“gis-integrated decision support system (dss) in water resources management” ivan maximov, ph.d...
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“GIS-integrated Decision Support System (DSS) in Water Resources
Management”
Ivan Maximov, Ph.D
The Ministry of Natural Resources Russian Federal Water Resources Agency
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STRUCTURE OF PRESENTATION
1. DSS (Decision Support Systems)
2. U.S EPA BASINS (Better Assessment Science Integrating point and Non-point Sources): Integrated GIS, data analysis and modeling system designed to support watershed based analysis.
3. Application of BASINS. Examples.
4. CONCLUSION
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1.DSS (Decision Support Systems)
Q: What is a Decision Support System? Process? Tool?
As a process: …is a systematic method of leading decision-makers and other stakeholders through the task of considering all objectives and then evaluating options to identify a solution that best solves an explicit problem while satisfying as many objectives as possible.
As a tool: …consists of models, data, and point-and-click interfaces that connect decision-makers directly to the models and data they need to make informed, scientific decisions. A DSS collects, organizes, and processes information, and then translates the results into management plans that are comprehensive and justifiable
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Q: What are the benefits of this technology for water resources management?
• Based on scientific data and models it can account for all stakeholder objectives, cause/effect relationships, risks, costs, and reliability, whereas traditional decision processes have had difficulty aggregating all of these considerations.
• Adaptable. Custom-designed. Scenario analysis and forecasts. Graphical interface links the decision-makers with the models.
• Aggregates all competing objectives to identify the best strategy – optimal solution.
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STRUCTURE OF DSS (GENERAL CASE)
DATABASE
TSS
MonitoringSystems
Laws, regulations
DATA TOOLS AND
ASSESSMENT
MODELS
USER' S DEMAND, MODEL SELECTION, MODEL'S
LIMITATION
MIKE SHE PLOAD
HSPFSWAT
DECISION-MAKING
OU
TP
UT
R
ES
UL
TS
/P
OS
T-
PR
OC
ES
SIN
G
SCENARIO ANALYSIS
MET
HYD
WQ
TOPOSOL
AGR
GEOL
SOCLU
STATS GIS
MAPSINFO-ANALYTICAL
SYS.
SIMULATION
MODELING SYS.
SYNTHESIS
‘what if’
‘forecasts’
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2. U.S EPA BASINS (Better Assessment Science Integrating
point and Non-point Sources)
• What Is BASINS?BASINS - Integrated into GIS, , multipurpose
environmental analysis system for performing watershed and water-quality-based studies.
• Main Objectives- To facilitate examination of environmental information- To support analysis of environmental systems- To provide a framework for examining management
alternatives
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BASINSGIS Interface
Watershed DelineationAutomated w/ SA
QUAL2E WIN HSPFSWAT
Schematic Diagram of BASINS 3.0
GenScn(Post-processor)
Output and Analysis
Watershed DelineationManual - AV only.
PLOAD
Watershed Parameterization
Tools/Utilities
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BASINS Spatially Distributed Data (Full set)
– Land use and land cover (shape and grid)
– Urbanized areas
– Populated place locations
– Reach file 1
– Reach file 3
– National Hydrographic Data (NHD)
– Major roads
– USGS hydrologic unit boundaries (accounting and catalog units)
– Drinking water supply sites– Dam sites– EPA region boundaries– State boundaries– County boundaries– DEM (shape and grid)– Ecoregions– NAQWA study unit boundaries– Managed area database (Federal
and Indian Lands)– Soil (STATSGO)
* Red color– those data that are required for model start-up
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BASINS Environmental Monitoring Data
• Drinking water supply sites
• Water quality monitoring station summaries
• Bacteria monitoring station summaries
• Weather station sites• USGS gauging
stations• Dam sites• Classified shellfish
area• 1996 Clean water
needs survey
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BASINS Point Source Data
• Permit compliance system sites (PCS)• Industrial facilities discharge sites• Toxic release inventory sites (annual releases)• Superfund national priority list sites• Mineral data• Hazardous and solid waste sites
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Assessment Tools in BASINS •Target: provides broad-based evaluation of watershed water quality and point source loadings.
•Assess: watershed-based evaluation of specific water quality stations and/or discharges and their proximity to waterbodies.
•Data mining: dynamic link of data elements using a combination of tables and maps. Allows for visual interpretation of geographic and historical data.
•Watershed reporting: automated report generation with user-defined selection options.
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3. APPLICATION OF BASINS. EXAMPLES
• HSPF (Hydrologic Simulation Program, FORTRAN)Project: Development of a local DSS - Integrated assessment of climate and land-use change effects on hydrology and water quality of the Great Miami River, OH, USA • SWAT (Soil and Water Assessment Tool).Project: To develop a DSS in order to decide how many and where to place water quality sampling stations in Swiss mezo-scale watershed (Thur River)
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HSPF is a comprehensive, physically-based, continuous, lumped model that can simulate hydrologic and associated water quality processes on pervious and impervious land surfaces. Model is capable to model point and non-point source pollution. Good for mid-size watersheds. Has a history of successful application (Chesapeake Bay Program and others.)
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Schematic structure of HSPF
LANDSACAPE DATA
Windows Interface, WinHSPF GUI
Land Use and pollutant specific data
Meteorological Data
HSPF code Post Processing and
decision-making process
GIS ArcView
Point Sources
Land Use distribution
Stream Data
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Hydrology in HSPF
Impervious Land Pervious Land
WaterBody
Runoff
Runoff
Interflow
Base flow
Surf
ace
Layers
Upp
erLo
wer
Gro
und
wat
er
Courtesy of Tetra Tech Inc.
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LOCATION OF STUDY AREA
Basin drainage area= 5,385 sq.mi
MA
D R
GR
EA
T M
IAM
I R
TW
IN C
R
STILL
WA
TE
R R
INDIA
N C
R
SEVEN
MILE
CR
INDIAN LAKE
GR
EA
T M
IAM
I R
DARKE
LOGAN
BUTLER
MIAMI
CLARK
HARDIN
PREBLE
WAYNE
MERCER
GREENE
SHELBY
WARREN
RANDOLPH
AUGLAIZE
HAMILTON
FRANKLIN
BOONE
CHAMPAIGN
MONTGOMERY
DEARBORN
UNION
0 40 80 Miles
N
County boundaries
Streams
DEM Great Miami river
151 - 185
186 - 219
220 - 253
254 - 287
288 - 321
322 - 355
356 - 389
390 - 423
424 - 457
No Data
Legend
Digital Elevation ModelGMR basin
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METHODOLOGY
Data collection
Characteristic of current water quantity and quality conditions
in the Great Miami River
Development, calibration and validation of GMR
hydrological model
Development, calibration and validation of GMR
water quality model
Hypothetical climate and land-use scenarios construction
Simulation of GMR hydrologic regime and water quality
Simulation of BMPs effect on water quality of Stillwater river
BASINS, HSPF, GenScn
BASINS, HSPF, WDMUtil
CLIMATE CHANGE ONLY
Simulation of GMR hydrologic regime and water quality
LAND USE CHANGE ONLY
Simulation of GMR hydrologic regime and water quality
COMBINED: CLIMATE AND LAND USE
HSPF, GenScn
HSPF, GenScn
Analysis of the results
Analysis of the results
HSPF, GenScn
HSPF, GenScn
Model development
USGS (National Water Information System), U.S EPA STORET, NCDC, US Census
Bureau, Ohio EPA, Miami conservancy group, National
Resource Inventory, Ohio Department of Development,
Stormwater managers resource center
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Upper GMR basin divided into sub-basins
$
$
$$$ $
$
$ $
$$ $ $$$ $$$$ $$ $$ $ $$$$ $
$$
$$$
$
$$
6
2
27
13
12
1
4
10
1815 19
3
58
7
17
22
14
9
26
20
24
11
21
25
23
28
16
MA
D R
*A
GR
EA
T M
IAM
I R
ST
I LLW
AT
ER
R
BU
CK
CR
LORAMIE
CR
GREENVILLE CR
LO
ST
CR
PAINTER CRMU
D C
R
HO
NE
Y C
R
SP
RIN
G C
R
MOSQU
ITO CR
LUDLOW CR
NETTLE CR
KINGS CR
BEAVER CR
HARRIS CR
IND
IAN
CR
CH
AP
MAN
CR
KR
AUT C
R
SWAM
P CRMUDDY CR
DO
NN
EL
S C
R
BO
YD
CR
AN
DE
RS
ON
CR
INDIAN L
LEA
TH
ER
WO
OD
CR
0 30 60 Miles
N
Cataloging Unit Boundaries
Streams
USGS Gage Stations$
Permit Compliance System
Watershed
Subbasins
Legend
Scale
Dayton
Springfield
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HSPF MODEL CALIBRATION AND VALIDATION
Very Good Good FairHydrology/Flow <10 10-15 15-25
Sediment <20 20-30 30-45
Water temperature <7 8-12 13-18
Water Quality/Nutrients <15 15-25 25-35
Pesticides/Toxics <20 20-30 30-40
Calibration, 1985-1990 Validation, 1990-1995Observed
mean annual
flow (ft3/s)
Simulated mean
annual flow (ft3/s)
% Error between
observed and simulated
Observed mean
annual flow (ft3/s)
Simulated mean
annual flow (ft3/s)
% Error between
observed and simulated
Upper GMR at Dayton,
OH
2,494 2,305 7.5
(good)
2,659 2,922 9.8
(good)Lower
GMR at Hamilton,
OH
3,646 2,998 17.7
(fair)
3,728 3,150 15.0
(good)
STREAMFLOW
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Great Miami River at Dayton, OH
Calibration
100)(
E%
1m,
1ms
n
ii
n
ii
X
XX
n
ii
n
ii
XX
XX
1
2mm
1
2ms
)(
)(1NS
Nash-Sutcliffe model efficiency
R=0.91; NS=0.74
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R= 0.78
R= 0.96
Upper GMR
Lower GMR
Upper GMR
Lower GMR
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Calibration and Validation of Water quality
CALIBRATION (1980-1985) VALIDATION (1985-1991)
Observed Simulated % Error Observed Simulated % Error
UPPER GMR
W T (F) 54.0 44.0 18.4 (fair) 53.4 44.8 16.1 (good)
DO (mg/l) 10.3 11.3 9.7 10.3 11.2 8.7
TP (mg/l) 0.34 0.33 2.9 (very good) 0.31 0.33 6.4(very good)
TAM (mg/l) 0.18 0.16 11.1(very good) 0.09 0.1 11.1(very good)
NO2+NO3 (mg/l)
3.50 3.12 10.8(very good) 3.80 3.20 15.7(good)
LOWER GMR
WT (F) 57.2 56.3 1.6(very good) 57.2 56.8 1.0(very good)
DO (mg/l) 9.9 9.4 5.0 10.5 9.3 11.4
TP (mg/l) 0.5 0.44 12.0(very good) 0.44 0.41 6.8(very good)
TAM (mg/l) 0.3 0.27 10.0(very good) 0.18 0.16 11.1(very good)
NO2+NO3 (mg/l)
4.18 3.61 13.6(very good) 3.9 3.44 11.7(very good)
WATER QUALITY
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WTEMP DO
NO3+NO2 OrthoP
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Hypothetical climate scenarios for HSPF simulations
Hot Scenario Group
Warm Scenario Group
Base Scenario(Current
Temperature and Precipitation)
Hot and Wet scenario(T+2oC, P+20%)
HW
Hot and Wet scenario(T+2oC, P+20%)
HW
Hot and Dry scenario(T+2oC, P-20%)
HD
Hot and Dry scenario(T+2oC, P-20%)
HD
Warm and Wet scenario(T+1.5oC, P+20%)
WW
Warm and Wet scenario(T+1.5oC, P+20%)
WW
Warm and Dry scenario(T+1.5oC, P-20%)
WD
Warm and Dry scenario(T+1.5oC, P-20%)
WD
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DEVELOPMENT OF LAND USE CHANGE SCENARIO
Hypothetical land-use change scenario includes an overall 30% increase in urban area: by 20% in Upper GMR basin and by 32% in the Lower GMR.
Major Roads
Streams
MRLC Land Use
Urban
Barren or Mining
Transitional
Agriculture - Cropland
Agriculture - Pasture
Forest
Upland Shrub Land
Grass Land
Water
Wetlands
Legend
Current Land Usefor the UPPER GMRBasin (Base Case)
6
2
27
13
12
1
4
10
1815 19
3
58
7
17
22
14
9
26
20
24
11
21
25
23
28
16
Ma d R
iv er
St il lw
a t er Ri ver
Grea t M
iami R
i ver
Greenvil
le Cree
k
Buc
k Cr
eek
Mile Creek
Los
t Cre
e k
Loramie C
reek
Mud Run
Tu r tle Cr e ek
Muc
hini
ppi C
reek
Honey C
reek
Dugan
Run
Mad River
GREENVILLE
BELLEFONTAINE
URBANA
PIQUA
SIDNEY
DAYTONSPRINGFIELD
TROY
0 20 40 Miles
N
Scale
Base case
Major Roads
Streams
FUTURE LAND USE SCENARIO
Urban
Barren or Mining
Transitional
Agriculture - Cropland
Agriculture - Pasture
Forest
Upland Shrub Land
Grass Land
Water
Wetlands
LEGEND
Future HypotheticalLand Use Scenariofor the UPPER GMR
Basin
6
2
27
13
12
1
4
10
1815 19
3
58
7
17
22
14
9
26
20
24
11
21
25
23
28
16
Ma d R
iv er
St il lw
a t er Ri ver
Grea t M
iami R
i ver
Greenvil
le Cree
k
Buc
k Cr
eek
Mile Creek
Los
t Cre
e k
Loramie C
reek
Mud Run
Tu r tle Cr e ek
Muc
hini
ppi C
reek
Honey C
reek
Dugan
Run
Mad River
TROY
SPRINGFIELDDAYTON
SIDNEY
PIQUA
GREENVILLE
URBANA
BELLEFONTAINE
0 20 40 Miles
N
Scale
Future scenario
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SIMULATIONS UNDER COMBINED CLIMATE AND LAND USE CHANGE SCENARIOS
UPPER GMR AT DAYTON, OH Mean annual flowft3/s (km3/year)
Difference between Base Case Scenario and Simulated combined Climate and Hypothetical
Land Use Scenario
Base Case Scenario 2347 (2.1) -
Hot and Dry Climate Scenario + Land Use Scenario (HD+LU)
1688 (1.5)
-28%
Hot and Wet Climate Scenario + Land Use Scenario (HW+LU)
3775 (3.36)
+61%
Warm and Dry Climate Scenario + Land Use Scenario (WD+LU)
1863 (1.66)
-21%
Warm and Wet Climate Scenario + Land Use Scenario (WW+LU)
4112 (3.6)
+75%
LOWER GMR AT HAMILTON, OH
Mean annual flowft3/s (km3/year)
Difference between Base Case Scenario and Simulated combined Climate and Hypothetical
Land Use Scenario
Base Case Scenario 2984 (2.66) -
Hot and Dry Climate Scenario + Land Use Scenario (HD+LU)
2653 (2.36)
-11%
Hot and Wet Climate Scenario + Land Use Scenario (HW+LU)
5080 (4.53)
+70%
Warm and Dry Climate Scenario + Land Use Scenario (WD+LU)
2928 (2.6)
-2%
Warm and Wet Climate Scenario + Land Use Scenario (WW+LU)
5464 (4.8)
+83%
ST
RE
AM
FL
OW
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Results from Water quality simulations under combined climate and land use change change scenarios:
UPPER GMR AT DAYTON, OH
Base Case
(Simulated)(Mean annual
values)
Difference between Base Case Scenario and Simulated combined Climate and
Hypothetical Land Use Scenario
Hot and Dry Climate Scenario + Land Use Scenario (HD+LU):
Water temperature (F)
DO (Mg/l)Total Phosphorus (Mg/l)
Total NH4 (Mg/l)
NO2+NO3 (Mg/l)
44.4 (47.8)11.2 (9.6)
0.33 (0.54)0.13 (0.16)3.16 (5.40)
+7.6%-14.0%+63.0%+9.0%
+12.9%
Hot and Wet Climate Scenario + Land Use Scenario (HW+LU):
Water temperature (F)
DO (Mg/l)Total Phosphorus (Mg/l)
Total NH4 (Mg/l)
NO2+NO3 (Mg/l)
44.4 (46.8)11.2 (10.1)0.33 (0.47)0.13 (0.10)3.16 (2.51)
+5.4%-9.8%
+42.0%+19.6%+22.8%
Warm and Dry Climate Scenario + Land Use Scenario (WD+LU):
Water temperature (F)
DO (Mg/l)Total Phosphorus (Mg/l)
Total NH4 (Mg/l)
NO2+NO3 (Mg/l)
44.4 (46.0)11.2 (10.2)0.33 (0.50)0.13 (0.14)3.16 (4.90)
+3.6%-8.9%
+51.0%+7.8%+9.2%
Warm and Wet Climate Scenario + Land Use Scenario (WW+LU):
Water temperature (F)
DO (Mg/l)Total Phosphorus (Mg/l)
Total NH4 (Mg/l)
NO2+NO3 (Mg/l)
44.4 (46.1)11.2 (11.1)0.33 (0.46)0.13 (0.12)3.16 (3.10)
+3.8%-1.0%
+39.0%+3.8%
+12.4%
WA
TE
R Q
UA
LIT
Y
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Results from BMPs simulations (SIMULATED construction dry/wet detention ponds, wetlands, aquatic
buffers)
No BMPs and
current conditions (base case)
With BMPs and current conditions
(% change to base case)
No BMPs and future land use scenario
With BMPs and future land-use
scenario(% change to
“No BMPs and future land use”)
Annual flow (ft3/s) at
Englewood, OH
675
612 (-9.3%)
816
715 (-12.3%)
Annual total phosphorus
(mg/l)
0.37
0.30 (-19.0%)
0.53
0.38 (-28.3%)
Annual ammonia
nitrogen (mg/l)
0.10 0.10 (no change)
0.11 0.10 (-10.0%)
Annual sum of nitrites and
nitrates (mg/l)
3.50
3.30 (-6.0%)
3.58
3.0 (-16.2%)
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HSPF in DSS for Great Miami River basin
• The effectiveness of the integrated approach when simulating “what if” scenarios in the context of combined future climate and land use changes. The important consideration is examining the combined effects rather than individual.
• Better understanding the complex hydrological cycle and interplay of climate and land-use changes and their effects on the stream ecosystem.
FOR THEORY
PRACTICAL
• the REAL measures, which could be applied in attempt to alleviate and minimize the consequences of predicted negative impacts on water quality.• capability of the GIS-based U.S EPA BASINS and HSPF tool.
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SWAT is a physically based model, capable of simulating long-term impacts, such as land-use changes, climate changes and agricultural management. SWAT has a history of successful applications.
Criteria for choosing this model: (a) model capabilities; (b) model accuracy; (c) model flexibility; and (d) data requirements.
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• SWAT input data: land-use, soils, slope and climatological data.• SWAT models evapotraspiration, lateral subsurface flow, return flow from groundwater, surface runoff, pollutants loads, erosion and sediment yield:(a) Land phase of the hydrologic cycle (controls the amount of water, sediment, nutrient, and pesticide loadings to the main channel in each subbasin (for water budget):
SWt = SW + ∑ (Ri-Qi-ETi-Pi-QRi)SWt is the final soil water content; SW is the soil water content for plant uptake; R – precip (mm); Q – surface runoff (mm); ET – evapotraspiration (mm); P – percolation (mm); QR – return flow (mm)
(b) Routing phase – movement of water, sediments etc., through channel network of the watershed to outlet (QUAL2E).
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Precipitation
(Rainfall & Snow)
Evaporation and Transpiration
Infiltration/plant uptake/ Soil moisture redistribution
Surface Runoff
Lateral Flow
Percolation to shallow aquifer
HYDROLOGICAL CYCLE IN SWAT
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LOCATION OF STUDY AREA
Basin drainage area= 1,700 km2
Gage stations$T
Streams
Subbasins
LEGEND
$T
$T
$T
$T
$T
$T
$T
$T$T
$T$T
$T
$T
$T
$T
$T
$T
$T
$T
121
1310
36
164
157 11
148 9
5
2
Murg-W„ngi
Thur-Halden
Necker-NeckerThur-Btschwil
Murg-FrauenfeldThur-Andelfingen
Sitter-Appenzell
Aubach-Fischingen
Thur-Neu St. Johann
Sitter-Bernhardzell
Thur-Stein, Iltishag
Thur-Jonschwil, Mhlau
Thur-Lichtensteig, Flotz
0 10 20 Kilometers
Delineation of theThur River Basin
(16 subchatchments)
N
Scale
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FLOW CALIBRATION, SUMMARY RESULTS (1991-1995)
1991-1995
Total Water Yield, mm
Surface runoff,
mm
Baseflow, mm
Mean annual flow, m3/s
Daily R (Monthly
R) between obs vs.
sim
Nash-Sutcliffe
coefficient
% Error
Observed 894 388 506 48.5 0.88 (0.92)
0.75 3.1
SWAT simulated
867 358 509 46.0
1991-1995 Summer, m3/s
%Error Fall, m3/s
% Error Winter, m3/s
% Error Spring, m3/s
% Error
Observed 50.6 +10.0 38.0 -5.2 48.7 -27.0 56.4 -10.4
SWAT 56.5 36.1 38.4 51.1
Very Good Good Fair
Hydrology/Flow <10 10-15 15-25
Sediment <20 20-30 30-45
Water temperature <7 8-12 13-18
Water Quality/Nutrients <15 15-25 25-35
Pesticides/Toxics <20 20-30 30-40
Target criteria for calibration (percent mean errors or differences between simulated and observed values )
MODEL CALIBRATION AND VALIDATION
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FLOW CALIBRATION (comparison between simulated and observed values)
Calibration
r daily = 0.88, rmonthly = 0.92NS = 0.75
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SIMULATED WATER BUDGET OF THE THUR RIVER
Streams
PRECIPITATION [mm] Average
999 - 1001
1001 - 1085
1085 - 1435
1435 - 1668
1668 - 1890
LEGEND
1
6
2
59
7
4
3
10
8
11
14
13
12
16
15
0 20 Kilometers
Thur River BasinPrecipitation
by subbasins, mm,1991-2000
N
Scale
#YAndelfingen
Streams
a.EVAPOTRASPIRATION [mm] Aver
406 - 410
411 - 516
517 - 540
541 - 591
592 - 627
LEGEND
1
6
2
59
7
4
3
10
8
11
14
13
12
16
15
0 20 Kilometers
Thur River BasinSimulated
Actual Evapotraspirationby subbasins, mm,
1991-2000
N
Scale
#YAndelfingen
P, mm ET, mm
Streams
GROUNDWATER CONTRIBUTION [mm] Aver
6 - 50
51 - 140
141 - 265
266 - 353
354 - 475
LEGEND
1
6
2
59
7
4
3
10
8
11
14
13
12
16
15
0 20 Kilometers
Thur River BasinSimulated
Groundwater Contribution to the flow
by subbasins, mm,1991-2000
N
Scale
#YAndelfingen
GW contribution, mm
Subbasins
Streams
Snow/ice melt amount (mm H2O), average
14 - 20
20 - 27
27 - 61
61 - 87
87 - 175
LEGEND
121
1310
36
164
157 11
148 9
5
2
0 10 20 Kilometers
SimulatedSnowmelt/Icemelt
amountsby subbasins, mm H2O,
Thur River Basin,1991-2000
N
Scale
#YAndelfingen
SNOWMELT, mmH2 0
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SIMULATED WATER BUDGET (continued)
Streams
SURFACE RUNOFF CONTRIBUTION [mm] Aver
28 - 91
92 - 175
176 - 244
245 - 545
546 - 768
LEGEND
1
6
2
59
7
4
3
10
8
11
14
13
12
16
15
0 20 Kilometers
Thur River BasinSimulated
Surface runoff Contribution to the streamlowby subbasins, mm,
1991-2000
N
Scale
#YAndelfingen
SURFACE RUNOFF, mm
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TSS calibration, 1991-1995: R = 0.78, NS = 0.60
TSS calibration, 1991-1995: R = 0.96, NS = 0.57
FLOW
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SIMULATED TSS LOADS BY SUBBABSINS
Gage stations$T
Streams
Total Sediment Yield, metric tonnes
513 - 2909
2910 - 12371
12372 - 35292
35293 - 49348
49349 - 102646
Subbasins
LEGEND
$T
$T
$T
$T
$T
$T
$T
$T$T
$T$T
$T
$T
$T
$T
$T
$T
$T
$T
121
1310
36
164
157 11
148 9
5
2
Murg-W„ngi
Thur-Halden
Necker-NeckerThur-Btschwil
Murg-FrauenfeldThur-Andelfingen
Sitter-Appenzell
Aubach-Fischingen
Thur-Neu St. Johann
Sitter-Bernhardzell
Thur-Stein, Iltishag
Thur-Jonschwil, Mhlau
Thur-Lichtensteig, Flotz
0 10 20 Kilometers
SimulatedTotal Sediment
Yield by Subbasins,metric tonnes,
Thur River Basin,1991-2000
N
Scale
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SIMULATED TN LOADS BY SUBBABSINS
Gage stations$T
Streams
Total Nitrogen Yields, kgN
16095 - 53436
53437 - 104879
104880 - 156425
156426 - 333093
333094 - 560155
Subbasins
LEGEND
$T
$T
$T
$T
$T
$T
$T
$T$T
$T$T
$T
$T
$T
$T
$T
$T
$T
$T
121
1310
36
164
157 11
148 9
5
2
Murg-W„ngi
Thur-Halden
Necker-NeckerThur-Btschwil
Murg-FrauenfeldThur-Andelfingen
Sitter-Appenzell
Aubach-Fischingen
Thur-Neu St. Johann
Sitter-Bernhardzell
Thur-Stein, Iltishag
Thur-Jonschwil, Mhlau
Thur-Lichtensteig, Flotz
0 10 20 Kilometers
SimulatedTotal Nitrogen
Loads by Subbasins,kgN,
Thur River Basin,1991-2000
N
Scale
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Gage stations$T
Streams
Total Phosphorus Yields, kgP
552 - 2147
2148 - 4932
4933 - 12320
12321 - 28298
28299 - 66720
Subbasins
LEGEND
$T
$T
$T
$T
$T
$T
$T
$T$T
$T$T
$T
$T
$T
$T
$T
$T
$T
$T
121
1310
36
164
157 11
148 9
5
2
Murg-W„ngi
Thur-Halden
Necker-NeckerThur-Btschwil
Murg-FrauenfeldThur-Andelfingen
Sitter-Appenzell
Aubach-Fischingen
Thur-Neu St. Johann
Sitter-Bernhardzell
Thur-Stein, Iltishag
Thur-Jonschwil, Mhlau
Thur-Lichtensteig, Flotz
0 10 20 Kilometers
SimulatedTotal Phosphorus
Loads by Subbasins,kgP,
Thur River Basin,1991-2000
N
Scale
SIMULATED TP LOADS BY SUBBABSINS
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• SUMMARY TABLE: SEDIMENT AND NUTRIENT LOADS BY LAND USE TYPE, THUR RIVER BASIN
IMPORTANT – LOADS PER LAND-USE
Land Use SYLD, tn
SYLD, SYLD
T-NO3,kg
N
T-NO3
, T-NO3 TN, kgN TN, TN
TP, kgP TP, TP
tn/ha
(% contributi
on) kgN/ha
(% contributi
on)
kgN/ha
(% contributio
n) kgP/ha
(% contributio
n)
FOREST 3,7760.02 0.9 91,484 0.54 30.7
106,664
0.63 5.0
1,869 0.01 0.9
PASTURE43,00
10.25 10.6 57,762 0.34 19.4
319,607
1.88 15.0
32,519 0.19 15.1
AGRICULTURE337,8
191.99 83.5 138,700 0.82 46.6
1,642,864
9.67 77.3
174,841 1.03 81.3
URML15,06
50.09 3.7 3,449 0.02 1.2 38,369
0.23 1.8
4,529 0.03 2.1
BROMGRASS/BAREN LAND 3,912
0.02 1.0 5,596 0.03 1.9 6,635
0.04 0.3 157 0.00 0.1
ORCHARDS 1,4800.01 0.4 911 0.01 0.3 10,427
0.06 0.5
1,176 0.01 0.5
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• SUMMARY TABLE: TSS AND NUTRIENT LOADS BY LAND USE AND SUBBASINS, THUR RIVER BASIN
Sub # LU Area,ha SYLD, tn SYLD,tn/ha T-NO3, kg T-NO3, kgN/ha TN, kg TN, kgN/ha TP, kg TP, kgP/ha
1 FRST 4610 250 0.05 1003 0.22 2483 0.54 191 0.04AGGR 12758 35042 2.75 10050 0.79 153942 12.07 18974 1.49
2 PAST 5772 3198 0.55 11913 2.06 12733 2.21 150 0.03FRST 5651 576 0.10 8301 1.47 8448 1.50 23 0.00BROM 1747 2040 1.17 3194 1.83 3739 2.14 82 0.05AGGR 2676 4062 1.52 4433 1.66 13948 5.21 1191 0.45
3 PAST 580 1035 1.78 557 0.96 7934 13.67 913 1.57FRST 2347 33 0.01 1632 0.70 1939 0.83 41 0.02AGRR 3677 6840 1.86 3560 0.97 53514 14.55 6176 1.68URML 459 3701 8.06 710 1.55 8271 18.01 972 2.12ORCD 580 762 1.31 420 0.72 1089 1.88 575 0.99
4 PAST 521 5202 9.98 937 1.80 24333 46.70 2898 5.56FRST 3076 244 0.08 3328 1.08 5540 1.80 276 0.09AGRR 4366 43902 10.05 8203 1.88 211139 48.36 25124 5.75
5 PAST 3079 2360 0.77 11169 3.63 26253 8.53 1873 0.61FRST 6074 144 0.02 24667 4.06 25145 4.14 60 0.01AGRR 4870 4292 0.88 19097 3.92 43238 8.88 2999 0.62
6 PAST 991 7976 8.05 455 0.46 30980 31.27 3778 3.81FRST 3707 98 0.03 1100 0.30 2025 0.55 118 0.03AGRR 9527 81426 8.55 9474 0.99 273632 28.72 35018 3.68URML 993 7550 7.60 2000 2.01 23205 23.37 2751 2.77ORCD 963 441 0.46 296 0.31 1251 1.30 366 0.38
7 PAST 896 519 0.58 3466 3.87 7504 8.37 500 0.56FRST 2908 9 0.00 9096 3.13 9179 3.16 11 0.00AGRR 4384 2381 0.54 17406 3.97 36753 8.38 2395 0.55
8 PAST 2189 1834 0.84 7560 3.45 15973 7.30 1055 0.48FRST 2573 201 0.08 8339 3.24 8596 3.34 35 0.01AGRR 1712 5006 2.92 3146 1.84 23859 13.94 2547 1.49
9 PAST 3332 2353 0.71 6668 2.00 7261 2.18 107 0.03FRST 2606 360 0.14 3654 1.40 3748 1.44 13 0.00BROM 1335 1872 1.40 2402 1.80 2896 2.17 75 0.06AGRR 1453 5257 3.62 2742 1.89 18554 12.77 1952 1.34
10 FRST 4563 68 0.01 2066 0.45 2641 0.58 78 0.02AGRR 8679 19936 2.30 11915 1.37 141802 16.34 17363 2.00
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SCENARIO ANALYSIS
No-Fertilizer
No-Tillage
No-Tillage +
No- Fertilizer
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Summary results from agricultural management scenarios simulations
Base case scenario No-Fertilizer scenario No-Tillage scenario No-Fertilize rand No-Tillage scenarioConstituent Observed Simulated Simulated % difference Simulated % difference Simulated % difference
to Base case to Base case to Base caseTSS (tn) 9,291 8,510 8,533 - 7,059 -17.0 7,443 -12.5TN (tn) 200 191 162 -15.0 187 - 182 -5.0
NO3 (tn) 145 130 108 -17.0 115 -11.5 113 -13.0TP (tn) 14 16 13 -18.0 12 -25.0 12 -25.0TN/TP 14 12 12 16 15
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• SWAT is capable to model alpine-pre-alpine watershed with acceptable degree of accuracy
• Results show relation between land-use and water quality parameters (winter wheat + summer pasture produced the highest sediment and nutrient loads)
• Spatial distribution, dynamic of nutrient and sediment loadings. Relative impacts of types of agricultural activities and land-uses on water resources.
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THANK YOU FORATTENTION!