show a slightly lower USLE sediment yield compared to
the USLE sediment yields from the USDA-SCS (1972) SDR equation. The SDR
values calculated using the Vanoni (1975) equation range from 0.41 (UE3 and
160
Conclusion and Recommendations
UW7) to 0.60 (UN2) while calculated SDR values from the USDA SCS (1972)
equation range from 0.50 (UE3 and UW7) to 0.70 (UN2).
The wetland reservoir sediment yields for 2002 from the sedimentation
survey exercise range from -12 t/ha/yr (UW7) to 867 t/ha/yr (UE1). For the 2004
sedimentation survey, the wetland reservoir sediment yields range from 20
t/ha/yr (UW7 and UN4) to 712 t/ha/yr (UE1). The highest average annual
reservoir sediment yield was from the UE1 wetland cell (536 t/ha/yr) while the
lowest was from UW7 (9.75 t/ha/yr).
Comparative analysis between the three sediment yield methods show
that the catchment sediment yields from both the USLE-SDR (Vanoni, 1975)
and USLE-SDR (USDA-SCS, 1972) approaches had overestimated and
underestimated the specific TSS yields and wetland reservoir sediment yields.
These results further show that USLE using USLE-SDR Vanoni (1975) and
USDA-SCS (1972) is consider a fair to poor sediment yield determinator as
there was a lot of factors affecting the sediment delivery processes from the
source of erosion to the sink area. However for management purposes, and
because of the simplicity of the equation, the USLE can give a rough idea of the
source of erosion and how to plan and place erosion and sediment mitigation
measures.
The linkages between wetland TSS yield, wetland reservoir sediment
yield and USLE total gross erosion can be analyzed in term of sediment delivery
ratio (SDR) defined as the portion of sediment transported from source area to
the catchment outlet or particular reservoir. The increasing (UW, UN, UE and
UB wetland subcatchment area) and reducing (LE wetland subcatchment area)
trends of SDR value towards the downstream stations observed indicate that
161
Conclusion and Recommendations
162
there was an increasing amount of sediment transported (increasing transport
capacity) from the upstream to downstream wetlands at UW, UN, UE and UB
wetland subcatchment area. The declining trend of SDR values in LE wetland
subcatchment area implied that the effectiveness of wetland filtration processes
within LE’s wetland cells.
Sedimentation due to bank erosion could be control by installing
sediment fences in the affected areas. However, this measure is only a
temporary measure and need to be monitored frequently as sediment fencing
tend to collapse or fail after heavy storms. The best solution is to remove the
sediment and repair the failure itself. The heavily affected of bank erosion
should be redesigned using permanent bank reinforced structure or heavy
stone to provide armor protection together with effective culvert enlargement
program should be considered for critical and major bank erosion inlets (UB1-
05, UN8-C5, UN8-17 and etc.). The management also needs to consider
wetland geotechnical monitoring or wetland slope and embankment monitoring
to detect potential slope embankment failures surrounding the wetland area.
Rehabilitation on wetland storage capacity is needed by using the siphon
dredging technique instead of removing all sediments, that easily implemented
and very effective for small to medium reservoirs together with controlled
sediment flushing and emptying through under-sluice method. The
management should also consider replanting and desilting exercise for wetland
cells with dead storage (almost zero storage capacity).
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APPENDIX 1
Total Suspended Solids (TSS) Reference:
Standard Methods; 2540 A, 2540 D, 2540 E
Scope and Application
The residue obtained after a thoroughly mixed sample is filtered and dried at 103° -105° C represents the amount of solids suspended in the sample solution. The amount of suspended solids in a water sample may be used as a general indicator of the overall quality of the sample. Suspended solids analyses are important in the control of biological and physical wastewater treatment processes and for assessing compliance with discharge regulations. The residue remaining after drying at 103° C is weighed and placed in a muffle furnace at 550° C. The weight loss from ignition determines "volatile suspended solids".
Apparatus
Vacuum pump and manifold Forceps or tweezers Desiccator and desiccant that contains a color indicator for moisture content Drying oven for use at 103° -105° C Muffle furnace for use at 550° C. Analytical balance - capable of weighting to 0.1 mg Magnetic stirplate and stirbar Magnetic stirbar retriever Crucible tongs Heat resistant gloves Cotton gloves Glass-fiber filter disks (Whatman AH-934 or equivalent) 40 mL Gooch crucible (permanently labeled) Aluminum dish for drying filter disks Side arm erlenmeyer flask Sample Delivery Mechanisms: (as required)
1. 1 mL - 5 mL variable pipette, 2. 25 mL graduated wide-mouth glass pipette, 3. 50 mL wide-mouth glass transfer pipette, OR 4. 250 mL glass graduated cylinder
Reagents
Distilled or deionized water
Storage / Preservation
Samples may be stored in a plastic or glass container and kept for 7 days at 4° C.
Raw Data Sheet Format
The following must be recorded on the data sheet:
o Sample identification (source, name, and date(s) of collection) o Analyst(s) o Raw data o Final results with correct units (reported to nearest mg/L) o Description of unusual sample characteristics
o Replicates are to be listed in an orderly cluster
Quality Control Requirements
o A check sample should be analyzed with every batch of samples. o Recoveries of suspended solids check samples should be between ±2σ. o Recoveries of suspended solids check samples should be between ±3σ. o Recovery statistics of suspended solids check samples should be reviewed on a yearly
basis and any changes in acceptable ranges documented appropriately. o Results of replicate analyses should yield RSD’s of less than 5% for the set.
Procedure for Total Suspended Solids
Filter Preparation
1. Pre-wash glass-fiber filter disks in Gooch crucibles. With vacuum operating wash the disks with three 20 mL portions of distilled or deionized water.
2. When all water has been vacuumed through the filter disks, place the Gooch crucible in a 103° -105° C oven to dry. If volatile suspended solids are to be analyzed, move the dry filters into a 550° C muffle furnace for 15 minutes; if the volatile portion does not need to be determined, place the crucibles into a desiccator to cool and skip step 3.
3. Remove the Gooch crucibles from the muffle furnace and place on a heat resistant surface. The surface temperature of the crucible must be greater than 103° C when placed into the desiccator.
4. Cool the filters thoroughly in a desiccator before use. Gooch crucibles and washed filters should be stored in a desiccator.
Sample Analysis
1. Weigh the Gooch crucible and filter (at room temperature) on an analytical balance. Use crucible tongs or wear lint free cotton gloves to transfer the crucible from the desiccator to the balance pan. Handling the crucible with your bare fingers may transfer oils and moisture from the skin.
2. Record the weight of the crucible and filter. 3. Place prepared crucible and filter on the vacuum manifold or side-arm Erlenmeyer flask
with vacuum gasket. Wet the filter with deionized water in order to seat the filter against the crucible. Turn on the vacuum. If there is a hole in the filter, you may hear an abnormal hissing or whistling. Use a different weighed crucible and filter.
4. Thoroughly mix the sample to be analyzed. Carefully measure the volume of sample transferred to the Gooch crucible. The volume of sample used should leave at least 2.5, but not more than 200, milligrams of residue on the filter.
5. Rinse the filter with three successive 10 mL portions of deionized water. If the sample takes excessive time to filter (longer than 10 minutes), begin again with a different weighed crucible and filter using a smaller volume of sample for filtering.
6. Allow the vacuum to continue until no traces of moisture are present. If solids are present on the side of the funnel, rinse the sides gently with deionized water.
7. Place the crucible in the oven to dry for at least 1 hour at 103° C. 8. Transfer the dried crucible to a desiccator to cool. When the crucible has cooled
sufficiently it should not feel warm to the touch on the inside of your forearm. 9. Weigh the dried and cooled crucible on an analytical balance. Record the weight. If the
sample is not going to be used for regulatory purposes, it may be acceptable to use this weight as the final dry weight.
10. Return the crucible to the drying oven for another thirty minutes. Cool, reweigh and record its weight. Repeat this procedure until the change in the weight of the residue remains within 4% or less than 0.5 mg from one weighing to the next. (This is referred to as constant weight.) Record the final dry weight on the benchsheet and calculate the total suspended solids.
APPENDIX 2
(mm) (gm) 100 4 2 11 21 18 45
10.000 0.005.000 1.083.350 1.192.000 1.631.180 2.180.600 4.010.425 2.740.300 4.090.212 4.920.150 6.430.063 9.19
< 0.063 62.54Total 37.46
2319
Fine Sand silt clay
0.00 200.00
GravelCoarse Sand
Medium Sand
BS Sieve
Sample Name
CWA
(%)
2.88
4.355.82
3.18
154.83
197.12
189.59183.77
193.94
17.1624.5362.54
10.707.3110.9213.13
(%)
141.70124.53100.0037.46
173.06165.75
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
CWA
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
(mm) (gm) 100 2 2 15 28 20 33
10.000 0.005.000 0.573.350 0.892.000 0.901.180 1.500.600 4.890.425 4.260.300 6.050.212 6.950.150 9.530.063 11.39
< 0.063 53.07Total 46.93
2319
Fine Sand silt clayGravelCoarse Sand
Medium Sand
BS Sieve
0.00 200.00
Sample Name
LE1 (1)
(%)
1.21
1.923.20
1.90
159.39
198.79
194.97191.77
196.89
20.3124.2753.07
10.429.0812.8914.81
(%)
144.58124.27100.0046.93
181.36172.28
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
LE1 (1)
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
(mm) (gm) 100 18 4 14 20 9 34
10.000 0.005.000 8.883.350 4.352.000 4.461.180 3.870.600 5.480.425 3.520.300 5.160.212 5.240.150 6.680.063 8.46
< 0.063 43.90Total 56.10
2319
Fine Sand silt clayGravelCoarse Sand
Medium Sand
BS Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
(%)
126.99115.08100.0056.10
151.80145.53
11.9115.0843.90
9.776.279.209.34
136.33
184.17
168.47161.57
176.4215.83
7.956.90
7.75
0.00 200.00
Sample Name
LE1 (2)
(%)
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
LE1 (2)
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 6 6 27 28 11 22
10.000 0.005.000 2.853.350 1.402.000 1.361.180 6.240.600 14.040.425 6.430.300 6.700.212 6.220.150 8.650.063 13.23
< 0.063 32.88Total 67.12
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
132.60119.71100.0067.12
161.43151.85
12.8919.7132.88
20.929.589.989.27
141.87
195.75
191.64182.35
193.674.25
2.039.30
2.09
0.00 200.00
Sample Name
LE2
(%)
PercentagePercentageRetained
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
LE2
Percentage retained (%)
0.005.00
10.0015.0020.0025.0030.0035.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 4 1 17 30 10 38
10.000 0.005.000 1.183.350 1.382.000 0.971.180 1.440.600 3.660.425 4.620.300 8.910.212 9.740.150 9.760.063 10.84
< 0.063 47.51Total 52.49
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
139.23120.65100.0052.49
183.57174.77
18.5820.6547.51
6.978.8016.9818.56
157.79
197.75
193.27190.54
195.112.25
1.842.74
2.64
0.00 200.00
Sample Name
UN1
(%)
PercentagePercentageRetained
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UN1
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 0 2 16 26 17 40
10.000 0.005.000 0.003.350 0.002.000 0.071.180 1.550.600 3.620.425 4.110.300 8.120.212 8.550.150 8.400.063 9.06
< 0.063 56.52Total 43.48
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
0.00 200.00
Sample Name
UN3
(%)
PercentagePercentageRetained
0.00
0.163.58
0.00
159.82
200.00
199.84196.27
200.00
19.3120.8456.52
8.339.4518.6619.67
Passing(%)
140.15120.84100.0043.48
187.94178.49
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
CLAY
SILT SAND GRAVEL
FINE FINE FINEMEDIUM MEDIUM MEDIUMCOARSE COARSE COARSECLAY
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UN3
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 6 2 18 27 11 35
10.000 0.005.000 3.473.350 1.122.000 1.351.180 2.010.600 5.320.425 5.010.300 8.000.212 8.180.150 8.720.063 10.36
< 0.063 46.45Total 53.55
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
135.64119.36100.0053.55
175.22165.87
16.2919.3646.45
9.939.3514.9515.28
150.92
193.52
188.89185.14
191.426.48
2.533.75
2.09
0.00 200.00
Sample Name
UN5
(%)
PercentagePercentageRetained
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UN5
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 2 2 14 21 9 52
10.000 0.005.000 0.003.350 0.352.000 1.481.180 1.600.600 3.550.425 3.540.300 7.000.212 7.340.150 7.310.063 6.69
< 0.063 61.14Total 38.86
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
0.00 200.00
Sample Name
UN7
(%)
PercentagePercentageRetained
0.00
3.814.12
0.91
154.91
200.00
195.28191.16
199.09
18.8217.2161.14
9.149.1118.0118.88
Passing(%)
136.03117.21100.0038.86
182.03172.92
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UN7
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 0 0 3 27 16 53
10.000 0.005.000 0.003.350 0.052.000 0.111.180 0.140.600 0.750.425 0.850.300 1.530.212 2.450.150 6.940.063 17.79
< 0.063 68.94Total 30.61
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
180.80158.13100.0031.06
196.59193.82
22.6758.1368.94
2.442.775.018.01
188.81
200.00
199.48199.03
199.830.00
0.350.45
0.17
0.00 200.00
Sample Name
UN8
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.0080.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UN8
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 4 2 9 15 9 60
10.000 0.005.000 0.443.350 1.242.000 1.951.180 2.380.600 3.600.425 2.270.300 3.280.212 3.460.150 4.320.063 7.49
< 0.063 69.57Total 30.43
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
0.00 200.001.45
6.42
Sample Name
UW1
(%)
PercentagePercentageRetained
7.83
4.07
150.19
198.55
188.06180.23
194.48
14.2024.6069.57
11.827.4510.7711.38
Passing(%)
138.81124.60100.0030.43
168.41160.96
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.0080.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UW1
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 0 0 1 3 25 71
10.000 0.005.000 0.003.350 0.172.000 0.091.180 0.180.600 0.270.425 0.210.300 0.340.212 0.220.150 0.620.063 2.52
< 0.063 95.38Total 4.62
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
167.97154.55100.00
4.62
184.63180.09
13.4254.5595.38
5.844.557.364.76
172.73
200.00
194.37190.48
196.320.00
1.953.90
3.68
0.00 200.00
Sample Name
UW2-8
(%)
PercentagePercentageRetained
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UW2-8
Percentage retained (%)
0.0020.0040.0060.0080.00
100.00120.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 8 2 17 32 9 31
10.000 0.005.000 2.513.350 2.942.000 3.041.180 2.160.600 4.720.425 4.780.300 7.930.212 9.150.150 10.500.063 12.12
< 0.063 40.15Total 59.85
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
0.00 200.00
Sample Name
UW4
(%)
PercentagePercentageRetained
4.19
5.083.61
4.91
153.08
195.81
185.81182.21
190.89
17.5420.2540.15
7.897.9913.2515.29
Passing(%)
137.79120.25100.0059.85
174.32166.33
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UW4
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 10 5 15 18 10 42
10.000 0.005.000 1.693.350 3.332.000 4.841.180 5.060.600 5.880.425 3.790.300 5.050.212 4.980.150 5.870.063 7.29
< 0.063 52.23Total 47.77
2319
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Fine Sand silt clayTotal GravelPassing
(%)
127.54115.26100.0047.77
156.48148.55
12.2815.2652.23
12.317.9410.5810.43
Coarse Sand
Medium Sand
10.58
6.96
137.97
196.47
179.38168.80
189.51
Sample Name
UW6
(%)
PercentagePercentageRetained
Dry Sieve
0.00 200.003.53
10.12
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UW6
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 8 1 18 33 9 31
10.000 0.005.000 1.683.350 2.842.000 3.361.180 1.020.600 5.020.425 4.740.300 8.010.212 9.180.150 11.170.063 12.86
< 0.063 40.12Total 59.88
2319
Fine Sand silt clay
0.00 200.002.81
5.611.70
Sample Name
UW8
(%)
PercentagePercentageRetained
4.74
155.46
197.19
186.84185.14
192.45
Total GravelCoarse Sand
Medium Sand
18.6521.4840.12
8.387.9213.3815.33
Passing(%)
140.13121.48100.0059.88
176.75168.84
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UW8
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 2 2 11 14 13 58
10.000 0.005.000 0.003.350 0.732.000 1.301.180 1.650.600 2.620.425 3.090.300 5.190.212 4.880.150 4.390.063 4.82
< 0.063 71.32Total 28.68
2319
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Fine Sand silt clayTotal GravelPassing
(%)
132.09116.79100.0028.68
177.99167.20
15.3016.7971.32
9.1410.7918.1117.01
Coarse Sand
Medium Sand
5.77
2.56
149.10
200.00
192.90187.14
197.44
Sample Name
UE1
(%)
PercentagePercentageRetained
Dry Sieve
0.00 200.000.00
4.54
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.0080.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UE1
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 18 6 15 16 6 39
10.000 0.005.000 5.263.350 5.642.000 7.501.180 5.990.600 6.820.425 3.710.300 4.430.212 4.160.150 4.500.063 6.87
< 0.063 45.12Total 54.88
2319
Fine Sand silt clay
0.00 200.009.58
13.6710.91
Sample Name
UE1(ioi)
(%)
PercentagePercentageRetained
10.28
128.30
190.42
166.47155.56
180.14
Total GravelCoarse Sand
Medium Sand
8.2012.5245.12
12.436.768.077.58
Passing(%)
120.72112.52100.0054.88
143.13136.37
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UE1(ioi)
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 6 3 12 16 12 50
10.000 0.005.000 1.503.350 1.832.000 2.751.180 3.190.600 4.750.425 3.290.300 4.170.212 4.120.150 4.520.063 6.94
< 0.063 62.94Total 37.06
2319
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Fine Sand silt clayTotal GravelPassing
(%)
130.92118.73100.0037.06
162.17153.29
12.2018.7362.94
12.828.8811.2511.12
Coarse Sand
Medium Sand
8.61
4.94
142.04
195.95
183.59174.99
191.01
Sample Name
UE3
(%)
PercentagePercentageRetained
Dry Sieve
0.00 200.004.05
7.42
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UE3
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 0 4 17 28 9 41
10.000 0.005.000 0.003.350 0.002.000 0.301.180 4.340.600 6.020.425 5.110.300 5.930.212 5.560.150 7.630.063 15.03
< 0.063 50.08Total 49.92
2319
Fine Sand silt clay
0.00 200.00
Sample Name
UB1 (1)
(%)
PercentagePercentageRetained
0.00
0.618.70
0.00
156.53
200.00
199.39190.69
200.00
Total GravelCoarse Sand
Medium Sand
15.2930.1150.08
12.0510.2411.8811.13
Passing(%)
145.39130.11100.0049.92
178.64168.40
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UB1 (1)
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 30 21 30 6 2 12
10.000 0.005.000 5.023.350 7.452.000 17.141.180 20.840.600 21.490.425 5.400.300 3.040.212 1.820.150 1.670.063 2.09
< 0.063 14.04Total 85.96
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
104.38102.44100.0085.96
116.32110.04
1.952.4414.04
25.006.283.542.12
Total GravelCoarse Sand
Medium Sand
106.50
194.16
165.56141.32
185.495.84
19.9324.24
8.67
0.00 200.00
Sample Name
UB1 (2)
(%)
PercentagePercentageRetained
Percentage retained (%)
0.005.00
10.0015.0020.0025.0030.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UB1 (2)
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 17 12 36 23 2 9
10.000 0.005.000 1.443.350 5.982.000 9.461.180 12.050.600 19.510.425 8.520.300 8.420.212 7.180.150 7.400.063 8.68
< 0.063 11.37Total 88.63
2319
Fine Sand silt clay
0.00 200.00
Sample Name
UB2 (1)
(%)
PercentagePercentageRetained
1.62
10.6713.59
6.74
126.25
198.38
180.96167.37
191.63
Total GravelCoarse Sand
Medium Sand
8.359.8011.37
22.019.619.508.10
Passing(%)
118.15109.80100.0088.63
145.36135.75
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
5.00
10.00
15.00
20.00
25.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UB2 (1)
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 6 3 17 27 11 35
10.000 0.005.000 1.753.350 1.952.000 2.551.180 3.450.600 6.790.425 4.480.300 5.680.212 6.170.150 8.440.063 12.87
< 0.063 45.87Total 54.13
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
139.37123.78100.0054.13
169.54161.27
15.6023.7845.87
12.548.2710.5011.40
Total GravelCoarse Sand
Medium Sand
150.77
196.77
188.46182.09
193.163.23
4.716.37
3.61
0.00 200.00
Sample Name
UB2 (2)
(%)
PercentagePercentageRetained
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UB2 (2)
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 3 1 6 26 8 55
10.000 0.005.000 2.103.350 0.432.000 0.891.180 1.210.600 2.790.425 1.450.300 1.880.212 2.450.150 3.740.063 20.27
< 0.063 62.80Total 37.20
2319
Fine Sand silt clay
0.00 200.00
Sample Name
UB2 (3)
(%)
PercentagePercentageRetained
5.65
2.393.24
1.15
171.10
194.35
190.82187.58
193.21
Total GravelCoarse Sand
Medium Sand
10.0554.4762.80
7.503.915.066.58
Passing(%)
164.52154.47100.0037.20
180.08176.17
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
UB2 (3)
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 4 2 9 15 18 52
10.000 0.005.000 1.973.350 1.082.000 1.001.180 2.380.600 4.050.425 2.170.300 2.710.212 2.730.150 4.210.063 7.72 26.071
< 0.063 69.99Total 30.01
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
139.73125.71100.0030.01
165.09157.84
14.0225.7169.99
13.497.259.029.09
Total GravelCoarse Sand
Medium Sand
148.82
193.42
186.49178.58
189.836.58
3.347.91
3.59
0.00 200.00
Sample Name
PL1
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.0080.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL1
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 3 2 5 7 15 68
10.000 0.005.000 0.003.350 0.922.000 1.771.180 1.990.600 2.040.425 1.260.300 1.340.212 1.360.150 1.880.063 4.20 26.071
< 0.063 83.24Total 16.76
2319
Fine Sand silt clay
0.00 200.00
Total GravelCoarse Sand
Medium Sand
Sample Name
PL2
(%)
PercentagePercentageRetained
0.00
10.5611.87
5.49
144.39
200.00
183.95172.08
194.51
11.2225.0683.24
12.177.528.008.11
Passing(%)
136.28125.06100.0016.76
159.90152.39
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
20.00
40.00
60.00
80.00
100.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL2
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 9 10 27 23 5 27
10.000 0.005.000 1.443.350 2.122.000 5.321.180 9.760.600 14.650.425 6.380.300 5.690.212 5.120.150 6.010.063 11.47 26.071
< 0.063 32.02Total 67.98
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL3
(%)
PercentagePercentageRetained
2.12
7.8314.36
3.12
133.25
197.88
186.93172.56
194.76
Total GravelCoarse Sand
Medium Sand
8.8516.8832.02
21.569.398.367.53
Passing(%)
125.72116.88100.0067.98
151.01141.62
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.005.00
10.0015.0020.0025.0030.0035.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL3
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 5 5 13 20 13 44
10.000 0.005.000 0.393.350 1.132.000 3.331.180 5.040.600 6.760.425 3.000.300 3.560.212 3.800.150 5.180.063 10.66 26.071
< 0.063 57.15Total 42.85
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
136.96124.87100.0042.85
161.13154.14
12.0924.8757.15
15.786.998.308.88
Total GravelCoarse Sand
Medium Sand
145.84
199.08
188.67176.91
196.440.92
7.7711.76
2.64
0.00 200.00
Sample Name
PL4
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL4
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 8 5 19 18 11 39
10.000 0.005.000 1.853.350 2.232.000 4.371.180 4.790.600 8.860.425 4.670.300 4.980.212 4.370.150 5.120.063 8.84 26.071
< 0.063 49.91Total 50.09
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL5
(%)
PercentagePercentageRetained
3.70
8.729.56
4.46
136.60
196.30
183.12173.56
191.85
Total GravelCoarse Sand
Medium Sand
10.2217.6549.91
17.699.329.958.73
Passing(%)
127.87117.65100.0050.09
155.88146.56
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL5
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 1 0 6 30 21 40
10.000 0.005.000 0.793.350 0.082.000 0.371.180 0.320.600 0.690.425 1.550.300 4.200.212 5.540.150 8.250.063 16.59 26.071
< 0.063 61.63Total 38.37
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
164.72143.23100.0038.37
194.14190.09
21.4943.2361.63
1.814.0510.9314.44
Total GravelCoarse Sand
Medium Sand
179.15
197.94
196.77195.94
197.722.06
0.950.83
0.22
0.00 200.00
Sample Name
PL6
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL6
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 10 6 18 18 8 40
10.000 0.005.000 2.463.350 2.972.000 4.441.180 6.230.600 8.200.425 4.600.300 5.360.212 4.830.150 4.770.063 8.56 26.071
< 0.063 47.57Total 52.43
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL7
(%)
PercentagePercentageRetained
4.70
8.4611.89
5.66
134.65
195.30
181.18169.30
189.65
Total GravelCoarse Sand
Medium Sand
9.1016.3247.57
15.648.7810.239.22
Passing(%)
125.43116.32100.0052.43
153.66144.88
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL7
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 31 5 12 10 11 30
10.000 0.005.000 14.483.350 10.002.000 6.831.180 5.120.600 6.020.425 3.170.300 3.260.212 2.640.150 2.860.063 4.93 26.071
< 0.063 40.70Total 59.30
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
113.14108.32100.0059.30
128.43123.08
4.828.3240.70
10.165.355.504.44
Total GravelCoarse Sand
Medium Sand
117.58
175.59
147.22138.58
158.7324.41
11.518.63
16.86
0.00 200.00
Sample Name
PL8
(%)
PercentagePercentageRetained
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL8
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 4 4 12 12 20 49
10.000 0.005.000 0.833.350 0.882.000 2.551.180 3.670.600 5.520.425 2.790.300 3.250.212 2.800.150 3.270.063 5.82 26.071
< 0.063 68.62Total 31.38
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL9
(%)
PercentagePercentageRetained
2.64
8.1411.69
2.80
137.90
197.36
186.41174.72
194.55
Total GravelCoarse Sand
Medium Sand
10.4118.5668.62
17.588.8810.368.92
Passing(%)
128.97118.56100.0031.38
157.14148.26
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.0080.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL9
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 1 1 16 32 13 37
10.000 0.005.000 0.613.350 0.072.000 0.801.180 1.320.600 3.660.425 3.880.300 8.130.212 7.830.150 9.580.063 14.43 26.071
< 0.063 49.70Total 50.30
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
147.72128.68100.0050.30
187.17179.46
19.0428.6849.70
7.287.7116.1615.57
Total GravelCoarse Sand
Medium Sand
163.29
198.80
197.08194.45
198.661.20
1.582.62
0.14
0.00 200.00
Sample Name
PL10
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL10
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 3 2 9 13 19 55
10.000 0.005.000 0.853.350 0.602.000 1.081.180 1.510.600 3.830.425 2.340.300 2.360.212 2.050.150 2.640.063 8.39 26.071
< 0.063 74.34Total 25.66
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL11
(%)
PercentagePercentageRetained
3.31
4.215.87
2.33
151.01
196.69
190.15184.28
194.36
Total GravelCoarse Sand
Medium Sand
10.3032.7174.34
14.949.129.218.00
Passing(%)
143.01132.71100.0025.66
169.34160.22
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.0080.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL11
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 10 4 9 18 12 48
10.000 0.005.000 4.243.350 2.282.000 3.191.180 3.530.600 3.940.425 2.280.300 3.080.212 2.860.150 4.400.063 10.44 26.071
< 0.063 59.75Total 40.25
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
136.88125.94100.0040.25
157.31151.65
10.9325.9459.75
9.785.657.667.11
Total GravelCoarse Sand
Medium Sand
143.99
189.45
175.85167.09
183.7910.55
7.948.77
5.66
0.00 200.00
Sample Name
PL12
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL12
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 3 2 7 11 20 56
10.000 0.005.000 1.153.350 0.722.000 1.531.180 2.200.600 3.340.425 1.850.300 2.300.212 2.220.150 2.580.063 5.81 26.071
< 0.063 76.31Total 23.69
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL13
(%)
PercentagePercentageRetained
4.83
6.469.28
3.05
144.78
195.17
185.65176.38
192.11
Total GravelCoarse Sand
Medium Sand
10.8824.5276.31
14.097.799.729.37
Passing(%)
135.41124.52100.0023.69
162.29154.50
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
20.00
40.00
60.00
80.00
100.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL13
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 3 4 12 20 15 46
10.000 0.005.000 0.713.350 0.542.000 2.001.180 3.960.600 6.330.425 2.720.300 3.350.212 3.490.150 5.010.063 11.32 26.071
< 0.063 60.57Total 39.43
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
141.41128.70100.0039.43
165.68158.77
12.7128.7060.57
16.056.918.508.86
Total GravelCoarse Sand
Medium Sand
150.27
198.19
191.75181.72
196.821.81
5.0710.03
1.37
0.00 200.00
Sample Name
PL14
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.0070.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL14
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 20 13 19 16 7 25
10.000 0.005.000 6.423.350 4.372.000 9.351.180 12.860.600 10.530.425 3.910.300 4.080.212 3.560.150 4.570.063 7.93 26.071
< 0.063 32.41Total 67.59
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL15
(%)
PercentagePercentageRetained
9.50
13.8319.02
6.47
123.77
190.50
170.20151.18
184.04
Total GravelCoarse Sand
Medium Sand
6.7711.7332.41
15.585.796.045.27
Passing(%)
118.50111.73100.0067.59
135.60129.81
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.005.00
10.0015.0020.0025.0030.0035.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL15
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 7 7 18 19 12 36
10.000 0.005.000 0.173.350 1.822.000 4.791.180 7.300.600 8.390.425 4.080.300 5.400.212 5.180.150 5.090.063 9.01 26.071
< 0.063 48.78Total 51.22
2319
Fine Sand silt clay
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
127.53117.59100.0051.22
156.14148.18
9.9417.59
137.63
48.78
16.377.9610.5510.10
Total GravelCoarse Sand
Medium Sand
199.67
186.76172.51
196.110.33
9.3514.25
3.56
0.00 200.00
Sample Name
PL16
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL16
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS Sieve WeightNo. Retained
(mm) (gm) 100 9 6 16 21 10 38
10.000 0.005.000 1.623.350 2.772.000 4.731.180 6.140.600 8.210.425 3.520.300 4.170.212 4.210.150 5.330.063 11.79 26.071
< 0.063 47.51Total 52.49
2319
Fine Sand silt clay
0.00 200.00
Sample Name
PL17
(%)
PercentagePercentageRetained
3.08
9.0011.69
5.28
140.65
196.92
182.64170.94
191.64
Total GravelCoarse Sand
Medium Sand
10.1622.4647.51
15.646.717.948.03
Passing(%)
132.62122.46100.0052.49
155.30148.59
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL17
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS SieveNo.
(mm) 100 16 5 12 11 14 43
10.0005.0003.3502.0001.1800.6000.4250.3000.2120.1500.063 26.071
< 0.063Total
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
118.84111.59100.0043.05
140.86133.84
7.2511.5956.95
11.757.027.947.06
43.05
WeightRetained
(gm)
3.043.124.9956.95
5.063.02
125.90
189.29
164.00152.61
178.68
3.42
4.61
6.32
10.71
14.684.90 11.38
4.57 10.62
0.000.00 200.00
Sample Name
PL18
(%)
PercentagePercentageRetained
Percentage retained (%)
0.0010.0020.0030.0040.0050.0060.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL18
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS SieveNo.
(mm) 100 1 2 18 48 7 24
10.0005.0003.3502.0001.1800.6000.4250.3000.2120.1500.063 26.071
< 0.063Total
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
0.000.00 200.00
Sample Name
PL19
(%)
PercentagePercentageRetained
8.34
0.00
1.00
0.00
1.451.67 2.42
0.00 0.00
169.98
200.00
198.55196.14
200.00
69.12
WeightRetained
(gm)
12.0914.6521.6330.88
4.235.51
21.2031.2930.88
6.127.9712.0717.49
Passing(%)
152.49131.29100.0069.12
190.02182.05
Dry Sieve
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Percentage retained (%)
0.005.00
10.0015.0020.0025.0030.0035.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL19
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
BS SieveNo.
(mm) 100 10 4 15 26 10 34
10.0005.0003.3502.0001.1800.6000.4250.3000.2120.1500.063 26.071
< 0.063Total
2319
Fine Sand silt clayTotal GravelCoarse Sand
Medium Sand
PARTICLE SIZE DISTRIBUTION
Test Method : BS 1377:1990, Part 2, Method 9.2, 9.3 & 9.5
Dry Sieve
Passing(%)
136.04122.73100.0055.39
164.07157.18
13.3122.7344.61
9.686.9010.1510.99
55.39
WeightRetained
(gm)
6.097.3712.5944.61
5.363.82
147.03
192.15
181.21173.75
187.83
5.62
4.35
3.67
7.85
6.634.13 7.46
2.39 4.31
0.000.00 200.00
Sample Name
PL20
(%)
PercentagePercentageRetained
Percentage retained (%)
0.00
10.00
20.00
30.00
40.00
50.00
10.0
005.
000
3.35
02.
000
1.18
00.
600
0.42
50.
300
0.21
20.
150
0.06
3
< 0.
063
Size (mm)
%
Series1
Series2
0
10
20
30
40
50
60
70
80
90
100
0.00001 0.00010 0.00100 0.01000 0.10000 1.00000 10.00000 100.00000Particle size (mm)
Per
cent
age
Pas
sing
PL20
SILT
SAND GRAVEL
FINE FINEMEDIUM MEDIUMCOARSE COARSECLAY
APPENDIX 3
REGRESSION RESULTS 10m Grid Size ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REGRESSION RESULTS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac03_10m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC03_10M [Dependent/Response] --- Cell Count: 499108 Mean: 0.2864 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3507 Variance: 0.1230 Sum: 142963.3500 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499108 Mean: 22.6025 Minimum: 0.0000 Maximum: 1357.2986 Range: 1357.2986 St. Dev: 51.6508 Variance: 2667.8010 Sum: 11281090.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003(t/ha/yr)] Regession Equation: Y-hat = 0.2215693 + 0.0028700*[Soil erosion 2003(t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: ------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2215693428 0.0004919095 450.42696 < 0.00001 0.22060521 0.22253347 [Soil erosion 2003 (t/ha/yr)] 0.0028699631 0.0000087249 328.93796 < 0.00001 0.00285286 0.00288706 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499106 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.178164 --> Adjusted R-Squared = 0.178162 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac03_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 10967.320 10967.3204275 108200 < 0.00001 Residuals 499106 50590.074 0.1013614 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499107 61557.394 --------------------------------- Analysis Began: March 18, 10:31:43 PM Analysis Complete: March 18, 10:40:26 PM Time Elapsed: 8 minutes, 43 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS usle2003 vs Rfac03 |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rfac_2003_10m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- Grids Created and Added to View: --> Predicted Values: [Y-Hat (Predicted Values)_2] Source = d:\putrausle\grid12 --------------------------------------------------- DESCRIPTIVE STATISTICS: --- RFAC_2003_10M [Dependent/Response] --- Cell Count: 499108 Mean: 3685.9629 Minimum: 518.4152 Maximum: 6443.8551 Range: 5925.4398 St. Dev: 2438.4422 Variance: 5946000.3140 Sum: 1839693619.2000 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499108 Mean: 22.6025 Minimum: 0.0000 Maximum: 1357.2986 Range: 1357.2986 St. Dev: 51.6508 Variance: 2667.8010 Sum: 11281090.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 3430.1870580 + 11.3162616*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3430.1870579 3.6725885328 933.99710 < 0.00001 3422.98889 3437.38521 [Soil erosion 2003 (t/ha/yr)] 11.316261626 0.0651402311 173.72154 < 0.00001 11.1885887 11.4439344 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499106 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.057019 --> Adjusted R-Squared = 0.057017 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rfac_2003_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 170511572810.358 170511572810.3584000 30179.175 < 0.00001 Residuals 499106 2819936199574.435 5649974.5536508 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499107 2990447772384.793 --------------------------------- Analysis Began: March 18, 9:15:36 PM Analysis Complete: March 18, 9:22:29 PM Time Elapsed: 6 minutes, 53 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS usle2003 vs. kfac |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_10m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- Grids Created and Added to View: --> Predicted Values: [Y-Hat (Predicted Values)] Source = d:\putrausle\grid12 --------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_10M [Dependent/Response] --- Cell Count: 499108 Mean: 0.4163 Minimum: 0.0796 Maximum: 0.7288 Range: 0.6493 St. Dev: 0.0978 Variance: 0.0096 Sum: 207766.5125 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499108 Mean: 22.6025 Minimum: 0.0000 Maximum: 1357.2986 Range: 1357.2986 St. Dev: 51.6508 Variance: 2667.8010 Sum: 11281090.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.4072663 + 0.0003986*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4072662904 0.0001507351 2701.8665 < 0.00001 0.40697085 0.40756172 [Soil erosion 2003 (t/ha/yr)] 0.0003986008 0.0000026735 149.08934 < 0.00001 0.00039336 0.00040384 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499106 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.042636 --> Adjusted R-Squared = 0.042634 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 211.555 211.5554938 22227.634 < 0.00001 Residuals 499106 4750.331 0.0095177 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499107 4961.886 --------------------------------- Analysis Began: March 18, 9:26:28 PM Analysis Complete: March 18, 9:33:27 PM Time Elapsed: 6 minutes, 59 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS usle2003 vs. Lsfac10m |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_10m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_10M [Dependent/Response] --- Cell Count: 499108 Mean: 0.0535 Minimum: 0.0000 Maximum: 0.3297 Range: 0.3297 St. Dev: 0.0511 Variance: 0.0026 Sum: 26680.4219 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499108 Mean: 22.6025 Minimum: 0.0000 Maximum: 1357.2986 Range: 1357.2986 St. Dev: 51.6508 Variance: 2667.8010 Sum: 11281090.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.0441526 + 0.0004116*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0441526262 0.0000719907 613.31008 < 0.00001 0.04401152 0.04429372 [Soil erosion 2003 (t/ha/yr)] 0.0004116174 0.0000012768 322.35935 < 0.00001 0.00040911 0.00041412 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499106 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.172325 --> Adjusted R-Squared = 0.172323 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 225.598 225.5980558 103916 < 0.00001 Residuals 499106 1083.547 0.0021710 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499107 1309.145 --------------------------------- Analysis Began: March 18, 10:22:14 PM Analysis Complete: March 18, 10:29:05 PM Time Elapsed: 6 minutes, 51 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rfac_2004_10m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RFAC_2004_10M [Dependent/Response] --- Cell Count: 499363 Mean: 2677.1572 Minimum: 187.5232 Maximum: 4702.4008 Range: 4514.8777 St. Dev: 1670.4029 Variance: 2790245.9486 Sum: 1336873267.2000 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499363 Mean: 15.6456 Minimum: 0.0000 Maximum: 707.5264 Range: 707.5264 St. Dev: 39.1042 Variance: 1529.1361 Sum: 7812816.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 2529.7237354 + 9.4233502*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 2529.7237354 2.4911338086 1015.4909 < 0.00001 2524.84119 2534.60628 [Soil erosion 2004 (t/ha/yr)] 9.4233501562 0.0591466411 159.32181 < 0.00001 9.30742457 9.53927573 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499361 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.048373 --> Adjusted R-Squared = 0.048371 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rfac_2004_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 67806775940.773 67806775940.7729490 25383.440 < 0.00001 Residuals 499361 1333942876340.749 2671299.6736644 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499362 1401749652281.522 --------------------------------- Analysis Began: March 19, 11:06:32 AM Analysis Complete: March 19, 11:13:46 AM Time Elapsed: 7 minutes, 14 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_10m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_10M [Dependent/Response] --- Cell Count: 499363 Mean: 0.4162 Minimum: 0.0796 Maximum: 0.7288 Range: 0.6493 St. Dev: 0.0978 Variance: 0.0096 Sum: 207861.1750 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499363 Mean: 15.6456 Minimum: 0.0000 Maximum: 707.5264 Range: 707.5264 St. Dev: 39.1042 Variance: 1529.1361 Sum: 7812816.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.4085549 + 0.0004920*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4085548817 0.0001485349 2750.5647 < 0.00001 0.40826375 0.40884600 [Soil erosion 2004 (t/ha/yr)] 0.0004920095 0.0000035266 139.51212 < 0.00001 0.00048509 0.00049892 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499361 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.037515 --> Adjusted R-Squared = 0.037513 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 184.846 184.8457567 19463.633 < 0.00001 Residuals 499361 4742.422 0.0094970 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499362 4927.268 --------------------------------- Analysis Began: March 19, 12:28:02 PM Analysis Complete: March 19, 12:36:46 PM Time Elapsed: 8 minutes, 44 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_10m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_10M [Dependent/Response] --- Cell Count: 499363 Mean: 0.0534 Minimum: 0.0000 Maximum: 0.3297 Range: 0.3297 St. Dev: 0.0511 Variance: 0.0026 Sum: 26691.8312 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499363 Mean: 15.6456 Minimum: 0.0000 Maximum: 707.5264 Range: 707.5264 St. Dev: 39.1042 Variance: 1529.1361 Sum: 7812816.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.0456162 + 0.0005008*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0456162381 0.0000720909 632.75946 < 0.00001 0.04547494 0.04575753 [Soil erosion 2004 (t/ha/yr)] 0.0005008142 0.0000017116 292.59228 < 0.00001 0.00049745 0.00050416 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499361 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.146349 --> Adjusted R-Squared = 0.146348 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 191.521 191.5207684 85610.244 < 0.00001 Residuals 499361 1117.133 0.0022371 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499362 1308.653 --------------------------------- Analysis Began: March 19, 1:24:00 PM Analysis Complete: March 19, 1:31:15 PM Time Elapsed: 7 minutes, 15 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac04_10m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC04_10MNEW [Dependent/Response] --- Cell Count: 499363 Mean: 0.2545 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3665 Variance: 0.1343 Sum: 127081.3875 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 499363 Mean: 15.6456 Minimum: 0.0000 Maximum: 707.5264 Range: 707.5264 St. Dev: 39.1042 Variance: 1529.1361 Sum: 7812816.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.1819525 + 0.0046361*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.1819525158 0.0004864902 374.01063 < 0.00001 0.18099901 0.18290602 [Soil erosion 2004 (t/ha/yr)] 0.0046361035 0.0000115506 401.37099 < 0.00001 0.00461346 0.00465874 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 499361 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.243919 --> Adjusted R-Squared = 0.243918 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac04_10mnew -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 16412.271 16412.2714750 161099 < 0.00001 Residuals 499361 50873.468 0.1018771 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 499362 67285.739 --------------------------------- Analysis Began: March 19, 1:39:35 PM Analysis Complete: March 19, 1:48:28 PM Time Elapsed: 8 minutes, 53 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rfac_2006_10m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RFAC_2006_10M [Dependent/Response] --- Cell Count: 497695 Mean: 3866.0535 Minimum: 1501.3465 Maximum: 5955.6930 Range: 4454.3465 St. Dev: 1941.6295 Variance: 3769925.0764 Sum: 1924115660.8000 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 497695 Mean: 24.4824 Minimum: 0.0000 Maximum: 969.3941 Range: 969.3941 St. Dev: 49.3431 Variance: 2434.7425 Sum: 12184788.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 3634.9500618 + 9.4395662*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3634.9500618 3.0068030856 1208.9085 < 0.00001 3629.05682 3640.84330 [Soil erosion 2006 (t/ha/yr)] 9.4395662015 0.0545868123 172.92759 < 0.00001 9.33257774 9.54655465 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 497693 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.056680 --> Adjusted R-Squared = 0.056678 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rfac_2006_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 107974292075.552 107974292075.5517600 29903.953 < 0.00001 Residuals 497693 1797021582422.563 3610702.9482483 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 497694 1904995874498.114 --------------------------------- Analysis Began: March 19, 2:05:26 PM Analysis Complete: March 19, 2:12:58 PM Time Elapsed: 7 minutes, 32 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_10m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_10M [Dependent/Response] --- Cell Count: 497695 Mean: 0.4162 Minimum: 0.0796 Maximum: 0.7288 Range: 0.6493 St. Dev: 0.0979 Variance: 0.0096 Sum: 207164.0375 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 497695 Mean: 24.4824 Minimum: 0.0000 Maximum: 969.3941 Range: 969.3941 St. Dev: 49.3431 Variance: 2434.7425 Sum: 12184788.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.4065366 + 0.0003966*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4065365917 0.0001590473 2556.0732 < 0.00001 0.40622486 0.40684831 [Soil erosion 2006 (t/ha/yr)] 0.0003966258 0.0000028874 137.36369 < 0.00001 0.00039096 0.00040228 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 497693 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.036528 --> Adjusted R-Squared = 0.036526 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 190.624 190.6243573 18868.785 < 0.00001 Residuals 497693 5028.008 0.0101026 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 497694 5218.633 --------------------------------- Analysis Began: March 19, 2:35:36 PM Analysis Complete: March 19, 2:44:26 PM Time Elapsed: 8 minutes, 50 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_10m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_10M [Dependent/Response] --- Cell Count: 497695 Mean: 0.0535 Minimum: 0.0000 Maximum: 0.3297 Range: 0.3297 St. Dev: 0.0511 Variance: 0.0026 Sum: 26616.2219 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 497695 Mean: 24.4824 Minimum: 0.0000 Maximum: 969.3941 Range: 969.3941 St. Dev: 49.3431 Variance: 2434.7425 Sum: 12184788.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.0416184 + 0.0004845*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0416184483 0.0000719012 578.82824 < 0.00001 0.04147752 0.04175937 [Soil erosion 2006 (t/ha/yr)] 0.0004844505 0.0000013053 371.13381 < 0.00001 0.00048189 0.00048700 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 497693 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.216766 --> Adjusted R-Squared = 0.216764 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 284.391 284.3905570 137740 < 0.00001 Residuals 497693 1027.580 0.0020647 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 497694 1311.971 --------------------------------- Analysis Began: March 19, 3:06:29 PM Analysis Complete: March 19, 3:13:35 PM Time Elapsed: 7 minutes, 6 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac06_10m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC06_10M [Dependent/Response] --- Cell Count: 497695 Mean: 0.2808 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3290 Variance: 0.1082 Sum: 139732.5125 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 497695 Mean: 24.4824 Minimum: 0.0000 Maximum: 969.3941 Range: 969.3941 St. Dev: 49.3431 Variance: 2434.7425 Sum: 12184788.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.2026378 + 0.0031909*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2026377797 0.0004577121 442.71881 < 0.00001 0.20174067 0.20353488 [Soil erosion 2006 (t/ha/yr)] 0.0031909218 0.0000083095 384.00862 < 0.00001 0.00317463 0.00320720 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 497693 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.228569 --> Adjusted R-Squared = 0.228568 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac06_10m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 12338.109 12338.1094264 147463 < 0.00001 Residuals 497693 41641.675 0.0836694 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 497694 53979.785 --------------------------------- Analysis Began: March 19, 3:15:54 PM Analysis Complete: March 19, 3:24:59 PM Time Elapsed: 9 minutes, 5 seconds...
REGRESSION RESULTS 20m Grid Size ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac03_20m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC03_20M [Dependent/Response] --- Cell Count: 124805 Mean: 0.2873 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3510 Variance: 0.1232 Sum: 35849.6344 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124805 Mean: 20.0762 Minimum: 0.0000 Maximum: 888.2681 Range: 888.2681 St. Dev: 50.8120 Variance: 2581.8634 Sum: 2505607.6000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.2351878 + 0.0025930*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2351878043 0.0009915135 237.20079 < 0.00001 0.23324445 0.23713115 [Soil erosion 2003 (t/ha/yr)] 0.0025929912 0.0000181481 142.87901 < 0.00001 0.00255742 0.00262856 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124803 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.140578 --> Adjusted R-Squared = 0.140571 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac03_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 2166.543 2166.5427940 20414.413 < 0.00001 Residuals 124803 13245.105 0.1061281 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124804 15411.648 --------------------------------- Analysis Began: March 29, 2:42:03 PM Analysis Complete: March 29, 2:43:08 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_20m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_20M [Dependent/Response] --- Cell Count: 124805 Mean: 0.0479 Minimum: 0.0000 Maximum: 0.2738 Range: 0.2738 St. Dev: 0.0544 Variance: 0.0030 Sum: 5978.7633 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124805 Mean: 20.0762 Minimum: 0.0000 Maximum: 888.2681 Range: 888.2681 St. Dev: 50.8120 Variance: 2581.8634 Sum: 2505607.6000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.0383226 + 0.0004773*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0383225686 0.0001484204 258.20267 < 0.00001 0.03803166 0.03861347 [Soil erosion 2003 (t/ha/yr)] 0.0004772953 0.0000027166 175.69500 < 0.00001 0.00047197 0.00048261 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124803 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.198294 --> Adjusted R-Squared = 0.198287 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 73.407 73.4073683 30868.736 < 0.00001 Residuals 124803 296.788 0.0023780 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124804 370.195 --------------------------------- Analysis Began: March 29, 2:46:44 PM Analysis Complete: March 29, 2:47:54 PM Time Elapsed: 1 minutes, 10 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_20m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_20M [Dependent/Response] --- Cell Count: 124805 Mean: 0.4162 Minimum: 0.0810 Maximum: 0.7278 Range: 0.6468 St. Dev: 0.0977 Variance: 0.0095 Sum: 51939.8219 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124805 Mean: 20.0762 Minimum: 0.0000 Maximum: 888.2681 Range: 888.2681 St. Dev: 50.8120 Variance: 2581.8634 Sum: 2505607.6000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.4089392 + 0.0003601*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4089391530 0.0002993186 1366.2332 < 0.00001 0.40835249 0.40952581 [Soil erosion 2003 (t/ha/yr)] 0.0003600616 0.0000054785 65.721740 < 0.00001 0.00034932 0.00037079 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124803 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.033452 --> Adjusted R-Squared = 0.033444 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 41.775 41.7752307 4319.347 < 0.00001 Residuals 124803 1207.051 0.0096717 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124804 1248.827 --------------------------------- Analysis Began: March 29, 2:56:01 PM Analysis Complete: March 29, 2:57:10 PM Time Elapsed: 1 minutes, 9 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rfac_2003_20m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RFAC_2003_20M [Dependent/Response] --- Cell Count: 124805 Mean: 3683.9953 Minimum: 518.4152 Maximum: 6443.8551 Range: 5925.4398 St. Dev: 2438.6111 Variance: 5946824.2434 Sum: 459781017.6000 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124805 Mean: 20.0762 Minimum: 0.0000 Maximum: 888.2681 Range: 888.2681 St. Dev: 50.8120 Variance: 2581.8634 Sum: 2505607.6000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 3474.1368052 + 10.4531046*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3474.1368052 7.2793501385 477.25919 < 0.00001 3459.86940 3488.40420 [Soil erosion 2003 (t/ha/yr)] 10.453104649 0.1332375225 78.454660 < 0.00001 10.1919613 10.7142479 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124803 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.047001 --> Adjusted R-Squared = 0.046993 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rfac_2003_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 35209169209.502 35209169209.5024410 6155.134 < 0.00001 Residuals 124803 713909734035.651 5720293.0541385 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124804 749118903245.154 --------------------------------- Analysis Began: March 29, 3:00:42 PM Analysis Complete: March 29, 3:01:46 PM Time Elapsed: 1 minutes, 4 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac_2004new INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC_2004NEW [Dependent/Response] --- Cell Count: 124711 Mean: 0.2541 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3655 Variance: 0.1336 Sum: 31691.7813 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124711 Mean: 14.2103 Minimum: 0.0000 Maximum: 573.1454 Range: 573.1454 St. Dev: 39.6589 Variance: 1572.8252 Sum: 1772179.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.1951645 + 0.0041489*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.1951644867 0.0009840755 198.32267 < 0.00001 0.19323571 0.19709325 [Soil erosion 2004 (t/ha/yr)] 0.0041489137 0.0000233592 177.61329 < 0.00001 0.00410313 0.00419469 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124709 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.201890 --> Adjusted R-Squared = 0.201884 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac_2004new -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 3376.400 3376.4002727 31546.481 < 0.00001 Residuals 124709 13347.527 0.1070294 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124710 16723.927 --------------------------------- Analysis Began: March 29, 3:11:55 PM Analysis Complete: March 29, 3:12:56 PM Time Elapsed: 1 minutes, 1 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_20m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_20M [Dependent/Response] --- Cell Count: 124711 Mean: 0.0479 Minimum: 0.0000 Maximum: 0.2738 Range: 0.2738 St. Dev: 0.0544 Variance: 0.0030 Sum: 5974.8492 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124711 Mean: 14.2103 Minimum: 0.0000 Maximum: 573.1454 Range: 573.1454 St. Dev: 39.6589 Variance: 1572.8252 Sum: 1772179.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.0399313 + 0.0005614*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0399312921 0.0001496142 266.89494 < 0.00001 0.03963805 0.04022453 [Soil erosion 2004 (t/ha/yr)] 0.0005614428 0.0000035514 158.08914 < 0.00001 0.00055448 0.00056840 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124709 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.166947 --> Adjusted R-Squared = 0.166940 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 61.830 61.8295788 24992.178 < 0.00001 Residuals 124709 308.525 0.0024740 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124710 370.354 --------------------------------- Analysis Began: March 29, 3:18:25 PM Analysis Complete: March 29, 3:19:34 PM Time Elapsed: 1 minutes, 9 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_20m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_20M [Dependent/Response] --- Cell Count: 124711 Mean: 0.4162 Minimum: 0.0810 Maximum: 0.7278 Range: 0.6468 St. Dev: 0.0977 Variance: 0.0095 Sum: 51902.9594 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124711 Mean: 14.2103 Minimum: 0.0000 Maximum: 573.1454 Range: 573.1454 St. Dev: 39.6589 Variance: 1572.8252 Sum: 1772179.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.4097294 + 0.0004544*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4097293738 0.0002978477 1375.6334 < 0.00001 0.40914559 0.41031315 [Soil erosion 2004 (t/ha/yr)] 0.0004543562 0.0000070700 64.264571 < 0.00001 0.00044049 0.00046821 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124709 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.032055 --> Adjusted R-Squared = 0.032047 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 40.493 40.4928306 4129.935 < 0.00001 Residuals 124709 1222.736 0.0098047 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124710 1263.229 --------------------------------- Analysis Began: March 29, 3:28:58 PM Analysis Complete: March 29, 3:29:59 PM Time Elapsed: 1 minutes, 1 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rfac_2004_20m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RFAC_2004_20M [Dependent/Response] --- Cell Count: 124711 Mean: 2675.8268 Minimum: 187.5232 Maximum: 4702.4008 Range: 4514.8777 St. Dev: 1671.0560 Variance: 2792428.3222 Sum: 333705036.8000 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124711 Mean: 14.2103 Minimum: 0.0000 Maximum: 573.1454 Range: 573.1454 St. Dev: 39.6589 Variance: 1572.8252 Sum: 1772179.8000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 2555.0257790 + 8.5009639*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 2555.0257789 4.9528569790 515.86908 < 0.00001 2545.31826 2564.73329 [Soil erosion 2004 (t/ha/yr)] 8.5009639005 0.1175672378 72.307252 < 0.00001 8.27053409 8.73139370 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124709 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.040237 --> Adjusted R-Squared = 0.040230 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rfac_2004_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 14174947210.134 14174947210.1343990 5228.339 < 0.00001 Residuals 124709 338108067279.661 2711176.1563292 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124710 352283014489.795 --------------------------------- Analysis Began: March 29, 3:33:54 PM Analysis Complete: March 29, 3:34:59 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac06_20m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC06_20M [Dependent/Response] --- Cell Count: 124411 Mean: 0.2800 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3287 Variance: 0.1080 Sum: 34833.7000 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124411 Mean: 21.8034 Minimum: 0.0000 Maximum: 846.0087 Range: 846.0087 St. Dev: 49.3188 Variance: 2432.3490 Sum: 2712576.2000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.2181161 + 0.0028378*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2181160838 0.0009236646 236.14206 < 0.00001 0.21630571 0.21992645 [Soil erosion 2006 (t/ha/yr)] 0.0028377668 0.0000171291 165.66844 < 0.00001 0.00280419 0.00287133 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124409 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.180738 --> Adjusted R-Squared = 0.180732 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac06_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 2436.902 2436.9021882 27446.032 < 0.00001 Residuals 124409 11046.135 0.0887889 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124410 13483.037 --------------------------------- Analysis Began: March 29, 3:55:17 PM Analysis Complete: March 29, 3:56:19 PM Time Elapsed: 1 minutes, 2 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_20m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_20M [Dependent/Response] --- Cell Count: 124411 Mean: 0.0479 Minimum: 0.0000 Maximum: 0.2738 Range: 0.2738 St. Dev: 0.0544 Variance: 0.0030 Sum: 5961.6383 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124411 Mean: 21.8034 Minimum: 0.0000 Maximum: 846.0087 Range: 846.0087 St. Dev: 49.3188 Variance: 2432.3490 Sum: 2712576.2000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.0359255 + 0.0005501*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0359255397 0.0001468919 244.57127 < 0.00001 0.03563763 0.03621344 [Soil erosion 2006 (t/ha/yr)] 0.0005500698 0.0000027240 201.92839 < 0.00001 0.00054473 0.00055540 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124409 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.246846 --> Adjusted R-Squared = 0.246840 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 91.563 91.5630651 40775.075 < 0.00001 Residuals 124409 279.368 0.0022456 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124410 370.932 --------------------------------- Analysis Began: March 29, 3:59:29 PM Analysis Complete: March 29, 4:00:34 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rfac_2006_20m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RFAC_2006_20M [Dependent/Response] --- Cell Count: 124411 Mean: 3865.0848 Minimum: 1501.3465 Maximum: 5955.6930 Range: 4454.3465 St. Dev: 1941.8174 Variance: 3770654.7373 Sum: 480859084.8000 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124411 Mean: 21.8034 Minimum: 0.0000 Maximum: 846.0087 Range: 846.0087 St. Dev: 49.3188 Variance: 2432.3490 Sum: 2712576.2000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 3678.4380405 + 8.5604661*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3678.4380405 5.9304558875 620.26227 < 0.00001 3666.81444 3690.06163 [Soil erosion 2006 (t/ha/yr)] 8.5604660671 0.1099792299 77.837115 < 0.00001 8.34490863 8.77602350 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124409 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.046438 --> Adjusted R-Squared = 0.046430 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rfac_2006_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 22175809479.305 22175809479.3049320 6058.617 < 0.00001 Residuals 124409 455363073539.872 3660210.0614897 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124410 477538883019.177 --------------------------------- Analysis Began: March 29, 4:13:24 PM Analysis Complete: March 29, 4:14:28 PM Time Elapsed: 1 minutes, 4 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_20m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_20M [Dependent/Response] --- Cell Count: 124411 Mean: 0.4161 Minimum: 0.0810 Maximum: 0.7278 Range: 0.6468 St. Dev: 0.0978 Variance: 0.0096 Sum: 51770.8938 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 124411 Mean: 21.8034 Minimum: 0.0000 Maximum: 846.0087 Range: 846.0087 St. Dev: 49.3188 Variance: 2432.3490 Sum: 2712576.2000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.4085155 + 0.0003491*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4085155222 0.0003149512 1297.0753 < 0.00001 0.40789822 0.40913282 [Soil erosion 2006 (t/ha/yr)] 0.0003491403 0.0000058407 59.776999 < 0.00001 0.00033769 0.00036058 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 124409 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.027920 --> Adjusted R-Squared = 0.027912 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_20m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 36.888 36.8879727 3573.290 < 0.00001 Residuals 124409 1284.306 0.0103233 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 124410 1321.194 --------------------------------- Analysis Began: March 29, 4:21:13 PM Analysis Complete: March 29, 4:22:20 PM Time Elapsed: 1 minutes, 7 seconds...
REGRESSION RESULTS 30m Grid Size ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rainfall Erosivity (Mj.mm/ha.h.yr) 2003 INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RAINFALL EROSIVITY (MJ.MM/HA.H.YR) 2003 [Dependent/Response] --- Cell Count: 55556 Mean: 3859.9074 Minimum: 1501.3465 Maximum: 5955.6930 Range: 4454.3465 St. Dev: 1942.4773 Variance: 3773218.2164 Sum: 214441024.0000 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55556 Mean: 17.8632 Minimum: 0.0000 Maximum: 918.0112 Range: 918.0112 St. Dev: 49.6483 Variance: 2464.9577 Sum: 992408.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 3723.3175288 + 7.6464472*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3723.3175288 8.6116182502 432.35979 < 0.00001 3706.43869 3740.19635 [Soil erosion 2003 (t/ha/yr)] 7.6464471954 0.1632097710 46.850425 < 0.00001 7.32655494 7.96633944 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55554 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.038009 --> Adjusted R-Squared = 0.037991 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rainfall Erosivity (Mj.mm/ha.h.yr) 2003 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 8006814352.693 8006814352.6928711 2194.962 < 0.00001 Residuals 55554 202650657492.498 3647813.9736562 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55555 210657471845.191 --------------------------------- Analysis Began: March 30, 11:32:08 AM Analysis Complete: March 30, 11:33:09 AM Time Elapsed: 1 minutes, 1 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: CP Factor 2003 INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CP FACTOR 2003 [Dependent/Response] --- Cell Count: 55556 Mean: 0.2869 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3512 Variance: 0.1233 Sum: 15936.7781 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55556 Mean: 17.8632 Minimum: 0.0000 Maximum: 918.0112 Range: 918.0112 St. Dev: 49.6483 Variance: 2464.9577 Sum: 992408.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.2432658 + 0.0024404*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2432657938 0.0014862816 163.67408 < 0.00001 0.24035267 0.24617891 [Soil erosion 2003 (t/ha/yr)] 0.0024404316 0.0000281684 86.637147 < 0.00001 0.00238522 0.00249564 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55554 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.119029 --> Adjusted R-Squared = 0.119014 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: CP Factor 2003 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 815.593 815.5933108 7505.995 < 0.00001 Residuals 55554 6036.437 0.1086589 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55555 6852.031 --------------------------------- Analysis Began: March 30, 11:39:45 AM Analysis Complete: March 30, 11:40:49 AM Time Elapsed: 1 minutes, 4 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_30m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_30M [Dependent/Response] --- Cell Count: 55556 Mean: 0.0425 Minimum: 0.0000 Maximum: 0.2586 Range: 0.2586 St. Dev: 0.0561 Variance: 0.0031 Sum: 2360.6521 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55556 Mean: 17.8632 Minimum: 0.0000 Maximum: 918.0112 Range: 918.0112 St. Dev: 49.6483 Variance: 2464.9577 Sum: 992408.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.0330192 + 0.0005303*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0330192230 0.0002235594 147.69771 < 0.00001 0.03258104 0.03345740 [Soil erosion 2003 (t/ha/yr)] 0.0005302618 0.0000042369 125.15147 < 0.00001 0.00052195 0.00053856 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55554 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.219932 --> Adjusted R-Squared = 0.219918 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_30m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 38.505 38.5053618 15662.891 < 0.00001 Residuals 55554 136.573 0.0024584 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55555 175.078 --------------------------------- Analysis Began: March 30, 11:46:13 AM Analysis Complete: March 30, 11:47:14 AM Time Elapsed: 1 minutes, 1 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_30m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_30M [Dependent/Response] --- Cell Count: 55556 Mean: 0.4162 Minimum: 0.0811 Maximum: 0.7281 Range: 0.6469 St. Dev: 0.0978 Variance: 0.0096 Sum: 23120.9906 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55556 Mean: 17.8632 Minimum: 0.0000 Maximum: 918.0112 Range: 918.0112 St. Dev: 49.6483 Variance: 2464.9577 Sum: 992408.0000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.4103033 + 0.0003287*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4103032951 0.0004383534 936.01027 < 0.00001 0.40944411 0.41116247 [Soil erosion 2003 (t/ha/yr)] 0.0003286756 0.0000083077 39.562319 < 0.00001 0.00031239 0.00034495 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55554 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.027402 --> Adjusted R-Squared = 0.027384 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_30m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 14.794 14.7936525 1565.177 < 0.00001 Residuals 55554 525.082 0.0094517 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55555 539.876 --------------------------------- Analysis Began: March 30, 11:48:30 AM Analysis Complete: March 30, 11:49:35 AM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cp30_2004new INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CP30_2004NEW [Dependent/Response] --- Cell Count: 55468 Mean: 0.2539 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3658 Variance: 0.1338 Sum: 14083.4922 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55468 Mean: 12.8602 Minimum: 0.0000 Maximum: 602.8631 Range: 602.8631 St. Dev: 39.0656 Variance: 1526.1242 Sum: 713329.1500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.2038812 + 0.0038897*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2038812240 0.0014896996 136.86062 < 0.00001 0.20096140 0.20680104 [Soil erosion 2004 (t/ha/yr)] 0.0038896606 0.0000362210 107.38661 < 0.00001 0.00381866 0.00396065 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55466 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.172123 --> Adjusted R-Squared = 0.172108 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cp30_2004new -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 1280.725 1280.7248920 11531.885 < 0.00001 Residuals 55466 6160.024 0.1110595 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55467 7440.749 --------------------------------- Analysis Began: March 30, 11:51:41 AM Analysis Complete: March 30, 11:52:41 AM Time Elapsed: 1 minutes, 0 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\ DEPENDENT GRID: Rainfall Erosivity (Mj.mm/ha.h.yr) 2004 INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RAINFALL EROSIVITY (MJ.MM/HA.H.YR) 2004 [Dependent/Response] --- Cell Count: 55468 Mean: 2676.4070 Minimum: 187.5232 Maximum: 4702.4008 Range: 4514.8777 St. Dev: 1671.1750 Variance: 2792825.8806 Sum: 148454950.4000 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55468 Mean: 12.8602 Minimum: 0.0000 Maximum: 602.8631 Range: 602.8631 St. Dev: 39.0656 Variance: 1526.1242 Sum: 713329.1500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 2574.5072146 + 7.9236822*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 2574.5072145 7.3733544930 349.16362 < 0.00001 2560.05538 2588.95903 [Soil erosion 2004 (t/ha/yr)] 7.9236822499 0.1792784009 44.197640 < 0.00001 7.57229537 8.27506912 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55466 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.034020 --> Adjusted R-Squared = 0.034003 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rainfall Erosivity (Mj.mm/ha.h.yr) 2004 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 5314795137.191 5314795137.1906738 1953.431 < 0.00001 Residuals 55466 150909024973.135 2720748.2957692 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55467 156223820110.326 --------------------------------- Analysis Began: March 30, 11:54:23 AM Analysis Complete: March 30, 11:55:29 AM Time Elapsed: 1 minutes, 6 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_30m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_30M [Dependent/Response] --- Cell Count: 55468 Mean: 0.0425 Minimum: 0.0000 Maximum: 0.2586 Range: 0.2586 St. Dev: 0.0561 Variance: 0.0031 Sum: 2357.0756 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55468 Mean: 12.8602 Minimum: 0.0000 Maximum: 602.8631 Range: 602.8631 St. Dev: 39.0656 Variance: 1526.1242 Sum: 713329.1500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.0344075 + 0.0006288*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0344075188 0.0002259799 152.25916 < 0.00001 0.03396459 0.03485044 [Soil erosion 2004 (t/ha/yr)] 0.0006288248 0.0000054945 114.44502 < 0.00001 0.00061805 0.00063959 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55466 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.191029 --> Adjusted R-Squared = 0.191015 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_30m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 33.473 33.4727861 13097.665 < 0.00001 Residuals 55466 141.751 0.0025556 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55467 175.223 --------------------------------- Analysis Began: March 30, 11:57:32 AM Analysis Complete: March 30, 11:58:34 AM Time Elapsed: 1 minutes, 2 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_30m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_30M [Dependent/Response] --- Cell Count: 55468 Mean: 0.4162 Minimum: 0.0811 Maximum: 0.7281 Range: 0.6469 St. Dev: 0.0977 Variance: 0.0095 Sum: 23087.0172 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55468 Mean: 12.8602 Minimum: 0.0000 Maximum: 602.8631 Range: 602.8631 St. Dev: 39.0656 Variance: 1526.1242 Sum: 713329.1500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.4109919 + 0.0004067*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4109918855 0.0004406208 932.75638 < 0.00001 0.41012826 0.41185550 [Soil erosion 2004 (t/ha/yr)] 0.0004067122 0.0000107134 37.962896 < 0.00001 0.00038571 0.00042771 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55466 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.025325 --> Adjusted R-Squared = 0.025308 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_30m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 14.003 14.0025410 1441.182 < 0.00001 Residuals 55466 538.908 0.0097160 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55467 552.911 --------------------------------- Analysis Began: March 30, 11:59:37 AM Analysis Complete: March 30, 12:00:42 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: Rainfall Erosivity (Mj.mm/ha.h.yr) 2006 INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- RAINFALL EROSIVITY (MJ.MM/HA.H.YR) 2006 [Dependent/Response] --- Cell Count: 55366 Mean: 3693.2578 Minimum: 518.4152 Maximum: 6443.8551 Range: 5925.4398 St. Dev: 2437.0199 Variance: 5939066.0905 Sum: 204480896.0000 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55366 Mean: 19.6861 Minimum: 0.0000 Maximum: 786.4747 Range: 786.4747 St. Dev: 49.5675 Variance: 2456.9361 Sum: 1089938.9000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 3509.9605826 + 9.3109967*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3509.9605825 10.976103575 319.78202 < 0.00001 3488.44734 3531.47382 [Soil erosion 2006 (t/ha/yr)] 9.3109966574 0.2058007549 45.242772 < 0.00001 8.90762573 9.71436757 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55364 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.035654 --> Adjusted R-Squared = 0.035636 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: Rainfall Erosivity (Mj.mm/ha.h.yr) 2006 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 11793136122.243 11793136122.2430420 2046.908 < 0.00001 Residuals 55364 318976256483.999 5761438.0551261 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55365 330769392606.242 --------------------------------- Analysis Began: March 30, 12:03:32 PM Analysis Complete: March 30, 12:04:34 PM Time Elapsed: 1 minutes, 2 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: CP Factor 2006 INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CP FACTOR 2006 [Dependent/Response] --- Cell Count: 55366 Mean: 0.2811 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3292 Variance: 0.1084 Sum: 15564.5906 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55366 Mean: 19.6861 Minimum: 0.0000 Maximum: 786.4747 Range: 786.4747 St. Dev: 49.5675 Variance: 2456.9361 Sum: 1089938.9000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.2303218 + 0.0025805*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2303218249 0.0013870504 166.05151 < 0.00001 0.22760319 0.23304045 [Soil erosion 2006 (t/ha/yr)] 0.0025805037 0.0000260070 99.223257 < 0.00001 0.00252952 0.00263147 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55364 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.150979 --> Adjusted R-Squared = 0.150964 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: CP Factor 2006 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 905.828 905.8284323 9845.255 < 0.00001 Residuals 55364 5093.854 0.0920066 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55365 5999.682 --------------------------------- Analysis Began: March 30, 12:06:03 PM Analysis Complete: March 30, 12:07:07 PM Time Elapsed: 1 minutes, 4 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_30m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_30M [Dependent/Response] --- Cell Count: 55366 Mean: 0.0425 Minimum: 0.0000 Maximum: 0.2586 Range: 0.2586 St. Dev: 0.0561 Variance: 0.0031 Sum: 2354.3176 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55366 Mean: 19.6861 Minimum: 0.0000 Maximum: 786.4747 Range: 786.4747 St. Dev: 49.5675 Variance: 2456.9361 Sum: 1089938.9000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.0309484 + 0.0005879*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0309484477 0.0002200889 140.61793 < 0.00001 0.03051707 0.03137982 [Soil erosion 2006 (t/ha/yr)] 0.0005879465 0.0000041266 142.47573 < 0.00001 0.00057985 0.00059603 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55364 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.268285 --> Adjusted R-Squared = 0.268272 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_30m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 47.023 47.0232477 20299.335 < 0.00001 Residuals 55364 128.250 0.0023165 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55365 175.274 --------------------------------- Analysis Began: March 30, 12:09:24 PM Analysis Complete: March 30, 12:10:26 PM Time Elapsed: 1 minutes, 2 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_30m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_30M [Dependent/Response] --- Cell Count: 55366 Mean: 0.4162 Minimum: 0.0811 Maximum: 0.7281 Range: 0.6469 St. Dev: 0.0979 Variance: 0.0096 Sum: 23041.5000 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 55366 Mean: 19.6861 Minimum: 0.0000 Maximum: 786.4747 Range: 786.4747 St. Dev: 49.5675 Variance: 2456.9361 Sum: 1089938.9000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.4097061 + 0.0003282*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4097061162 0.0004592746 892.07209 < 0.00001 0.40880593 0.41060629 [Soil erosion 2006 (t/ha/yr)] 0.0003281937 0.0000086113 38.111763 < 0.00001 0.00031131 0.00034507 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 55364 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.025565 --> Adjusted R-Squared = 0.025547 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_30m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 14.652 14.6520226 1452.507 < 0.00001 Residuals 55364 558.479 0.0100874 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 55365 573.131 --------------------------------- Analysis Began: March 30, 12:12:43 PM Analysis Complete: March 30, 12:13:47 PM Time Elapsed: 1 minutes, 4 seconds...
REGRESSION RESULTS 40m Grid Size ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_40m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_40M [Dependent/Response] --- Cell Count: 31025 Mean: 0.0390 Minimum: 0.0000 Maximum: 0.2629 Range: 0.2629 St. Dev: 0.0568 Variance: 0.0032 Sum: 1211.2110 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 16.3686 Minimum: 0.0000 Maximum: 825.5882 Range: 825.5882 St. Dev: 49.5214 Variance: 2452.3651 Sum: 507837.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.0300785 + 0.0005475*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0300785116 0.0003002651 100.17317 < 0.00001 0.02948997 0.03066704 [Soil erosion 2003 (t/ha/yr)] 0.0005474691 0.0000057570 95.096153 < 0.00001 0.00053618 0.00055875 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.225708 --> Adjusted R-Squared = 0.225683 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_40m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 22.804 22.8042719 9043.279 < 0.00001 Residuals 31023 78.230 0.0025217 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 101.034 --------------------------------- Analysis Began: March 21, 4:57:11 PM Analysis Complete: March 21, 5:07:11 PM Time Elapsed: 10 minutes, 0 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: 2003 Rainfall Erosivity (Mj.mm/ha.h.yr) INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- 2003 RAINFALL EROSIVITY (MJ.MM/HA.H.YR) [Dependent/Response] --- Cell Count: 31025 Mean: 3692.2766 Minimum: 518.4152 Maximum: 6443.8551 Range: 5925.4398 St. Dev: 2438.7641 Variance: 5947570.1403 Sum: 114552883.2000 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 16.3686 Minimum: 0.0000 Maximum: 825.5882 Range: 825.5882 St. Dev: 49.5214 Variance: 2452.3651 Sum: 507837.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 3550.3968333 + 8.6677713*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3550.3968332 14.487440766 245.06721 < 0.00001 3522.00086 3578.79280 [Soil erosion 2003 (t/ha/yr)] 8.6677713470 0.2777687763 31.204988 < 0.00001 8.12333329 9.21220939 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.030433 --> Adjusted R-Squared = 0.030402 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: 2003 Rainfall Erosivity (Mj.mm/ha.h.yr) -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 5716258464.807 5716258464.8071899 973.7512793 < 0.00001 Residuals 31023 182115792938.384 5870347.5788410 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 187832051403.191 --------------------------------- Analysis Began: March 25, 1:03:46 PM Analysis Complete: March 25, 1:04:54 PM Time Elapsed: 1 minutes, 8 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_40m INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_40M [Dependent/Response] --- Cell Count: 31025 Mean: 0.4161 Minimum: 0.0844 Maximum: 0.7277 Range: 0.6433 St. Dev: 0.0979 Variance: 0.0096 Sum: 12909.2227 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 16.3686 Minimum: 0.0000 Maximum: 825.5882 Range: 825.5882 St. Dev: 49.5214 Variance: 2452.3651 Sum: 507837.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.4108505 + 0.0003202*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4108504709 0.0006199201 662.74745 < 0.00001 0.40963540 0.41206553 [Soil erosion 2003 (t/ha/yr)] 0.0003201553 0.0000118857 26.936009 < 0.00001 0.00029685 0.00034345 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.022853 --> Adjusted R-Squared = 0.022821 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_40m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 7.799 7.7986320 725.5486133 < 0.00001 Residuals 31023 333.454 0.0107486 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 341.252 --------------------------------- Analysis Began: March 25, 1:11:21 PM Analysis Complete: March 25, 1:12:28 PM Time Elapsed: 1 minutes, 7 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: CP factor 2003 INDEPENDENT GRID: [Soil erosion 2003 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CP FACTOR 2003 [Dependent/Response] --- Cell Count: 31025 Mean: 0.2859 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3502 Variance: 0.1226 Sum: 8869.3039 --- [SOIL EROSION 2003 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 16.3686 Minimum: 0.0000 Maximum: 825.5882 Range: 825.5882 St. Dev: 49.5214 Variance: 2452.3651 Sum: 507837.4000 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2003 (t/ha/yr)] Regession Equation: Y-hat = 0.2484980 + 0.0022835*[Soil erosion 2003 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2484979739 0.0019970209 124.43433 < 0.00001 0.24458373 0.25241221 [Soil erosion 2003 (t/ha/yr)] 0.0022835145 0.0000382890 59.638871 < 0.00001 0.00220846 0.00235856 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.102858 --> Adjusted R-Squared = 0.102829 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: CP factor 2003 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 396.739 396.7386837 3556.795 < 0.00001 Residuals 31023 3460.426 0.1115439 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 3857.164 --------------------------------- Analysis Began: March 25, 1:18:34 PM Analysis Complete: March 25, 1:19:34 PM Time Elapsed: 1 minutes, 0 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_40m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_40M [Dependent/Response] --- Cell Count: 31025 Mean: 0.0391 Minimum: 0.0000 Maximum: 0.2629 Range: 0.2629 St. Dev: 0.0568 Variance: 0.0032 Sum: 1211.4918 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 11.5001 Minimum: 0.0000 Maximum: 584.8555 Range: 584.8555 St. Dev: 37.6049 Variance: 1414.1278 Sum: 356790.9500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.0313041 + 0.0006735*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0313041026 0.0003035739 103.11852 < 0.00001 0.03070908 0.03189912 [Soil erosion 2004 (t/ha/yr)] 0.0006734531 0.0000077198 87.237052 < 0.00001 0.00065832 0.00068858 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.196988 --> Adjusted R-Squared = 0.196962 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_40m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 19.898 19.8982647 7610.303 < 0.00001 Residuals 31023 81.114 0.0026146 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 101.012 --------------------------------- Analysis Began: March 25, 1:22:15 PM Analysis Complete: March 25, 1:23:20 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: cpfac_2004new INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CPFAC_2004NEW [Dependent/Response] --- Cell Count: 31025 Mean: 0.2539 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3657 Variance: 0.1338 Sum: 7875.9953 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 11.5001 Minimum: 0.0000 Maximum: 584.8555 Range: 584.8555 St. Dev: 37.6049 Variance: 1414.1278 Sum: 356790.9500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.2110452 + 0.0037230*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2110451651 0.0020190265 104.52817 < 0.00001 0.20708779 0.21500253 [Soil erosion 2004 (t/ha/yr)] 0.0037229609 0.0000513433 72.511116 < 0.00001 0.00362232 0.00382359 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.144921 --> Adjusted R-Squared = 0.144894 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: cpfac_2004new -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 608.103 608.1032823 5257.862 < 0.00001 Residuals 31023 3587.996 0.1156560 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 4196.099 --------------------------------- Analysis Began: March 25, 1:28:07 PM Analysis Complete: March 25, 1:29:09 PM Time Elapsed: 1 minutes, 2 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: 2004 Rainfall Erosivity (Mj.mm/ha.h.yr) INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- 2004 RAINFALL EROSIVITY (MJ.MM/HA.H.YR) [Dependent/Response] --- Cell Count: 31025 Mean: 2680.7269 Minimum: 187.5232 Maximum: 4702.4008 Range: 4514.8777 St. Dev: 1670.4635 Variance: 2790448.2380 Sum: 83169548.8000 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 11.5001 Minimum: 0.0000 Maximum: 584.8555 Range: 584.8555 St. Dev: 37.6049 Variance: 1414.1278 Sum: 356790.9500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 2597.6959516 + 7.2200121*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 2597.6959516 9.8896745357 262.66748 < 0.00001 2578.31178 2617.08011 [Soil erosion 2004 (t/ha/yr)] 7.2200120704 0.2514918218 28.708735 < 0.00001 6.72707791 7.71294622 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.025880 --> Adjusted R-Squared = 0.025848 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: 2004 Rainfall Erosivity (Mj.mm/ha.h.yr) -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 2287052994.053 2287052994.0534668 824.1914659 < 0.00001 Residuals 31023 86085876852.950 2774904.9689892 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 88372929847.004 --------------------------------- Analysis Began: March 25, 1:30:59 PM Analysis Complete: March 25, 1:32:05 PM Time Elapsed: 1 minutes, 6 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_40m INDEPENDENT GRID: [Soil erosion 2004 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_40M [Dependent/Response] --- Cell Count: 31025 Mean: 0.4161 Minimum: 0.0844 Maximum: 0.7277 Range: 0.6433 St. Dev: 0.0979 Variance: 0.0096 Sum: 12909.6188 --- [SOIL EROSION 2004 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 31025 Mean: 11.5001 Minimum: 0.0000 Maximum: 584.8555 Range: 584.8555 St. Dev: 37.6049 Variance: 1414.1278 Sum: 356790.9500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2004 (t/ha/yr)] Regession Equation: Y-hat = 0.4113739 + 0.0004113*[Soil erosion 2004 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4113739226 0.0006155527 668.30007 < 0.00001 0.41016741 0.41258043 [Soil erosion 2004 (t/ha/yr)] 0.0004112835 0.0000156533 26.274484 < 0.00001 0.00038060 0.00044196 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 31023 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.021768 --> Adjusted R-Squared = 0.021737 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_40m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 7.421 7.4213520 690.3485485 < 0.00001 Residuals 31023 333.502 0.0107502 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 31024 340.923 --------------------------------- Analysis Began: March 25, 1:35:45 PM Analysis Complete: March 25, 1:36:46 PM Time Elapsed: 1 minutes, 1 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: kfac_40m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- KFAC_40M [Dependent/Response] --- Cell Count: 30994 Mean: 0.4160 Minimum: 0.0844 Maximum: 0.7277 Range: 0.6433 St. Dev: 0.0980 Variance: 0.0096 Sum: 12895.0391 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 30994 Mean: 17.4515 Minimum: 0.0000 Maximum: 869.9435 Range: 869.9435 St. Dev: 47.7473 Variance: 2279.8047 Sum: 540892.3500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.4104536 + 0.0003207*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.4104535982 0.0006338314 647.57534 < 0.00001 0.40921126 0.41169593 [Soil erosion 2006 (t/ha/yr)] 0.0003206557 0.0000124680 25.718275 < 0.00001 0.00029621 0.00034509 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 30992 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.020896 --> Adjusted R-Squared = 0.020864 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: kfac_40m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 7.265 7.2652981 661.4296945 < 0.00001 Residuals 30992 340.423 0.0109842 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 30993 347.689 --------------------------------- Analysis Began: March 25, 1:39:15 PM Analysis Complete: March 25, 1:40:20 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: LSfac_40m INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- LSFAC_40M [Dependent/Response] --- Cell Count: 30994 Mean: 0.0391 Minimum: 0.0000 Maximum: 0.2629 Range: 0.2629 St. Dev: 0.0568 Variance: 0.0032 Sum: 1210.7180 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 30994 Mean: 17.4515 Minimum: 0.0000 Maximum: 869.9435 Range: 869.9435 St. Dev: 47.7473 Variance: 2279.8047 Sum: 540892.3500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.0281802 + 0.0006236*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.0281802277 0.0002946105 95.652480 < 0.00001 0.02760277 0.02875767 [Soil erosion 2006 (t/ha/yr)] 0.0006235991 0.0000057952 107.60532 < 0.00001 0.00061224 0.00063495 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 30992 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.271991 --> Adjusted R-Squared = 0.271968 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: LSfac_40m -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 27.478 27.4780723 11578.905 < 0.00001 Residuals 30992 73.548 0.0023731 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 30993 101.026 --------------------------------- Analysis Began: March 25, 1:44:39 PM Analysis Complete: March 25, 1:45:41 PM Time Elapsed: 1 minutes, 2 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: CP Factor 2006 INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- CP FACTOR 2006 [Dependent/Response] --- Cell Count: 30994 Mean: 0.2774 Minimum: 0.0000 Maximum: 1.0000 Range: 1.0000 St. Dev: 0.3262 Variance: 0.1064 Sum: 8598.1133 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 30994 Mean: 17.4515 Minimum: 0.0000 Maximum: 869.9435 Range: 869.9435 St. Dev: 47.7473 Variance: 2279.8047 Sum: 540892.3500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 0.2353507 + 0.0024102*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 0.2353507132 0.0018576317 126.69395 < 0.00001 0.23170967 0.23899174 [Soil erosion 2006 (t/ha/yr)] 0.0024101898 0.0000365412 65.958098 < 0.00001 0.00233856 0.00248181 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 30992 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.123095 --> Adjusted R-Squared = 0.123066 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: CP Factor 2006 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 410.467 410.4665208 4350.471 < 0.00001 Residuals 30992 2924.092 0.0943499 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 30993 3334.559 --------------------------------- Analysis Began: March 25, 1:47:16 PM Analysis Complete: March 25, 1:48:21 PM Time Elapsed: 1 minutes, 5 seconds...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~| REGRESSION RESULTS |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEPENDENT GRID: 2006 Rainfall Erosivity (Mj.mm/ha.h.yr) INDEPENDENT GRID: [Soil erosion 2006 (t/ha/yr)] MASK GRID: No Mask Grid used in analysis --------------------------------------------------------------------- <<<<<<< Polygon ID: No Polygon Analysis Area Specified >>>>>>>>>>>>>> --------------------------------------------------------------------- DESCRIPTIVE STATISTICS: --- 2006 RAINFALL EROSIVITY (MJ.MM/HA.H.YR) [Dependent/Response] --- Cell Count: 30994 Mean: 3869.6996 Minimum: 1501.3465 Maximum: 5955.6930 Range: 4454.3465 St. Dev: 1941.6803 Variance: 3770122.2709 Sum: 119937472.0000 --- [SOIL EROSION 2006 (T/HA/YR)] [Independent/Predictor] --- Cell Count: 30994 Mean: 17.4515 Minimum: 0.0000 Maximum: 869.9435 Range: 869.9435 St. Dev: 47.7473 Variance: 2279.8047 Sum: 540892.3500 --------------------------------------------------------------------------------------- Regression Model = B0 + B1*[Soil erosion 2006 (t/ha/yr)] Regession Equation: Y-hat = 3742.5337067 + 7.2868143*[Soil erosion 2006 (t/ha/yr)] --------------------------------------------------------------------------------------- Parameter Coefficients: --------------------------------------------------------------------------------------------------- | 95% CI Coefficient | Value | Std. Error | t-Value | P (>|t|) | Lower | Upper --------------------------------------------------------------------------------------------------- [Intercept] 3742.5337067 11.707438477 319.67143 < 0.00001 3719.58665 3765.48076 [Soil erosion 2006 (t/ha/yr)] 7.2868143010 0.2302954582 31.641155 < 0.00001 6.83542587 7.73820272 -- -- -- -- -- -- -- -- -- P-values calculated on t-distribution with 30992 df. --------------------------------------------------------------------------------------------------- Coefficient of Multiple Determination (R-Squared): --> R-Squared = 0.031293 --> Adjusted R-Squared = 0.031262 --------------------------------------------------------------------------------------- ANOVA Table Dependent Grid: 2006 Rainfall Erosivity (Mj.mm/ha.h.yr) -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Df Sum of Sq Mean Sq F-Value P-Value Regression 1 3751894782.886 3751894782.8862305 1001.163 < 0.00001 Residuals 30992 116143683677.771 3747537.5476823 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Total 30993 119895578460.657 --------------------------------- Analysis Began: March 25, 1:51:50 PM Analysis Complete: March 25, 1:52:49 PM Time Elapsed: 59 seconds..
APPENDIX 4
Image 2003 of Putrajaya by SPOT-4
Image 2004 of Putrajaya by SPOT-5
Image 2006 of Putrajaya by SPOT-4