use of the swat model to evaluate the impact of land …
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USE OF THE SWAT MODEL TO EVALUATE THE IMPACT OF
LAND-USE CHANGE ON EBONYI RIVER WATERSHED
BY
OGBU, KINGSLEY NNAEMEKA
PG/M.ENG/08/48464
A PROJECT REPORT SUBMITTED IN PARTIAL FULFILMENT OF
THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER
OF ENGINEERING (M. ENG) IN AGRICULTURAL AND BIORESOURCES
ENGINEERING
DEPARTMENT OF AGRICULTURAL AND BIORESOURCES
ENGINEERING
UNIVERSITY OF NIGERIA, NSUKKA
DECEMBER, 2010
APPROVAL
USE OF THE SWAT MODEL TO EVALUATE THE IMPACT OF LAND-USE
CHANGE ON EBONYI RIVER WATERSHED
BY
OGBU, KINGSLEY NNAEMEKA
PG/M.ENG/08/48464
A PROJECT REPORT SUBMITTED IN PARTIAL FULFILMENT OF
THE REQUIREMENTS FOR THE AWARD OF THE DEGREE
OF MASTER OF ENGINEERING (M. ENG) IN
AGRICULTURAL AND BIORESOURCES ENGINEERING
DEPARTMENT OF AGRICULTURAL AND BIORESOURCES
ENGINEERING
UNIVERSITY OF NIGERIA, NSUKKA
PROJECT ADVISER:
Engr. Dr. C. C. Mbajiorgu ..……………………..…………….
EXTERNAL EXAMINER: .……………………...…………….
Engr. Prof. C.D. Okereke
HEAD OF DEPARTMENT:
Engr. Dr. B. O. Ugwuishiwu ...…………………….…………….
DEDICATION
To my parents, Mr. & Mrs. S.O. Ogbu and my siblings, for their affection, love and
generous moral and financial support throughout the duration of this programme.
ACKNOWLEDGMENT
I hereby express my profound gratitude to my project adviser, Engr. Dr. C. C.
Mbajiorgu for his patience, guidance, support and encouragement throughout the
duration of this programme. I also acknowledge the online contributions of; Prof.
Raghavan Srinivasan (Director of the Spatial Science Laboratory, Texas Agricultural
& Mechanical University, USA), Chris George (International Institute for Software
Technology, United Nations University, Macoa) and Nancy Sammons (United State
Department of Agriculture – Agricultural Research Service) for their immeasurable
assistance in using MapWindow-SWAT (MWSWAT) model.
I am indebted to my colleagues in Eco-Hydrological System Research Unit
(EHSRU), University of Nigeria, Nsukka for their useful assistance and motivation in
this project work. I am highly indebted to all the lecturers in the Department of
Agricultural and Bioresources Engineering, who in various ways assisted me in the
course of this study. Immense contribution of the MapWindow GIS team in providing
all the datasets required for this study is highly appreciated.
I thank my immediate family members and friends for their prayers, moral and
financial support throughout the duration of this programme.
ABSTRACT
SWAT was applied a large watershed (3,765km2) in south-eastern Nigeria, to predict streamflow and sediment discharge from the Ebonyi River Watershed using globally available data downloaded from the internet. Digital elevation, land-use and soil maps of the study area from global databases and historical weather data measured locally were used. SWAT model was setup to use the MapWindow GIS interface. The model predicted an average daily streamflow and sediment discharge of 24.32m3/s and 341.31 metric tonnes, respectively, at the watershed outlet for the case of no land-use change. The major land-use of the study area (savanna) was then altered to grassland and row-crop agriculture to simulate six land-use change scenarios, representing combinations of decreasing grassland (93.76% - 0.09%) and increasing agricultural land (0.09% - 93.76%). Other model inputs were kept constant and experimental runs were performed to obtain streamflow and sediment discharges for a one-year period in daily time steps. Results show that expansion of agricultural land to about 19% of the watershed area increased average daily streamflow and sediment discharge to about 29% and 44%, respectively. A further respective increase of streamflow and sediment discharge to about 52% and 79% was simulated for expansion of agricultural land to 56%. Also, about 72% and 99% increase were obtained for streamflow and sediment discharge, respectively, when agricultural land was expanded to 94% of the watershed area. Generally, as more of the watershed is allocated to row-crop agriculture, streamflow and sediment discharge increased and the measure of increase reflected watershed characteristics and conditions.
TABLE OF CONTENTS
APPROVAL - - - - - - - - - i DEDICATION - - - - - - - - ii ACKNOWLEDGMENT - - - - - - - iii ABSTRACT - - - - - - - - - iv TABLE OF CONTENTS - - - - - - - v LIST OF TABLES - - - - - - - - vi LIST OF FIGURES - - - - - - - - vii CHAPTER ONE: INTRODUCTION 1.1 Background - - - - - - - - 1 1.2 Objectives of the Study - - - - - - 2 1.3 Justification of the Study - - - - - - 3 1.4 Scope of the Study - - - - - - - 3 CHAPTER TWO: LITERATURE REVIEW 2.1 Land-Use/Cover Change - - - - - - 4 2.2 Water Yield - - - - - - - - 5 2.3 Sediment Yield - - - - - - - 12 2.4 Water Quality - - - - - - - - 15 2.5 Watershed Modelling - - - - - - - 20 2.6 The Soil and Water Assessment Tool (SWAT) - - - 22 2.7 SWAT2005 Model Description - - - - - 25 2.8 Theory of the SWAT Model - - - - - - 26 2.9 Geographic Information System (GIS) Interface - - - 44 2.10 Performance of SWAT on Hydrologic Studies - - - 46 2.11 Performance of SWAT on Sediment Studies - - - - 48 2.12 SWAT Model: Advantages and Disadvantages - - - 49 CHAPTER THREE: METHODOLOGY 3.1 The Study Area - - - - - - - 51 3.2 Model Inputs - - - - - - - - 52 3.3 MapWindow-SWAT(MWSWAT) Model Setup - - - 56 3.4 Calculation Methods - - - - - - - 58 3.5 Land-use Scenarios - - - - - - - 58 CHAPTER FOUR: RESULTS AND DISCUSSION 4.1 Simulated Streamflow and Sediment Discharge - - - 60 4.2 Land-Use Scenarios - - - - - - - 62 4.3 Discussion - - - - - - - - 65 CHAPTER FIVE: CONCLUSION AND RECOMMENDATION 5.1 Conclusion - - - - - - - - 68 5.2 Recommendation - - - - - - - 69 REFERENCES - - - - - - - - 70 APPENDICES - - - - - - - - 81
LIST OF TABLES
Table Page
2.1 Potential Evapotranspiration Methods and Their Required Inputs - 32
2.2 P-Factor Values and Slope Length Limits for Contouring - - 41
2.3 P-Factor Values, Maximum Strip Width and Slope Length Limits
for Continuous Strip-Cropping - - - - - 42
2.4 P-Factor Values for Contour-Farmed Terraced Field - - - 42
3.1 Distribution of Land-Use Types Ebonyi River Watershed - - 54
3.2 Distribution of Soil Types in Ebonyi River Catchment - - 55
3.3 Distribution of Land-Use Types For Scenario 0 - - - 57
3.4 Land-Use Scenarios - - - - - - - 59
3.5 Areal Coverage of Land-Use Types for Different Scenarios - 59
4.1 Average Weekly Streamflow and Sediment Discharge for Control Simulation
- - - - - - - - - 61
4.2 Average Weekly Streamflow for Land-Use Scenarios 1 to 6 - 62 4.3 Average Weekly Sediment Discharge for Land-Use Scenarios 1 to 6- 63
4.4 Average Daily Streamflow and Sediment Discharge for all Scenarios- 65
LIST OF FIGURES
Figure Page
2.1 Schematic of SWAT Development History - - - 23
2.2 Definition Sketch Illustrating Time of Concentration - 31
2.3 Behaviour of Water Table As Assumed in the Kinematic
Storage Model - - - - - - - 36
3.1 Location of Ebonyi River Watershed in Nigeria - - 52
3.2 DEM Data for Ebonyi River Watershed - - - 53
3.3 Delineated Ebonyi River Watershed - - - - 53
3.4 Digital Land-Use Map of Ebonyi River Watershed - - 54
3.5 Digital Soil Map of Ebonyi River Watershed - - - 55
4.1 Simulated Average Weekly Streamflow for Scenario 0 - 60
4.2 Simulated Average Weekly Sediment Discharge for Scenario 0 60
4.3 Average Weekly Streamflow for Land-Use Scenarios 1 to 6 64
4.4 Average Weekly Sediment Discharge for Land-Use
Scenarios 1 to 662 - - - - - - 64
4.5 Average Daily Streamflow for Scenarios 0 to 6 - - 65
4.6 Average Daily Sediment Discharge for Scenarios 0 to 6 - 66
CHAPTER ONE
INTRODUCTION
1.1 Background
Land-use change, which occur continuously in response to population growth
and changes in primary production activities (Cao et al., 2009) has transformed a vast
part of the natural landscape of the developing world for the last 50 years (Solaimani
et al., 2009a). Natural watershed systems maintain a balance between precipitation,
runoff, infiltration or evapotranspiration, completing the natural hydrologic cycle
(Arnold et al., 2009). Land cover and land-use changes alter the hydrological cycle of
a catchment by modifying rainfall, evaporation, and runoff (Cao et al., 2009). Today,
land-use changes as a result of human activities (deforestation, commercial
agriculture, animal husbandry and urbanisation) have altered these hydrologic
processes particularly in small catchments (Cao et al., 2009). Deforestation activities
accounts for the largest percentage of land-use change occurring on the planet today
(Calder, 2000), resulting to increases in annual streamflow and stormflow volumes in
temperate, humid and dry tropical areas (Wilk, 2002). The severity of these problems
is more pronounced in arid or semi-arid regions, where high rainfall intensities of
short duration on grazing lands and rain-fed farms, and human mismanagement of
land have accelerated soil losses by erosion (Omani et al., 2007). Soil compaction by
deforestation, pasture installation and cattle trampling increase bulk density and
penetration resistance and reduces macroporosity, infiltration rates and hydraulic
conductivities (Germer et al., 2010). Domestic and industrial waste discharge,
agricultural wastes and pollution from other non-point sources reduces water quality
(Demirci et al., 2006).
Physically based distributed hydrological models, whose parameters have a
physical representation of the spatial variability of hydrological processes and are
capable of simulating the impact of human activities on the hydrologic cycle, are
increasingly being used to simulate complex water resource systems including the
simulation of the impacts of land-use on water resources in river basins during past
decades (Xu et al., 2009). The SWAT2005 model interfaced with MapWindow GIS is
a good example of such development. The model was developed initially by Dr. Jeff
Arnold for the USDA Agricultural Research Service (ARS) in the early 1990s in
Texas A & M University, Texas and is now being applied across the world (Neitsch et
al., 2005). The SWAT model key strength lies in its ability to predict the relative
impacts of changes in land-use on water quantity and quality (Govender and Everson,
2005), providing a scientific basis for water resources planning and management, as
well as measures to control water and soil erosion (Xu et al., 2009).
1.2 Objectives of the Study
This study is aimed at achieving the following objectives:
i. To obtain relevant historical climatic data such as rainfall, relative humidity,
sunshine duration, minimum & maximum air temperature and wind speed
from meteorological stations or the met agency (NIMET) for Nsukka.
ii. To obtain spatial data sets of digital elevation map (DEM), land cover and soil
data of Ebonyi River Basin; and
iii. To simulate the effects of land-use change on runoff and sediment yield, and
analyze simulated effects to draw useful inferences.
1.3 Justification of the Study.
The justification for this study is informed by the following considerations:
• The need to make an efficient and cost effective evaluation of impact of land-
use change on unguaged watersheds.
• The need to apply physical based distributed hydrological models in studying
streamflow and sediment discharge.
• The need for providing a scientific basis for water resources planning and
management, and also measures to control soil erosion.
1.4 Scope of the Study
The SWAT model was used for predicting the effects of land-use change on
runoff and sediment discharge using historical weather data and spatial data sets of
digital elevation map, soil and land-use for Ebonyi River watershed.
CHAPTER TWO
LITERATURE REVIEW
2.1 Land-Use/Cover change
Land cover refers to the physical and biological cover over the surface of land,
including water, vegetation, bare soil and or artificial structures while land-use refers
to human activities such as agriculture, forestry and building construction that alter
land surface processes (Erle, 2007). Land cover conversions constitute the
replacement of one cover type by another and are measured by a shift from one land-
cover category to another, as in the case of agricultural expansion, deforestation, or
change in urban extent (Lambin and Geist, 2006). Therefore, Land-use/cover change
is the modification/alteration of the Earth’s terrestrial surface by man in his quest to
obtain food and other essential services. Land-use change involves both the manner in
which biophysical attributes of land are manipulated and the purpose for which the
land is used (Lambin and Geist, 2006). Land can be used for forestry, parks, livestock
herding, and farmlands. The current population pressure, inadequate cultivation
practices, forest removal and high grazing intensities on rangelands and wetlands, and
marginal agricultural lands result to unwanted sediment and streamflow changes that
mainly impact the downstream natural communities (Olago and Odada, 2007).
There are many factors which trigger land-use change pattern such as
population growth, urbanization, production method and industrialization (Solaimani
et al., 2009b). Others include commercial exploitation of forest trees, demand for
firewood, forest fires, building of access roads, conventional farming, development of
settlement and urbanisation (Carpenter and Lawedrau, 2002). Demirci et al. (2006)
also pointed out the important factors that deteriorate a water body to include rapid,
unplanned urban and industrial expansions, domestic and industrial waste discharge,
agricultural pollution, leakages from garbage dumps and other non-point sources such
as run-off from streets and highways. Alterations in vegetative cover decreases time
of concentration of flow, increase the intensity of peak flows for a given precipitation
event, and increase the frequency and intensity of extreme flow events, especially
channel-forming flows (Hubbart, 2009). These alterations tends to deteriorate water
quality by transporting sediment and other pollutants from the landscape and
increasing erosive forces within the stream channel (Hubbart, 2009).
Presently, physical expansion of urban areas and extensive use of land for
agricultural purposes are the main causes of land-use change in the developing
countries (Solaimani et al., 2009a).
2.2 Water Yield
The relationship between timber harvest and water yield has long been of
interest with worldwide studies showing that water yield usually increases
immediately after forest harvest (Hubbart et al., 2007). Experimental studies have
generally been in agreement that a removal of forest cover is associated with
increased annual streamflow in both temperate and tropical environments (Wilk and
Hughes, 2002). It is believed that forest use more water than grassland hence, reduce
annual streamflow (Zhang et al., 2008). For example, clear-cutting forest in the
United States may result in up to 540mm increase in annual water yield, and it may
take up to 30 years to return to pre-harvesting water yield conditions with natural
forest regeneration (Stednick, 1996). However, this increase depends on forest types
(coniferous, deciduous, or scrub), rainfall amounts and intensities, topography,
climate regimes, soil type and depth, and vegetation (Sun et al., 2006; Lorup and
Hansen, 1997), and tends to decrease as forest regenerate (Bari et al., 1996). The
higher evapotranspiration losses from a forest than from any other alternative land-use
cover are the main reason for this situation (Lorup and Hansen, 1997). Forest removal
has a negative effect on evapotranspiration, thus resulting to increased streamflow.
Also, the area of cleared vegetation has an effect on water yield but in the case of
partial cuts or canopy thinning, vegetation removal may result in smaller water yield
increases than predicted by area alone because of the increased use of available
moisture by retained vegetation and vegetation in surrounding uncut areas (Hubbart et
al., 2007). Brooks et al. (2003) generalized that water yield usually increases when:
i) Forest is clear-cut or thinned.
ii) Vegetation on a watershed is converted from deep-rooted species
to shallow-rooted species.
iii) Vegetative cover is changed from plant species with high
interception capacities to species with lower interception
capacities.
iv) Species with high annual transpiration losses are replaced with
species with low annual transpiration losses.
Also, Hubbart et al. (2007) after studying results from previous studies on vegetation
management and water yield concluded that:
a. A reduction in forest cover increases water yield.
b. Afforestation reduces water yield.
Lorup and Hansen (1997) compared one year stream flow from three
catchments with similar physiographic and climatic characteristics and noted that the
annual runoff from the evergreen montane forest catchment was 30% and 36% lower
than from the other two cultivated catchments and largest difference in runoff found
during the dry season. They attributed the higher water yields from the cultivated
catchments to a considerable higher evapotranspiration from the forested catchments,
in particular during the dry season where the evergreen and deeper rooted forest cover
has a higher water demand and a better ability to extract soil moisture to a larger
depth. After studying ninety-five (95) catchments in the United States, Stednick
(1996) found that increased water yield was detectable after 20% - 30% of a
watershed has been harvested. Generally, higher water yield responses would be
expected in regions with deep soils and high annual precipitation, whereas responses
would be lower in magnitude in dry climates (Brooks et al., 2003). Hubbart et al.
(2007) designed treatments (50% clear-cut and 50% partial cut) on a watershed in the
continental/maritime hydro-climatic region of the US and noted that water yield
increased in excess of 270mm/yr and evapotranspiration reduced by 35% for the
clear-cut area while water yield increased by more than 140mm/yr and
evapotranspiration reducing by 14% for the partial cut area. In another study and
following 21-33% patch harvest units in four sub-watersheds with areas ranging from
3.6ha – 14.2ha, annual water yield was significantly increased by a range of 13% -
29%, or an average of 358mm/yr (King, 1994). In a study to asses the potential water
yield reduction due to afforestation across China (Sun et al., 2006) observed that the
average water yield reduction vary from about 50mm/yr (50%) in the semi-arid Loess
Plateau region in northern China to about 300mm/yr (30%) in the tropical southern
region. They also concluded that afforestation in China may have a higher potential to
greatly reduce annual water yield in headwater watersheds, especially in the semi-arid
Loess Plateau region.
The hydrological effects of land-use change in the humid tropics have been a
cause of controversy and debate for many years as Brooks et al. (2003) reports that
relatively little is known about forest management-water yield relationships in this
region. This they attributed to the availability of few controlled watershed
experiments in the tropical ecosystem which is surprising as people believe that the
humid tropical forests have a strong influence on precipitation. However, such studies
are rarely documented, but nevertheless are often stated as general facts (Lorup and
Hensen, 1997).
The few controlled documented experiments in the humid tropics show that
water yield responses to changes in vegetation cover are similar to those in the
temperate climates but would normally be of shorter duration because of rapid
regrowth of vegetation (Brooks et al., 2003). After studying runoff for one year under
different land-use/vegetation types in the Opa Reservoir Basin, South-western
Nigeria, Adediji (2006) recorded a total runoff of 5.62mm on a cleared basin, 5.24mm
on a field crop basin and a total runoff of 1.09mm in a thick bush part of the basin.
The author concluded that the mean total runoff from the cleared part of the basin was
higher than the mean total runoff recorded in the cultivated field crops, cocoa
farmlands and forested part of the basin. Also, in a study to examine the effect of
changes in land-use and vegetation cover on water yield from the Ruhudji catchment
in southern Njombe highlands of Tanzania, Atwitye (1999) reported that 88% of
variation in water discharge in the catchment is due to increased settlement,
cultivation areas and an increase in deforestation.
Contradictory results on the impacts of vegetation removal on water yield still
exist. The widespread belief that trees and tree planting will increase streamflow
during and at the end of the dry season are in conflict with the present and most other
catchment studies dealing with the effect of deforestation and afforestation on dry
season flow (Lorup and Hansen, 1997). Results from a study in northeast Thailand
found no significant changes in water balance after a decline in forest cover from 80%
to 27% (Wilk and Andersson, 2001). Lui and Fu (1996), after comparing watershed
hydrology of an old-growth fir forest watershed with a clear-cut watershed from 1965
– 1967 found that the annual water yield from the 331ha forested watershed was
709mm/yr and that from the 291ha clear-cut watershed was only 276mm/yr. Wei et
al. (2003) also reported similar positive correlations between forests and water yield
for large basins (>100km2) for northern China which they corroborated with Russian
literature that suggests that streamflow is generally higher for large forested basins.
One unsubstantiated hypothesis was that forest increased ‘fog drip’ precipitation and
forests have lower evapotranspiration, thus increasing streamflow (Sun et al., 2006).
Wei et al. (2003) attributed these inconsistent findings to the following reasons and
include:
i. Heterogeneous large basins have large buffering capacity and may mask
the forest cover effects.
ii. Inconsistent methods and measurement error.
iii. Differences in climate and watershed characteristics among the contrasting
basins that could have obscured the forest cover effect.
Forest that occurs along coastal areas or on mountain islands (cloud forest)
sometimes produces more moisture for a watershed than they consume by
transpiration (Brooks et al., 2003). In these regions, precipitation inputs are horizontal
via fog interception by vegetation (Bruijnzeel et al., 2005). This horizontal
precipitation via fog interception is a notably important source of water for a variety
of ecosystems, from temperate evergreen forests growing in the sub-humid seasonal
climates, such as the redwoods in California (Burges and Dawson, 2004), to tropical
montane cloud forest (TMCFs) that have seasonally low rainfall (Bruijnzeel, 2001).
Fog precipitation occurs when fog droplets are filtered by the forest canopy
and coalesce on the vegetative surfaces to form larger water droplets that drip to the
forest floor (Holder, 2005). Environments that are likely to have fog precipitation are
high elevation regions where cool temperature results in the condensation of water
vapour (LaBastille and Pool, 1978) and coastal regions on the western side of
continents where cool air from the oceans condenses and moves inland (Cereceda and
Schemenauer, 1991). Because fog occurs under conditions of high relative humidity
and low solar radiation, water intercepted from fog is less likely to be evaporated,
yielding additional precipitation through subsequent dripping of intercepted fog to the
forest floor (Gonzalez, 2000). The net precipitation in this areas are significantly
enhanced by direct canopy interception of cloud water, and when combined with low
water use by the vegetation due to reduced solar radiation and vapour deficit, canopy
wetting, and general suppression of evapotranspiration, results in net additions to the
water yield of the watershed (Hamilton, 1995).
Fog interception is influenced by several meteorological variables, including
fog liquid water content (LWC), fog drop size, wind speed and direction, and duration
and frequency of fog events (Bruijnzeel and Proctor, 1995). It is also influenced by
biotic variables related to structural characteristics of the forest, such as height, size,
spatial pattern, orientation relative to prevailing wind direction, biomass and physical
characteristics of leaves and epiphytes (Bruijnzeel et al., 2005). Several different
approaches have been used to estimate fog interception (Bruijnzeel et al., 2005)
including:
i) Water and mass balance techniques such as wet canopy water budget approach
that estimates fog interception through a comparison of wet and gross
precipitation for periods with and without fogs.
ii) Mass balance and isotopic techniques that trace the origin of the water that
reaches the forest floor.
iii) Micrometeorological methods based on the eddy covariance techniques, under
which exchanges of cloud water between the forest and the atmosphere are
estimated as the covariance between the turbulent components of vertical wind
speed and liquid water content.
iv) Using gauges designed to intercept fog, referred to as ‘fog gauges’.
By far, the most common approach for estimating fog interception is the use of
the fog gauges (Villegas et al., 2008). Holder (2003) examined fog precipitation at
two sites having different elevations in the Sierra de las Minas Biosphere Reserve,
Guatemala. His study found that fog precipitation at the 2100m site totalled 23.4mm
from 30 July, 1995 to 7 June, 1996 and 203.4mm at the 2550m site during the same
time interval. In another study, Holwerda et al. (2006) studied the deposition of fog to
a wind-exposed 3m tall Puerto Rican cloud forest at 1010m elevation using the water
budget and eddy covariance methods. Their results showed that best estimates of
annual fog deposition amounted to 770mm/year for the summit cloud forest just
below the ridge top (according to the water budget method) and 785mm/year for the
cloud forest on the lower windward slope (using the eddy-covariance method). They
however attributed the discrepancies in their results to be related to effect of footprint
mismatch and methodological problems with rainfall measurement under the
prevailing wind conditions.
Changes in water yield that accompany land-use practices can be estimated by
the following methods (Brooks et al., 2003):
i) Regional relationships: This method involves the use of mathematical
expressions which are often the results of localized experiments or regression
relationships. This method is only used in the region or area for which they
were developed or in other regions that have similar climates, soils,
topography, and vegetative types of the area for which they were developed.
ii) Water budget approach: This method can be used to estimate water yield for
watersheds with deep soils and high infiltration capacities. Water yield
changes associated with changes in the types of vegetative cover can be
estimated by water budget analyses using different effective rooting depths.
iii) Computer simulation models: Computer models for hydrologic simulation
include simplified empirical relationships at one extreme and detailed process-
oriented model at the other extreme. This method can contain derivations or
elements of the previously discussed methods. Also, it can consider nutrients
and complex relationships and perform sensitivity analysis where data or
assumptions are weak.
2.3 Sediment Yield
In recent times, due to various developmental activities within the river basins,
the rate of soil erosion, its transport and deposition downstream have considerably
been altered (Chandramohan and Balchand, 2007). Sediment yield is the total
sediment outflow from a watershed or drainage basin measured for a specific period
and at a defined point in a stream channel (Brooks et al., 2003). Ali and Boer (2008)
defined it as “the total sediment outflow from a basin, with suspended sediment as the
dominant component”. Soil loss occurs when the earth surface is exposed to impact of
rain drops or wind. This action detaches soil particles and makes it easy for runoff to
transport it downstream. Knowledge on sediment yields from a catchment is needed
to estimate the quantity of sediment delivered to a downstream reservoir and then to
further determine the amount of pollutant into such reservoir (Xu et al., 2009).
Generally, a decrease in vegetative cover leads to an increase in runoff
generation and sediment detachment (Boix-Fayos et al., 2008). Also, a change in
landscape structure has an important consequence for sediment yield (ibid). For
example, the topographic modifications from road construction to skid-trail use are a
common cause for gullying in logged areas, which results in the transport of soils
from a watershed (Brooks et al., 2003).
The rate of sediment generation and transport depends on several factors
related to watershed topographic, geomorphologic and land-use/cover characteristics
(Seegar et al., 2004). Streams discharging large quantities of sediment annually are
those that drain areas undergoing active geologic erosion or improper land use
(Brooks et al., 2003). Erosion is one of the most significant forms of land degradation
(loss of soil fertility and slope instability) and is greatly influenced by land-use and
management (Solaimani et al., 2009a).
Sediment can be carried downstream as bed load (particles that move along
the river bed by rolling, skipping or sliding) or as suspended load (supported by fluid
flow and maintained by fluid turbulence) (Franeke et al., 2008). However, bed load is
flow dependent and generally accounts for around 10% of the total solid transport of a
river (ibid). They further pointed out that sediment yields are often based on the data
concerning suspended load as it is the major transporting mechanism in streams
worldwide and are composed of particles finer than 0.062mm in diameter.
Soil loss from a drainage basin is influenced by the following factors (Brooks
et al., 2003)
i) Road construction and deforestation on steep slopes.
ii) Undercutting a slope and improper drainage.
iii) The permanent conversion of forest to pasture or crop land reduces
evapotranspiration and increases soil moisture.
Investigations of land-use change on soil erosion process using geographical
information system (GIS) (Solaimani et al., 2009b) revealed that the total erosion of
the basin decreased to 89.24% after appropriate land-use practices was implemented
on the watershed. The results showed that after appropriate land management
practices on the watershed, the sediment yield decreased from 144465.1m3/km2/yr to
15542.9 m3/km2/yr. In another study, Alansi et al. (2009) reported a marked increase
in sediment load amount from 300,000 tonnes in the 1980s to 400,000 tonnes in the
1990s. They attributed this increase to the replacement of old oil palms with new
ones, replacement of most of the rubber trees with oil palms and deforestation and
urbanisation which resulted to open bare lands, aiding runoff and sediment load
transports. Xu et al. (2009) assessed sediment yields from two basins (Chao River
Basin and Bai River Basin) into the Miyun Reservoir due to land-use changes in the
basins. Their results showed that in 1986, sediment yield from the Chao River Basin
and Bai River Basin was 246,000tonnes and 254,000tonnes respectively. In 1991, the
sediment yield was 281,000tonnes and 208,000tonnes for the Chao River Basin and
Bai River Basin respectively. They however attributed the increase in sediment yield
from the Chao River Basin to high annual precipitation (average of 587mm) which
resulted to an increase in runoff and more intensive farmlands located near the river in
the Chao River Basin. Adediji (2006) examined effects of land-use/vegetation types
and slope on soil loss within a catchment. His results showed that soil loss on slope
was highest in the built-up basins (with mean specific yield of 30.07t/km2/yr). This
was distantly followed by field crops, cocoa dominated and forested basins with mean
specific sediment yield of 18.04t/km2/yr, 16.88t/km2/yr and 14.20t/km2/yr
respectively. His results also showed that soil loss from the valley slopes of the
studied basin correlated well with runoff and slope angle. He therefore concluded that
runoff was the most important predictor of soil loss in south-western Nigeria.
2.4 Water Quality
Water pollution occurs because human activities such as agriculture, forest
harvest, and urbanisation have altered the structure of rural landscape and increased
the quantity of substances like sediment, nitrogen, chlorine, etc., loaded to the river
system (Anbumozhi et al., 2005). Forest cover helps maintain clean water supplies by
filtering freshwater and reducing soil erosion and sedimentation (Fuentes-Junco et al.,
2004). However, deforestation undermines this process by degrading the quality of
water supplies (Mulligan, 1998). Demirci et al. (2006) studied the relationship
between land-use changes and water quality and noted that the most important factors
that affect water quality in the studied area were rapid, unplanned urban and industrial
expansions, domestic and industrial waste discharge, agricultural pollution, leakages
from closed garbage dump area and runoffs from streets and highways.
The quality of water can be completely defined and estimated by studying its
physical, chemical and biological characteristics (Garg, 2005). Water quality standard
refers to the physical, chemical or biological characteristics of water in relation to a
specified use (Brooks et al., 2003). Biological contamination of water result from
human wastes, animal wastes, industrial wastes, and chemical contaminants result
from industrial process, agricultural use of fertilizers and pesticides while physical
contaminants results from erosion and disposal of solid wastes (Schwab et al., 1993).
Suspended sediment concentrations, levels of thermal pollutions and dissolved
oxygen are the most important physical characteristics of surface water (Brooks et al.,
2003). Throughout the world, an increase in the sediment load of streams is the most
widespread cause of degradation in the quality of water in forests (ibid), which most
times occur due to soil erosion (Schwab et al., 1993). Suspended sediments restrict
sunlight from reaching photosynthetic plants and increases turbidity. The potential
sources of suspended sediment in mountainous forested watersheds are streambed,
stream bank, forest floor (Mizugaki et al., 2008) and forest roads (Madej, 2001).
Higher concentrations of suspended sediments are often the result of accelerated
erosion caused by disturbances (road constructions, logging, heavy grazing, bush
fires, etc) in drainage areas (Brooks et al., 2003). Agricultural nutrients (Nitrogen and
Phosphorus), heavy metals and pesticides (altrazine) are known to adsorb to eroded
sediments and are transported to streams, resulting to eutrophication (Schwab et al.,
1993).
Ploughing (Edeso et al., 1999), construction of forest roads and skid trails
causes major disturbances in forested watersheds, resulting in increases in suspended
sediments (Grayson et al., 1993) and organic materials (Kreutzweiser and Capell,
2001). The dense coverage of forest canopy prevents sunlight from reaching the forest
floor and does not permit understory vegetation. In this case, sediment detachment by
raindrop impact is considered the most dominant process for sediment transport
(Miura et al., 2002), which increases with the kinetic energy of the rain drops
increases (Nanko et al., 2008). The overland flow on a forest road may emanate from
the upslope forest floor (Wemple et al., 2001) and transport the sediment produced
from the road surface and forest floor to the stream channel (Mizugaki et al., 2008).
Suspended sediment also increases during intensive grazing on steep, unstable terrains
and fragile soils or when livestock are allowed to overgraze riparian plant community
resulting to stream-bank erosion and sediment deposition into stream channel (Brooks
et al., 2003).
Forest harvesting may also increase suspended sediment yields even when
unaccompanied by soil surface disturbances created by harvesting treatments (Hotta et
al., 2007). Results from a study (Mizugaki et al., 2008) showed that the considerable
higher contribution (69%) of the forest floor to the suspended sediment in the
watershed without a truck trail network suggests the direct transport of the sediment
from the forest floors to the stream channel by interrill erosion while the lower
contribution (46%) of the forest floor in the watershed with the truck trail networks
suggest contribution from subsurface materials of the truck trail and stream bank.
Basnyat et al., (1999) is of the opinion that land cover changes near streams have a
greater effect on stream water physico-chemical characteristics than similar land
cover changes elsewhere in the watershed. Water temperature increases due to land-
use practices results to increased biological activities and places greater demand on
the dissolved oxygen in a stream (Brooks et al., 2003). Stream temperature increases
due to urbanisation have been recognised as the significant factor in habitat loss for
both coldwater fish species (Nelson and Palmer, 2007) and invertebrates (Wang and
Kanchl, 2003). Urbanisation influences stream temperature through changes in stream
shading, channel geometry, groundwater input and inflows of storm water and waste
water (Herb et al., 2008). Clearing of riparian vegetation adjacent to a stream channel
increases the stream exposure to solar radiation, and subsequently a rise in water
temperature (Brooks et al., 2003). Brooks et al. also noted that studies in north-eastern
and north-western United States reported that annual maximum stream temperature
rise as much as 4ºC and 15ºC when riparian vegetation was removed from small
streams.
A study (Herb et al., 2008) on the analysis of stream temperature changes due
to surface runoff from a paved surface gave the result that rainfall producing high heat
export rates lead to the greatest instantaneous change in stream temperature and was
attributed to the following causes:
i) Event-based surface temperature is strongly related to dew-point temperature
during the rainfall, air temperature and solar radiation prior to the rainfall.
ii) Rainfall events with high heat export (thermal impacts) occur from May
through September.
iii) Rainfall events with the highest heat export rates occurs mostly in the
afternoon, have runoff temperature significantly above 20ºC and have
relatively low total precipitation (average of 12mm).
iv) Rainfall events that gave the highest event-average change in stream
temperature tend to be of short duration (2hrs).
Conversely, Brown and Hannah (2007) found that 75% of summer precipitation
events led to temperature decrease in an alpine stream system and attributed it to
lower ambient air and ground surface temperature. Generally, the relationship
between stream temperature and ambient atmospheric temperature is an important
factor in determining the magnitude and direction of stream temperature changes due
to surface runoff (Herb et al., 2008).
The dissolved oxygen concentration of a water body is determined by the
solubility of oxygen, and is inversely related to temperature, pressure and biological
activities in the water body (Brooks et al., 2003) and is estimated from:
32 000077774.00079910.041022.0652.14 TTTOs −+−= …………….2.1
where:
Os = solubility of oxygen (mg/l)
T = temperature of water (ºC)
If sufficient oxygen is present in a water body, the useful aerobic bacteria
production will flourish, which causes the biological decomposition of waste and
organic matter thus reducing the carbonaceous material from the water (Garg, 2005).
Dissolved oxygen varies in response to biological activity, with higher levels being
associated with the presence of aquatic plants (Schneider et al., 2000). Oxygen
depletion in water bodies as a result of excessive amounts of nutrients causes
eutrophication and limits aquatic plant growth (Tafangenyasha and Dzinomwa, 2005).
Increased nutrient loads in water bodies depletes dissolved oxygen by stimulating
algal blooms and decreases habitability for aquatic organisms (Brooks et al., 2005).
Demirci et al. (2006) related land-use changes in a basin to water quality in a lake and
found that the chemical oxygen demand (COD) in the lake shows a large value spread
(between 4.4mg/l and 732mg/l) and that the average coliform value was 1600 per
100ml. They attributed these results to the runoffs from impervious surfaces and
many industrial facilities discharging their wastes into the streams feeding the lake.
The chemical composition of a water body is determined largely from the
watershed system it drains (Tafangenyasha and Dzinomwa, 2005). Water is an
effective solvent, and as it comes in contact with any part of the watershed system,
chemical reactions occurs (Brooks et al., 2003). This action determines the chemical
constituents of a water body. During streamflow, there are large cumulative
increments in the chemical parameters that reduce the high quality of stream water
from forested uplands (Swank and Bolstad, 1994). The major sources of dissolved
chemical constituents in a water system that drains upland watersheds are geologic
weathering of parent rock, biological inputs and meteorological events (Brooks et al.,
2003). Precipitations, dusts and aerosols add organic compounds and mineral ions to
the stream system. Water quality is also threatened by the application of waste water
and fertilizers from agricultural lands (Anbumozhi et al., 2005). Commercial
agriculture supports the application of large amounts of fertilizer to maximize yields
which result to the introduction of excess Nitrogen to downstream ecosystems.
Demirci et al. (2005) after assessing the relationship between land-use change and
water quality in a lake watershed concluded that the Phosphate and Nitrate levels in
the lake are as a result of sewage discharge and agricultural application of fertilizers.
Tafangenyasha and Dzinomwa, (2005) attributed the pollution of the Runde River in
Zimbabwe to agricultural runoff, domestic and industrial effluents. They concluded
that it led to the increase in the nutrient (Nitrogen, Nitrates and Phosphorus) levels,
resulting to eutrophication and distribution of macro invertebrates. Results from a
study (Akintola and Gbadegesin, 1997) on the effects of land-use practices on water
quality of a reservoir indicated fairly low and tolerable levels of nitrate (< 10mg/l),
nitrate-nitrogen (< 6.0mg/l), dissolved oxygen (< 8.0mg/l) and total dissolved solids
(< 220mg/l). The significant increase in some of these parameters, especially the
nitrate-nitrogen content was attributed to the increased land area (900% - 1150%)
devoted to arable farming and increase in the amount nitrogenous fertilizer used.
2.5 Watershed Modelling
Watershed models are powerful tools for simulating the effect of watershed
processes and management on soil and water resources (Moriasi et al., 2007). Borah
and Bera (2003) defined watershed models as “useful tools for assessing the
environmental conditions of a watershed and evaluating best management practices
(BMP), implementation of which can help reduce the damaging effects of stormwater
runoff on water bodies and the landscape”. Watershed models link sources of
pollutants to receiving water bodies as non-point source loads. Therefore, watershed
modelling is the application of watershed models to simulate the effect of watershed
processes and management activities on water quality, water quantity and soil quality.
Watershed modelling, in which physical and chemical processes of hydrology and
water quality are represented in a computer simulation, is often employed by
hydrologist and environmental scientists to evaluate potential impacts from changes
in land-use or climatic conditions (Johnson, 2009). Watershed models provides viable
alternatives to measuring water and sediment supply from an ungauged watershed and
supports environmental policy decisions (Lai, 2005).
Geographic information systems (GIS) stores and displays the data (land-use,
soil, climate and DEM) needed by watershed models to predict both water and
pollutant runoffs from a watershed. Examples of watershed-scale hydrologic and non-
point source pollution models are SWAT (Soil and Water Assessment Tool), HSPF
(Hydrologic Simulation Program Fortran), AGNPS (Agricultural Non-Point Source
Pollution), AnnAGNPS(Annualised Agricultural Non-Point Source Pollution),
ANSWERS (Areal Non-Point Source Watershed Environment Response Simulation),
PRMS (Precipitation-Runoff Modelling System), KINEROS (Kinematic Runoff and
Erosion), WEPP (Water Erosion Prediction Project), DWSM (Dynamic Watershed
Simulation Model), etc. (Borah, 2002). Clear understandings of these models are
necessary in other not to misuse them and also the choice of use of any of these
models depends on the problem, watershed size, desired spatial and temporal scales,
expected accuracy, user skills and computer resources (Borah and Bera, 2003).
2.5.1 Importance of Watershed Modelling
The importance of modelling a watershed includes:
i) It enables researchers to study long-term impacts of land management
practices in large complex watersheds (George and Leon, 2008).
ii) Results obtained from watershed modelling provides scientific basis for water
resources planning and management, as well as measures to control water and
soil losses from the study area (Xu et al., 2009).
iii) Studying the impacts of changes in land management practices and vegetation
on water quality and quantity is less tedious (Govender and Everson, 2005).
2.6 The Soil and Water Assessment Tool (SWAT)
The development of the Soil and Water Assessment Tool (SWAT) model is a
continuation of the United States Department of Agriculture (USDA), Agricultural
Research Service modelling experience that spans a period of roughly 30 years
(Gassman et al., 2007). It emerged mainly from the Simulator for Water Resources in
Rural Basins (SWRRB) model, and contains features from Agricultural Research
Service (ARS) models: Chemicals, Runoff, and Erosion from Agricultural
Management Systems (CREAMS), Groundwater Loading Effects on Agricultural
Management Systems (GLEAMS), Erosion Productivity Impact Calculator which is
known today as Environmental Impact Policy Climate (EPIC), Routing Outputs to
Outlets (ROTO) and QUAL2E model (Borah and Bera, 2003) as shown in Figure 2.1.
Fig. 2.1: Schematic of SWAT Development History (Gassman et al., 2007)
SWAT was developed to assist water resources managers in analyzing the impacts of
land management practices on water, sediment, and agricultural chemical yields in
large complex watersheds (Setegn et al., 2008).
Development of SWRRB began in the early 1980s with the modification of
the daily rainfall hydrology model from CREAMS (Neitsch et al., 2005). Surface
runoff and other computations were expanded to ten sub-basins. Other modifications
include an improved peak runoff rate method, calculation of transmission losses,
groundwater return flow, reservoir storage, EPIC crop growth sub-model, weather
generator, sediment transport component, GLEAMS pesticide fate component,
optional USDA-SCS technology for estimating peak runoff rates and newly
developed sediment yield equations (Gassman et al., 2007). The limitation of
simulating streamflow for basins extending to over several thousand square
kilometres led to the development of the ROTO (Routing Outputs to Outlet) model
(Arnold et al., 1995) which links multiple SWRRB runs together (Neitsch et al.,
2005). This led to the merging of ROTO and SWRRB into a single model – SWAT
CREAMS
EPIC
GLEAMS Pesticide component
Daily rainfall hydrology component
Crop growth component
SWRRB (multiple subbasin, other components)
QUAL2E
In-stream kinetics
SWAT
Routing structure
ROTO
(Fig. 2.1) with SWAT retaining all the features of SWRRB. Also, the in-stream
kinetics component from the QUAL2E model was incorporated into SWAT (Fig. 2.1).
Since the development of the SWAT model in the early 1990s, it has
undergone continued review and expansion of its capabilities (Arnold and Fohrer,
2005). The most significant improvements of the model between releases include
(Neitsch et al., 2005):
• SWAT94.2: Multiple hydrologic response units (HRUs) incorporated.
• SWAT96.2: Auto-fertilization and auto-irrigation added as management
options; canopy storage of water incorporated; a CO2 component added to
crop growth model for climatic change studies; Penman-Monteith potential
evapotranspiration equation added; lateral flow of water in the soil based on
kinematic storage model incorporated; in-stream nutrient water quality
equations from QUALZ2E added; in-stream pesticide routing.
• SWAT98.1: Snow melt routines improved; in-stream water quality improved;
nutrient cycling routines expanded; grazing, manure applications, and tile flow
drainage added as management options; model modified for use in Southern
Hemisphere.
• SWAT99.2: Nutrient cycling routines improved, rice/wetland routines
improved, reservoir/pond/wetland nutrient removal by settling added; bank
storage of water in reach added; routing of metals through reach added; all
year references in model changed from last 2 digits of year to 4-digit year;
urban build up/wash off equations from SWMM added along with regression
equations from USGS.
• SWAT2000: Bacteria transport routines added; Green & Ampt infiltration
added; weather generator improved; allow daily solar radiation, relative
humidity, and wind speed to be read in or generated; allow potential ET values
for watershed to be read in or calculated; all potential ET methods reviewed;
elevation band processes improved; enabled simulation of unlimited number
of reservoirs; Muskingum routing method added; modified dormancy
calculations for proper simulation in tropical areas.
• SWAT2005: Bacteria transport routines improved; weather forecast scenarios
added; sub-daily precipitation generator added; the retention parameter used in
daily CN calculation may be a function of soil water content or plant
evapotranspiration.
Today, the model has gained international acceptance as a robust
interdisciplinary watershed modelling tool, as evidenced by international SWAT
conferences, hundreds of SWAT-related papers presented at numerous other scientific
meetings, and dozens of articles published in peer-reviewed journals (Gassman et al.,
2007). Applications of the SWAT model include studies by Setegn et al. (2008),
Govender and Everson (2005), Xu et al. (2009), and Kaur et al. (2003).
2.7 SWAT2005 Model Description
SWAT is an acronym for Soil and Water Assessment Tool, a river basin, or
watershed scale model developed by Dr. Jeff Arnold for the USDA Agricultural
Research Service (Neitsch et al., 2005). It is a physically based, distributed,
hydrological model that can operate on daily, monthly or annual time-steps, and can
be used to predict the impacts of land management practices on water, sediment and
agricultural chemicals in catchments (Cao et al., 2009).
The main driving force behind SWAT modelling is the hydrological cycle
which is divided into the land phase (controls the amount of water and sediment in
receiving waters) and the water routing phase (simulates movement of water and
sediment through the channel networks). The SWAT model delineates watersheds
into sub-basins interconnected by stream network and each sub-basin is further
divided into hydrologic response units (HRUs) based upon unique soil or land-use
characteristics (Somura et al., 2007). Flow and sediment loadings from each HRU in a
sub-basin are summed up and routed through channels to the watershed outlets
(Arnold et al., 2001).
The SWAT2005 model has the following characteristics (Neitsch et al., 2005):
a. It is physically based; requires specific information about weather, soil
properties, topography, vegetation and land management practices to directly
model physical processes such as water movement, sediment movement, etc.
b. It uses readily available inputs.
c. It is computationally efficient; it can simulate very large basins.
d. It enables users to study long-term impacts.
SWAT2005 has eight major components – hydrology, weather, sedimentation,
soil temperature, crop growth, nutrients, pesticides, and agricultural management
(Borah and Bera, 2003).
2.8 Theory of the SWAT Model
2.8.1 Overview
The SWAT model allows a number of different physical processes to be
simulated in a watershed (Neitsch et al., 2005). It has eight major components –
hydrology, weather, sedimentation/erosion, soil temperature, crop growth, nutrient,
pesticides, and agricultural management (Arnold and Fohrer, 2005). In SWAT, a
watershed is subdivided into sub-basins that are spatially related to one another, and,
further, into hydrological response units (HRUs), which are homogenous units that
posses unique land-use/land-cover and soil attributes and account for the complexity
of the landscapes within the sub-basins (Githui et al., 2009). Alternatively, a
watershed can be subdivided into only sub-watersheds that are characterized by
dominant land-use, soil type, and management (Gassman et al., 2007).
Water balance is the driving force behind everything that happens in a
watershed (Neitsch et al., 2005). The daily water budget in each HRU is computed
based on daily precipitation, runoff, evapotranspiration, percolation, and return flow
from the subsurface and groundwater flow (Borah and Bera, 2003). The hydrologic
cycle simulated by SWAT is based on the water balance equation (Setegn et al., 2008)
as shown in equation 3.
( )∑=
−−−−+=t
igwseepasurfday QWEQRSWSWt
1o ………………………..2.2
where:
SWt = final soil water content (mm water)
SWº = initial soil water content in day i (mm water)
t = time (days)
Rday = amount of precipitation in day i (mm water)
Qsurf = amount of surface runoff in day i (mm water)
Ea = amount of evapotranspiration in day i (mm water)
Wseep = amount of water entering the vadose zone from the soil profile in day i
(mm water)
Qgw = amount of return flow in day i(mm water)
Once the loadings (water and sediment) to the main channel are determined, they are
routed through the stream network of the watershed to obtain the total runoff and
sediment yield (Githui et al., 2009).
2.8.2 Hydrology
2.8.2.1 Surface Runoff
Surface runoff is the portion of the precipitation that makes its way towards
rivers, oceans, etc as surface flow (Garg, 2005). Runoff occurs only when the rate of
precipitation exceeds the rate at which water may infiltrate into the soil (Schwab et
al., 1993). As rain falls, a part of it is intercepted by vegetation and some is stored in
depressions on the ground surface, which later infiltrates into the soil or evaporates.
The amount of precipitation that will be absorbed by the soil will depend on the
prevailing soil moisture content at the time of precipitation. If the rain continues
further, water starts infiltrating into the soil, and when the rate of precipitation
exceeds the rate of infiltration, water starts ponding on the soil surface and results to
surface flows.
Surface runoff volume is computed using a modification of the SCS Curve
Number (CN) method or the Green and Ampt infiltration method. The USDA SCS
curve number equation is given as (USDA SCS, 1972):
( )( )SIR
IRQ
aday
adaysurf +−
−=
2
……………………………………………………2.3
where:
Qsurf = accumulated runoff/rainfall excess (mm)
Rday = rainfall depth for the day (mm)
Ia = initial abstractions which includes surface storage, interception and infiltration
prior
to runoff (mm)
S = retention parameter
and
−= 1010004.25
CNS …………………………………………………….2.4
where:
Ia = 0.2S
Therefore,
( )( )SR
SRQ
day
daysurf 8.0
2.0 2
−
−= ……………………………………………………2.5
The Green and Ampt equation was developed to predict infiltration assuming
excess water at the surface at all times (Green and Ampt, 1911). Their equation
assumes that the soil profile is homogeneous and antecedent moisture is uniformly
distributed in the profile. Mein and Larson (1973) developed a methodology for
determining ponding time with infiltration using the Green and Ampt equation as:
∆⋅+⋅=
t
vwfet F
Kfinf,
,inf 1θϕ
……………………………………………..2.6
where:
tf ,inf = infiltration rate at time t (mm/hr)
Ke = effective hydraulic conductivity (mm/hr)
wfϕ = wetting front matric potential (mm)
vθ∆ = change in volumetric moisture content across the wetting front (mm/mm)
tFinf, = cumulative infiltration rate at time t (mm)
Nearing et al. (1996) developed an equation to calculate the effective
hydraulic conductivity as a function of saturated hydraulic conductivity and curve
number. The equation is given as:
( ) 2062.0exp051.01
82.56 286.0
−×+
×=
CNKK sat
e ………………………………………2.7
where:
Ks= effective hydraulic conductivity (mm/hr)
Ksat= saturated hydraulic conductivity (mm/hr)
CN = curve number
Wetting front matric potential is calculated as a function of porosity, percent
sand and percent clay (Rawls and Brakensiek, 1985).
8.2...
00799.0
003479.00000136.0
001602.0006108.
049837.0000344.0
809479.3001583.032561.75309.6
exp10
2
22
222
2
2
××
−××−××
−××+××
+××−××+
×+×+×−
=
soils
soilccs
soilcsoils
soilscssoil
csoil
wf
M
MMM
MM
MMM
M
φ
φ
φφ
φφ
φ
ϕ
where:
soilφ = porosity of the soil (mm/mm)
Mc = percent clay content
Ms = percent sand content
( )soilv FCSW φθ +×
−=∆ 95.01 ……………………………………...…..2.9
where:
vθ∆ = change in volumetric moisture content across the wetting front (mm/mm)
SW = soil water content of the entire profile excluding the amount of water held in the
profile at wilting point (mm).
FC = amount of water in the soil profile at field capacity (mm)
soilφ = porosity of the soil (mm/mm)
PEAK RUNOFF RATE: The peak runoff rate is the maximum runoff flow rate that
occurs with a given rainfall event and indicates the erosive power of a storm (Neitsch
et al., 2005). The modified rational formula used to estimate peak flow rate is given as
(ibid):
conc
surftcpeak t
AreaQq
6.3××
=α
……………………………………………..2.10
where:
qpeak = peak runoff rate (m3/s)
tcα = fraction of daily rainfall that occurs during the time of concentration
Qsurf = surface runoff (mm)
Area = subbasin area (km2)
tconc = time of concentration for the subbasin (hr)
3.6 = unit conversion factor
In the beginning of a rainfall event, only a certain amount of runoff will reach
the watershed outlet, but after some time, the runoff from the entire watershed area
will reach the outlet. At this time, the runoff rate equals the rainfall rate and the time
required to reach this equilibrium condition is known as the time of concentration
(Garg, 2005). Michael and Ojha (2003) defined the time of concentration of a
watershed “as the time required for runoff water to flow from the most remote point
(in time of flow) of the watershed area to the outlet” (A to B,) as shown in Fig. 2.2.
Fig 2.2: Definition Sketch Illustrating Time of Concentration (Michael and Ojha, 2003)
Kirpich (1940) developed a method for computing time of concentration. It is given
as:
385.077.00195.0 −= gc SLT ………………………………………….……….2.11
where:
Tc = time of concentration (min)
B
A
B
L = maximum length of flow (m)
Sg = watershed gradient (m/m)
2.8.2.2 Evapotranspiration
Evapotranspiration includes evaporation from the plant canopy, transpiration
through the tissues of living plants, sublimation and evaporation from the soil
(Neitsch et al., 2005). Penman (1956) defined potential evapotranspiration (PET) as
“the amount of water transpired by a short green crop, completely shading the ground,
of uniform height and never short of water”. Numerous empirical methods have been
developed to estimate potential evapotranspiration and are based primarily on the
assumptions that the energy available for evaporation is proportional to temperature
(Schwab et a., 1993). Examples are the Penman-Monteith method (Monteith, 1965),
Priestly-Taylor method (Priestly and Taylor, 1972) and Hargreaves method
(Hargreaves et al., 1985). These three methods vary in the amount of required inputs
as shown in Table 2.1.
Table 2.1: Potential Evapotranspiration Methods and Their Required Inputs (Neitsch et al., 2005). S/N PET Methods Input(s) 1 Penman-Monteith Solar radiation, air temperature, relative humidity and
wind speed 2 Priestly-Taylor Solar radiation, air temperature, and relative humidity. 3 Hargreaves Air temperature
PENMAN-MONTEITH METHOD: The Penman-Monteith equation combines
components that account for energy needed to sustain evaporation, the strength of the
mechanism required to remove the water vapour and aerodynamic and surface
resistance terms (Neitsch et al., 2005). The Penman-Monteith equation is given as
(Monteith, 1965):
( ) [ ]
.1
++∆
−+−∆
=
a
e
a
zzpairnet
rr
reeCGH
Eγ
λ
o
l
……………………………………..2.12
where:
Eλ = latent heat flux density (MJ/m2/day)
E = depth rate evaporation (mm/day)
∆ = slope of the saturation vapour pressure temperature curve (kPa/ºC)
Hnet = net radiation (MJ/m2/day)
G = heat flux density to the ground (MJ/m2/day)
airl = air density (kg/m3)
Cp = specific heat at constant pressure (MJ/kg/ºC) o
ze = saturation vapour pressure of air at height z (kPa)
ze = water vapour pressure of air at height z (kPa)
γ = psychrometric constant (kPa/ºC)
re =plant canopy resistance (s/m)
ra = diffusion resistance of the air layer (s/m)
PRIESTLEY-TAYLOR METHOD: Priestley and Taylor (1972) developed a
simplified version of the combination equation for use under humid conditions. The
equation is given as:
( )GHE netpet −⋅+∆∆
⋅=γ
αλ o ……………………………………………2.13
where:
λ = latent heat of vaporisation (MJ/kg)
oE = potential evapotranspiration (mm/day)
petα = coefficient
∆ = slope of the saturation vapour pressure-temperature curve (kPa/ºC)
γ = psychrometric constant (kPa/ºC)
netH = net radiation (MJ/m2/day)
G = heat flux density to the ground (MJ/m2/day)
HARGREAVES METHOD: The Hargreaves equation which was originally derived
from eight years of cool-season Alta fescue grass lysimeter data from Davis,
California is given as (Hargreaves et al., 1985):
( ) ( )8.170023.0 5.0minmax +−⋅= avTTTHE ooλ ………………………………2.14
where:
λ = latent heat of vaporisation (MJ/kg)
oE = potential evapotranspiration (mm/day)
oH = extraterrestrial radiation (MJ/m2/day)
maxT = maximum air temperature for a given day (ºC)
minT = minimum air temperature for a given day (ºC)
avT = mean air temperature for a given day (ºC)
2.8.2.3 Soil Water
Soil water is essentially the soil moisture held in the pore spaces of the soil
mass and lying near enough to the surface and within the crop root zone (Michael and
Ojha, 2003). Water returns back to the earth surface through precipitation. One part of
this precipitation percolates into the ground, forming ground water reservoir; another
infiltrated part and before joining the watertable, joins the river channels (Garg,
2005).
PERCOLATION: This is the downward flow of water in saturated or nearly
saturated soil at hydraulic gradient of the order of 1.0 or less (Micheal and Ojha,
2003). Water is allowed to percolate if the water content exceeds the field capacity
water content for that layer and the layer below is not saturated (Neitsch et al., 2005).
They gave the equation for the calculation of the amount of water which percolates to
the next layer as:
∆−−=
percexcesslylyperc TT
tSWw exp1,, …………………………………….2.15
where:
lypercw , = amount of water percolating to the underlying soil layer on a given day
(mm)
excesslySW , = drainable volume of water in the soil layer on a given day (mm)
t∆ = length of the time step (hrs)
percTT = travel time for percolation (hrs)
LATERAL FLOW: During rainfall, rainwater percolates vertically until it
encounters an impermeable layer, and ponds above it forming a saturated zone of
water. This saturated zone is the source of water for lateral subsurface flow (Neitsch
et al., 2005). The SWAT model incorporates a kinematic wave storage model for
subsurface flow developed by Sloan et al. (1983) and summarised by Sloan and
Moore (1984). This kinematic storage model simulates flow in a two-dimensional
cross-section along a flow path down a steep hill slope (Fig. 2.3). The kinematic
wave approximation of lateral flow assumes that the lines of flow in the saturated
zone are parallel to the impermeable boundary and the hydraulic gradient equals the
slope of the bed.
Fig. 2.3: Behaviour of the Water Table as Assumed in the Kinematic Storage Model (Neitsch et al., 2005).
where:
Dperm = depth of permeable soil surface layer (mm)
Lhill= length of hillslope segment (mm)
αhill= angle of hillslope segment to the horizontal (º)
From Fig. 2.3, the drainable volume of water stored in the saturated zone of
the hillslope segment per unit area is calculated from the equation given by Neitsch et
al. (2005):
2
.1000,
hilldoexcessly
LHSW ⋅⋅=
φ …………………………………………..2.16
where:
excesslySW , = drainable volume of water stored in the saturated zone of the hillslope per
unit area (mm)
oH = saturated thickness normal to the hillslope length at the outlet expressed as a
fraction of the total thickness (mm/mm)
dφ = drainable porosity of the soil (mm/mm)
hillL = hillslope length (m)
1000 = factor needed to convert meters to millimetres
The drainable porosity of the soil layer is calculated from the equation:
fcsoild φφφ −= ………………………………………………………….2.17
Lhill
Dperm
Impermeable layer
αhill Ho Steady state water table Transient water table
where:
dφ = drainable porosity of the soil layer (mm/mm)
soilφ = total porosity of the soil layer (mm/mm)
fcφ = porosity of the soil layer filed with water when the layer is at field capacity
water content (mm/mm)
The net discharge at the hillslope outlet, Qlat is given as:
latlat VHQ o24= ……………………………………………...………….2.18
where:
latQ = water discharge from the hillslope outlet (mm/day)
oH = saturated thickness normal to the hillslope at the outlet expressed as a fraction
of the total thickness (mm/mm)
latV = velocity of flow at the outlet (mm/hr)
Equation for the velocity of flow at the outlet is given as:
( )hillsatlat SinKV α⋅= ………………………………………………….2.19
where:
= saturated hydraulic conductivity (mm/hr)
= slope of the hillslope segment
But
slpKV satlat ⋅= ………………………………………………………..2.20
slp = increase in elevation per unit distance
Therefore,
⋅
⋅⋅=
hilld
satexcesslylat L
slpKSWQ
φ,2
024.0 ……………………….…………2.21
BASE FLOW/GROUNDWATER FLOW: This is the water that percolates to the
ground watertable, and later after long times, joins the river system (Garg, 2005). An
aquifer is a geologic unit that can store enough water and transmit it at a rate fast
enough to be hydrological significant (Dingman, 1994). An unconfined aquifer is an
aquifer whose steady upper boundary is the water table and contributes return flow to
streams within the watershed while a confined aquifer is an aquifer bounded above
and below by geologic formations whose hydrologic conductivity is significantly
lower that that of the aquifer and contributes return flow to streams outside the
watershed (Arnold et al., 1993).
The steady-state response of groundwater flow to recharge is given as
(Hooghoudt, 1940):
wthlgw
satgw h
LKQ ⋅
⋅=
8000 …………………………………………………2.22
where;
gwQ = groundwater flow or base flow into the main channel on day I (mm)
satK = hydraulic conductivity of the aquifer (mm/day)
gwL = distance from the subbasin divide for the groundwater system to the main
channel (m)
wthlh = water table height (m)
2.8.3 Erosion
Water erosion is the detachment, transport, and deposition of soil particles by
the erosive forces of raindrops and surface flow of water (Schwab et al., 1993).
During rainfall, raindrop impact on unprotected soil surface detaches soil particles,
initiating transport of these particles by the action of flowing water to the rills. These
particles moves from smaller rills to larger rills, then into ephemeral channels, and
finally into continuously flowing rivers (Neitsch et al., 2005). Non-point erosion
refers to soil erosion from the land surface rather than from channels and gullies
(Schwab et al., 1993). Geologic erosion occurs without human influence while
accelerated erosion occurs when human activities increases erosion rate (Neitsch et
al., 2005). There is a direct relationship between total runoff and soil loss from
agricultural lands (Michael and Ojha, 2005)
Erosion caused by rainfall and runoff is computed with the Modified
Universal Soil Loss Equation (MUSLE) (Williams, 1995). MUSLE is a modified
version of the Universal Soil Loss Equation (USLE) developed by Wischmeier and
Smith (1978).
Modified Universal Soil Loss Equation (MUSLE): The Modified Universal Soil
Loss Equation is given as (Williams, 1995) is given as:
( ) CFRGLSPCKareaqQSed USLEUSLEUSLEUSLEhrupeaksurf ⋅⋅⋅⋅⋅⋅⋅= 56.08.11 ….2.23
where:
Sed = sediment yield on a given day (meric tons)
surfQ = surface runoff volume (mm/ha)
peakq = peak runoff rate (m3/s)
hruarea = area of the HRU (ha)
USLEK = USLE soil erodibility factor (0.013 metric ton m2hr/ (m3-metric ton cm)
USLEC = USLE cover and management factor
USLEP = USLE support practice factor
USLELS = USLE topographic factor
CFRG = coarse fragment factor
Soil erodibility factor is the soil loss rate per erosion index unit for a specified
soil as measured on a unit plot (Wischmeier and Smith, 1978) and is given as:
hisandorgcsiclcsandUSLE ffffK ⋅⋅⋅= − …………………………………………2.24
where:
csandf = a factor that gives low soil erodibility for soils with high coarse sand contents
and high values for soils with little sand.
siclf − = a factor that gives low soil erodibility factors for soils with high clay to silt
ratios.
orgcf = a factor that reduces soil erodibility for soil with high organic carbon content.
.hisandf = a factor that reduces soil erodibility for soils with extremely high sand
contents.
These factors are calculated from;
( )
28.2.......................................
10019.2251.5exp
1001
10017.0
1
27.2..........................................................95.272.3exp
25.01
26.2.........................................................................................
25.2.............................................100
10256.0exp3.02.0
3.0
−⋅+−+
−
−⋅−=
⋅−+
⋅−=
+
=
−⋅⋅−⋅+=
−
ss
s
hisand
orgc
siltc
siltsicl
siltscsand
MM
M
f
orgCorgCorgCf
MMMf
MMf
where:
Ms = percent sand content (0.05 – 2.00mm diameter particles)
Msilt = percent silt content (0.002 – 0.05 diameter particles)
Mc = percent clay content (< 0.002mm diameter particles)
OrgC = percent organic carbon content of the layer (%)
The USLE cover and management factor, CUSLE, is defined as the “ratio of soil
loss from land cropped under specified conditions to the corresponding loss from
clean tilled, continuous fallow (Wischmeier and Smith, 1978) and is given as:
( ) ( )[ ] [ ]
[ ]
+
⋅−⋅−=
mnUSLE
surfmnUSLEUSLE CLn
rsdCLnLnC
,
, 00115.0exp8.0exp ………….2,29
where:
CUSLE= minimum value for the cover and management factor for the land cover
rsdsurf = amount of residue on the soil surface (kg/ha)
The minimum factor C can be estimated from a known average annual C
factor using the equation below (Arnold and William, 1995):
[ ] 1034.0463.1 ,, += aaUSLEmnUSLE CLnC ……………………………….…2.30
where:
mnUSLEC , = minimum C factor for the land cover
aaUSLEC , = average annual C factor for the land cover
The support practice factor, PUSLE, is defined as the “ratio of soil loss with a
specific support practice to the corresponding loss with up-and-down slope culture
(Neitsch et al., 2005). Values for PUSLE, for contour tillage, strip cropping on the
contour and terrace systems are given in Tables 2.2, 2.3 and 2.4 respectively.
Table 2.2: P Factor Values and Slope Length Limits for Contouring (Wischmeier and Smith, 1978) Land slope (%) PUSLE Maximum length (m) 1 – 2 0.60 122 3 – 5 0.50 91 6 – 8 0.50 61 9 – 12 0.60 37 13 – 16 0.70 24 17 – 20 0.80 18 21 – 23 0.90 15
Table 2.3: P Factor Values, Max Strip Width and Slope Length Limits for Continuous Strip-cropping (Wischmeier and Smith, 1978) Land Slope (%)
PUSLE values Strip Width (m) Max. Length (m)
A B C 1 – 2 0.30 0.45 0.60 40 244 3 – 5 0.25 0.38 0.50 30 183 6 – 8 0.25 0.38 0.50 30 122 9 – 12 0.30 0.45 0.60 24 73 13 – 16 0.35 0.52 0.70 24 49 17 – 20 0.40 0.60 0.80 18 37 21 – 23 0.45 0.68 0.90 15 30 AFor 4-year rotation of row crop, small grain with meadow seeding, and 2 yrs of meadow. A second row crop can replace the small grain if meadow is established in it. BFor 4-yr rotation of 2 yrs row crop, winter grain with meadow seeding and 1-yr meadow. CFor alternate strips of row crop and winter grain
Table 2.4: P Factor Values for Contour-Farmed Terraced Fields (Wischmeier and Smith, 1978) Land slope (%)
Farm Planning Computing Sediment Yield2 Contour P Factor1
Stripcrop P Factor
Graded Channels Sod Outlet
Steep Backslope Underground Outlet
1 – 2 0.60 0.30 0.12 0.05 3 – 8 0.50 0.25 0.10 0.05 9 – 12 0.60 0.30 0.12 0.05 13 – 16 0.70 0.35 0.14 0.05 17 – 20 0.80 0.40 0.16 0.06 21 – 23 0.90 0.45 0.18 0.06 1Use these values for control on interterrace erosion within specified soil loss tolerances. 2These values include entrapment efficiency and are used for control of offsite sediment within limits and for estimating the field’s contribution to watershed sediment yield.
The topographic factor, LSUSLE, is the expected ratio of soil loss per unit area
from a field slope to that from a 22.1m length of uniform 9% slope under otherwise
identical conditions and is given as (Neitsch et al., 2005):
( )( )065.056.441.651.22
2 ++⋅
= hillhill
mhill
USLE SinSinLLS αα ……………..2.31
where;
hillL = slope length
m = exponential term
hillα = angle of the slope
The exponential term, m, is calculated from:
[ ]( )slpm ⋅−−= 835.35exp16.0 ………………………………………2.32
The relationship between hillα and slp is given as;
hillslp αtan= …………………………………………………………2.33
The coarse fragment factor is caudated from:
( )rockCFRG ⋅−= 053.0exp …………………………………………2.34
where:
Rock = % rock in the first soil layer (%)
UNIVERSAL SOIL LOSS EQUATION (USLE): The universal soil loss equation
(Williams, 1995) is:
CFRGLSPCKEISed USLEUSLEUSLEUSLEUSLE ⋅⋅⋅⋅⋅⋅= 292.1 …………..2.35
where:
Sed = sediment yield on a given day (metric tons/ha)
USLEEI = rainfall erosion index (0.017m-metric ton cm/m2hr)
USLEK = USLE soil erodibility factor (0.013 metric ton m3 hr/ (m3-metric ton cm)
USLEC = USLE cover and management factor
USLEP = USLE support practice factor
USLELS = USLE topographic factor
CFRG = coarse fragment factor
2.8.3.1 Sediment in Lateral and Base Flow
The amount of sediment contributed by lateral and groundwater flow is
calculated from (Neitsch et al., 2005):
( )
1000sedhrugwlat
lat
ConcareaQQSed
⋅⋅+= ………………………………..2.36
where:
latSed = sediment loading in lateral and groundwater flow (metric tons)
latQ = lateral flow for a given day (mm)
gwQ = groundwater flow for a given day (mm)
hruarea = area of the HRU (km2)
sedConc = concentration of sediment in lateral and groundwater flow (mg/L)
2.9 Geographic Information System (GIS) Interface
A second trend that has paralleled the historical development of the SWAT
model is the creation of various Geographic Information System (GIS) interface tools
to support the input of topographic, land-use, soil and other digital data into SWAT
(Gassman et al., 2007). GIS interfaces have been developed for SWAT using both
GRASS (Graphical Resources Analysis Support System) and ArcView (Arnold and
Fohrer, 2005). The GRASS input interface automatically subdivides a basin (grids or
sub-watersheds) and then extracts model input data from map layers and associated
relational databases for each sub-basin (Arnold and Fohrer, 2005). Input data are
collected and written to appropriate data files. By selecting a sub-basin from a GIS
map, maps and graph outputs can be displayed.
The ArcView-SWAT (AVSWAT) interface tool was designed to generate
model inputs from ArcView 3x GIS data layers and execute SWAT within the same
framework (Gassman et al., 2007). The SWAT ArcView system consists of three key
components (Diluzio et al., 1998):
i. Pre-processor generating sub-basin topographic parameters and
model input parameters.
ii. Editing input data and execute simulation.
iii. Postprocessor viewing graphical and tabular results.
The export of data from GIS to the SWAT model and the return of results for
display are accomplished by Avenue routines addressed directly by the interactive
tools of GIS (e.g. setting up parameter values via customized menus) and the
exchange of data is fully automatic (Arnold and Fohrer, 2005). The most recent
version of the interface is denoted AVSWAT-X, which provides additional input
generation functionality, including soil data input from both the USDA-NRCS State
Soils Geographic (STATSGO) and Soil Survey Geographic (SSURGO) databases for
applications of SWAT2005 (Gasman et al., 2007).
There is a substantial price tag on these current commercial GIS systems that
is currently used by SWAT (George and Leon, 2008). The Waterbase Project, a
project of the United Nations University with aim to advance the practice of
Integrated Water Resources Management (IWRM) in developing countries developed
a suitable, open source GIS (MapWindow) system that would support the generation
of SWAT input data (ibid). MWSWAT is an interface between SWAT and the open
source GIS MapWindow, and is specifically designed to use freely available GIS data
for anywhere in the world, as well local data when available (George and Leon,
2009).
A variety of other tools have been developed to support executions of SWAT
simulations, including (Gassman et al., 2007):
i) The interactive SWAT (i_SWAT) software which supports SWAT simulations
using a Window interface with an Access database.
ii) The Conservation Reserve Program (CRP) Decision Support System (CRP-
DSS).
iii) The AUTORUN system, which facilitates repeated SWAT simulations with
variations in selected parameters.
iv) A generic interface (iSWAT) program, which automates parameter selection
and aggregation for iterative SWAT calibration simulations.
2.10 Performance of SWAT on Hydrologic Studies
Hydrologic components of the SWAT model such as surface runoff,
evapotranspiration, recharge and stream flow have been validated at smaller scales
with the EPIC, GLEAMS and SWRRB models. Interactions between surface flow and
subsurface flow in SWAT are based on a linked surface-subsurface flow model
developed by Arnold et al. (1993). Current reach and reservoir routing techniques are
based on the ROTO approach, which was developed to estimate flow and sediment
yields in large basins using sub area inputs from the SWRRB model (Arnold, 1995).
Configuration of routing schemes in SWAT is based on the approach given by Arnold
et al. (1994).
Surface runoff, evapotranspiration and streamflow components of SWAT have
been refined and validated at larger scales including a U.S. national assessment of
streamflow and evapotranspiration (Arnold et al., 1998). Measured data from Illinois
watersheds were used to successfully validate surface runoff, groundwater flows, ET
in the soil profile, ground water recharge and groundwater height parameters (Arnold
and Allen, 1996). Arnold et al., (2000) compared groundwater recharge and discharge
(baseflow) results from SWAT to filtered estimates for the 491,700km2 Upper
Mississippi River Basin. SWAT’s capability to predict surface and subsurface flow
for a 33.4km2 watershed in Maryland, United State was evaluated by Chu and
Shirmohammadi (2004). Their result showed that SWAT was not able to simulate an
extremely wet year, but with the wet year removed, the surface runoff, base flow, and
streamflow results were within acceptable accuracy on a monthly basis. The authors
then used the corrected base flow result to improve the subsurface flow result.
Rosenthal et al. (1995) using SWAT and with no calibration simulated 10
years of monthly stream flow, and observed that SWAT underestimated the extreme
events, though their results showed R2 = 0.75. Rosenthal and Hoffman (1999)
successfully used SWAT to simulate flows and sediment loadings on a 9,000 km2
watershed in central Texas to locate potential water quality monitoring sites.
Srinivasan et al., (1998) validated SWAT for streamflow and sediment loads for the
Mill Creek watershed in Texas for 1965 – 1968 and 1968 – 1975. Their results
showed monthly streamflow rates were well predicted but the SWAT model
overestimated streamflow in a few years during the spring/summer months. However,
they noted that the SWAT model predicted soil erosion and sediment transport
satisfactorily considering the model’s limitations.
As part of the HUMUS (Hydrologic Unit Model for the United States) project,
Arnold, (1999) validated annual runoff and ET rates across the entire continental
United States. Bingner (1996) simulated runoff for 10 years for a watershed in
Northern Mississippi, and observed that the SWAT model produced reasonable
results in the predicted daily and annual runoffs from multiple sub-basins, with the
exception of wooded sub-basins.
Arnold (1999) interfaced SWAT with a GIS to evaluate streamflow and
sediment yield data in the Texas Gulf Basin with drainage areas ranging from
10,000km2 to 110,000 km2 and observed that simulated sediment yield agreed
reasonably well considering inputs uncertainties, sampling errors and model
assumptions. Mapfumo et al. (2004) tested the SWAT’s ability to simulate soil-water
patterns in small watersheds under three grazing intensities in Alberta, Canada. These
authors observed that SWAT had a tendency to over predict soil-water in dry
conditions and to under predict it in wet conditions. However, they concluded that the
model was adequate in simulating soil-water patterns for all three watersheds within a
daily time step.
2.11 Performance of SWAT on Sediment Studies
Comparisons of SWRRB–ROTO sediment output for three watersheds in
Texas compared favourably with measured data as (Arnold, 1995). Sediment loss
predictions using SWAT were further tested in nine watersheds in Texas (Arnold,
1999), New York (Benaman and Shoemaker, 2005) and India (Tripathi et al., 2004).
Though these studies varied in watershed sizes, interval and duration of measured
sediment loss, validation criteria and other factors, these authors all noted that the
SWAT sediment predictions agreed with measured values.
SWAT was interfaced with HUMUS to conduct a national research on the
effect of management scenarios of water quality and quantity (Jayakrishnan et al.,
2005). A study (Kirsch et al., 2002) using SWAT showed that implementation of
improved tillage practices can reduce sediment yields by almost 20% in Rock River in
Wisconsin. Also, SWAT has been applied in Indian watersheds: identification of
critical or priority areas for soil and water management in a watershed (Kaur et al.,
2004) and the impact of different tillage systems on sediment losses (Tripathi et al.,
2005).
This wide range of SWAT applications has underscored the model’s flexibility
and robustness in studying watershed’s runoff and sediment transport processes, even
thought some studies have shown the low performance capability of the tool in some
areas, especially when comparing predicted streamflow and sediment yield with
measured data. Model users have addressed the tool weaknesses by modifying its
components to accurately predict specific hydrologic processes. This trend is likely to
continue in the future in other to respond to the needs of the growing user population
and also to provide prediction accuracy.
2.12 SWAT Model: Advantages and Disadvantages
Some of the key features that made the SWAT model applicable for a wide
range of studies area:
a) It can realistically represent the spatial variability of catchment characteristics
(Mishra et al., 2007).
b) It is characterized by its availability (i.e. it is an open source model) and user-
friendliness in handling input data (Xu et al., 2009).
c) It is an operational model that assists water resource managers in assessing
water supplies and non-point source pollution on large river basins (Arnold
and Fohrer, 2005).
d) Watersheds with no monitoring data (e.g. stream gauge data) can be modelled
with SWAT i.e. SWAT can be applied to a large ungauged basin (Neitsch et
al., 2005).
e) It has reduced the laborious and tedious process involved in evaluating land
management impacts on water resources as a result of human activities on the
watershed. It is capable of modelling catchment areas varying between few
hectares to thousand of sq. km.
f) Physical based hydrological models (e.g. SWAT) assist water managers to
identify the most vulnerable erosion-prone areas of a catchment and select
appropriate management practices (Xu et al., 2009).
The SWAT model limitations are as follows;
a) Accuracy of the predicted impacts on water resources as a result of land-use
changes on a watershed depends highly on the use of local data as inputs
(George and Leon, 2009).
b) For accurate results, meteorological stations should be situated within and
around the watershed area. Due to the heterogeneity of watersheds, a number
of meteorological stations are required to represent the spatial variation in the
hydro-meteorological characteristics of the area.
c) Where local spatial data sets are lacking, the only alternative is to obtain such
online and this requires accessibility to the internet.
d) The SWAT model requires a GIS to display, analyze and represent the spatial
variations in catchment characteristics. Most GIS have high price tagged on
them and are expensive.
e) Real time land-use maps, one of the basic inputs to the SWAT can only be
extracted through remote sensing as satellite imageries and processed using
digital image processing technique. However, the acquisition of satellite
imageries is expensive and also the expertise required for the image
interpretation is another major limitation in model’s application in developing
countries.
CHAPTER THREE
METHODOLOGY
3.1 The Study Area
The study area is the River Ebonyi watershed (3,765km2) located on the
western border of the Cross River Plains, headed by the Udi-Nsukka Escarpment
(Fig.3.1). Geographically, the River Ebonyi catchment is situated between latitudes
5º78'N and 6º50'N and longitudes 7 º47'E and 8 º00'E. It is situated in the transition
zone between the Guinea-Congolian wetter-type forest and Guinea savannah eco-
climatological zones with elevation ranging between 105m and 565m above sea level
(Campling et al., 2002). The mean annual temperature is 23ºC and the mean annual
rainfall is 1577mm with the peak rainfall period between mid-August and mid-
September (Campling et al., 2000).
Campling et al. (2002) reported that two major landforms are found within the
catchment: (1) the sandstone escarpment of the Udi-Nsukka Cuesta, which is
characterised by deeply weathered aerosols with low water-holding capacity and high
hydraulic conductivity, and (2) the shale peneplains of the Cross River Plains. Major
ravines are found near the sandstone escarpment in association with spring lines and
excessive surface runoff caused from tarmac roads (Gobin et al., 1999). The upper
part of the catchment has a rolling to undulating topography, intersected by incised
streams that flow to narrow valley bottom while the lower part has a flatter
topography, with extensive areas of waterlogging in the wet season and a wide valley
bottom (Campling et al., 2002).The basin has a rural set-up and is used extensively for
agriculture (Agbo, 1991).
Fig 3.1: Location of Ebonyi River Watershed in Nigeria
3.2 Model Inputs
The spatially distributed data sets (GIS input) needed for the MWSWAT
interface include the digital elevation model (DEM), land-use, soil and historical local
weather data.
3.2.1 Digital Elevation Map (DEM)
Topography was defined by a DEM that describes the elevation at any point in
a given area at a specific spatial resolution. DEM was derived using the NASA 90-m
Shuttle Radar Topographic Mission (SRTM) dataset, version 4 (SRTM, 2004).
Subbasin parameters such as slope gradient, slope length of the terrain and the stream
network characteristics were derived from the DEM. The DEM (Fig. 3.2) has a
resolution of 90m and was provided in mosaic 5 deg × 5 deg tiles. The DEM was used
to delineate the watershed into 29 sub-basins using the TauDEM (Terrain Analysis
Using Digital Elevation Model) software as shown in Fig. 3.3.
Fig. 3.2: DEM Data for Ebonyi River Watershed
As the DEM covered a larger area in which part of it was not required for the
modelling work, a mask was created for the study area so that only the masked
portion of the DEM will be used for the modelling study.
Fig. 3.3: Delineated Ebonyi River Watershed
3.2.2 Land-Use
This is one of the most important factors that affect runoff and soil erosion in a
watershed. Digital land-use map of the study area (Fig. 3.4) was obtained from the
University of Maryland, Global Land Cover Classification (Hensen et al., 1998) at
1km spatial resolution. SWAT calculated the area covered by each land-use. The
different land-use/cover types within the study area are presented in Table 3.1.
Parameters values for the different land-use types are shown in Appendix 1.
Parameters values for residential land-use type are shown in Appendix 2.
Fig. 3.4: Digital Land-Use Map of Ebonyi River Watershed Table 3.1: Distribution of Land-Use Types in Ebonyi River Watershed. Land-use Types Area (ha) Watershed (%) Savannah (SAVA) 350234.78 93.03 Cropland/woodland mosaic (CRWO) 4745.16 1.26 Evergreen broadleaf forest (FOEB) 163.48 0.04 Dry land, cropland and pasture (CRDY) 20882.27 5.55 Residential medium density (URMD) 362.54 0.10
3.2.3 Soil Data
SWAT model requires different soil textural and physico-chemical properties
such as soil texture, available water content, hydraulic conductivity, bulk density,
sand percent, silt percent, clay percent and organic content carbon content for the
different layers of each soil type. The soil map of the study area (Fig. 3.5) was
obtained from the Digital Soil Map of the World and Derived Soil properties CD-
ROM (FAO, 2003) at a scale of 1:5000000. Major soil types in the study area are
shown in Table 3.2 and their parameter value are presented in Appendix 3.
Fig. 3.5: Digital Soil Map of Ebonyi River Watershed
Table 3.2: Distribution of Soil Types in Ebonyi River Watershed Soil Types Area (ha) Watershed (%) Gd16-2-3a-1201 2103.22 0.56 Nd5-1a-1567 22039.34 5.85 Nd16-2-3a-1553 341376.36 90.68 Ap15-1a-1068 10782.07 2.86 Bf6-1105 87.25 0.02 3.2.4 Weather Data
SWAT requires daily meteorological data that can either be read from a
measured data set or generated by a weather generator model. The weather data used
in this study for driving the hydrological cycle are daily values of rainfall, and
maximum and minimum temperature for the period 1973 – 1982. These data were
obtained from University of Nigeria, Nsukka (UNN) Agrometeorological Service
(6º87'N, 7 º43'E, 442m) and is presented in Appendix 4. Most of the daily data were
however missing. For this study, a weather generator file which contains the statistical
data needed to generate representative daily climate data for the watershed was
developed and used to generate missing temperature and rainfall data and also to
simulate other weather parameters such as relative humidity, solar radiation and wind
speed. However, the unavailability of breakpoint (30 min) rainfall data, which will be
needed to prepare this weather generator file, limited the use of 20 years climate
record to prepare this file as stated in the SWAT User Manual. Thus, one year of
climate record was used to create a weather generator file is presented in Appendix 5.
3.3 MapWindow-SWAT (MWSWAT) Model Setup
The Soil and Water Assessment Tool (SWAT 2005) was interfaced with
MapWindow GIS version 4.7.5. This model setup is based around three basic steps.
i) Watershed delineation
ii) Hydrologic response unit (HRU) definition
iii) SWAT setup and run
The required spatial datasets (DEM, land-use and soil) were projected to the
same projection called UTM zone 32N using MapWindow GIS, which is the
transverse mercator projection parameters for Nigeria using MWSWAT. The DEM
was used to delineate the watershed into several hydrologically connected sub-
watersheds. The watershed shapefile was created and used as a mask which was
superimposed on the DEM. The MWSWAT interface used only the masked area for
stream delineation and helped reduced the processing time of GIS functions. The
initial stream network was defined based on drainage area threshold approach. An
output was manually added where model outputs were later measured. In this study,
an initial threshold area (the minimum drainage area required to form the origin of a
stream) of 100km2 was used to create 29 sub-basins with one outlet as shown in
Figure 3.3.
The land-use and soil map in a projected grid format were loaded into the
MWSWAT interface to determine the area of each land-soil category simulated
within each sub-watershed. These maps were then related to the SWAT land-use and
soil classes in the SWAT database. An intermediate slope band of 5% was selected
and the DEM was used for slope classification. In this study, HRUs were formed
based on a particular combination of land-use, soil and slope. A minimal percentage
of 5% was selected as the threshold level for HRU definition which resulted to 96
HRUs in the whole basin. Land-uses, soils and slope that cover a percentage of the
subbasin area less than the threshold level were eliminated and reapportioned so that
100% of the land area in the subbasin was modelled. The threshold level is a function
of the project goal and the amount of detail required. Land-use types remaining in the
study area after setting the threshold level is shown in Table 3.3. The Soil and Water
Assessment (SWAT) tool uses the Hydrological Response Units (HRUs) as the basis
for its modelling HRUs enable the model to reflect differences in ET and other
hydrologic conditions for different land-uses and soils. Runoff and sediment yield
were estimated separately for each HRU and routed to obtain total runoff and
sediment yield for the watershed.
Table 3.3: Distribution of Land-Use Type for Scenario 0. Land-use Types Area (ha) Watershed (%) Savanna (SAVA) 353800.04 93.86 Cropland/woodland mosaic (CRWO) 3124.06 0.83 Dry land, cropland and pasture (CRDY) 19540.37 5.17 Evergreen broadleaf forest (FOEB) 163.48 0.04 Residential medium density (URMD) 370.98 0.10 From Table 3.3, most portion of the watershed is covered by savanna, which accounts
for 93.86% of the watershed area. URMD and FOEB land-use types were eliminated
as they fall below the threshold level of 5% but were exempted during HRU
definition.
Ten years of daily rainfall and minimum and maximum temperature data was
prepared in Notepad and read into the SWAT model. The weather generator
component of the SWAT model used the weather generator file to generate other
weather parameters (solar radiation, relative humidity, wind speed) and also to fill
missing climatic data.
3.4 Calculation Methods
In this study, the Soil Conservation Society (SCS) curve number method was
used to estimate surface runoff. Penman-Monteith method was used for the estimation
of potential evapotranspiration. Peak runoff rate was computed using a modification
to the Rational formular. Modified Universal Soil Loss Equation (MUSLE) was used
to compute sediment yield, the Green-Ampt equation was used to estimate infiltration
rate. Streamflow was routed using the variable storage method.
3.5 Land-use Scenarios
The ‘Split Landuse’ option of the SWAT model can be used to define more
precise land-uses than the land-use map provides and affect how HRUs are defined.
The Savanna land-use type which covers 93.86% of the watershed was split into
Grassland (GRSS) and Agriculture-Row Crop (AGRR). Land-use scenarios were
created from the split Savanna land-use type for different percentage combinations of
grassland and agriculture as shown in Table 3.4. Watershed area in percent for the
different land-use types for all scenarios is shown in Table 3.5.
Table 3.4: Land-Use Scenarios Scenario Grassland (GRSS) Agriculture-Row Crops (AGRR) 1 99.9% 0.1% 2 80% 20% 3 60% 80% 4 40% 60% 5 20% 80% 6 0.1% 99.9%
Table 3.5: Areal Coverage of Land-Use Types for Different Scenarios Scenario SAVA CRWO CRDY FOEB URMD
0 93.86% 0.83% 5.17% 0.04% 0.10% GRSS AGRR 0.83% 5.17% 0.04% 0.10%
1 93.76% 0.09% 0.83% 5.17% 0.04% 0.10% 2 75.08% 18.77% 0.83% 5.17% 0.04% 0.10% 3 56.31% 37.54% 0.83% 5.17% 0.04% 0.10% 4 37.54% 56.31% 0.83% 5.17% 0.04% 0.10% 5 18.77% 75.08% 0.83% 5.17% 0.04% 0.10% 6 0.09% 93.76% 0.83% 5.17% 0.04% 0.10%
These scenarios were not determined from realistic location factors, and as
such are not plausible and should not be seen as future projections. Herein, the
emphasis was on expansion of agriculture (row crop). In each of the sub-basins
already delineated, new HRUs representing the scenarios were determined. Sample
outputs of the distribution of HRUs within the sub-basins for Scenarios 0,1,2,3,4,5,
and 6 are presented in Appendices 6,7,8,9,10,11, and 12. Other model inputs (DEM,
soil and weather data) were kept constant while varying land-use scenarios. Model
experimental run was performed using Scenarios 1 – 6, 164 HRUs were created for
each simulation, and streamflow and sediment discharge were predicted for one year
at a daily time step at the watershed outlet.
CHAPTER FOUR
RESULTS AND DISCUSSION
4.1 Simulated Streamflow and Sediment Discharge
The watershed delineation and HRU definition of River Ebonyi Basin for
Scenario 0 resulted in 29 subbasins and 96 HRUs. Results for the control simulation
(Scenario 0) processed for average weekly streamflow and sediment discharge are
shown in Figures 4.1 and 4.2, respectively, and in Table 4.1.
Fig. 4.1: Simulated Average Weekly Streamflow for Scenario 0
Fig. 4.2: Simulated Average Weekly Sediment Discharge for Scenario 0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 10 20 30 40 50 60Time (Week)
Avera
ge W
eekly
Sedim
ent D
ischa
rge (
tonne
s)
0
50
100
150
200
250
300
0 10 20 30 40 50 60
Time (week)
Avera
ge w
eekly
Strea
mflow
(m3 /s)
Table 4.1: Average Weekly Streamflow and Sediment Discharge for Control Simulation Week Streamflow (m3/s) Sediment Discharge (tonnes)
1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 10 0 0 11 0 0 12 0 0 13 0 0 14 0 0 15 0.08 0.029 16 0 0 17 0.00051 0.000011 18 0 0 19 1.83 7.54 20 249.16 4359.5 21 55.11 524.24 22 23.34 148.43 23 0 0 24 26.31 294.15 25 102.86 1399.47 26 9.36 23.19 27 0 0 28 2.03 8.3 29 39.3 351.18 30 8.99 52.16 31 2.6 9.09 32 0 0 33 3.39 12.43 34 0.1 0.14 35 0.35 0.56 36 5.09 31.59 37 205.21 3527.72 38 150.77 2130.06 39 131.07 1675.64 40 0 0 41 1.95 6.29 42 146 2063.85 43 43.96 373.1 44 59.37 790.01 45 0 0 46 0 0 47 0 0 48 0 0 49 0 0 50 0 0 51 0 0 52 0 0
4.2 Land-use Scenarios
Similarly, results for the land-use change simulations for Scenarios 1 to 6,
processed for average weekly streamflow and sediment discharge are shown in Table
4.2 and 4.3 respectively, and plotted in Figures 4.3 and 4.4.
Table 4.2: Average Weekly Streamflow for Land-Use Scenarios 1 to 6 Week Scenarios
1 2 3 4 5 6 1 0 0 0 0 0 0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 6 0 0 0 0 0 0 7 0 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 0 10 0 0 0 0 0 0 11 0 0 0 0 0 0 12 0 0 0 0 0 0 13 0 0 0 0 0 0 14 0 0 0 0 0 0 15 0.16 0.66 1.29 1.89 2.43 2.91 16 0.00026 0.053 0.19 0.32 0.49 0.66 17 0.0086 0.068 0.19 0.28 0.38 0.46 18 0 0 0 0 0 0 19 4.31 8.28 12.1 15.81 19.29 22.45 20 283.99 312.64 337.79 358.38 374.35 386.33 21 62.99 64.98 67.7 72.23 78.93 87.25 22 26.14 24.17 24.78 27.38 31.94 38.32 23 0 0 0 0 0 0 24 34.63 46.88 56.77 64.24 69.56 72.77 25 118.38 122.62 129.02 137.55 148.07 160.37 26 7.96 6.88 5.9 4.94 4.05 3.26 27 0 0.0061 0.032 0.071 0.12 0.15 28 4.28 7.56 10.21 12.27 13.85 14.97 29 50.85 58.72 66.9 75.12 83.18 91.33 30 11.85 16.01 19.44 22.2 24.45 26.32 31 3.48 3.05 3.39 4.31 5.78 7.6 32 0 0 0 0 0 0 33 7.11 12.55 17.31 21.57 25.35 28.64 34 0.22 0.11 0.3 0.63 1.11 1.78 35 0.65 2.14 3.36 4.43 5.39 6.36 36 9.07 13.56 17.86 21.66 25.15 28.68 37 236.89 262.19 284.13 301.68 314.69 323.78 38 169.29 172.66 179.05 189.41 203.81 221.45 39 147.8 164.06 177.62 188.46 196.82 202.77 40 0 0 0 0.0045 0.024 0.047 41 4.31 9.72 14.55 18.85 22.6 25.98 42 170.81 185.04 199.63 214.26 228.89 243.31 43 55.49 64.6 72.75 81.09 89.21 96.98 44 66.8 74.49 81 85.99 89.42 91.71 45 0 0 0 0 0 0
46 0 0 0 0 0 0 47 0 0 0 0 0 0 48 0 0 0 0 0 0 49 0 0 0 0 0 0 50 0 0 0 0 0 0 51 0 0 0 0 0 0 52 0 0 0 0 0 0 Table 4.3: Average Weekly Sediment Discharge for Land-Use Scenarios 1 to 6
Week Scenario 1 2 3 4 5 6
1 0 0 0 0 0 0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 6 0 0 0 0 0 0 7 0 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 0 10 0 0 0 0 0 0 11 0 0 0 0 0 0 12 0 0 0 0 0 0 13 0 0 0 0 0 0 14 0 0 0 0 0 0 15 0.15 1.41 3.71 6.21 8.94 11.68 16 0.0000039 0.052 0.32 0.63 1.27 1.78 17 0.002 0.057 0.33 0.49 0.87 1.09 18 0 0 0 0 0 0 19 24.37 60.03 95.94 132.91 167.16 199.29 20 5153.04 5891.51 6539.33 7033.77 7377.13 7561.33 21 688.78 836.2 971.19 1086.05 1181.4 1264.97 22 186.14 170.87 181.09 210.98 264.62 340.97 23 0 0 0 0 0 0 24 416.81 588.83 737.24 848.58 917.33 949.56 25 1706.93 1907.05 2124.24 2304.33 2475.64 2631.09 26 19.63 13.81 8.94 8.01 8.13 6.52 27 0 0.0016 0.02 0.063 0.12 0.21 28 25.63 57.46 86.85 111.64 130.33 146.09 29 493.52 632.15 762.57 875.31 970.27 1046.15 30 86.46 141.41 189.1 229.83 258.26 275.48 31 15 12.39 14.47 20.62 31.26 45.95 32 0 0 0 0 0 0 33 35.34 78.43 118.44 155.92 191.06 228.13 34 0.36 0.17 0.56 1.61 3.75 7.54 35 1.67 9.77 17.8 26.33 34.43 41.06 36 65.26 115.48 164.16 206.59 240.64 274.92 37 4252.24 4948.89 5526.99 5968.2 6238.94 6384.14 38 2513.34 2749.45 2980.76 3205.84 3435.41 3681.26 39 2050.93 2507.24 2879.62 3135.47 3292.89 3347.35 40 0 0 0 0.00096 0.015 0.033 41 19.83 58.31 97.47 134.47 169.14 199.43 42 2567.69 2982.33 3383.17 3708.54 3970.82 4160.01 43 535.96 697.45 854.56 993.06 1111.62 1212.33 44 948.42 1154.52 1315 1436.06 1487.31 1497.32 45 0 0 0 0 0 0 46 0 0 0 0 0 0
47 0 0 0 0 0 0 48 0 0 0 0 0 0 49 0 0 0 0 0 0 50 0 0 0 0 0 0 51 0 0 0 0 0 0 52 0 0 0 0 0 0 Fig. 4.3: Average Weekly Streamflow for Land-Use Scenarios 1 to 6 Fig. 4.4: Average Weekly Sediment Discharge for Land-Use Scenarios 1 to 6
0
1000
2000
3000
4000
5000
6000
7000
8000
0 10 20 30 40 50 60Time (Week)
Aver
age
Wee
kly
Sedi
men
t Dis
char
ge (t
onne
s)
SCE 1SCE 2SCE 3SCE 4SCE 5SCE 6
0
50
100
150
200
250
300
350
400
450
0 10 20 30 40 50 60Time (week)
Ave
rage
Wee
kly
Stre
amflo
w (m
3 /s)
SCE 1SCE 2SCE 3SCE 4SCE 5SCE 6
4.3 Discussion
In order to evaluate the impacts of the land-use change scenarios, their digital
land-use maps were imported into SWAT with the same set of DEM, soil and weather
data. These results were compared to the results from the control or no-change
simulation. Figures 4.3 and 4.4 show that all scenarios follow the same pattern with
increases in streamflow and sediment discharge as more of the watershed area is
allocated to row-crop agriculture. Average daily streamflow and sediment discharge
for all scenarios are shown in Table 4.4 and plotted in Figures 4.5 and 4.6
respectively.
Table 4.4: Average Daily Streamflow and Sediment Discharge for all Scenarios Scenario Streamflow (m3/s) Sediment Discharge (tonnes) 0 24.32 341.31 1 28.34 418.22 2 31.33 491.29 3 34.20 557.20 4 36.92 610.66 5 39.49 651.46 6 41.93 680.98
Fig. 4.5: Average Daily Streamflow for Scenarios 0 to 6
0
5
10
15
20
25
30
35
40
45
Sce 0 Sce 1 Sce 2 Sce 3 Sce 4 Sce 5 Sce 6Scenario
Avera
ge D
aily S
tream
flow(
m3 /s)
Fig. 4.6: Average Daily Sediment Discharge for Scenarios 0 to 6
Comparisons between simulated average daily streamflow rates from the
control land-use scenario and that from the six land-use change scenarios show that
the average daily streamflow increased by 16.53% for scenario 1, 28.82% for scenario
2, 40.63% for scenario 3, 51.81% for scenario 4, 62.38% for scenario 5 and 72.41%
for scenario 6. Consequently, average daily sediment discharge increased by 22.53%
for scenario 1, 43.94% for scenario 2, 63.25% for scenario 3, 78.92% for scenario 4,
90.87% for scenario 5 and 99.52% for scenario 6.
Expectedly, scenario 6 yielded the most streamflow and sediment, since
streamflow and sediment discharge increases as more of the watershed area is alloted
to row-crop agriculture. These increases were observed to be higher during the wet
season and lower during the dry season of the year. Removal of vegetative cover,
especially grass, would generally increase average surface runoff. Row-crop
agriculture is also known to leave the soil bare for a longer period of time, particularly
during periods of crop planting and early growth stages. Therefore, as more of the
watershed is converted to row-crop agriculture, the rates of overland flow increase
leading to higher runoff rates. This results to increased detachment and transportation
0
100
200
300
400
500
600
700
800
Sce 0 Sce 1 Sce 2 Sce 3 Sce 4 Sce 5 Sce 6
Scenarios
Aver
age D
aily S
eidme
nt D
ischa
rge (
tonn
es)
of sediments. Also, ridging which characterizes row-crop agriculture creates a very
loose and porous upper soil condition which facilitates soil erosion.
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
5.1 Conclusion
The need to provide food and shelter to the ever increasing world population
has led to alterations in our natural watershed systems, affecting the hydrologic
balance. The capability of MapWindow GIS in using freely available global, spatial
data (DEM, land-use and soil) from online sources, and interfaced with the Soil and
Water Assessment Tool has been utilized in this study to evaluate the impacts of land-
use change on streamflow and sediment discharge from the Ebonyi River watershed.
Digital elevation, land-use and soil maps of the study area from global
databases and historical weather data measured locally were inputted into the model
and a one year simulation run in daily time steps performed. Simulated average daily
streamflow and sediment discharge are 24.32m3/s and 341.31tonnes respectively for
the control simulation. The major land-use of the study area (savanna) was then
altered to grassland and row-crop agriculture to create six land-use change scenarios
representing different combinations of decreasing grassland (93.76% - 0.09%) and
increasing agricultural land (0.09% - 93.76%). Other model inputs were kept constant
and experimental runs were performed to obtain streamflow and sediment discharge
for one year in daily time steps.
Average daily simulated streamflow and sediment discharge increased by 29%
and 44% respectively as about 19% of the watershed is allocated to row-crop
agriculture. A further increase by 72% and 99% for streamflow and sediment
discharge, respectively, was obtained as the agricultural area increased to 94% of the
watershed area. Therefore, with DEM, climatic and soil inputs held constant,
conversion of watershed area from grassland to row-crop agricultural land has
accounted for a major difference in streamflow and sediment discharge from the
Ebonyi River watershed.
5.2 Recommendation
It is recommended that further research in this area should be conducted with
fine resolution spatial data sets which will improve spatial accuracy. The coarse/poor
resolution data sets used in this study were the only ones available from global data
bases.
Conduct of similar research should be done with rainfall being spatially
represented within the study area. In this study, rainfall was not spatially represented
as only one weather station outside the study area was used. Also, unavailability of
breakpoint rainfall data limited the use of only one year of weather record to calculate
parameters in the weather generator file instead of twenty years, as recommended for
the SWAT model.
Use of measured data to validate the SWAT model is highly recommended
and would be a major contribution to watershed management in Nigeria.
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APPENDICES
Land-Use Scenarios and SWAT Model Input Data for Experimental Runs
APPENDIX 1 Land Cover Parameter Values for Different Land-Use Types
PARAMETERS SAVA GRSS AGRR CRWO CRDY FOEB Biomass-energy ratio (MJ/m2) 34 34 39 24.25 34.25 15 Harvest index for optimal growing condition 0.89999 0.89999 0.5 0.61 0.68 0.75999 Maximum potential leaf area index 2 2.5 3 4 3.5 5 Fraction of the plant growing season 0.05 0.05 0.15 0.1 0.1 0.15 Fraction of maximum leaf area index corresponding to the 1st point on the optimal leaf development curve
0.1 0.1 0.05 0.05 0.05 0.69999
Fraction of the plant growing season 0.25 0.25 0.5 0.449999 0.5 0.25 Fraction of maximum leaf area index corresponding to the 2nd point on the optimal leaf development curve
0.69999 0.69999 0.95 0.94999 0.94999 0.99
Fraction of growing season when leaf area begins to decline 0.34999 0.34999 0.7 0.81999 0.81999 0.99 Maximum canopy height (m) 1 1 2.5 3.5 0.75 10 Maximum root depth (m) 2 2 2 2.75 2 3.5 Optimal temperature for plant growth (ºC) 25 25 25 30 27.5 30 Minimum temperature for plant growth (ºC) 12 12 8 10.5 11.5 0 Normal fraction of Nitrogen in yield (kg N/kg yield) 0.016 0.016 0.014 0.0107 0.0217 0.0015 Normal fraction of Phosphorus in yield (kg P/kg yield) 0.00219 0.00219 0.0016 0.00179 0.00329 0.003 Normal fraction of Nitrogen in plant biomass at emergence (kg N/kg biomass) 0.01999 0.01999 0.047 0.025 0.052 0.006 Normal fraction of Nitrogen in plant biomass at 50% maturity (kg N/kg biomass)
0.012 0.012 0.0177 0.0092 0.01979 0.002
Normal fraction of Nitrogen in plant biomass at maturity (kg N/kg biomass) 0.00499 0.00499 0.0138 0.00719 0.0131 0.0015 Normal fraction of Phosphorus in plant biomass at emergence (kg P/kg biomass)
0.00139 0.00139 0.0048 0.0034 0.00719 0.00069
Normal fraction of Phosphorus in plant biomass at 50% maturity (kg P/kg biomass)
0.001 0.001 0.0018 0.0013 0.0027 0.00039
Normal fraction of Phosphorus in plant biomass at maturity (kg P/kg biomass) 0.00069 0.00069 0.0014 0.00109 0.0019 0.0003 Lower limit of harvest index 0.89999 0.89999 0.3 0.12999 0.57499 0.6
Minimum value of USLE C factor for water erosion applicable to the land cover/plant
0.003 0.003 0.2 0.101 0.10199 0.001
Maximum stomatal conductance at high solar radiation and low vapour pressure deficit (m.s-I)
0.00499 0.00499 0.007 0.004 0.00499 0.002
Vapour pressure deficit corresponding to the 2nd point on the stomatal conductance curve
4 4 4 4 4 4
Fraction of max. stomatal conductance corresponding to the 2nd point on the stomatal conductance curve
0.75 0.75 0.75 0.75 0.75 0.75
Rate of decline in radiation use efficiency per unit increase in vapour pressure deficit
10 10 7.2 8.25 9.25 8
Elevated CO2 atmospheric concentration (µL CO2/L air) 660 660 660 660 660 660 Biomass energy ratio 39 39 45 26 36 16 Plant residue decomposition coefficient 0.05 0.05 0.05 0.05 0.05 0.05 Mannings “n” value for overland flow 0.15 0.15 0.14 0.11999 0.15 0.1 SCS runoff curve number for soil Hydrologic Group A 44 49 67 51.5 58 25 SCS runoff curve number for soil Hydrologic Group B 65 69 78 68.5 73 55 SCS runoff curve number for soil Hydrologic Group C 76.5 79 85 78 81 70 SCS runoff curve number for soil Hydrologic Group D 82 84 89 83 85.5 77 Minimum leaf area index for plant during dormant period (m2/ m2) 0.75 Fraction of tree biomass accumulated each year that is converted to residue during dormancy
0 0 0 0 0 0.3
SAVA: Savanna GRSS: Grassland AGRR; Agricultural land – row crops CRWO: Cropland/woodland mosaic CRDY: Cropland, dryland and pasture FOEB: Evergreen broad leaf forest
APPENDIX 2
Land Cover Parameter Values for Residential Medium Density (URMD) Land-Use Type Fraction of total impervious area 0.38 Fraction directly connected impervious area 0.3 Curb length density in urban (km/ha) 0.24 Wash-off coefficient for removal of constituents from impervious area (mm-1) 0.18 Max. amount of solids allowed to build up on impervious area (kg/ curb km) 225 Number of days for amount of solids on impervious areas to build up from 0kg/curb km to half the maximum allowed 0.75 Concentration of total Nitrogen in suspended solid load from impervious area (mg N/kg sed) 550 Concentration of total Phosphorus in suspended solid load from impervious areas (mg P/kg sed) 223 Concentration of nitrate in suspended solid load from impervious areas (mg NO3-N/kg sed) 7.2 Manning’s “n” value for overland flow 0.1 SCS runoff curve number for soil hydrologic group A 31 SCS runoff curve number for soil hydrologic group B 59 SCS runoff curve number for soil hydrologic group C 72 SCS runoff curve number for soil hydrologic group D 79 Curve number for moisture condition II 98
APPENDIX 3
Soil Parameter Value for Gd16-2-3a-1201 Soil Type Soil hydrologic group D Maximum rooting depth(mm) 1000 Porosity fraction from which anions are excluded 0.5 Crack volume potential of soil 0.5 Texture Clay-loam Layer
1 2 Depth(mm) 300 1000 Bulk density(g/cm3) 1.4 1.4 Available water capacity of soil(mm H2O) 0.175 0.175 Ksat(mm/hr) 4.01 4.1 Organic carbon(% soil weight) 2.1 1.2 Clay(% soil weight) 33 37 Silt(% soil weight) 38 36 Sand(% soil weight) 29 27 Rock fragments (% total weight) 0 0 Soil albedo (moist) 0.0085 0.0484 Soil erodibility, K (metric ton m2hr) 0.2536 0.2536 Electrical conductivity (ds/m) 0 0
Soil Parameter Value for Nd5-1a-1567 Soil Type Soil hydrologic group C Maximum rooting depth(mm) 820 Porosity fraction from which anions are excluded 0.5 Crack volume potential of soil 0.5 Texture Sandy-loam Layer
1 2 Depth(mm) 300 1000 Bulk density(g/cm3) 1.0 1.1 Available water capacity of soil(mm H2O) 0.087 0.087 Ksat(mm/hr) 77.15 28.55 Organic carbon(% soil weight) 1.1 0.5 Clay(% soil weight) 15 27 Silt(% soil weight) 18 19 Sand(% soil weight) 67 53 Rock fragments (% total weight) 0 0 Soil albedo (moist) 0.0587 0.1867 Soil erodibility, K (metric ton m2hr) 0.2878 0.2878 Electrical conductivity (ds/m) 0 0
Soil Parameter Value for Nd16-2-3a-1553 Soil Type Soil hydrologic group C Maximum rooting depth(mm) 1000 Porosity fraction from which anions are excluded 0.5 Crack volume potential of soil 0.5 Texture Clay-loam Layer
1 2 Depth(mm) 300 1000 Bulk density(g/cm3) 1.2 1.3 Available water capacity of soil(mm H2O) 0.175 0.175 Ksat(mm/hr) 13.12 8.73 Organic carbon(% soil weight) 1.9 0.5 Clay(% soil weight) 36 50 Silt(% soil weight) 28 22 Sand(% soil weight) 37 28 Rock fragments (% total weight) 0 0 Soil albedo (moist) 0.0125 0.1867 Soil erodibility, K (metric ton m2hr) 0.2846 0.2846 Electrical conductivity (ds/m) 0 0
Soil Parameter Value for Ap15-1a-1068 Soil Type Soil hydrologic group C Maximum rooting depth(mm) 820 Porosity fraction from which anions are excluded 0.5 Crack volume potential of soil 0.5 Texture Sandy-laom Layer
1 2 Depth(mm) 300 1000 Bulk density(g/cm3) 1.5 1.6 Available water capacity of soil(mm H2O) 0.087 0.087 Ksat(mm/hr) 13.4 3.45 Organic carbon(% soil weight) 0.8 0.3 Clay(% soil weight) 17 30 Silt(% soil weight) 18 17 Sand(% soil weight) 66 52 Rock fragments (% total weight) 0 0 Soil albedo (moist) 0.1047 0.2747 Soil erodibility, K (metric ton m2hr) 0.2504 0.2504 Electrical conductivity (ds/m) 0 0
Soil Parameter Value for Bf6-1105 Soil Type Soil hydrologic group C Maximum rooting depth(mm) 910 Porosity fraction from which anions are excluded 0.5 Crack volume potential of soil 0.5 Texture Sandy-loam Layer
1 2 Depth(mm) 300 1000 Bulk density(g/cm3) 1.1 1.2 Available water capacity of soil(mm H2O) 0.16 0.16 Ksat(mm/hr) 40.36 0 Organic carbon(% soil weight) 2.1 0.6 Clay(% soil weight) 20 30 Silt(% soil weight) 21 19 Sand(% soil weight) 59 53 Rock fragments (% total weight) 0 0 Soil albedo (moist) 0.0085 0.154 Soil erodibility, K (metric ton m2hr) 0.2186 0.2186 Electrical conductivity (ds/m) 0 0
APPENDIX 4 RAINFALL AND TEMPERATURE DATA
Data format: Rainfall = year + julian date + rainfall depth(mm) Temperature = year + julian date + max temp(xxx.x) + min temp(xxx.x)
Daily Rainfall (mm) Max. and Min. Daily Air Temperature (ºC)
1973001000 1973001032.2022.8 1973002000 1973002031.7023.3 1973003000 1973003031.1023.3 1973004000 1973004031.7022.8 1973005000 1973005033.3023.3 1973006000 1973006032.8023.9 1973007000 1973007031.7023.3 1973008000 1973008030.6023.3 1973009000 1973009030.0023.3 1973010000 1973010030.6020.6 1973011000 1973011030.6022.2 1973012000 1973012028.3022.2 1973013000 1973013028.9022.8 1973014000 1973014030.0021.7 1973015000 1973015029.4017.8 1973016000 1973016029.4016.1 1973017000 1973017030.6018.9 1973018000 1973018032.2018.3 1973019000 1973019032.8021.7 1973020000 1973020031.1023.3 1973021000 1973021032.2023.3 1973022000 1973022033.9020.0 1973023000 1973023033.3018.9 1973024000 1973024032.2022.2 1973025000 1973025032.2022.2 1973026000 1973026033.9022.2 1973027000 1973027034.4024.4 1973028000 1973028032.8022.8 1973029008 1973029032.8023.9 1973030000 1973030030.6023.3 1973031000 1973031031.1020.0 1973032000 1973032031.7018.9 1973033000 1973033031.7018.3 1973034000 1973034033.9018.3 1973035000 1973035033.9023.3 1973036000 1973036033.9024.4 1973037000 1973037035.6023.9 1973038000 1973038032.8024.4 1973039000 1973039028.9024.4 1973040000 1973040033.3023.3 1973041000 1973041031.7022.2 1973042000 1973042033.9022.8 1973043000 1973043033.9023.3 1973044000 1973044035.0022.8 1973045000 1973045034.4024.4
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1982053009 1982053033.0024.5 1982054004 1982054030.1022.6 1982055000 1982055030.8021.0 1982056011 1982056032.8022.5 1982057000 1982057027.4021.0 1982058000 1982058031.4021.4 1982059032 1982059031.0023.1 1982060023 1982060032.0022.0 1982061001 1982061031.5019.5 1982062000 1982062030.6020.6 1982063000 1982063033.0020.6 1982064000 1982064033.0018.3 1982065000 1982065033.0020.0 1982066000 1982066033.2020.5 1982067000 1982067033.8023.0 1982068000 1982068035.1022.1 1982069000 1982069033.8023.5 1982070001 1982070032.0024.0 1982071000 1982071030.1023.0 1982072000 1982072031.1022.5 1982073000 1982073032.0023.2 1982074000 1982074033.5021.5 1982075003 1982075030.5022.7 1982076000 1982076031.5020.5 1982077000 1982077031.0023.0 1982078000 1982078027.0021.5 1982079000 1982079032.6019.5 1982080020 1982080032.6022.5 1982081000 1982081030.8020.0 1982082000 1982082031.5022.6 1982083000 1982083029.5021.5 1982084000 1982084031.1020.0 1982085000 1982085032.3023.0 1982086000 1982086031.9022.0 1982087000 1982087031.0023.2 1982088009 1982088031.3023.4 1982089000 1982089031.0022.5 1982090000 1982090032.0022.0 1982091000 1982091032.6022.0 1982092000 1982092033.3023.5 1982093000 1982093032.5023.0 1982094000 1982094033.6023.5 1982095000 1982095033.8023.6 1982096000 1982096033.1020.0 1982097000 1982097033.0023.6 1982098026 1982098033.0023.0 1982099000 1982099032.0019.5 1982100000 1982100031.7022.1 1982101009 1982101030.0022.5 1982102000 1982102030.6020.5 1982103000 1982103031.0022.6 1982104000 1982104031.0023.0 1982105000 1982105030.5022.0 1982106007 1982106032.0023.5
1982107000 1982107030.5020.5 1982108000 1982108031.2020.5 1982109000 1982109032.6022.5 1982110002 1982110032.0023.0 1982111000 1982111031.7023.2 1982112003 1982112031.4020.5 1982113000 1982113031.5022.5 1982114000 1982114033.0020.0 1982115000 1982115032.0022.5 1982116005 1982116033.0022.0 1982117000 1982117029.5019.5 1982118000 1982118029.8021.5 1982119013 1982119032.0022.3 1982120018 1982120028.5021.4 1982121005 1982121028.0019.5 1982122014 1982122030.0021.0 1982123000 1982123027.0020.0 1982124000 1982124029.3020.6 1982125009 1982125030.2021.8 1982126000 1982126027.7019.0 1982127000 1982127030.5020.0 1982128042 1982128029.2022.0 1982129000 1982129028.5019.0 1982130003 1982130030.2021.0 1982131000 1982131031.0022.0 1982132000 1982132031.0021.6 1982133000 1982133030.0022.5 1982134014 1982134030.5022.0 1982135000 1982135028.5019.5 1982136012 1982136030.3020.5 1982137000 1982137031.0021.0 1982138006 1982138031.0021.7 1982139027 1982139029.0018.5 1982140000 1982140027.0017.8 1982141000 1982141029.3019.2 1982142000 1982142030.0021.0 1982143011 1982143029.3021.7 1982144000 1982144028.5019.6 1982145000 1982145029.8019.5 1982146040 1982146029.5021.5 1982147006 1982147029.0018.5 1982148000 1982148031.0019.5 1982149028 1982149030.0022.0 1982150000 1982150029.3019.5 1982151000 1982151029.5021.1 1982152041 1982152025.9021.1 1982153034 1982153028.0017.0 1982154045 1982154028.2018.6 1982155000 1982155028.8018.8 1982156000 1982156028.5020.2 1982157000 1982157030.5020.5 1982158000 1982158029.3020.5 1982159000 1982159029.6021.0 1982160022 1982160029.0020.5
1982161000 1982161027.0017.0 1982162000 1982162028.6018.0 1982163000 1982163028.5020.4 1982164000 1982164029.2020.0 1982165000 1982165028.4020.5 1982166001 1982166029.0020.3 1982167000 1982167028.5020.2 1982168001 1982168029.7021.0 1982169029 1982169026.7020.5 1982170000 1982170027.6017.5 1982171000 1982171028.0020.1 1982172000 1982172027.0020.0 1982173000 1982173026.0020.4 1982174009 1982174029.8020.0 1982175015 1982175029.0020.5 1982176009 1982176029.0019.0 1982177020 1982177027.5018.5 1982178000 1982178025.5018.5 1982179000 1982179027.4018.5 1982180000 1982180029.2020.2 1982181000 1982181029.2019.8 1982182007 1982182029.0020.3 1982183000 1982183027.5018.4 1982184001 1982184028.3020.0 1982185029 1982185028.8020.0 1982186001 1982186026.5018.0 1982187027 1982187027.0018.0 1982188003 1982188025.0017.0 1982189000 1982189027.2018.0 1982190000 1982190028.5020.0 1982191037 1982191026.0020.5 1982192005 1982192027.0018.5 1982193021 1982193027.6019.7 1982194027 1982194027.8018.3 1982195003 1982195027.7017.3 1982196001 1982196028.1020.0 1982197053 1982197025.0020.0 1982198000 1982198027.5018.5 1982199000 1982199027.5019.0 1982200041 1982200027.8019.5 1982201000 1982201028.0019.5 1982202000 1982202026.8019.5 1982203006 1982203025.5020.0 1982204001 1982204024.0018.5 1982205002 1982205025.3018.0 1982206001 1982206024.5018.5 1982207000 1982207026.5019.0 1982208031 1982208025.5019.0 1982209000 1982209028.5019.0
1982210-99.0 1982210027.8019.8 1982211000 1982211027.4019.0 1982212015 1982212026.0018.5
1982213-99.0 1982213024.0019.0 1982214001 1982214026.5019.0
1982215000 1982215027.5019.0 1982216000 1982216026.5019.0 1982217003 1982217026.0019.0
1982218-99.0 1982218026.0019.1 1982219-99.0 1982219026.5018.5
1982220005 1982220025.5019.0 1982221001 1982221025.3018.6 1982222003 1982222026.8018.9 1982223000 1982223026.0018.6 1982224000 1982224027.6019.5 1982225054 1982225025.2019.0 1982226006 1982226025.5019.0 1982227000 1982227027.5019.0 1982228003 1982228027.5019.0 1982229022 1982229026.5018.0 1982230001 1982230025.5019.5
1982231-99.0 1982231026.0019.0 1982232000 1982232026.0019.0 1982233000 1982233025.3019.0 1982234000 1982234027.5019.5 1982235000 1982235026.0019.7 1982236015 1982236026.2019.5 1982237028 1982237027.5019.0 1982238006 1982238026.0019.1
1982239-99.0 1982239026.2019.5 1982240005 1982240028.4019.5 1982241012 1982241027.6019.5 1982242004 1982242027.0019.6 1982243013 1982243028.0020.1 1982244000 1982244026.5018.0 1982245007 1982245027.0019.0 1982246004 1982246029.0020.0 1982247005 1982247029.0019.5 1982248010 1982248027.0020.0 1982249030 1982249027.3019.0 1982250001 1982250028.0019.5 1982251019 1982251027.4019.3 1982252006 1982252025.0018.0 1982253000 1982253027.2018.4 1982254044 1982254027.5019.2
1982255-99.0 1982255028.0018.0 1982256001 1982256027.0018.0 1982257002 1982257027.5022.0 1982258012 1982258025.2018.5 1982259000 1982259029.0018.5 1982260000 1982260028.5019.5 1982261026 1982261028.0019.5
1982262-99.0 1982262024.0018.0 1982263000 1982263027.5018.0 1982264000 1982264028.6020.0 1982265013 1982265029.0019.5 1982266000 1982266025.5018.6 1982267009 1982267026.4020.0 1982268000 1982268027.0016.5
1982269000 1982269028.0017.5 1982270001 1982270028.0020.0 1982271000 1982271028.0018.1 1982272037 1982272027.0020.0 1982273003 1982273025.0018.0 1982274017 1982274027.5018.8 1982275005 1982275028.0019.5 1982276027 1982276028.0017.0 1982277046 1982277027.5019.6 1982278001 1982278028.8018.0 1982279023 1982279026.8019.3 1982280009 1982280028.5018.8 1982281007 1982281031.4018.2 1982282058 1982282028.8018.0 1982283022 1982283026.5018.8 1982284000 1982284028.2019.0 1982285000 1982285029.0019.6 1982286042 1982286028.0020.0 1982287008 1982287028.5018.0 1982288000 1982288029.0019.7 1982289000 1982289029.0020.0 1982290000 1982290027.5020.5 1982291005 1982291029.3019.5 1982292004 1982292028.6018.0 1982293000 1982293028.5018.2 1982294000 1982294028.0019.5 1982295010 1982295028.2020.0 1982296000 1982296028.1018.7 1982297007 1982297029.0019.5
1982298-99.0 1982298028.0017.0 1982299000 1982299028.4019.5 1982300007 1982300029.0019.6 1982301005 1982301029.5019.6 1982302000 1982302027.5017.5 1982303000 1982303027.0020.0 1982304000 1982304028.8020.0 1982305017 1982305027.7020.1 1982306002 1982306029.5020.0 1982307000 1982307029.5017.5 1982308000 1982308030.5019.7 1982309000 1982309030.5021.2 1982310000 1982310030.5021.0 1982311000 1982311029.5020.3 1982312000 1982312029.8021.0 1982313000 1982313030.5019.5 1982314000 1982314030.8018.7 1982315000 1982315030.7019.0 1982316000 1982316031.5019.3 1982317000 1982317030.8018.3 1982318000 1982318030.5020.8 1982319000 1982319030.4020.0 1982320000 1982320030.0020.1 1982321000 1982321031.0021.0 1982322000 1982322031.5020.5
1982323000 1982323031.0021.0 1982324000 1982324030.2015.5 1982325000 1982325030.3015.8 1982326000 1982326029.2015.2 1982327000 1982327028.5014.5 1982328000 1982328028.8017.5 1982329000 1982329029.5016.1 1982330000 1982330030.0013.5 1982331000 1982331030.0014.0 1982332000 1982332031.0014.0 1982333000 1982333030.5016.0 1982334000 1982334032.0016.0 1982335000 1982335031.5015.8 1982336000 1982336031.5016.5 1982337000 1982337032.0016.0 1982338000 1982338032.5016.5 1982339000 1982339030.5019.5 1982340000 1982340030.2020.0 1982341000 1982341031.5021.0 1982342000 1982342031.3020.0 1982343000 1982343032.0019.5 1982344000 1982344030.7019.5 1982345000 1982345030.0019.0 1982346000 1982346031.5016.6 1982347000 1982347030.7012.8 1982348000 1982348031.3013.5 1982349000 1982349031.6014.3 1982350000 1982350031.2016.5 1982351000 1982351033.0017.5 1982352000 1982352032.5019.2 1982353000 1982353032.4021.0 1982354000 1982354033.2019.6 1982355000 1982355032.3020.0 1982356000 1982356032.0019.5 1982357000 1982357031.0018.4 1982358000 1982358030.5019.0 1982359000 1982359032.0018.0 1982360000 1982360033.0018.5 1982361000 1982361032.8020.0 1982362000 1982362031.8018.5 1982363000 1982363030.7017.0 1982364000 1982364032.0016.5 1982365000 1982365032.5019.5
APPENDIX 5 Statistical Meteorological Data (2009) For Nsukka
JAN FEB MARCH APR MAY JUN JUL AUG SEP OCT NOV DEC TMPMX 30.8 32.8 32.8 30.9 29.2 28.4 27.1 27.3 27.1 28.1 30.3 30.6 TMPMN 19.1 22.1 22.1 21.1 21.3 21.1 20.4 20.9 20.4 20.5 20.7 18.5 TMPSTDMX 1.38 1.45 1.19 0.97 1.19 1.39 0.94 1.4 1.21 1.55 0.98 1.11 TMPSTDMN 2.81 2.33 1.34 1.42 1.37 1.27 0.73 0.59 0.65 1.34 1.8 2.37 PCPMM 0 0 38.4 134.6 254 186.6 178.4 132.7 429.6 301.3 1.8 0 PCPSTD 0 0 38.03 132.79 250.26 183.81 173.65 130.71 416.4 296.76 1.8 0 PCPSKW 0 0 3.66 2.92 1.12 1.77 1.68 1.74 1.88 1.98 5.01 0 PR_W(1) 0 0 0.11 0.36 0.47 0.39 0.5 0.62 0.63 0.5 0.03 0 PR_W(2) 0 0 0 0 0.25 0.36 0.59 0.5 0.7 0.53 0 0 PCPD 0 0 3 8 12 11 17 18 22 17 1 0 RAINHHMX 0 0 10.4 25.6 28.8 26.4 26 23.1 38.4 30.1 0.4 0 SOLARAV 13 14 14.8 14.6 12.8 11.7 9.9 9.9 11.4 12.1 15.4 14.7 REL HUM 0.43 0.51 0.61 0.69 0.73 0.76 0.8 0.81 0.81 0.79 0.65 0.41 WNDAV 42.38 51.29 56.09 52.41 43.97 44.98 46.11 52.47 45.31 41.23 41.4 49.34
TMPMX; Mean daily maximum air temperature for month (ºC) TMPMN: Mean daily minimum air temperature for month (ºC) TMPSTDMX: Standard deviation for daily maximum air temperature in month (ºC) TMPSTDMN: Standard deviation for daily minimum air temperature in month (ºC) PCPMM: Mean daily precipitation in month (mm H2O) PCPSTD: Standard deviation for daily precipitation in month (mm H2O/day) PCPSKW; Skew coefficient for daily precipitation in month PR_W(1): Probability of a wet day following a dry day in the month PR_W(2): Probability of a wet day following a wet day in the month PCPD; Average number of days of precipitation in month RAINHHMX: Maximum 0.5 hour rainfall in entire period of record for month (mm H2O) SOLARAV: Average daily solar radiation in month (MJ/m2/day) REL HUM: Relative humidity WNDAV: Average daily wind speed in month (m/s)
APPENDIX 6 SCENARIO 0 - CONTROL RUN
Sample Output of HRU Distribution Number of HRUs: 96 Number of subbasins: 29 --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area (ha) Watershed (%) Landuse SAVA 353331.05 93.86 CRWO 3117.14 0.83 FOEB 163.48 0.04 CRDY 19481.82 5.17 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 ---------------------------------------------------------------------------
Area [ha] Watershed(%) Subbasin(%) Subbasin 1 16643.64 4.42 Landuse SAVA 13923.77 3.70 83.66 CRDY 2634.32 0.70 15.83 FOEB 85.55 0.02 0.51 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21 HRUs: 1 SAVA/Nd16-2-3a-1553/5-67 1415.42 0.38 8.50 2 SAVA/Nd16-2-3a-1553/0-5 12508.35 3.32 75.15 3 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 4 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 5 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 6 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 ---------------------------------------------------------------------------
Area(ha) Watershed(%) Subbasin(%) Subbasin 2 24706.69 6.56 Landuse SAVA 22920.26 6.09 92.77 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 7 SAVA/Nd16-2-3a-1553/5-67 2367.18 0.63 9.58 8 SAVA/Nd16-2-3a-1553/0-5 12181.72 3.24 49.31 9 SAVA/Ap15-1a-1068/5-67 2985.19 0.79 12.08 10 SAVA/Ap15-1a-1068/0-5 5386.17 1.43 21.80 11 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 12 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 13 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 14 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 3 16172.68 4.30 Landuse SAVA 16172.68 4.30 100.00 Soil Nd16-2-3a-1553 16172.68 4.30 100.00 Slope 0-5 13306.27 3.53 82.28 5-67 2866.41 0.76 17.72 HRUs: 15 SAVA/Nd16-2-3a-1553/5-67 2866.41 0.76 17.72 16 SAVA/Nd16-2-3a-1553/0-5 13306.27 3.53 82.28 ---------------------------------------------------------------------------
APPENDIX 7 Scenario 1
Sample Output of HRU Distribution Number of HRUs: 164 Number of subbasins: 29 --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area [ha] %Watershed Landuse GRAS 352977.72 93.76 CRWO 3117.14 0.83 CRDY 19481.82 5.17 AGRR 353.33 0.09 FOEB 163.48 0.04 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 1 16643.64 4.42 Landuse GRAS 13909.85 3.69 83.57 FOEB 85.55 0.02 0.51 AGRR 13.92 0.00 0.08 CRDY 2634.32 0.70 15.83 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21
HRUs: 1 GRAS/Nd16-2-3a-1553/5-67 1414.00 0.38 8.50 2 GRAS/Nd16-2-3a-1553/0-5 12495.85 3.32 75.08 3 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 4 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 5 AGRR/Nd16-2-3a-1553/5-67 1.42 0.00 0.01 6 AGRR/Nd16-2-3a-1553/0-5 12.51 0.00 0.08 7 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 8 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 2 24706.69 6.56 Landuse GRAS 22897.34 6.08 92.68 AGRR 22.92 0.01 0.09 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 9 GRAS/Nd16-2-3a-1553/5-67 2364.81 0.63 9.57 10 GRAS/Nd16-2-3a-1553/0-5 12169.54 3.23 49.26 11 GRAS/Ap15-1a-1068/5-67 2982.20 0.79 12.07 12 GRAS/Ap15-1a-1068/0-5 5380.78 1.43 21.78 13 AGRR/Nd16-2-3a-1553/5-67 2.37 0.00 0.01 14 AGRR/Nd16-2-3a-1553/0-5 12.18 0.00 0.05 15 AGRR/Ap15-1a-1068/5-67 2.99 0.00 0.01 16 AGRR/Ap15-1a-1068/0-5 5.39 0.00 0.02 17 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 18 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 19 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 20 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 ---------------------------------------------------------------------------
APPENDIX 8 Scenario 2
Sample Output of HRU Distribution Number of HRUs: 164 Number of subbasins: 29 --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area [ha] Watershed(%) Landuse GRAS 282664.84 75.08 CRWO 3117.14 0.83 CRDY 19481.82 5.17 AGRR 70666.21 18.77 FOEB 163.48 0.04 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 1 16643.64 4.42 Landuse GRAS 11139.02 2.96 66.93 FOEB 85.55 0.02 0.51 AGRR 2784.75 0.74 16.73 CRDY 2634.32 0.70 15.83 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21 HRUs:
1 GRAS/Nd16-2-3a-1553/5-67 1132.33 0.30 6.80 2 GRAS/Nd16-2-3a-1553/0-5 10006.68 2.66 60.12 3 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 4 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 5 AGRR/Nd16-2-3a-1553/5-67 283.08 0.08 1.70 6 AGRR/Nd16-2-3a-1553/0-5 2501.67 0.66 15.03 7 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 8 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 2 24706.69 6.56 Landuse GRAS 18336.21 4.87 74.22 AGRR 4584.05 1.22 18.55 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 9 GRAS/Nd16-2-3a-1553/5-67 1893.74 0.50 7.66 10 GRAS/Nd16-2-3a-1553/0-5 9745.38 2.59 39.44 11 GRAS/Ap15-1a-1068/5-67 2388.15 0.63 9.67 12 GRAS/Ap15-1a-1068/0-5 4308.94 1.14 17.44 13 AGRR/Nd16-2-3a-1553/5-67 473.44 0.13 1.92 14 AGRR/Nd16-2-3a-1553/0-5 2436.34 0.65 9.86 15 AGRR/Ap15-1a-1068/5-67 597.04 0.16 2.42 16 AGRR/Ap15-1a-1068/0-5 1077.23 0.29 4.36 17 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 18 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 19 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 20 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 ----------------------------------------------- ----------------------------
APPENDIX 9 Scenario 3
Sample Output of HRU Distribution Number of HRUs: 164 Number of subbasins: 29 --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area [ha] Watershed(%) Landuse GRAS 211998.63 56.31 CRWO 3117.14 0.83 CRDY 19481.82 5.17 AGRR 141332.42 37.54 FOEB 163.48 0.04 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 1 16643.64 4.42 Landuse GRAS 8354.26 2.22 50.19 FOEB 85.55 0.02 0.51 AGRR 5569.51 1.48 33.46 CRDY 2634.32 0.70 15.83 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21 HRUs:
1 GRAS/Nd16-2-3a-1553/5-67 849.25 0.23 5.10 2 GRAS/Nd16-2-3a-1553/0-5 7505.01 1.99 45.09 3 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 4 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 5 AGRR/Nd16-2-3a-1553/5-67 566.17 0.15 3.40 6 AGRR/Nd16-2-3a-1553/0-5 5003.34 1.33 30.06 7 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 8 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 --------------------------------------------------------------------------- Area [ha] Watershed%) Subbasin%) Subbasin 2 24706.69 6.56 Landuse GRAS 13752.15 3.65 55.66 AGRR 9168.10 2.44 37.11 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 9 GRAS/Nd16-2-3a-1553/5-67 1420.31 0.38 5.75 10 GRAS/Nd16-2-3a-1553/0-5 7309.03 1.94 29.58 11 GRAS/Ap15-1a-1068/5-67 1791.11 0.48 7.25 12 GRAS/Ap15-1a-1068/0-5 3231.70 0.86 13.08 13 AGRR/Nd16-2-3a-1553/5-67 946.87 0.25 3.83 14 AGRR/Nd16-2-3a-1553/0-5 4872.69 1.29 19.72 15 AGRR/Ap15-1a-1068/5-67 1194.08 0.32 4.83 16 AGRR/Ap15-1a-1068/0-5 2154.47 0.57 8.72 17 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 18 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 19 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 20 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 ---------------------------------------------------------------------------
APPENDIX 10 Scenario 4
Sample Output of HRU Distribution Number of HRUs: 164 Number of subbasins: 29 Landuses exempt from thresholds: FOEB URMD --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area [ha] Watershed(%) Landuse GRAS 141332.42 37.54 CRWO 3117.14 0.83 CRDY 19481.82 5.17 AGRR 211998.63 56.31 FOEB 163.48 0.04 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 1 16643.64 4.42 Landuse GRAS 5569.51 1.48 33.46 FOEB 85.55 0.02 0.51 AGRR 8354.26 2.22 50.19 CRDY 2634.32 0.70 15.83 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21 HRUs:
1 GRAS/Nd16-2-3a-1553/5-67 566.17 0.15 3.40 2 GRAS/Nd16-2-3a-1553/0-5 5003.34 1.33 30.06 3 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 4 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 5 AGRR/Nd16-2-3a-1553/5-67 849.25 0.23 5.10 6 AGRR/Nd16-2-3a-1553/0-5 7505.01 1.99 45.09 7 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 8 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 2 24706.69 6.56 Landuse GRAS 9168.10 2.44 37.11 AGRR 13752.15 3.65 55.66 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 9 GRAS/Nd16-2-3a-1553/5-67 946.87 0.25 3.83 10 GRAS/Nd16-2-3a-1553/0-5 4872.69 1.29 19.72 11 GRAS/Ap15-1a-1068/5-67 1194.08 0.32 4.83 12 GRAS/Ap15-1a-1068/0-5 2154.47 0.57 8.72 13 AGRR/Nd16-2-3a-1553/5-67 1420.31 0.38 5.75 14 AGRR/Nd16-2-3a-1553/0-5 7309.03 1.94 29.58 15 AGRR/Ap15-1a-1068/5-67 1791.11 0.48 7.25 16 AGRR/Ap15-1a-1068/0-5 3231.70 0.86 13.08 17 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 18 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 19 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 20 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 ---------------------------------------------------------------------------
APPENDIX 11 Scenario 5
Sample Output of HRU Distribution Number of HRUs: 164 Number of subbasins: 29 --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area [ha] Watershed(%) Landuse GRAS 70666.21 18.77 CRWO 3117.14 0.83 CRDY 19481.82 5.17 AGRR 282664.84 75.08 FOEB 163.48 0.04 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 1 16643.64 4.42 Landuse GRAS 2784.75 0.74 16.73 FOEB 85.55 0.02 0.51 AGRR 11139.02 2.96 66.93 CRDY 2634.32 0.70 15.83 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21 HRUs:
1 GRAS/Nd16-2-3a-1553/5-67 283.08 0.08 1.70 2 GRAS/Nd16-2-3a-1553/0-5 2501.67 0.66 15.03 3 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 4 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 5 AGRR/Nd16-2-3a-1553/5-67 1132.33 0.30 6.80 6 AGRR/Nd16-2-3a-1553/0-5 10006.68 2.66 60.12 7 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 8 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 --------------------------------------------------------------------------- Area [ha] Watershed(%) Subbasin(%) Subbasin 2 24706.69 6.56 Landuse GRAS 4584.05 1.22 18.55 AGRR 18336.21 4.87 74.22 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 9 GRAS/Nd16-2-3a-1553/5-67 473.44 0.13 1.92 10 GRAS/Nd16-2-3a-1553/0-5 2436.34 0.65 9.86 11 GRAS/Ap15-1a-1068/5-67 597.04 0.16 2.42 12 GRAS/Ap15-1a-1068/0-5 1077.23 0.29 4.36 13 AGRR/Nd16-2-3a-1553/5-67 1893.74 0.50 7.66 14 AGRR/Nd16-2-3a-1553/0-5 9745.38 2.59 39.44 15 AGRR/Ap15-1a-1068/5-67 2388.15 0.63 9.67 16 AGRR/Ap15-1a-1068/0-5 4308.94 1.14 17.44 17 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 18 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 19 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 20 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 ---------------------------------------------------------------------------
APPENDIX 12 Scenario 6
Sample Output of HRU Distribution Number of HRUs: 164 Number of subbasins: 29 --------------------------------------------------------------------------- Area [ha] Watershed 376464.47 --------------------------------------------------------------------------- Area [ha] Watershed(%) Landuse GRAS 353.33 0.09 CRWO 3117.14 0.83 CRDY 19481.82 5.17 AGRR 352977.72 93.76 FOEB 163.48 0.04 URMD 370.98 0.10 Soil Nd5-1a-1567 21739.48 5.77 Nd16-2-3a-1553 341927.88 90.83 Ap15-1a-1068 10715.96 2.85 Gd16-2-3a-1201 2081.15 0.55 Slope 0-5 315377.78 83.77 5-67 61086.69 16.23 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Area [ha] Watershed(% Subbasin(% Subbasin 1 16643.64 4.42 Landuse GRAS 13.92 0.00 0.08 FOEB 85.55 0.02 0.51 AGRR 13909.85 3.69 83.57 CRDY 2634.32 0.70 15.83 Soil Nd16-2-3a-1553 16643.64 4.42 100.00 Slope 0-5 14944.46 3.97 89.79 5-67 1699.18 0.45 10.21 HRUs:
1 GRAS/Nd16-2-3a-1553/5-67 1.42 0.00 0.01 2 GRAS/Nd16-2-3a-1553/0-5 12.51 0.00 0.08 3 CRDY/Nd16-2-3a-1553/5-67 275.29 0.07 1.65 4 CRDY/Nd16-2-3a-1553/0-5 2359.03 0.63 14.17 5 AGRR/Nd16-2-3a-1553/5-67 1414.00 0.38 8.50 6 AGRR/Nd16-2-3a-1553/0-5 12495.85 3.32 75.08 7 FOEB/Nd16-2-3a-1553/5-67 8.47 0.00 0.05 8 FOEB/Nd16-2-3a-1553/0-5 77.08 0.02 0.46 --------------------------------------------------------------------------- Area [ha] Watershed(% Subbasin(% Subbasin 2 24706.69 6.56 Landuse GRAS 22.92 0.01 0.09 AGRR 22897.34 6.08 92.68 CRDY 1415.45 0.38 5.73 URMD 370.98 0.10 1.50 Soil Nd16-2-3a-1553 15964.35 4.24 64.62 Ap15-1a-1068 8742.34 2.32 35.38 Slope 0-5 18975.54 5.04 76.80 5-67 5731.15 1.52 23.20 HRUs: 9 GRAS/Nd16-2-3a-1553/5-67 2.37 0.00 0.01 10 GRAS/Nd16-2-3a-1553/0-5 12.18 0.00 0.05 11 GRAS/Ap15-1a-1068/5-67 2.99 0.00 0.01 12 GRAS/Ap15-1a-1068/0-5 5.39 0.00 0.02 13 AGRR/Nd16-2-3a-1553/5-67 2364.81 0.63 9.57 14 AGRR/Nd16-2-3a-1553/0-5 12169.54 3.23 49.26 15 AGRR/Ap15-1a-1068/5-67 2982.20 0.79 12.07 16 AGRR/Ap15-1a-1068/0-5 5380.78 1.43 21.78 17 CRDY/Nd16-2-3a-1553/5-67 183.76 0.05 0.74 18 CRDY/Nd16-2-3a-1553/0-5 1231.69 0.33 4.99 19 URMD/Ap15-1a-1068/5-67 195.03 0.05 0.79 20 URMD/Ap15-1a-1068/0-5 175.96 0.05 0.71 ---------------------------------------------------------------------------