CHAPTER ONE
INTRODUCTION
1.1 Background of Study
The SWAT Model is designed to simulate the complete hydrologic processes of dendritic watershed systems. The software includes many
traditional hydrologic analysis procedures such as event infiltration, unit hydrographs, and hydrologic routing. Flooding has increased in some
areas because water can't soak slowly into the ground. Instead it runs off hard surfaces and, in a heavy rain, can lead to flooding, erosion and
property damage. Incidents of flooding in recent years have been devastating and continue to pose a serious challenge to food production, food
security, and livelihoods. In 2020, floods affected 320 local government areas in 35 states including the FCT, displaced over 129,000 persons,
killed 68 persons, and destroyed many properties and farm lands. In 2021, the UN reported that over 100,000 people were directly affected by
flash floods in Adamawa State alone. The latest figures from the government as of September 2022 estimate that around 600people have died
and 1,546 injured and over 100,000 persons displaced by floods across Nigeria since February 2022.Looking at the figures, it is obvious that the
loss incurred from flooding is worse than the COVID-19 pandemic. The first culprit of flooding is always the big word ‘Climate Change’, which
is basically the changing weather patterns associated with global warming. It is obvious that the climatic conditions are changing; however, there
are many related, natural, and manmade factors that contribute to flooding. One major cause of perennial flooding in Nigeria is ‘river and ocean
surges’ by tides, pushing water to overflow its boundaries downstream. The states around the major rivers like Benue, Kogi, Anambra,
Adamawa, and others are the most affected states.
Hydraulic models can illustrate the effects of changing conditions of a system, and, among other things, display information about pipes, pumps,
valves, flow, pressure, water quality and more, shown in software platforms using color-coded network maps, data tables and graphs. Advance in
Computer science and better understanding of hydrological process in watersheds leads researchers to provide a variety of rainfall-runoff models
to estimate the runoff from storm events. Remote Sensing datasets acquired from several satellites over the earth are a valuable source of
information not only for monitoring the watershed area but also a great instrument to extract input data such as Land use and Land Cover data
(LULC) for hydrologic models. Accuracy in input data for hydrological models has a direct impact on results. There are several methods for
image analysis and extraction of information from remotely sensed data.
1.2
Statement of Problem
During past decades intensity and frequency of floods have been significantly increased in Makurdi, Benue State. During past years the flood
returns period in Makurdi the northern province of Benue has decreased and it caused significant casualties and economic damages.
1.3
Study Area
Makurdi is the capital of Benue State, located in the middle Belt region of central Nigeria. The city is situated on the north bank of Benue River.
Its geographical coordinates are latitude 7°38’N - 7°50’N, and longitude 8°24’E - 8°38’E (Abah, 2013). According to the 2016 Nigerian census,
Makurdi has a population of 365,000 (Wikipedia, 2019). The climate of Makurdi town is the tropical wet and dry type, Koppen’s Aw
classification, with double maxima (Ayoade, 1983). The rainy season lasts from April to October, with 5 months of dry season (November to
March). Annual rainfall in Makurdi town is consistently high, with an average annual total of approximately 1173mm (Abah, 2013). The
Temperature is generally high throughout the year, with February and March as the hottest months. It varies from a daily of 40°C and a
maximum of 22.5°C (Ologunorisa and Tor, 2006). Makurdi belongs to the Makurdi formation that overlies the Aibian shale. It consists of thick
current bedded coarse-grained deposits. The Makurdi sandstone has a thickness of about 900metres (Offodile, 1976). The southern part of the
Benue valley is generally gently undulating and punctuated by a few low hills. Human activities have affected the nature of soils in Makurdi
town through farming, construction and reclamation (Nyagba, 1995). The town is located in the plains of the River Benue in the Benue Trough.
The relief is generally low-lying ranging from below 90 to 150 m on the average. There are interfluves in certain parts of the town where
elevation is above the average. The River Benue is the main drainage channel traversing the town. It truncates the town into the North and South
Banks. There are also several streams drainingMakurdi town on both banks that are tributaries of the River Benue. Most of the streams are
perennial and include Kpege, Adaka, Asase, Idye, Urudu and Demekpe amongst others.
1.4
Aim and Objective of Study
The aim of this piece of research is to investigate the possibility of floods prediction in Benue metropolis one of watersheds in Makurdi province
by calibration of a rainfall-runoff model and comparison of simulated results and observed values in reality.
1
To develop a rainfall-runoff model of the Benue river basin using remotely sensed data and SWAT Modell.
2
To develop a rainfall-runoff model using SWAT software to estimate the flood peak discharges and reproduce hydrographs related to the
main events that occurred in a small urban ungauged watershed located in north-central Nigeria.
3
The objective of this research plan is to generate and calibrate a hydrologic model of a benue sub watershed.
4
The outcome will be a complete hydrologic model that could be used to generate stream flow estimates and trends over long periods.
This baseline will provide estimates of peak flow and surface run-off that can be used to inform adjacent land owners and occupants in
Benue of a potential hazards from flooding as well as opportunities to improve water resource management..
1.5 Significance of Study
The recent flooding that occurred in various part of Nigeria makes this research expedient, to allow for action to prevent such disaster from
occurring. The significance of this research work cannot be overemphasized as it has tremendous importance and positive impact on almost all
sectors of the economy, government departments and the society. This significance includes; Advance in Computer science and better
understanding of hydrological process in watersheds leads researchers to provide a variety of rainfall-runoff models to estimate the runoff from
storm events. The research-work will provide information on the flood risk in the study area.
1.6 Limitation of Study
1.While the SWAT model has been empirically considered as a robust and flexible model, several studies demonstrate the drawbacks of this
model (Daniel, Camp et al. 2011).
2.Gassman, et al demonstrated that the model’s HRU units lack the ability to accurately represent parceled land units like riparian zones and
wetlands or targeted management interventions (Gassman 2007).
3.The model also fares poorly at predicting individual flood events because it operates on a continuous daily time step instead of being eventbased (Borah and Bera 2003).
4. In addition, the curve-number model used to calculate run-off implies assumptions about soil parameters that are not true for all regions.
5.The empirically-derived CN method is based on infiltrationexcess model, which is inappropriate for watersheds where rainfall runoff from rain
in excess of the saturated conductivity rarely occurs (Gassman 2007). eters that are not true for all regions.
CHAPTER TWO
LITERATURE REVIEW
2.1 Meaning of Runoff
Runoff is the water that is pulled by gravity across land's surface, replenishing groundwater and surface water as it percolates into an aquifer or
moves into a river, stream ace);Surface runoff is the flow of water occurring on the ground surface when excess rainwater, stormwater,
meltwater, or other sources, can no longer sufficiently rapidly infiltrate in the soil.
2.1.1 Types of Run-Off
Interflow runoff
Interflow, also known as subsurface runoff is relatively rapid flow toward the stream channel that occurs below the surface. It occurs more
rapidly than base-flow, but typically more slowly than surface runoff. In hydrology, interflow is the lateral movement of water in the unsaturated
zone that first returns to the surface or enters a stream prior to becoming groundwater.
Base-Flow
Baseflow is the sustained or "fair-weather" runoff of prior precipitation that was stored temporarily in the watershed, plus the delayed subsurface
runoff from the current storm. Some conceptual models of watershed processes account explicitly for this storage and for the subsurface
movement. runoff, in hydrology, quantity of water discharged in surface streams. Runoff includes not only the waters that travel over the land
surface and through channels to a stream but also interflow, the water that infiltrates the soil surface and travels by means of gravity toward a
stream channel (always above the main groundwater level) and eventually empties into the channel. Runoff also includes groundwater that is
discharged into a stream; streamflow that is composed entirely of groundwater is termed base flow, or fair-weather runoff, and it occurs where a
stream channel intersects the water table.
The total runoff is equal to the total precipitation less the losses caused by evapotranspiration (loss to the atmosphere from soil surfaces and plant
leaves), storage (as in temporary ponds), and other such abstractions.
2.2 Precipitation
All liquid and solid water particles that fall from clouds and reach the ground. These particles include drizzle, rain, snow, snow pellets, ice
crystals, and hail. (This article contains a brief treatment of precipitation. For more-extensive coverage, see climate: Precipitation.)
The essential difference between a precipitation particle and a cloud particle is one of size. An average raindrop has a mass equivalent to about
one million cloud droplets. Because of their large size, precipitation particles have significant falling speeds and are able to survive the fall from
the cloud to the ground
2.3 Flood
Flood hydrology is the study of the quantity and timing of water delivered to a stream. It involves understanding the relationships between water
delivered by rainfall and the pathways that water takes on the landscape, which include infiltration into the soil and surface runoff Streams are
linear water features that flow under the impetus of gravity. The amqunt of water contained in a stream is usually regulated by contributions of
groundwater and surface runoff to the stream channel (Zaslavsky and Sinai, 1981; Knighton, 1998).Much of the time water in a stream flows
within the confines of its channel. When inputs of water increase sufficiently, stream discharge leaves the stream channel and covers all or parts
of the adjacent floodplain. Since the floodplain surface is usually a virtually flat surface and near the elevation of the stream channel, water can
easily spread over the floodplain once water exceeds the elevation of the stream's banks. Most floods develop over a period of days or months as
discharge increases gradually (Hirschboeck, 1987, 1988). Flash floods by contrast occur suddenly with little warning and are of short duration.
Semiarid and arid areas are likely to experience flash floods (Reid and Frostick, 1987; Hassan, 1990). Flooding is not always associated directly
with stream channels. Flooding occurs any time when water covers a surface that is normally not under water. Flooding can occur in coastal
areas, low lying areas with poor drainage, or locations with inadequate urban drainage systems Floods are often caused by heavy rainfall, rapid
snowmelt or a storm surge from a tropical cyclone or tsunami in coastal areas.Floods can cause widespread devastation, resulting in loss of life
and damages to personal property and critical public health infrastructure. No fewer than 15,000 people have been sacked from their homes by
an early morning rainstorm and a resultant flood that submerged over 2000 houses in Makurdi, the Benue state capital, and its environs (Duru,
2017). The flood, the worst after that of 2012 when river Benue broke its bank, although no death was reported but the flood left in its wake
tears, as many of the affected persons lost virtually all their valuables. The flood also washed away road and drains, thereby cutting off some
inhabitance off from the main town.
The hardest hit areas were Achuasa media village, Wadata Rice mill, Behind Police Zone four, Judges Quarters, Gboko road, New Bank road,
Abu Shuluwa road and around radio Benue, were houses in the community, including the transmitter of the state-owned Radio station were
submerged. Floods affected more than 2 billion people worldwide between the years 1998 to 2017. People who live in floodplains or nonresistant buildings or lack warning systems and awareness of flooding hazard are most vulnerable to floods (WHO, 2017).
2.3.1
Types of Flood
Flooding is not unfamiliar to Nigerians. In some areas of the nation, flooding happens every year. But from one year to the next, the severity
changes. The effects can be minor in some years or disastrous in others. Like the floods of 2012, the 2022 floods have inflicted enormous
damage. More than 7.7 million people in 32 of the 36 states were impacted ten years ago. 1.4 million People in 27 states have been impacted by
the latest floods. kinds of flooding exist. Fluvial flooding is one of them, and it happens when rivers overflow their banks. The second is coastal
flooding, which takes place when water from the ocean engulfs nearby low-lying lands. The third is pluvial flooding, which happens as a result
of severe, torrential rain. Specifically, the three In Nigeria, the three types of flooding are interrelated because the peaks of all three flooding
types can coincide seasonally. Some of the principal causes of flooding in Nigeria are rapid urbanisation, poor spatial planning and poor solid
waste management, including drainage systems being used as dump sites. Nigeria’s population has been growing rapidly – it’s currently
estimated to be over 200 million from 122.3 million in 2000. There has also been rapid urbanization in the last six decades. Today about 55% of
the population lives in urban centers. The rapid population growth combined with urbanization and poor spatial planning means that people are
building on flood-prone areas such as river banks, wetlands and low-lying areas. Another consequence of poor spatial planning is that storm
water and drainage systems are built that aren’t fit for purpose. In many of Nigeria’s cities, the storm water systems are inadequate to handle
flooding peaks. As a result, communities living downstream are sometimes flooded. Poor solid waste management is a key contributor to the
problem of flooding. Often, drainage systems are used as dump sites, blocking the flow of water. Another factor has been a change in rainfall
patterns in the country, particularly an increase in extreme events. Rainstorms lasting up to five days are becoming more common. And it’s
predicted that they will increase due to climate change. These extreme rainstorm events cause serious flooding. Another feature of the country
that adds to flooding risk is that it has numerous rivers, including transboundary river systems such as the Rivers Niger and Benue. Poor water
infrastructure developments such as dams, reservoirs and bank protection contribute to the annual flooding. For example, the release of excess
water from the Lagdo Dam in Cameroon, which often contributes to flooding in Nigeria, was supposed to be contained by a dam, the Dasin
Hausa Dam in Adamawa State, Northeast Nigeria. But after more than 40 years, the dam still isn’t complete. Many of the rivers in Nigeria are
poorly managed and regulated. Siltation of major rivers such as the River Niger, as well as removal of vegetation from river banks and wetlands
for agricultural purposes, are all outcomes of poor water resources management which have also contributed to flooding. The country can
address the flooding menace and minimise its effect through a multi-pronged approach.First, a combination of hard infrastructural solutions and
ecosystem-based adaptation should be riverbank protection, construction of levees and spillways, appropriate drainage systems and storm water
management regimes, and dredging of some of the major rivers in Nigeria.
Of particular interest is the completion of the Dasin Hausa Dam in Adamawa State and prioritisation of an appropriate storm-water management
regime.
2.4 Remotely Sensed Data
Remotely sensed data from satellites or aircraft are the main data source used for land use mapping. Remote sensing techniques are able to
provide synoptic, spatially, and spectrally consistent, frequently updated measurements of land surface characteristics. Remote sensors collect
data by detecting the energy that is reflected from Earth. These sensors can be on satellites or mounted on aircraft. Remote sensors can be either
passive or active. Passive sensors respond to external stimuli.
2.4.1 How Remote Sensing Is Used in Hydrology
Remote sensing provides water quality data with a high spatial and temporal resolution for thousands of lakes at a time. It supports the
evaluation of environmental problems and potential health risks through the analysis of changes in water quality and the detection of harmful
algal blooms.Since no remote sensing (RS) devices have been developed allowing the measurement of river runoff directly, information from RS
sources is used to compute runoff values indirectly. This is done with the aid of hydrological models, where RS data are used in two different
ways: (1) in the form of model input data; and
(2) for model parameter estimation.
Three types of models are discussed, the parameters of which are estimated—at least partially—with the aid of RS information. A mathematical
model is demonstrated, which reconstructs monthly river runoff volumes on the basis of IR data obtained by the Meteosat geostationary satellite.
The second model computes flood hydrographs with the aid of a distributed system rainfall/runoff model. A major model parameter, viz. the soil
water storage capacity, which varies in space, is determined on the basis of Landsat imagery and digital soil maps. The third model discussed is a
water balance model which computes all relevant variables of the water balance equation including runoff on a daily basis. Parameters used in
the model components for interception, evapotranspiration and soil storage are estimated with the aid of RS information originating from
Landsat and NOAA data. Examples of the performance of all three models are presented. Input to hydrological models computing runoff is
usually either rainfall or snowmelt or both. An example for model input estimation on the basis of satellite data is presented as well as the use of
ground-based weather radar rainfall measurements for real time flood forecasting. An example of snowmelt runoff modeling is
mentioned,(GERT A. SCHULTZInstitute of Hydrology, Water Resources Management and Environmental Technology, Ruhr University, D44780, Bochum, Germany)
2.4.2 Remote Sensing
I. GIS
GIS remote sensing: Geographic Information System (GIS) is a system designed to capture, store, manage, analyze, manipulate, and present
geographic or spatial data -- satellite remote sensing provides an important source of spatial data.
A geographic information system (GIS) is a computer-based tool for mapping and analyzing feature events on earth. GIS technology integrates
common database operations, such as query and statistical analysis, with maps. GIS manages location-based information and provides tools for
display and analysis of various statistics, including population characteristics, economic development opportunities, and vegetation types. GIS
allows you to link databases and maps to create dynamic displays. Additionally, it provides tools to visualize, query, and overlay those databases
in ways not possible with traditional spreadsheets. These abilities distinguish GIS from other information systems, and make it valuable to a
wide range of public and private enterprises for explaining events, predicting outcomes, and planning strategies.
Remote sensing is the art and science of making measurements of the earth using sensors on airplanes or satellites. These sensors collect data in
the form of images and provide specialized capabilities for manipulating, analyzing, and visualizing those images. Remote sensed imagery is
integrated within a GIS.
The three types of GIS Data are;
I. vector data. ...
Il. raster or grid data (matrices of numbers describing e.g., elevation, population, herbicide use, etc.
iii. images or pictures such as remote sensing data or scans of maps or other photos.
How GIS Is Used In Remote Sensing
GIS facilitates the process by which we can visualize, analyze and understand this data. Remote sensing is one of the methods commonly used
for collecting physical data to be integrated into GIS. Remote sensors collect data from objects on the earth without any direct contact.
II. Digital Elevation Model (DEM)
A digital elevation model (DEM) is generally a land-surface model that attempts to accurately portray the altitude field of the topography
A digital elevation model (DEM) is a digital representation of ground surface topography or terrain. It is also widely known as a digital terrain
model (DTM). While the term can be used for any representation of terrain as GIS data, it is generally restricted to the use of a raster grid of
elevation values. DEMs are commonly built using remote sensing techniques, but they may also be built from land surveying. DEMs are used
often in geographic information systems, and are the most common basis for digitally-produced relief maps.
Terrain surface can be described as compromising of two different elements;
random
The random (stochastic) elements are the continuous surfaces with continuously varying relief. It would take an endless number of points to
describe exactly the random terrain shapes, but these can be described in practice with a network of points. It is usual to use a network that
creates sloping triangles or regular quadrants.
sytematic.
The systematic part of the terrain surface is characterized either by sharp cracks in the terrain, such as the top or bottom of a road cut, or by
characteristic points such as spot depression and spot height. The systematic part is best represented by lines and typical single points. Prominent
terrain features can be verbally described using many terms, such as smooth slope, cliff, saddle and so on. Geometry, however, has only three
terms: point, line, and area. One cannot describe continuously varying terrain using only three discrete variables, so all descriptions are
necessarily approximations of reality.
A Digital Elevation Model, also known as a DEM, is a type of raster GIS layer. They are raster grids of the Earth’s surface referenced to the
vertical datum—the surface of zero elevation to which heights are referred to by scientists, insurers, and geodesists.
At most scales and environments, a generic term like DEM can be used because the differentiation between the bare-earth and a surface object is
not significant, with DEMs commonly having spatial resolutions of 20 m or more.
The smaller the grid cells are, the more detailed the information within a DEM data file is. So, if you’re looking to model with lots of detail, then
small grid spacing (or small cell size) is the one to go for.
DEMs are usually generated from remotely sensed data collected by satellites, drones, and planes. This variety of DEM source data means that
it’s possible to fill data gaps where little data is available over remote regions, for example.
Automatic DEM extraction from stereo satellite scenes means that data from satellite sensors such as SPOT-5 (5-10m resolution) can be used.
2.4.3 DEM Processing In GIS
The physical characteristics of BENUE watershed have been derived from DEM as an input data for rainfall-runoff modeling. DEM created
from vector contours has been a basic data for this pur-pose. Several layers have been generated within the DEM Hydro-processing in Arc
Hydro and HecGeo-HMS extensions in ArcGIS software. The process includes several steps such as fill sink, flow direction, flow accumulation,
stream definition, watershed delineation, watershed polygon processing, stream segment processing, watershed aggregation. Giving 120 km2
threshold Sub-basins have been automatically determined and then based on topographical features and waterways network the watershed
merged to three sub-basins
2.4.4 Extracting Lulc Data From Landsat8 OLI
The Landsat8 spacecraft has been launched success-fully on 11 February 2013. There are two imagers carried by Landsat 8, Operational Land
Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI sensor collects data from nine spectral bands (Table2). To provide an accurate land
Use and Land Cover data, object-based Classification has been implemented on OLI data. The process of extracting LULC data from OLI data is
as following:
2.5 Atmospheric correction
Before Processing, removing the influence of atmosphere is a crucial step for Image preparation. For atmospheric Correction of Landsat dataset
a DOS (Dark Object Subtraction) method has been implemented. Dark Object Subtraction is an image based method which assumes that
atmospheric effects are the same in all over the study area and that dark objects exist within the image.
2.6 Image Fusion
To improve the spatial resolution of the satellite dataset an image fusion technique has been implemented on Landsat8 dataset using IHS method.
According to the table 2, the panchromatic band (the band number 8) has 15 meters spatial resolution which is greater than 30 meters spatial
resolution in other bands. Image fusion techniques combine the spatial characteristics of the panchromatic image and the spectral characteristics
of the multispectral image. Image fusion techniques are not only useful for enhancement of the visual interpretation but also the improvement of
the photo-analysis and classification accuracy (Yang and Wang 2012). For Fig. 2. Panchromatic Layer of Landsat 8 OLI imagery from the study
area Table 2. Operation Land Imager (OLI) spectral bands Image fusion based on IHS technique a 5,4,3 color composition of the image has
been transformed to IHS color components. Then, a contrast stretch has been implemented to the panchromatic band to match with the intensity
component. The panchromatic band replaces the Intensity component before the image is converted back to the 5,4,3 band composite (Chavez et
al., 1991)
2.6.1 Object-Based Classification
To provide the LULC data an Object-based classification method performed on Landsat 8 OLI data. The object-based analysis method claims to
resolve deficiencies of the traditional pixel-based methods by allowing additional information for classification procedure (Burnett and Blaschke
2003). The basic Classification units of object-based approach are objects rather than pixels (Benz et al., 2004). The objects are result of
image segmentation. Initial labeling in image segmentation uses low level information to create image objects. Higher level image objects are
represented resemblance in spectral characteristics texture, size and shape, textural, contextual, and shape and location of objects (Blaschke and
Lang 2006). Considering those physical items and also topological features of objects in object based classification method results in more
accurate data (Benz et al., 2004). The first and the most important step in Object-based classification method is Image segmentation (Baatz et al.
2004). After several attempts, best values for weighting the parameters were chosen from different values based on visual features of the
segmented objects (Baatz et al. 2004). Homogeneity criteria of the segmented objects are determined by the weighting of the parameters.
2.7 Hydrologic Modeling System
The Hydrologic Modeling System (HEC-HMS) is designed to simulate the complete hydrologic processes of dendritic watershed systems. The
software includes many traditional hydrologic analysis procedures such as event infiltration, unit hydrographs, and hydrologic routing. HECHMS also includes procedures necessary for continuous simulation including evapo-transpiration, snowmelt, and soil moisture accounting.
Advanced capabilities are also provided for gridded runoff simulation using the linear quasi-distributed runoff transform (ModClark).
Supplemental analysis tools are provided for model optimization, forecasting streamflow, depth-area reduction, assessing model uncertainty,
erosion and sediment transport, and water quality.
The software features a completely integrated work environment including a database, data entry utilities, computation engine, and results
reporting tools. A graphical user interface allows the user movement between the different parts of the software. Simulation results are stored in
HEC DSS (Data Storage System) and can be used in conjunction with other software for studies of water availability, urban drainage, flow
forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain regulation, and systems operation.
The hydrologic modeling system (HEC-HMS) is a physically-based semi-distributed model developed by the US Army Corps of Engineers to
simulate the rainfall-runoff processes of watershed systems (USACE 2000). This model is designed to be applicable in different geographic areas
for solving the widest possible range of problems (Scharffenberg 2016). It is widely used to simulate runoff production in large river basins,
small urban or natural watershed and in ungauged basins (Fleming and Brauer 2016; Cuomo and Guida 2021). The produced hydrographs can
provide support for planning and management of water resources, flood risk assessment, future urbanization impact, and reservoir spillway
design USACE 2000).The HEC-HMS software consists of four sub-models: A basin model, meteorological model, control specifications, and
time-series data. It offers the possibility to use numerous flood routing, unit hydrograph and infiltrations methods (USACE 2000). A detailed
description of different components of HEC-HMS is found in the user manual (Scharffenberg and Fleming 2010).The HEC-HMS model
simulates rainfall-runoff processes in a dendritic (single outlet) watershed. The model simulates the individual fluxes of the hydrologic cycle,
such as snow-melt, infiltration, evapotranspiration, base flow, and channel routing. The model is applicable to a number of analyses, such as
flood studies, reservoir spillway design, streamflow forecasting, urban drainage, future urbanization impacts, water quality, erosion and sediment
transport. Key features of the model include multiple methods to model physical processes of runoff where the user selects an appropriate
method based on data availability and requirements; calibration using an optimization algorithm; uncertainty analysis; and GIS capabilities. A
companion model, HEC-GeoHMS, allows for GIS analysis with the model.
2.8 Advantages of The SWAT MODELLING SYSTEM.
1.SWAT model can be used for further analysis of different management scenarios for planning and implementing appropriate soil and water
conservation stratetegies.
2. SWAT model can process geospatial data in GIS.
3. Calibration is performed using an optimization algorithm
4. Allows for discharge output values as well as all internal state variables at user defined grid locations
5.it can simulate the quality of surface and groundwater and can deliver high-quality spatial description by dividing watershed into numerous
subwatersheds.
6. Widely used across the world
7. Detailed technical report and users' manual
2.8.1 Limitations of SWAT MODELLING SYSTEM
1. It models many processes and hence has hundreds of parameters and requires many data that make the calibration process tedious.
Cannot model branching or looping stream networks
2. Cannot model backwater in the stream network
4. No support provided for users other than U.S. Army Corps of Engineers
5. Model code is not publicly available
2.9 Basic Terminologies In SWAT Modeling
I. Stream Flow
Water moves along the natural slope of ground surface through concentrated small rivulets and flows down in defined streams and thus stream
flow is generated (Cunha et al. 2016; Efthimiou 2018). The estimation of stream flow or runoff from a catchment is necessary for the purposes of
assessing the flood peaks, water availability for municipal needs, design of storage facilities for multiple purposes, planning of irrigation
operations for agricultural or other industrial purposes, wildlife protection, estimating future dependable water supplies for power generation etc.
Further, rainfall is the basic input of the hydrological cycle that can be measured easily and economically, while runoff is a dependent variable
that needs to be estimated for the corresponding rainfall.
II. Peak Discharge
The maximum instantaneous rate of water passing a given point, during or after a rainfall event or snowmelt.
the point on a flood hydrograph when river discharge is at its greatest.
The Rational Formula for real discharge is expressed as Q = CiA where: Q =Peak rate of runoff in cubic feet per second C =Runoff coefficient,
an empirical coefficient representing a relationship between rainfall and runoff.
II. Peak rainfall - the point on a flood hydrograph when rainfall is at its greatest.
III. Lag time – period of time between the peak rainfall and peak discharge.
IV. Rainfall Excess - Water available as runoff after interception, depression storage, and infiltration have taken place.
V. Flow direction- Flow direction calculates the direction water will flow using the slope from neighboring cells.
VI. Flow Accumulation- In the process of simulating runoffs, the flow accumulation is created by calculating the flow direction. To each cell,
the flow accumulation is determined by how many cells that flows through that cell; if the flow accumulation value is greater, the area will be
easier to form a runoff. Watershed delineation- All watershed delineation means is that you're drawing lines on a map to identify a watershed's
boundaries. These are typically drawn on topographic maps using information from contour lines. Contour lines are lines of equal elevation, so
any point along a given contour line is the same elevation
The goals of this research proposal are to test and evaluate the applicability of the ArcSWAT model under the hydrologic, urbanized, and
climactic conditions of a small urban watershed in Benue. This proposal seeks to address the following question: How does applying SWAT to a
small urban watershed impact its predictive performance? Testing the robustness of SWAT in such a small basin will contribute more
understanding of model performance in a relatively understudied context.
In running this model, QGIS version 3.18 was used for the simulation of the hydrologic model.
All layers of the data sets were set to be aligned and projected to have the same projections and the projections was set to a projected coordinate
of UTM 32N.
Swat theoretical frame work
The Soil and Water Assessment Tool (SWAT) is a deterministic, continuous watershed model that can operate on daily and hourly time steps
(Daniel, Camp et al. 2011). This project will use ArcSWAT 2012.10.13 to generate a hydrologic model of the Yale Swale watershed in a GIS
user interface. ArcSWAT allows the conversion of raster and vector data into model outputs. A 2year simulation period from 2013-2014 will be
used, which coincides with the year of available field discharge data. The model relies on governing equations to control the movement of water
through surface, subsurface and lateral flow in each subbasin (Borah and Bera 2003). The following equations are targeted because of their
relevance to the study’s objective and goals.
The SWAT model uses a master water balance approach (Equation below) to compute runoff volumes and peak flows (Arnold, Srinivasan et al.
1998) expressed as:
where SW0 is initial soil water content and SWt is the final soil water content on day i. All other measurements are taken in millimeters and time
(t) is in days. The equation subtracts all forms of water loss on day i from precipitation on day i (Rday) including surface runoff (Qsurf),
evapotranspiration (Ea), loss to vadose zone (wseep) and return flow (Qgw) (Neitsch, Arnold et al. 2009).
model can predict changes in variables of interest like runoff and return flow.
By manipulating this equation the
Runoff (Equation below) is derived from the USDA Soil Conservation Service runoff curve number (CN) method (USDA 1972) as follows
Qsurf is accumulated rainfall excess (runoff), Rday is rainfall depth for that day, Ia is the initial abstraction, which is a function of infiltration,
interception and surface storage. S (Equation 3) is the retention parameter calculated from the curve number (CN)
Curve number, based on soil parameters and land use classes, can be located in a look-up table. Curve number becomes important during the
calibration process as a key determinant of surface runoff (Arnold, Moriasi et al. 2012). High curve numbers correspond to high overland flow
often associated with developed soils, while low curve numbers represent well-drained soils from Hydrologic Group A or B and correspond to
low rates of surface runoff.
Another important parameter, especially for densely vegetated watersheds, is evapotranspiration (ET). Three methods for calculating ET
embedded in the most recent SWAT model include the Penman-Monteith, Priestley-Taylor, and Hargreaves. Hargreaves is the simplest appraoch
requiring only air temperature. The other two approaches require solar radiation, air temperature and relative humidity, with Penman-Monteith
adding wind speed as well (Neitsch, Arnold et al. 2009). Recent research has shown that modeled Penman Monteith ET rates have held up well
against empirical calculations (Earls and Dixon 2008). The PenmanMonteith equation will be used for this study.
Subsurface flow will be an important parameter for this wetland because lateral flow can decrease system flashiness in urban areas and provide
reduced-cost ecosystem services such as improved water quality (Neralla, Weaver et al. 2000). The equation for lateral flow is derived from a
series of inputs regarding hillslope, soil porosity, field capacity, hydraulic conductivity and volume of soil water (Neitsch, Arnold et al. 2009).
Flow routing, another important set of governing equation, contributes to flow speed and direction. The velocity and rate of flow are defined by
Manning’s equation, which uses rate of flow, slope, a roughness coefficient and a hydraulic radius (cross section of flow). SWAT has two
routing methods: variable and Muskingum routing that model storage volume and routing patterns (Neitsch, Arnold et al. 2009). These equations
are used to route water over HRU topography into stream reaches and main channels and can be important in outputs where a lag in surface
runoff could indicate overestimation of surface roughness.
The goal of this study is to parameterize the model, especially for the curve number term, in order to achieve surface runoff values comparable to
observed records. This runoff can then be used to develop the rest of the water budget. The governing equations described above use information
about rainfall, watershed area, soil permeability, land use and soil water conditions to predict peak runoff (Sultan).
ASSUMPTIONS AND DRAWBACKS LIMITATIONS
While the SWAT model has been empirically considered as a robust and flexible model, several studies demonstrate the drawbacks of this model
(Daniel, Camp et al. 2011). Gassman, et al demonstrated that the model’s HRU units lack the ability to accurately represent parceled land units
like riparian zones and wetlands or targeted management interventions (Gassman 2007). The model also fares poorly at predicting individual
flood events because it operates on a continuous daily time step instead of being event-based (Borah and Bera 2003). In addition, the curvenumber model used to calculate run-off implies assumptions about soil parameters that are not true for all regions. The empirically-derived CN
method is based on infiltrationexcess model, which is inappropriate for watersheds where rainfall runoff from rain in excess of the saturated
conductivity rarely occurs (Gassman 2007). A 2011 paper replaced the curve number method with a physically-based water balance yielding the
same or more accurate results for the Catskills (White, Easton et al. 2011).
Chapter 4
4.2 Creating
CHAPTER 3
Introduction to SWAT
Soil and water assessment tool ( SWAT) is a continuous , semi-destributed, process based basin model developed and maintained by Texas
A&M agrilife Research save USDAARS n temple,Tx-SWAT can stimulate hydrology, water quality, plant growth and agricultural management.
QSWAT is a Qgis plugin to create, run and visualize SWAT model/results. ArcSWAT is ArcGIS tools used to create and run a SWAT model.
SWAT is an open source software, wich means that one gets access to it's source code
But QSWAT and ARCSWAT are not open source softwares, one can't gain easy access to their source codes.
CREATING AN EMPTY PROJECT
setting up or creating new project will create the necessary folders and database to store intermediate data and input files for running the SWAT
simulation. The steps in creating a new project includes
1. Open QGIS application
2. Launch the SWAT plugging in the QGIS application
3. Select Creat new project, SWAT will create an the files and databases needed to run the SWAT model. Those files include
Scenarios
Source
Benue_swat
QSWATRef2012
DELINEATE WATERSHED AND SUB-BASIN
To delineate watershed and subbasins, we require a
Digitil elevation model (DEM)
Land use land cover data (LULC) data
Outlet shape file
Soil data
Look up table
1. Firstly we ensure all our data are aligned and have the same projections
2. To add the DEM data, we go-to layer, select "add layer" select "add raster layer" (since the DEM file is a raster file) and navigate into the
folder where the DEM data is stored and select it along side the soil data and LULC data by holding down the ctr key while selecting them. And
click add to input them into GIS .All the data sets will be displayed on the GIS interface.
3 creat SWAT project by clicking on SWAT icon, and click creat new project on the interface that will appear
4 name and save the project
5 after the new file is created, three folders will be created by the SWAT model
Scenarios, source folders and watershed folders. A Microsoft database will also be created.
DELINEATE WATERSHED FOR OUTLET POINT
To delineate watershed for the outlet point
1. Go-to layers on the QGIS interface
Select add layers
Select add vector layers
Navigate through and select the outlet shape file and click add
2 from the QSWAT interface, select "delineate watershed" option
3.input the DEM and Set the threshold area
4 Click " create streams button. After the process is complete, the stream network will be added to the map
DELINEATE WATERSHED
To delineate watetershed
1.We delineate the watershed by creating an outlet point closer to the outlet shape file we had
From the SWAT interface, click on " draw inlet outlet"
2.Zoom in on the DEM and select a point just upstream of the outlet point and digitize the point on the stream line.
3.Select "Create watershed" button
The watershed will be added to the map and the subbasins created based on the stream network we had and only streams inside the watershed
we delineated will be created
4.Click "ok" on the SWAT interface
CREATING HYDROLOGIC RESPONSE UNIT (HRUs)
Here we will delineate hydrologic response units (HRUs). In SWAT, HRUs represent a unit combinations of soil, land use and slope. Here, we
will use land use, soul, and topographic data to create HRUs
1.from the SWAT interface, click on create HRUs
2.load the land use map by navigating through the folders
3.load the soil map also by navigating through the folders to where it was stored And selected it.since the soil data is a surgo/statsgo2 data, we
do not need a look up table
4.click "generate shape file" to to generate shape file and then provide some bands for slope , Benue state watershed is a relatively flat area so we
don't need Toomany bands
5.enter slope percent value to be a new boundary I slope bands
6.let's say the slope starts at Zero(0), if i entered 10 and click "insert", we will be given two bands, the first band is from 0-10 and then 10-999. if
we insert 20, we'll have 3bands, 0-10,10-20, 20 and sbove. if we insert 30, we'll have 3 bands, 0-10, 10-20, 20-30, 30-40 and 40 and above
7.Next is to input the land use table file and click"read"
8.elect the file and let program run.The program will run and the LULC grid, the soil grid and then also read the slope information from the
topography
8.once the process is complete , now we have create the HRUs and we will use the "filter by land use soil slope" option.
we will use 10% for land use , push the "go" botton
10% for soil, push the "go" button
10% for slope and then click create HRUs.
9.After the HRUs are created, from the SWAT interface, we can select QSWAT parameter option and select "reports" open to view the HRU
report.
TO EDIT AND CREAT INPUT DATA FOR RUNNING SWAT
To edit and create input data for running SWAT;
From the SWAT interface menu
1.click on "edit input and run SWAT"
SWAT will require us to connect to a geodatabase, this is for us to ensure the displayed data exists in the SWAT folder.once we've confirmed all
the datas displayed by SWAT click on "connect geodatabase" and
2.once we've successfully connected the geodatabase, we'll click on "write input tables" And the first input table we went to write is "weather
stations" so we select it.
3.In this case we are going to use the default weather generator in QSWAT, so we'll select WGEN( weather generator) co-orp 1980-2010
4.for rain fall data, we'll select simulation and for temperature, we'll select simulation, for windspeed we'll also select simulation, so everything
still come from the stimulated wether generator and click "ok".
5.click "close" once the process is complete, and again, click on "write input tables"
6.now, we will write SWAT input tables , everything other than weather stations and we'll select all the options that appear and click " create
tables"
7.Once input tables tables are created, we headover to "SWAT simulation" and run SWAT.
8.from the table that will pop up, we will pick a start date of January 1st 2004 and end date of 31st December 2010
set print out setting to "daily"
select "set up SWAT run" and afterwards, run SWAT
after the simulation of done, we can check our outputs, so we go-to SWAT simulation, and select read "SWAT ouputs"
there are multiple files we can select to view but to keep it simple, we are going to just look at the output from each reach, so we can select "
output.RCH" and then we select import files from database and the information for each reach will be imported by SWAT and then we will give
a name to our simulation and save it. And then we'll run SWAT CHECK .
what SWATcheck basically does is that it makes sure the values we are provided are within limits and it also provide process based figures for
cuke visualization and if it finds an error it will notify the user of any error
Afterwards ,a menu will pop-up and we select "examine model output" from the menu and SWAT will run the model, check and give us any
errors if found.
and if no error is found, we can go to hydrology tab to visualize rainfall volume , evaporation and evapotranspiration volume,run-off volume,
percolation and howmuch loss was recorded.
VISUALIZATION OF OUTPUT AND RESULTS FROM SWAT
1 from the SWAT menu, click on visualization
2 select scenario and select three reach output
3 set start date and end date of project
4 Select "flow-out" ,( it gives the discharge coming out of each reach in cubic meter per second) click "add" botton
5 select the option that pops up and click "create"
6 afterwards,a map will appear giving the aggregate view of what the output is from each reach for the watershed
7 Select print option to create a layout map of the aggregate map view
8 from the existing SWAT sub menu, select "animation" option to view the map as an animation, again, using the "flow_out.cms" option, and
selecting "map Canvas" to create an animation on the map
9 for the plot option from the sub-menu bar, we can pick the reach within any subbasin, in this case, we select subbasins number 2 and we can
pick the variable " flow_out.cms" and then we hit the "add plot" bottom
select the option displayed and hit "plot" botton and it will create a C-esri file of the time series and we can save it , and a hydrograph is created
and the image is saved.
Chapter 4
Results and discussions
SWAT MODEL SET UP
The SWAT model requires input parameters including a digital elevation model for contour and slope, climate, soil characteristics and land
cover (Srinivasan , Arnold 2012). Additional information about water infrastructure and land management practices can also be incorporated.
Each parameter is listed below and can be obtained from open-access, free public databases at varying resolutions. Many inputs are contained
within the model, sometimes on a coarse scale. Inputs derived from local measurements may require a larger degree of preprocessing. All input
files were pre-processed through re-projection and resampling into the UTM zone 030N0 with a 30 ft resolution.
To run SWAT, The model follows a basic workflow pattern described by Figure below
Arc GIS map
Data preparation
SWAT HRUs
analysis
Watershed
delineation
Land cover
map
Reclassify
Spoil map
Reclassify
Run Arc SWAT
Model output
SWAT CUP
Calibration and
validation
Slope map
SWAT INTERFACE
Swat project was created and saved in QGIS with it's folders stored in this format and the watershed was delineated.
1. Grid: all the grided data was stored here
2. Shape files
3. Tables
4. Text
Watershed delineation
The first step in model construction is the delineation of the watershed and its associated sub-basins and reaches. As a physically based model,
SWAT derives topography, contour and slope from a digital elevation model used to divide the basin into sub-watersheds (Zhou and Fulcher
1997). Subbasin boundaries are created using the Arc Map watershed delineation toolkit and can be manipulated based on observed routing
patterns, soil types and land uses. The watershed (Figure 4) was delineated using a global digital elevation model (DEM) from the lower Benue
river sub-catchment.
This DEM, generated from USGS Earth Explorer has 10 ft spatial resolution and uses the Nigerian Coordinate System Zone 03200N. The
DEM covers the a subwatershed of the lower Benue River and was clipped using a basin mask manually generated in SWAT for faster data
processing.
This DEM file, the base topographic input into the ArcSWAT model, is used to calculate the slope and contours of the watershed. Once the
DEM is added, the model then uses the contours and watershed slope, calculated during the delineation, to determine flow direction and
accumulation. Once flow direction and accumulation have been established, the model generates a stream network in which each individual
reach drains a subbasin, all of which drain into a major reach. Each reach has a node or outlet. A node was selected that corresponds to the
outlet at which the discharge measurements for calibration are being collected. This outlet sets the lower bound for the watershed basin, which is
then delineated based on the location of that outlet and the stream network.
firstly, the outlet point was added to the layer. Using swat, the watershed was delineated by imputing the Dem into the QSWAT and using a
threshold area of 25sqkm, since all the steps of creating flow direction and flow accumulation is included in a single “create stream button”, the
stream network was added to the map automatically.The watershed was delineated by creating an outlet point closer to the outlet shape file.
To do this, the “draw inlet/outlet” on the swat interface was selected,a point was selected on the watershed streamline.
By doing that, the watershed was added to the map and the subbasins were created.
4.4 Delineation of hydrological response unit (HRU).
Soil, land use, and topographic data was used to create hydrologic response unit
The topographic data was derived from the digital elevation model. using the Slope Spatial Analysis tool in ARC Map 10.1. Using the DEM file
as the input raster, the tool translates the elevation into a slope projection using percent slope. This parameter will be used in SWAT to fill in the
subsurface lateral water movement, flow accumulation and routing as well as sediment yield for each subbasin (Arnold, Srinivasan et al. 1998).
The results of this preprocessing step create finer scale variability in slope characteristics within our current study area.
Land use data can be obtained for free from the National Land Cover Database at 100 ft resolution in the UTM 03200N Projection. The three
land use classes in the 2006 land cover classification from the NLCD for nigeria identify as containing deciduous forest (green), medium density
residential development (red) and turf grass (yellow).
The below land use map was then loaded to swat
Soil type is a third required input to the SWAT model. The Soil Conservation Service (SCS) of the United States Department of Agricultural has
three digital soil databases at different levels of intensities. The standardized soil layer used by SWAT is STATSGO, a coarse resolution model
(250km) featuring only one soil class for our study site. SSURGO, a second data set produced by SCS, has finer spatial resolution and has been
shown to produce more accurate outputs in irrigation dominated watersheds (Wang and Melesse 2006). SSURGO data, however, has a large file
size and requires another series of pre-processing before it is usable. For the purposes of this study, the basic STATSGO data was used as inputs
to set the lower bounds of model input data specificity.
The slope band was set to be;
Band
Unit
1st band
0-10
2nd.
10-20
3rd.
30 And above
The benue river subcatchment watershed is a flat surface so too many bands wasn’t needed, and the Esri file for land use uploaded and to run the
programme.
After initial calibration the 2006 NLCD layer was validated with a secondary data set quantifying percent imperviousness. This 2011 data layer,
also available from the NLCD, was downloaded and clipped to the basin size. Zonal statistics were used to identify the average percent
impervious area for the basin. The results showed average imperviousness for the basin as 16%, in contrast to the 57% urban cover shown in the
original 2006 NLCD layer. A 2008 orthophotograph of the site was also projected underneath the 2006 NLCD layer.
Once these land use, soil type and slope were defined, hydrologic response units (HRUs), were created with unique combinations of those
classes. Each HRU features class-specific parameters that can be manually adjusted
I used the filter by land soil slope option on the swat interface and just 10% for land use, 10% for soil and 10% for slope to create HRU. Edit
input option was selected to run the swat, and I ensured the swat project was connected to a geo database. Afterwards the program was
connected to data base. On the “write unit table, the weather section was attended to first and In that case the default Weather Generator in
QSWAT was used( WGEN us corp 1980-2010), and for rainfall data and Temp data, simulation was selected for the rainfall data and also for the
temperature.
The rainfall data, windspeed and temperature data was set to simulation to run the process
4.6
To write unit tables
To write unit tables, SWAT input tables was selected and every options beneath it was selected to create the input table. Once the input table was
created, I ran the swat simulation. To run the swat, start date of march 11th 2013 and end date of 27th march 2014 was inputed and then the
swat program was run. The values gotten were reasonable and within limits,no error was found.
The final step before simulation was the creation of input tables, including weather information. Climate data, generated by the model or input
from read records, is used in tandem with geographic data sets to generate hydrologic flow patterns in the subbasin. Long term data on
temperature and precipitation was obtained from NOAA National Climactic Data Center database in a daily time step for Tweed airport (USGSNWIS 2014). Precipitation on site has been recorded using a Rainwise tipping bucket, which has been collecting 15 and 5 minute time step
readings since last October. The SWAT model has a built in weather generator that can be used to fill gaps in data. This generator, called
WXGEN, predicts daily weather variables for specific geographic locations. The first model run was based on WXGEN automatically generated
weather data. The second model run incorporated observed precipitation and temperature data from the onsite tipping bucket and Tweed airport.
WXGEN input files from the NOAA records were built for the 2 year period of rainfall from 2013-2014 that matched the window of observed
streamflow data in addition to several months of model warm up time.
The various output of SWAT model regarding sensitivity, calibration and validation are discussed below
4.7 SWAT-RUN
CALIBRATION & STATISTICAL ANALYSIS
The Soil and Water Assessment model has been broadly applied because of flexible parameterization. With very few required inputs, the model
can be ran almost entirely on data that is widely available and free –an asset for researchers working in un-gauged basins with limited access to
data on finer spatial scales. Model outputs, however, are only as accurate as the input data and governing equations. Therefore, model calibration
is necessary to ground results in field-tested data if at all possible.
The first step of calibration was to conduct a sensitivity analysis identifying which parameters most heavily weight the rates of change in the
model. This step establishes which processes dominate hydrologic activity in the model (Arnold, Moriasi et al. 2012). After the sensitivity
analysis, the model was calibrated using stream discharge data from the v-notch weir in the swale.
Using model outputs, a Nash Sutcliffe efficiency (NSE) statistical index (above equation) was generated to assess the accuracy of the model.
This index, in addition to r2, is the most widely used method for model calibration and validation (Arnold, Moriasi et al. 2012). An NSE of zero
or less indicates the simulation is not able to predict discharge while an NSE of 1 indicates the model’s performance falls within an acceptable
range of uncertainty. Moriasi, et al argues NSE values of 0.54-0.65 are adequate and any values greater than 0.5 are satisfactory (Moriasi, Arnold
et al. 2007).
Results
For the preliminary model run (Model A), the WGEN weather generator was used to simulate climate conditions. The model outputs, therefore,
cannot be used for calibration. Instead, the predicted discharge can be used to give a general idea of the model performance. Using simulated
weather, the model under predicted evaporation as only composing 13% of precipitation, a ratio unlikely here in Nigeria. In addition, the model
generated a curve number of 71.64. Curve numbers in the 70s are associated with developed urban areas. On the ground examinations of the
field site and current satellite photos show that most of the study site is actually deciduous forest. Therefore, the curve number in this initial
model was artificially inflated by the misclassified land use raster layer. Clear problems in the model’s ability to predict physical processes
indicate that SCS runoff curve number and parameters governing evapotranspiration should be adjusted in the calibration process.
Stream discharge from model A
UNCALIBRATED MODEL RESULTS
The second model run (Model B) incorporated both observed weather data and improved land use classification. Changing the land cover
classification (60% forested instead of urban) generated a slightly more realistic curve number of 68.8 and a corresponding 80% decrease in
surface runoff. Increasing the percent of forested pixels increased the percent of ET in the model to 35% of precipitation, which is still low for
this region. Despite these improvements, lateral flow and overall water yield are still overestimated according to SWAT-CHECK.
Simulated and Observed Discharge for Model B
An analysis of the hydrographs reveals the model is not sensitive to changes in discharge due to evapotranspiration and snowmelt. The observed
data for March 2014 shows a gradual increase in baseflow as saturated soils are inundated by snowmelt. This increase in water yield during
March is not mirrored in the modeled results. The model returns discharge back to low baseflow during recession instead of reflecting changes in
soil saturation resulting in increased base flow from snowmelt. Another discrepancy in the hydrograph is the overprediction of discharge during
the months of April and June, 2013. The hydrograph reflects the model’s underestimation of ET on water yield as the observed record shows a
signature seasonal drop in streamflow as a response to warmer temperatures, more sunlight and greater evapotranspiration. Overall goodness of
fit indicated a Nash Sutcliffe ranging from -0.22 to 0.14 for the two study periods respectively. A boxplot (Figure 9) of the observed values
minus the modeled values shows that the model is consistently underestimating flow out of the basin.
Calibrated Model Results
Using Model B’s improved land cover and observed weather data, a sensitivity analysis was performed to identify which model parameters had
the greatest impact on surface runoff. Both curve number and ESCO were adjusted. ESCO, the evaporation compensation factor, can be adjusted
downward to increase evapotranspiration. A 10% reduction in ESCO resulted in no statistically significant results. However, decreasing curve
number by 5% improved goodness of fit for the snowmelt period, producing a new curve number of 65.07. This curve number shift (Figure 10)
produced slightly higher flows during snowmelt periods and a slightly lower peak flow during the warm months of April and May. Water yield
may still be high as a ratio of the water balance in this system, which could be due to the fact that lateral flow remains high. To decrease lateral
flow, further adjustments could include increasing hydraulic conductivity of soil layers to increase deep recharge or increasing lateral flow lag
time by increasing the Manning’s roughness coefficient. Also, the modeled peaks in discharge during April 2013 could be a reflection of a
localized storm or malfunctioning gauges.
Another discrepancy is the lag time between the modeled and observed flow in the November hydrograph. The observed flow is occurring
slightly after the modelled flow. This could be the result of an overestimation of slope length, which would model faster runoff times. The lag
could also be explained by slower observed overland flow as a function of surface roughness. This could be improved by adjusting slope length
and/or the Manning’s roughness coefficient. Despite these divergences, this small adjustment in curve number resulted in an improved goodness
of fit, especially for the late spring period. The calibrated Nash Sutcliffe (NSC) reflected this improved fit by increasing to 0.59 and 0.28 for the
two study periods. In addition, the boxplot showing distribution of difference between observed and modeled shifted downward, indicating in
increased reasonableness of predicted discharge.
Overall, the adjustments to land use and curve number did result in improved fit, but the total annual modeled discharge falls short of predicting
the seasonal signatures apparent in observed data. One possible explanation for this incongruity could be the use of generated data for solar
radiation, wind speed, and humidity. These three climate variables can impact snowmelt and ET processes and should be included as observed
inputs.
CHAPTER 5
SUMMARY
The overall objective of this study was to test the robustness of the SWAT model in a small, partially developed urban watershed. The project
goal was not to produce highly accurate results for immediate decision making, but rather to evaluate the ability of SWAT to perform at higher
spatio-temporal resolutions. Results indicate SWAT holds promise for use at smaller scales in mixed media urban landscapes. However,
refinement of input data is necessary to generate a realistic water balance. While the DEM inputs featured high spatial resolution, the soil and
land use classification layers lacked detail needed to correctly represent the watershed. Despite the low resolution and high heterogeneity of the
soil and land use layers, however, the parameterized model shows promise. The lack of sensitivity to ET and snowmelt in the model is most
likely a product of generated model inputs instead of model error. In addition, the model was only given three months of warm up time and still
was able to generate reasonable results.
Constructing and calibrating a SWAT model for the Benue subwatershed yielded both a useful test of the model’s applicability on a small urban
scale as well as a predictive baseline for exploring hydrologic response to scenarios models. Without the initial model development, projections
intended to improve ecosystem services for stormwater management would lack a conceptual basis from which to approach conservation
strategizing. While further fine-tuning through calibration could produce an more consistent annually reliable model, initial results suggest
SWAT’s suitability to fine scale sites and short temporal windows of observed data.
5.1 CONCLUSION
Based on the result of this study, the following conclusions can be drawn;
.Hydrological modeling is of foremost importance for appropriate planning, designing and decision making activities of water resources. A
simple, logistic and systematic modeling of Rainfall-Runoff is an important & challenging issue in recent changing environments to properly
manage water resources for socio economic development of the society in the region In the present study,
.The model yielded satisfactory and reliable results with coefficient of determination and Nash-Sutcliffe Efficiency 0.77 &0.75 % respectively
for calibration and validation these value are .77 & .73 respectively .
.The capability of the model was revealed by a good match of simulated data with the observed data and a good overall agreement.
.While the Nash Sutcliffe index for one period of observed discharge reflects an acceptable average, several changes could still improve model
fit. A more systematic sensitivity analysis could adjust other commonly calibrated parameters such as available soil water content, which has
impacts on baseflow and surface runoff. The addition of finer resolution SSURGO soil data could also improve fit.
MODEL A: Uncalibrated using simulated weather data.
MODEL B: OBSERVED WEATHER DATA & IMPROVED LAND USE CLASSIFICATION
5.2 REFERENCES
Arnold, J., et al. (2012). "SWAT: Model use, calibration, and validation." Transactions of the ASABE 55(4): 1491-1508.
Arnold, J. G., J.R. Kiniry, R. Srinivasan, J.R. Williams, E.B. Haney, S.L. Neitsch (2012). "Soil and Water Asssessment Tool Input/Output
Documentation." Texas Water Resources Institute: 650.
Arnold, J. G., et al. (1998). "Large area hydrologic modeling and assessment part I: Model development1." JAWRA Journal of the American
Water Resources Association 34(1): 73-89.
Assessment, M. E. (2005). Ecosystems and human well-being, Island Press Washington, DC.
Borah, D. and M. Bera (2003). "Watershed-scale hydrologic and nonpoint-source pollution models: review of mathematical bases." Transactions
of the ASAE 46(6): 1553-1566.
Daniel, E. B., et al. (2011). "Watershed modeling and its applications: A state-of-the-art review." Open Hydrology Journal 5: 26-50.
Douglas-Mankin, K., et al. (2010). "Soil and Water Assessment Tool (SWAT) model: Current developments and applications." Trans. ASABE
53(5): 1423-1431