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SWAT-based LULC impacts on groundwater recharge

2.2.6.2. Slope map formats compatible with the SWAT model, ensuring
The slope map was constructed from DEM-derived seamless integration and reliable model inputs.
elevation data. The study area was discretized into five
slope classes, each interpreted as having different levels 2.2.6.6. Hydrological data (river flow)
of steepness. These classes were then further re-classified To substantiate the SWAT model, data on river discharge
into percentages, providing a more readily recognizable from observed gauging stations in the watershed were
picture of the five slope classes. They were created used. The discharge data from 2000 to 2014, collected
using ArcSWAT dunging model parametrizations based from the Ministry of Water and Energy of Ethiopia, were
on slope percentage gradients of the study area. This analyzed to calibrate and validate the SWAT model.
was done to provide a more detailed description of the These data were of particular importance, as they tested
topography of the target region. and calibrated the model, assessing the fidelity with
which the model can reproduce river flow patterns in
2.2.6.3. Soil data the watershed. Hydrogeologists have continuously
In the Dire Dawa watershed, key soil characteristics, such applied this observed data for watershed hydrological
as texture, bulk density, saturated hydraulic conductivity, modeling. In this study, based on the availability of the
and available water content, were re-established and data, discharge data spanning from 2000 to 2014 were
input into the SWAT model for hydrological simulation. utilized to calibrate and validate the SWAT model.
These features influence the water flow, infiltration, and
runoff processes. Such soil information was manually 2.2.7. The SWAT model setup
entered into the file (soil.dat) using ArcSWAT to meet The SWAT model was executed at a 12.5 m DEM in the
specific modeling needs. In addition, the calibration of Earthdata Search database to delineate the watershed.
soil parameters was at the core of improving the accuracy With the SWAT model, flexible modeling of hydrological
of water balance and runoff forecasts, considering local processes can be achieved using the computationally
land-use management variables. This approach laid efficient Soil Conservation Service (SCS) Curve
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the groundwork for assessing recharge and land-use- Number (CN) procedure for surface runoff calculation.
associated effects at the watershed scale. ArcSWAT was downloaded for free (https://swat.tamu.
edu/software/arcswat/) based on ArcGIS (version 10.8).
2.2.6.4. LULC In this study, potential evapotranspiration was estimated
40
Land use has a significant impact on a watershed’s using the Hargreaves techniques, selected based on
hydrology. The LULC is a key variable determining the availability of climate data. The calibration and
groundwater recharge in a watershed. To validate the validation of the model were performed using SWAT-
scenarios considered by the SWAT model, a realistic CUP and the SUFI-2 algorithm, both of which are also
land-use map was generated to delineate the hydrological recognized for producing accurate results with minimal
38
response units (HRUs). All six LULC types were iterations. Detailed model equations and units are
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assigned different codes for model feasibility and were presented in the SWAT documents. SWAT simulates
associated with a SWAT code to assess the influence of the water balance components daily (Equation IV):
different land covers on groundwater recharge during t
the study period (2000–2022). SW SW R day Q surf E W seep Q (IV)
a
t
gw
0
i1
2.2.6.5. Meteorological data where SW is the final soil water content (mm) at time
t
Accurate weather data are essential for the SWAT t, SW is the initial soil water content at a t of 0 (or i = 0),
0
model; hence, daily precipitation and temperature data R day is the total precipitation for day i (mm), Q surf is the
(1982–2022) were used in the study. These data were surface runoff for day i (mm), E is the evapotranspiration
a
primarily sourced from the local weather stations within for day i (mm), W seep is the inflow to the vadose zone
and around the watershed. In cases where data gaps from the soil profile for day i (mm), and Q gw is the
existed, linear regression and arithmetic methods were return flow for day i (mm). This equation considers the
employed to interpolate missing values. To address the contributing factors in soil water processes, including
uneven distribution of weather stations, local data were precipitation, runoff, evapotranspiration, seepage, and
supplemented with records from the National Centers return flow, and is used to model hydrological processes
for Environmental Prediction’s Climate Forecast System in watershed applications. ArcSWAT partitions the
Reanalysis. All meteorological data were processed into watershed into sub-basins and HRUs according to

Volume 22 Issue 6 (2025) 109 doi: 10.36922/AJWEP025180139