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Article

Groundwater Recharge Estimation in Upper Gelana Watershed, South-Western Main Ethiopian Rift Valley

by
Endale Siyoum Demissie
1,
Demisachew Yilma Gashaw
1,
Andarge Alaro Altaye
1,
Solomon S. Demissie
2,* and
Gebiaw T. Ayele
3,*
1
Arba Minch Water Technology Institute, Arba Minch University, Arba Minch P.O. Box 21, Ethiopia
2
Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa P.O. Box 150461, Ethiopia
3
Australian Rivers Institute and School of Engineering and Built Environment, Griffith University, Nathan, QLD 4111, Australia
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(3), 1763; https://doi.org/10.3390/su15031763
Submission received: 1 November 2022 / Revised: 11 January 2023 / Accepted: 13 January 2023 / Published: 17 January 2023
(This article belongs to the Special Issue Sustainable Water Use)
Browse Figures
Versions Notes

Abstract

:
Estimating the spatial and temporal patterns of groundwater recharge through integrated water balance modeling plays an important role in sustainable groundwater resource management. Such modeling effort is particularly essential for data-scarce regions, such as the Rift Valley Lake basin in the Basement Complex of Ethiopia, which has shallow aquifers, a proliferation of wells, and poor groundwater monitoring networks. A spatially distributed water balance model (WetSpass), along with GIS and remote sensing tools, was used for groundwater recharge estimation for its suitability and efficiency in data-scarce hydrogeological regions. The WetSpass model depicted a very good performance in simulating the groundwater recharge in the Upper Gelana watershed within the Rift Valley Lake basin. The water balance analysis revealed that about 7% of the mean annual rainfall is converted to groundwater recharge, and the remaining rainfall amounts are partitioned into surface runoff (19%) and evapotranspiration (75%). The model simulation outputs are also used to investigate the relative influences of biophysical driving factors on the water balance components. While the land use types had a greater influence on the actual evapotranspiration processes, the soil texture classes were the dominant factors in the surface runoff and groundwater recharge processes in the watershed. The groundwater recharge rates were found to be higher than 400 mm/yr in the central parts (Fisehagenent, Tore, and Gedeb) and lower than 165 mm/yr in the southern parts (Hageremariam) of the watershed. The areal proportions of the low, medium, and high recharging parts of the watershed are, respectively, estimated as 15%, 68%, and 17% of the watershed area. Therefore, the spatial and temporal patterns of groundwater recharge should be taken into consideration in developing a sustainable groundwater resources management plan for the Upper Gelana watershed. Managed aquifer recharge can be adopted in high and medium groundwater recharging parts of the watershed to capture stormwater runoff during the wet season to improve the groundwater supply during dry months. Furthermore, monthly groundwater withdrawals should be regulated according to the spatial and temporal patterns of the groundwater recharge in the watershed.

1. Introduction

Water resources, the primary need of all living things, must be available in sufficient quantity and quality to satisfy the growing demands of domestic, agricultural, and industrial processing operations [1]. However, the natural distribution of water on the earth’s surface impedes its availability for biological use. Since 97.5% of the global water is saline water residing in the oceans, only 2.5% is considered freshwater available for productive consumption. The majority of the freshwater, about 68.7%, is stored in the polar ice. Groundwater, surface water, and other sources account for 30.1%, 0.9%, and 0.3% of the meager freshwater resource, respectively [2]. Therefore, groundwater is a limited, vital, and multifunctional natural resource found on the planet earth. However, the demand for freshwater is increasing worldwide in response to urbanization, economic intensification, and population growth [3]. Due to its limitation, efficient water resource planning and management of water distribution, demand management, equitable utilization, and environmental functions are critical for the sustainable use of water resources [3].
Groundwater availability is decreasing globally due to overexploitation and a lack of efficient groundwater management [4]. The current global water issue is the depletion and contamination of groundwater resources. A proper understanding of the methodologies and approaches to groundwater recharge estimation and surface water conservation is vital in maintaining groundwater levels at the national, regional, and local scales for sustainable livelihoods. The total groundwater resource in the world is estimated to be 1.36 billion Km3 [4]. However, its spatial and temporal distribution remains uneven across the Earth’s surface.
Ethiopia’s groundwater potential is estimated to be between 13.5 billion m3 [5] and 28 billion m3 [6]. The Rift Valley Lake basin has an estimated groundwater potential of 0.10 billion m3; representing about 20% of the surface water resource of the basin. The estimated annual direct groundwater recharge of the basin is 1080 million m3 per year; whereas the estimated groundwater resource availability of the basin is reported to be 53 million m3/year. Thus, if groundwater demand reaches 5% of the calculated recharge, the water resources must be considered under pressure. Under such circumstances, comprehensive investigations are warranted to re-evaluate the available resources and to ensure the groundwater resource is not over-utilized [5]. However, increased population growth, improved living standards, uncontrolled groundwater use, and global climate change [7,8] have concurrently posed threats to this water potential.
Robust and efficient groundwater recharge estimation is very essential for the sustainable development and management of such scarce water resources. Several methods have been used for groundwater recharge estimation in different hydrologic and climatic zones, such as the tracers approach, localized empirical technique, and water budget modeling approach [9,10,11]. While the tracers approach requires expensive experimental and monitoring wells, the localized empirical technique does not provide spatially explicit and reliable groundwater recharge estimation [12]. However, the water budget modeling approach attempts to mimic functions and processes within the hydrosystem, and hence, it resolves the groundwater recharge amount and pattern in transient conditions. The WetSpass model is frequently and widely used to estimate groundwater recharge from spatial analysis of hydrosystem drivers and water balance computation in data-scarce regions with poor observation networks [9,13,14,15]. Many researchers identified and mapped groundwater potential zones in the Rift Valley Lake basin and Abaya-Chamo subbasin using integrated GIS, remote sensing techniques, and hydrological models [16,17,18,19,20,21,22,23]. However, very few researchers applied the WetSpass model in this basin to identify and map groundwater recharge zones [24].
Understanding the spatial and temporal variabilities of water balance components in a region is critical for efficient, equitable, and sustainable groundwater resource management [9]. The spatial variation in groundwater recharge arises from irregularities of land use land cover, soil texture, topography, hydrogeological regime, and climatic conditions [10]. These important basin attributes of the hydrosystems should be considered in groundwater recharge estimation [25,26]. In this study, the long-term monthly average climatic time-series data were gridded and used, along with biophysical characteristics of the Upper Gelana watershed and the groundwater water level measurements, as input to the modeling experiment. The WetSpass model, integrated with ArcGIS and remote sensing tools, is applied to simulate the distribution of water balance components (groundwater recharge, runoff, and actual evapotranspiration). Reliable groundwater estimation that encompasses accurate partitioning of the water balance components is very instrumental for wise and equitable utilization, sustainable management, and dynamic planning of the water resources in the watershed.
Increased attention to sustainable groundwater management is emanated from the exponential growth of urban and rural populations, scarcity of surface water during erratic rainy seasons, global climate change, urbanization, deforestation, land degradation, and expansive irrigation development [17,27]. Intensification of human activities is the main driving factor for the depletion and deterioration of the water environment [28]. According to the integrated development master plan study (2010) of the Rift Valley Lakes basin, the surrounding community extensively used the surface water of the Upper Gelana watershed for production activities, such as coffee processing, irrigation, and livestock farming. Such uncontrolled water uses posed a serious challenge to the provision of sustainable drinking water supply from surface water sources. Consequently, local communities are compelled to use groundwater not only for domestic needs but also for irrigation demands, especially in parts of the watershed within the West Guji zone. In order to address these issues, the current study applied advanced geospatial techniques, such as GIS and remote sensing along with the WetSpass model, to come up with reliable water balance partitioning, groundwater recharge distribution, and a map of potential groundwater recharge zones in the study area.

2. Materials and Methods

2.1. Description of the Study Area

The Upper Gelana watershed is part of the Abaya-Chamo subbasin within the Rift Valley Lakes basin. The Abaya-Chamo subbasin lies in Southern Ethiopia, in the middle of the Main Ethiopian Rift (MER) Valley. The watershed is located between 37°50′–38°20′ east longitude and 5°25′–6°18′ north latitude [29]. The watershed elevation ranges from 1295 m to 3062 m above sea level, and it covers an area of 1238 km2 (Figure 1). The topography of the watershed area is characterized by hills and ridges that produce an undulating land surface with a tiny and narrow plateau at some ephemeral stream sides. The watershed predominantly drains towards the south from the northeast peak to the southwest outlet.
The climate of the Gelana River basin varies from semi-arid on the rift floor to humid in the escarpment mountains [29]. The mean annual precipitation ranges from 842.94 mm in the lowland rift floors up to 1387.13 mm in the highland areas. Precipitation in this region has a bimodal pattern, with two hyetograph peaks in April–May during the “little rainy season” and in September–October during the “major rainy season”. The warm seasons of the year are from June to August and from November to March, and the annual average temperature is about 21.2 °C. The mean monthly maximum temperature varies from 21.2 to 28.15 °C, and the mean monthly minimum temperature varies from 8.97 to 13.32 °C.
The geology of the watershed (Figure 2) is extraordinarily intricate and faulted, as numerous earlier studies have described [30,31]. Since the study area lies at the escarpment of the Main Ethiopia Rift Valley, local structural features are observed following the main regional structure in the north–south direction. The geological unit of the study area is classified as Lower Basalt: aphyric to porphyritic basalt (Pgl), Alluvium (Qa), Eluvium (Qe), Shale Ignimbrite: rhyolitic ignimber (Pgs), Hornblende-biotite-quartz-feldspar gneiss (Phbg), Magnetite-quartz-feldspar gnesiss (Pmfg), and Rhyolites and trachytes were separated from TV1(Try).

2.2. WetSpass Model

WetSpass model is a physically based and spatially distributed water balance model [32,33] that resolves water and energy transfer among soil, plants, and atmosphere in quasi-steady-state conditions. The model name, WetSpass, was derived from the first underlined letters of its dominant functions. The WetSpass model is commonly used to estimate the long-term mean spatial patterns of actual evapotranspiration, surface runoff, and groundwater recharge of watersheds at wide ranges of spatial scales. A special version of the model, the WetSpass-M model, is applied for the estimation of spatial groundwater recharge on a monthly, seasonal, and yearly basis for the Upper Gelana watershed. The model categorizes the land use land cover of the watershed into four major classes: vegetated, bare soil, open water, and impervious surfaces. The water balance components of the four predominant land use land cover classes are used to calculate the total water balance components of a raster cell with the following equations [32]:
ETraster = av × ETv + as × ETs + ao × ETo + ai × ETi
Sraster = av × Sv + as × Ss + ao × So + ai × Si
Rraster = av × Rv + as × Rs + ao × Ro + ai × Ri
where, ETraster, Sraster, and Rraster are total evapotranspiration, surface runoff, and groundwater recharge of a grid cell, respectively. The water balance components of the four land use land cover (LULC) classes have respective subscripts of (v) vegetated, (s) bare soil, (o) open water, and (i) impervious area. The terms av, as, ao, and ai are the fraction area of vegetated, bare soil, open water, and impervious area, respectively.
The transpiration component of the evapotranspiration process can only occur in the vegetated land cover class of the watershed. The total evapotranspiration (ET) is the sum of actual evapotranspiration (AET) and evaporation of water intercepted by vegetation (I). The actual evapotranspiration is often computed from the potential evaporation (PET) of open water with a proper application of vegetation coefficient and water content function [32].
The WetSpass model assigns interception percentages for different land use land cover classes for both summer and winter seasons. The monthly interception amount for each grid cell is calculated by multiplying the respective interception ratio with the precipitation amount of the grid cell. The remainder amount of the precipitation contributes to the runoff amount at the grid cell with the proper application of a surface runoff coefficient and a soil moisture accounting coefficient for Hortonian runoff. The Hortonian runoff coefficient varies with soil texture and seasons. The detailed mathematical description of the monthly water balance computation of the WetSpass model is widely available in the literature [32].

2.3. Study Framework

The research methodology was designed in recursive and adaptive ways that enable to make a robust investigation of the research problem and achieve its intended objectives. Accordingly, the research method comprises four major stages. In the first stage, collection and pre-processing of spatial and hydro-meteorological data were conducted. The activities in this stage include satellite image downloading and processing, land use land cover classification, accuracy assessment, DEM processing to develop topography and slope maps, and gridding soil map at common spatial resolution for the study area. In the second stage, preparation of the model input grid maps, such as land use, soil, rainfall, evapotranspiration, wind speed, temperature, and groundwater depth, was carried out. The lookup tables for land use, soil, and runoff coefficient parameters were also prepared. In the third stage, the WetSpass model was simulated for the watershed using meteorological and biophysical data gathered and processed in the previous stages. The WetSpass model simulation produced average temporal and spatial variabilities of surface runoff, actual evapotranspiration, and groundwater recharge on a seasonal and annual basis for the Upper Gelana watershed. In the final fourth stage, model validation was performed. The simulated groundwater recharge was validated against the baseflow derived from streamflow observation data in the watershed. The overall research methodology framework developed for the estimation of groundwater recharge for the Upper Gelana watershed using the GIS-based WetSpass model is schematically illustrated in Figure 3.

2.4. Materials and Software Used

The major software and resources used in this research study were ERDAS Imagine 2014, ArcGIS 10.3, Microsoft Office Package Visio 2016, WetSpass model, CROPWAT 8.0 program, BFI+3 tools, zonal statistics as table tools in ArcGIS, and GPS. The materials and software extensively used throughout the research project are listed in Table 1.

2.5. Input Data for WetSpass

The WetSpass model requires two major types of input data: geo-spatially referenced grid maps and parameter tables [33]. The model input grid maps were prepared for the following biophysical variables; slope angle, land use, soil texture, groundwater depth, and average meteorological maps of precipitation, potential evapotranspiration, temperature, and wind speed on monthly, seasonal and annual temporal scales. Initially, the model input grid maps were prepared at the spatial resolution of the coarser biophysical factor, 12.5 × 12.5 m. The hydro-meteorological observations in the watershed were very sparse, and their grid maps at 12.5 m resolution could not portray clear spatial variability. Therefore, the model input grid maps were further resampled to a spatial resolution of 100 m, and this spatial scale was found to produce coherent outputs during model simulation.
The biophysical parameters of land use, soil, and runoff have to be provided in four lookup tables for the seamless application of the WetSpass model. The attribute tables contain model parameters for different land use classes, soil types, and the two dominant seasons. Relevant literature reviews and the model user guide were carefully used to modify and estimate parameter values of the watershed features. Previous studies in the region, for example [34], made certain parameter adjustments to the leaf area index, root depth, and bareness for the Geba basin in Ethiopia. Accordingly, through professional consultation and frequent field observations, the relative differences between the Geba basin and the environmental setup of the Upper Gelana watershed are taken into account in parameter estimation for the watershed. The parameter lookup tables are listed in Table 2, Table 3 and Table 4.
The land use map of the watershed was derived from cloud-free Copernicus open access hub Sentinel-2A satellite images on 23 February 2019, using the standard ERDAS IMAGINE supervised image classification method. The six land use classes identified in the land use map (Figure 4a) of the watershed are agriculture (54.00%), shrub and bare land (21.91%), forest (15.12%), and urban and grassland (8.98%).
The soil map of the watershed was extracted from the soil map data of the Rift Valley Lake basin master plan (scale 1:250,000), which was obtained from the Ministry of Agriculture of Ethiopia. The soil code system used in WetSpass is based on the soil texture triangle developed by the United States Department of Agriculture (USDA), which is characterized by its percentage of clay, silt, and sand, ranging from the fine textures (clay), through the intermediate textures (loam), to the coarser textures (sand). The percentage of the topsoil textures (coarse, medium, and fine) was used to identify the soil type from the universal soil texture triangle. The soil map of the Upper Gelana watershed in Figure 4b revealed four types of soil texture classes: sandy loam (58.53%), loam (40.98%), loamy sand (0.24%), and clay (0.25%).
The watershed’s digital elevation model (DEM) was obtained from ALOS-PALSAR. It was downloaded from the Alaska Satellite Facility (ASF) website (https://www.asf.alaska.edu (accessed on 2 November 2018)) at a spatial resolution of 12.5 m. The DEM is used to create a topographic elevation map, a slope map (Figure 4c), and a river network map (Figure 1) for the Upper Gelana watershed. The slope angle of the watershed ranges from 0° to 55° with a mean slope angle of 13°. Groundwater depth data were collected from the Regional Water Resources Office. The groundwater level elevation contours were interpolated from the static water levels of 85 boreholes and springs found within the watershed as shown in Figure 4d.
The meteorological data of the watershed, such as rainfall amount (mm), temperature (°C), sunshine hour, relative humidity (%), and wind speed (m/s), were kindly provided by the National Meteorological Agency (NMA) of Ethiopia. Based on the availability of data and station locations, a total of six meteorological stations in the proximity of the study area were selected. While the Chlelektu, Fesehagenete, Gedebe, Hageremariam, and Yiregachefe weather stations are located within the watershed, the Dilla weather station is situated outside the watershed (Figure 1). The record length of these daily meteorological variables spans from 1988 to 2019. The Upper Gelana watershed’s hydro-meteorological characteristics and other biophysical features need to be accurately captured by the spatially distributed WetSpass model. The annual and seasonal meteorological grid maps of precipitation, temperature, potential evapotranspiration, and wind speed were prepared from the available meteorological stations in the format of the model input requirements.
In the Ethiopian context, there are three major seasons: July–August–September–October (JASO), November–December–January–February (NDJF), and March–April–May–June (MAMJ) are, respectively, referred to as the Kiremt, Bega, and Belg seasons. The Kiremt and Belg seasons are commonly known as wet seasons, whereas the Bega season is typically dry.
Streamflow data were collected from the Ministry of Water, Irrigation, and Energy (MoWIE) of Ethiopia for the Tore gauging station located near the Tore town. The daily streamflow data cover the period from 1990 to 2019. The streamflow data were used to validate the flow simulation of the WetSpass model, which comprises both surface runoff and groundwater recharge. In addition, the baseflow estimates and results of various previous regional and fragmented studies were used as a supplementary confirmation approach for validation of the total simulated flow [35,36,37]. The basic assumption of this validation exercise is that the stream baseflow is equivalent to the groundwater discharge in that watershed, which implies that groundwater discharge to rivers is equivalent to groundwater recharge [36]. The BFI+3 baseflow separation program was used to separate the gauging river flow records into the surface runoff and baseflow components. Using a numerical digital filter, the BFI+3 tool can partition the rainfall-runoff discharge into its components; overland surface flow and subsurface baseflow flow.
The groundwater level measurements were obtained from the South Region Water Works & Construction Enterprise (SWWCE). The monthly groundwater level data for the wells in Figure 4d span from 2017 to 2018. The groundwater level at the wells in the watershed is interpolated to produce a grid input map for the WetSpass model (Figure 4d).
Table
Table 2. Lookup parameters for summer land use land cover.
Table 2. Lookup parameters for summer land use land cover.
NoLand Use TypeRunoff VegetationRunoff ClassImpervious Runoff ClassVegetated AreaBare AreaImpervious AreaOpen Water AreaRoot DepthLAIMin Stomatal OpenInterception PercentageVegetation Height
21AgricultureCrop100.80.2000.44180150.6
7Bare landBare soil4001000.05011000.01
33ForestForest301000253753516
23GrasslandGrass2010000.32100100.2
2Built-upGrass210.200.800.32100100.12
36Shrub landGrass2010000.60.6110152
Table
Table 3. Lookup parameters for winter land use land cover.
Table 3. Lookup parameters for winter land use land cover.
NoLand Use TypeRunoff VegetationRunoff ClassImpervious Runoff ClassVegetated AreaBare AreaImpervious AreaOpen Water AreaRoot DepthLAIMin Stomatal OpenInterception PercentageVegetation Height
21AgricultureCrop1001000.35018000.6
7Bare landBare soil4001000.05011000.01
33ForestForest300.50.50024.55003815
23GrasslandGrass2010000.32100100.2
1Built-upGrass210.200.800.32100100.12
36Shrub landGrass200.20.8000.6011052
Where LAI is Leaf Area Index.
Table
Table 4. Soil parameter attribute table.
Table 4. Soil parameter attribute table.
Soil Type NumberSoil TextureField CapacityWilting PointPWAResidual Water ContentAIBare Soil ETTension Saturated HeightPf_SumPf_Win
12Clay0.460.330.130.090.210.050.370.950.85
5Loam0.250.120.130.0270.370.050.110.150.02
2Loamy sand0.150.070.080.0350.470.050.090.090.01
3Sandy loam0.210.090.120.0410.440.050.150.090.01
Where PAW is plant available water content; AI is the calibration parameter dependent on the sand content of the soil; Pf_Sum is the fraction of summer precipitation contributing to Hortonian runoff; and Pf_Win is the fraction of winter precipitation contributing to Hortonian runoff.

3. Results

3.1. Model Validation

The results obtained from the WetSpass model simulation are validated against streamflow observations. The streamflow measurements account for direct surface runoff and subsurface baseflow draining the groundwater reservoir. The model simulation of the mean monthly groundwater recharge was compared to the mean monthly observed baseflow, alias recharge, of the watershed (Figure 5b). The Nash–Sutcliffe model efficiency criterion coefficient, the coefficient of determination (R2), and its p-vale are used to validate the performance of the WetSpass model. As stated by [38], R2 and a Nash–Sutcliffe efficiency value greater than 0.75 shows a very good performance of a hydrological model in a watershed. The values of R2 and Nash–Sutcliffe coefficients for groundwater recharge and baseflow relationship in the Upper Gelana watershed are 0.91 and 0.85, respectively. The p-value of the coefficient of determination in Figure 5b further confirmed the significance of the simulated groundwater recharge and baseflow relationship. These efficiency coefficients and graphical displays in Figure 5 revealed that the WetSpass model had very good performance in the estimation of groundwater recharge in the Upper Gelana watershed.

3.2. Water Budget Components in the Upper Gelana Watershed

Simulation results of several water balance components are analyzed on annual, seasonal, and monthly aggregation levels from the WetSpass model outputs. The spatial distribution of mean monthly, annual, dry season, and wet season amounts of actual evapotranspiration, surface runoff, and groundwater recharge are obtained, in the form of raster maps, from the model simulation outputs. Figure 6 and Table 5 summarized the mean monthly water budget and the ratio of each water balance component to the rainfall amount, respectively. Annual and seasonal groundwater recharge, evapotranspiration, and surface runoff are the major outputs of the WetSpass model as presented in Figure 7. The simulation results of each water balance component of the Upper Gelana watershed are discussed in detail in the following subsections.

3.2.1. Interception Rate

Figure 8 depicted the spatial distribution of the mean monthly interception in the Upper Gelana watershed during the dry month of January and the wet month of April. The mean monthly interception in the watershed ranges from 1.5 mm in January to 20 mm in April (Table 5). For the wet summer and dry winter seasons, the average interception rate is 28.76 mm and 47.76 mm, respectively (Figure 9). The wet summer season accounts for 37.6% of the interception rate, whereas the dry winter season accounts for 62.4%. These seasonal differences in interception rate have captured the seasonal patterns of canopy storage. In the wet season, the canopy storage is relatively higher than that of the dry season; therefore, the interception rate could be lower in the wet season and higher in the dry season. The estimated annual interception rate ranges from 0 to 252.3 mm (Figure 10b), with an average annual interception rate of 76.5 mm.

3.2.2. Actual Evapotranspiration

The mean monthly actual evapotranspiration (AET) exhibited high spatial and temporal variability across the watershed (Figure 8). The spatial variability of this water balance component is mostly derived from variations in precipitation and land use land cover across the watershed. The mean monthly actual evapotranspiration in the watershed ranges from 21 mm in January to 128 mm in April (Table 5).
The annual actual evapotranspiration varies from 465.1 to 1233.3 mm/yr (Figure 10c). The mean annual actual evapotranspiration was found to be 887.2 mm/yr, and it accounted for 74.5% of the mean annual rainfall. The total amount of actual evapotranspiration in the watershed was estimated to be 1.09 billion m3/yr. The mean actual evapotranspiration during the wet summer and dry winter seasons are 377.92 mm and 509.1 mm, respectively. Generally, the seasonal evapotranspiration showed significant temporal and spatial variabilities in the watershed as depicted in Figure 9, mainly caused by the seasonal distribution of precipitation and seasonal changes in vegetation cover and soil moisture.
Watershed areas within the forest and sandy loam classes consumed the highest actual evapotranspiration amount, but bare land areas overlayed with clay soil had the least water consumption (Table 6). The mean and standard deviation of AET for each land use and soil class in Table 6 is used to determine which biophysical driving factor, among land use and soil texture, dominates the evapotranspiration processes in the watershed [39]. A biophysical factor that produced a lower standard deviation of AET across classes of another factor has a dominant role in the underlying physical processes. Accordingly, the land use classes, particularly the built-up and grassland classes, had a greater influence on the actual evapotranspiration of the watershed than the soil texture classes.

3.2.3. Surface Runoff

The estimated mean monthly surface runoff ranged from 6 to 27 mm. The highest mean rainfall amount occurred in April, and 13.79% of this highest rainfall amount became surface runoff. The lowest surface runoff of 6 mm occurred in January and December months with the lowest mean monthly rainfall amount (Table 5). The mean monthly spatial pattern of surface runoff in the watershed revealed that the highest surface runoff was simulated in built-up areas in central parts, such as Yirga Chefe, Gedeb, and Hageremariyam, as well as in the bare lands of the northeastern region (Figure 8).
The annual surface runoff depth varied from 2.1 to 686.5 mm (Figure 10a), with an average surface runoff of 221.01 mm which accounted for about 18.56% of the average annual rainfall (1190.81 mm). The mean surface runoff in the rainy season is estimated at 149.85 mm, while the mean surface runoff in the dry season is found to be 71.11 mm (Figure 9). The wet summer season accounted for 67.8% of the surface runoff, while the dry winter season accounted for the remaining 32.2% of the surface runoff. The average annual surface runoff volume in the watershed is estimated to be 273.6 million m3 per year.
The mean annual surface runoff for various combinations of land use and soil types is summarized in Table 7. Watershed areas with clay soil produced the largest portion of the surface runoff and the surface runoff contribution of sandy loam areas was found very low. Surface runoff generation processes are found to be more influenced by soil textures than land use types, as shown by the lower standard deviation values for different soil types in Table 7.

3.2.4. Groundwater Recharge

The estimated mean monthly groundwater recharge in the Upper Gelana watershed is presented in Table 5. The mean monthly groundwater recharge ranged from 0 mm in January, February, and December months to 21 mm in April. The largest average rainfall occurred in April and 10.7% of that rainfall amount went to recharge the groundwater system, whereas only 1.6% of rainfall in August converted to groundwater recharge. The mean monthly spatial distribution patterns of groundwater recharge in the watershed are presented in Figure 8. Areas located in the central part of the watershed, such as Fisehagenent, Tore, and Gedeb had higher groundwater recharge rates in April. All the wet months in the study area are found to have a similar recharge pattern to that of April. Meanwhile, Hageremariyam in the southern part of the watershed had a very low groundwater recharge rate in January. This low recharge pattern is consistently common to dry months in the study area.
The annual groundwater recharge in”the ’pper Gelana watershed ranged from 0 to 538.6 mm/yr, with an average groundwater recharge rate of 82.7 mm/yr (Figure 10d). The annual long-term groundwater recharge for the watershed was estimated to be 6.94% of the mean annual rainfall (1190.81 mm). The total groundwater recharge in the watershed was estimated to be 102.26 million m3/yr. The wet summer season produced 67.8% of the annual groundwater recharge, while the winter dry season generated only 32.2% of the annual groundwater recharge. The dry winter season groundwater recharge exhibited greater spatial variations between −101.1 and 205.7 mm/yr (Figure 9). Negative groundwater recharge occurred in areas where actual evapotranspiration exceeded infiltration. This phenomenon happened when the water table approaches the land surface in shallow groundwater zones around valleys, wetlands, lakes, and rivers.
The largest groundwater recharge occurred In watershed areas with overlapped combinations of loamy sand soil texture class and bare land, shrubland, and agricultural land use types. Whereas, the lowest groundwater recharge occurred in the clay soil texture class for all land use types in the watershed. The soil texture classes had smaller standard deviations of groundwater recharge than the land use types as shown in Table 8. Consequently, the soil texture had a greater influence on groundwater recharge processes than the land use land cover in the watershed.

4. Discussion

Groundwater recharge is one of the process components of the hydrological cycle, and this study conceptualized it as the water balance residual of the hydrological cycle. In other words, it was conceptualized as the residual water that enters the groundwater system, after inflow from rainfall and outflow through surface runoff and actual evapotranspiration were satisfied [32]. The direct measurement of these various components of the hydrological cycle is usually difficult because of the complex nature of the environmental system. Therefore, it often requires modeling of the watershed to improve our understanding of the water balance components and inherent physical processes [10]. The groundwater recharge in the Upper Gelana watershed exhibited significant variations over space and time because of the spatial and dynamic patterns of biophysical factors that control groundwater recharge, such as climate, slope, soil type, land use land cover, and geology.
Groundwater recharge plays a prominent role in the interactions of surface water and groundwater resources. Wide ranges of models are available to evaluate groundwater recharge and investigate the interactions of surface water and groundwater resources [10]. However, most of these models are data-intensive and very expensive to apply in the study region. However, the WetSpass model is found to be the most efficient and suitable modeling tool for data-scarce regions that have shallow aquifers. Such a noble modeling study finally enabled us to understand the spatial dynamics of groundwater resources and identify groundwater recharge areas of such larger watersheds in data-scarce, and remote regions. The study results could provide invaluable inputs to the development of an integrated master plan for sustainable management and equitable use of water resources in a healthy and cost-effective manner.
A spatially distributed monthly, seasonal, and annual water balance model was applied to evaluate the water balance components of the Upper Gelana watershed in Ethiopia with particular emphasis on groundwater recharge estimation. Most of this watershed is underlain by the Basement Complex aquifer, which makes the groundwater occurrence in most parts of the watershed predominantly shallow. Most of the inhabitants of this watershed depended on groundwater for their consumptive water use since the public water supply systems are non-resilient due to the erratic nature and unwise use of the surface water sources. In this study, the major components of watershed water balance, such as actual evapotranspiration, interception, surface runoff, and groundwater recharge, were simulated to have a holistic understanding of the water budget of the study area. This study mainly focused on the groundwater recharge component of the water balance that plays a prominent role in the interactions of surface water and groundwater resources. The results of the water balance analysis indicated that about 6.94% of the total rainfall in the Upper Gelana watershed resulted in groundwater recharge, while the remaining large portions of the rainfall amount were consumed by evapotranspiration (74.5%), and surface runoff (18.65%). April is the month with the highest average groundwater recharge rate that ranged from 0 to 21 mm.
The mean annual actual evapotranspiration of 887.22 mm utilized 74.5% of annual precipitation (1190.81 mm) in the Upper Gelana watershed. Other studies in the Abaya-Chamo subbasin have come up with similar evapotranspiration patterns. According to [30], the mean annual evapotranspiration demand of 842.3 mm consumed 70.7% of annual mean precipitation. In other Ethiopian watersheds, evapotranspiration demands consumed 81% of rainfall in the Illala catchment [40], 90.7% of rainfall in the Werii watershed [9], 58.5% of rainfall in the Birki watershed [41] in the Tekeze River basin, and 70.8% of rainfall in the Upper Bilate catchment [42] in Rift Valley Lake basin. Similarly, a study by [43] claimed that 75% of annual precipitation is often used to satisfy evapotranspiration demands. As a result, evapotranspiration absorbs a large portion of the average annual precipitation [15,43]. This study confirmed that evapotranspiration played a significant role in the water balance dynamics of the watershed due to acute solar radiation and dry winds.
The mean annual surface runoff in the Upper Gelana watershed accounted for 18.56% of the mean annual precipitation (1190.81 mm). This simulated result is also in good agreement with another study [24] in the Abaya-Chamo subbasin that reported 21.2% of mean annual precipitation is converted to surface runoff. Similarly, several scholars have estimated that surface runoff accounted for 7% of rainfall in the Illala catchment [40] in the Blue Nile basin, 6% of rainfall in the Werii watershed [9], 27.4% of rainfall in the Birki watershed [31] in Tekeze River basin, and 20% of rainfall in the Upper Bilate catchment [42] in Rift Valley Lake basin.
The average annual groundwater recharge of 82.35 mm in the watershed accounted for 6.94% of the mean annual precipitation. This result is also comparable to groundwater recharge estimated by [24] for the Abaya-Chamo subbasin, which reported a mean annual groundwater recharge of 96.2 mm that accounted for 8.1% of mean annual precipitation. Furthermore, different studies have estimated groundwater recharge in different parts of Ethiopia using the WetSpass model and reported that groundwater recharge accounted for 12% of rainfall in the Ilala catchment [40] in the Blue Nile basin, 4.2% of rainfall in the Werii watershed [9], 7.4% of rainfall in the Birki watershed [41] in Tekeze River basin, and 9.4% of rainfall in the Upper Bilate catchment [42] in Rift Valley Lake basin.

5. Conclusions

Groundwater recharge is an integral component of the hydrosystem that plays a prominent role in the interactions of surface water and groundwater resources [10]. Different biophysical controlling factors of the watershed have both direct and indirect influences on the water balance components of the watershed. The results of this study revealed that while the soil texture factor had a relatively greater influence on the surface runoff and groundwater recharge processes, the land use factor had dominantly influenced the evapotranspiration processes. Since the study watershed is located in shallow aquifer zones, topographic factors are expected to significantly control the recharge mechanism, and hence the interactions of surface water and groundwater resources. Therefore, the surface topography (Figure 1) and slope (Figure 4a) of the watershed are found to be the dominant controlling factors for the high groundwater recharge rates in the central parts (Fisehagenent, Tore, and Gedeb) of the watershed.
Groundwater recharge estimation through the integration of spatial analysis of biophysical factors [39] and spatially distributed modeling of the water balance components provided new hydrological insights for the Upper Gelana watershed. This study came up with a better understanding of the spatial patterns and temporal dynamics of the groundwater recharge of the watershed. This will assist the stakeholders and government agencies in the area to develop and use groundwater resources in a sustainable and equitable manner without causing an adverse effect on environmental, economic, and social norms. The integrated water balance system approach of the study has also improved our understanding of the interconnection between water balance components and their interactions with biophysical features of the watershed. In addition to the groundwater recharge, the spatio-temporal patterns of other water balance components, such as interception, surface runoff, and evapotranspiration, of the watershed were also thoroughly investigated.
A reasonable amount of groundwater recharge was found in most parts of the built-up and bare land areas. This suggested that there are more barren surfaces within the urban and rural settlements in the watershed that allows infiltration and percolation of rainwater. This study accurately estimated the spatial and temporal patterns of groundwater recharge and the interactions between the water balance components of the watershed. The groundwater recharge rates were found to be higher than 400 mm/yr in the central parts (Fisehagenent, Tore, and Gedeb) and lower than 165 mm/yr in the southern parts (Hageremariam) of the watershed. The areal proportions of the low, medium, and high recharging parts of the watershed are, respectively, estimated as 15%, 68%, and 17% of the watershed area. Managed aquifer recharge can be adopted in high and medium groundwater recharging parts of the watershed to capture stormwater runoff during the wet season so that the groundwater reserve and supply could be improved during dry months. This improved understanding of the spatial patterns and temporal dynamics of the groundwater recharge in the watershed could provide tangible evidence for the development of a sustainable groundwater management plan for the Upper Gelana watershed. In addition, concerted efforts should be made towards improving meteorological and hydrological data collection in the watershed on a smaller time and finer spatial scales to estimate daily groundwater recharge and improve groundwater management decisions.

Author Contributions

Conceptualization, E.S.D., D.Y.G. and S.S.D.; Methodology, E.S.D., D.Y.G., A.A.A., S.S.D. and G.T.A.; Software, E.S.D. and A.A.A.; Validation, S.S.D. and G.T.A.; Formal analysis, E.S.D. and S.S.D.; Investigation, E.S.D., D.Y.G., A.A.A., S.S.D. and G.T.A.; Resources, S.S.D. and G.T.A.; Data curation, E.S.D.; Writing–original draft, E.S.D.; Writing–review & editing, E.S.D., D.Y.G., A.A.A., S.S.D. and G.T.A.; Visualization, E.S.D., D.Y.G., A.A.A., S.S.D. and G.T.A.; Supervision, S.S.D.; Funding acquisition, E.S.D., S.S.D. and G.T.A. All authors have read and agreed to the published version of the manuscript.

Funding

Endale S. Demissie received funding from Arba Minch Water Technology Institute, Arba Minch University. Gebiaw T. Ayele covered the APC cost.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Research data is not available due to binding restrictions from data-supplying organizations.

Acknowledgments

Arba Minch Water Technology Institute of Arba Minch University provided financial assistance for this research. As a result, the authors express their gratitude to the Water Resources Research Center for supporting the research endeavor. We would like to express our gratitude to Belete Baychiken for his technical assistance in the modeling experiment. We also thank the National Meteorological Service Agency (NMA), the Ministry of Water Resource and Energy (MoWRE), the Geological Survey, the Mapping Agency, and the South Region Water Works & Construction Enterprise (SWWCE) for providing us with meteorology data, flow data, groundwater well data, and other documents that aided us in our research. Gebiaw T. Ayele acknowledges Griffith Graduate Research School, the Australian Rivers Institute and School of Engineering, Griffith University, Queensland, Australia.

Conflicts of Interest

We hereby declare that the results in this paper are based on original research work that has not been published or is under consideration for publication elsewhere in article form, and that the manuscript does not contain any previously published data arising from the same work. We agree to wait until we receive a final decision based on peer review before submitting this paper for publication. The authors state that the submitted manuscript contains no conflicts of interest.

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Figure 1. Location map of the study area.
Figure 1. Location map of the study area.
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Figure 2. Geological map of the Upper Gelana watershed.
Figure 2. Geological map of the Upper Gelana watershed.
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Figure 3. General framework of the research study.
Figure 3. General framework of the research study.
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Figure 4. Input grid maps used for the WetSpass model: (a) land use, (b) soil type, (c) slope angle, and (d) water level elevation contours.
Figure 4. Input grid maps used for the WetSpass model: (a) land use, (b) soil type, (c) slope angle, and (d) water level elevation contours.
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Figure 5. (a) Average monthly simulated and observed values of groundwater recharge; and (b) the linear relationship between simulated and observed groundwater recharge (2017–2018).
Figure 5. (a) Average monthly simulated and observed values of groundwater recharge; and (b) the linear relationship between simulated and observed groundwater recharge (2017–2018).
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Figure 6. Mean monthly water balance for Upper Gelana watershed (1988–2019).
Figure 6. Mean monthly water balance for Upper Gelana watershed (1988–2019).
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Figure 7. The variation of simulated seasonal and annual water balance components.
Figure 7. The variation of simulated seasonal and annual water balance components.
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Figure 8. Spatial patterns of the mean monthly water budget in the Upper Gelana watershed; (a) January runoff, (b) January interception, (c) January actual evapotranspiration, (d) January recharge, (e) April runoff, (f) April interception, (g) April actual evapotranspiration, and (h) April recharge.
Figure 8. Spatial patterns of the mean monthly water budget in the Upper Gelana watershed; (a) January runoff, (b) January interception, (c) January actual evapotranspiration, (d) January recharge, (e) April runoff, (f) April interception, (g) April actual evapotranspiration, and (h) April recharge.
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Figure 9. Spatial distribution of mean seasonal water balance components simulated by WetSpass model in Upper Gelana watershed: (a) wet season runoff, (b) wet season interception, (c) wet season actual evapotranspiration, (d) wet season recharge, (e) dry season runoff, (f) dry season interception, (g) dry season actual evapotranspiration, and (h) dry season recharge.
Figure 9. Spatial distribution of mean seasonal water balance components simulated by WetSpass model in Upper Gelana watershed: (a) wet season runoff, (b) wet season interception, (c) wet season actual evapotranspiration, (d) wet season recharge, (e) dry season runoff, (f) dry season interception, (g) dry season actual evapotranspiration, and (h) dry season recharge.
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Figure 10. Spatial patterns of mean annual water balance components in the Upper Gelana watershed: (a) mean annual runoff, (b) mean annual interception, (c) mean annual actual evapotranspiration, and (d) mean annual groundwater recharge.
Figure 10. Spatial patterns of mean annual water balance components in the Upper Gelana watershed: (a) mean annual runoff, (b) mean annual interception, (c) mean annual actual evapotranspiration, and (d) mean annual groundwater recharge.
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Table
Table 1. Materials and software used in the research study.
Table 1. Materials and software used in the research study.
NoInputSourceSpatial Resolution *Period and FrequencyProcessing Tools/Methods
1DEMALOS PALSAR
(https://www.asf.alaska.edu)		12.5 × 12.5 m2 November 2018ArcGIS10.3
2LULC mapSentinel-2
https://scihub.compernicus.eu 		10 × 10 m23 February 2019ERDAS IMAGINE2013 and ArcGIS10.3
3Soil map Ministry of Agriculture, Ethiopia (12.5 × 12.5 m)2012ArcGIS10.3
4Slope mapCalculated from ALOS DEM(12.5 × 12.5 m)2 November 2018ArcGIS10.3
5Rainfall, temperature, and wind speed map Ethiopian Meteorological Agency(12.5 × 12.5 m)1988–2019 DailyInverse distance weight (IDW) interpolation in ArcGIS10.3
6PET mapCalculated using FAO Penman–Monteith equation from observed climate data(12.5 × 12.5 m)1988–2019 DailyCROPWAT8 and (IDW) in ArcGIS10.3
7Groundwater depth mapRegional Water Office (12.5 × 12.5 m)2017–2018 Monthly(IDW) interpolation in ArcGIS10.3
8Soil, runoff, and LULC parameter lookup tableWetSpass User Guide and literature review
9StreamflowMinistry of Water Resources and Energy (MoWRE), Ethiopia 1990–2019 DailyBFI+3 tools
* The spatial resolutions in parentheses are for mean monthly spatial input data gridded from point observations obtained from the respective agencies.
Table
Table 5. Mean monthly water budget and the ratio of water budget components to rainfall amount, 1988–2019.
Table 5. Mean monthly water budget and the ratio of water budget components to rainfall amount, 1988–2019.
Month Rainfall (mm) Interception (mm) Runoff (mm) ET (mm) Recharge (mm) Interception/Rainfall (%) Runoff/Rainfall (%) ET/Rainfall (%) Recharge/Rainfall (%)
Jan281.5621017.2320.4959.780.00
Feb40101020024.5425.0550.380.00
Mar85162441518.4927.9147.855.46
Apr19620271282110.2013.7965.4710.76
May18619241241910.4512.8466.5710.23
Jun110121970911.1417.0464.148.12
Jul84101651612.2518.8460.467.59
Aug113161976213.8116.7467.001.61
Sep1321420871010.5615.4666.087.87
Oct17919261161810.4814.4165.2010.04
Nov75181342323.3316.9855.554.04
Dec301.5623015.9121.7263.780.00
Table
Table 6. Mean annual AET amount for different land use and soil texture combinations.
Table 6. Mean annual AET amount for different land use and soil texture combinations.
Land Use and SoilClayLoamLoamy SandSandy LoamMeanSTD
Agriculture537.9762.0688.2944.8733.2169.1
Bare land491.4718.4623.5787.1655.1128.1
Built-up631.7601.5612.6762.5652.174.7
Forest792.3851.8-1131.5925.2181.1
Grassland775.1803.6-939.9839.688.1
Shrubland 647.6736.8700.3951.0758.9133.2
Mean646.0745.7656.2919.5
STD121.6185.4144.5133.5
Table
Table 7. Mean annual surface runoff in mm/yr for different land use and soil class combinations.
Table 7. Mean annual surface runoff in mm/yr for different land use and soil class combinations.
Land Use and SoilClayLoamLoamy SandSandy LoamMeanSTD
Agriculture608.315.026.710.2165.1295.6
Bare land649.614.27.19.3170.1319.7
Built-up475.0260.9239.1322.0324.2106.5
Forest369.17.8-4.4127.1209.6
Grassland380.57.6-4.7130.9216.2
Shrubland 504.210.416.67.0134.6246.5
Mean497.852.772.459.6
STD115.0102.1111.41128.6
Table
Table 8. Mean annual recharge in mm/yr for different land use and soil class combinations.
Table 8. Mean annual recharge in mm/yr for different land use and soil class combinations.
Land Use and SoilClayLoamLoamy SandSandy LoamMeanSTD
Agriculture8.6286.5433.8326.0262.0184.4
Bare land5.3316.0508.3460.6322.6226.8
Built-up1.6157.1240.8165.8147.189.9
Forest19.9299.0-153.3157.4139.6
Grassland14.4302.2-313.3209.3170.6
Shrubland 4.0334.4435.4316.4272.1187.5
Mean11.0282.6404.6289.2
STD9.663.6114.6114.7
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Demissie, E.S.; Gashaw, D.Y.; Altaye, A.A.; Demissie, S.S.; Ayele, G.T. Groundwater Recharge Estimation in Upper Gelana Watershed, South-Western Main Ethiopian Rift Valley. Sustainability 2023, 15, 1763. https://doi.org/10.3390/su15031763

AMA Style

Demissie ES, Gashaw DY, Altaye AA, Demissie SS, Ayele GT. Groundwater Recharge Estimation in Upper Gelana Watershed, South-Western Main Ethiopian Rift Valley. Sustainability. 2023; 15(3):1763. https://doi.org/10.3390/su15031763

Chicago/Turabian Style

Demissie, Endale Siyoum, Demisachew Yilma Gashaw, Andarge Alaro Altaye, Solomon S. Demissie, and Gebiaw T. Ayele. 2023. "Groundwater Recharge Estimation in Upper Gelana Watershed, South-Western Main Ethiopian Rift Valley" Sustainability 15, no. 3: 1763. https://doi.org/10.3390/su15031763

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