GIS based water balance components estimation in northern Ethiopia catchment

Soil & Tillage Research 197 (2020) 104514

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GIS based water balance components estimation in northern Ethiopia catchment

T

Teklebirhan Arefaine Gebrua, Gebreyesus Brhane Tesfahunegnb,* a b

Department of Water Resource and Irrigation Engineering, School of Water Technology, P.O. Box 314, Shire-Campus, Aksum University, Shire, Ethiopia Department of Soil Resources and Watershed Management, College of Agriculture, P.O. Box 314, Shire-Campus, Aksum University, Shire, Ethiopia

ARTICLE INFO

ABSTRACT

Keywords: Evapotranspiration Groundwater Management strategy Runoff WetSpass model

A tool to understand water balance components is crucial for developing water resources management strategies. However, there are inadequate reports on the application of water balance models integrated with Geographical Information System (GIS) at ungauged sub-catchment scale. The objective of this study was to estimate hydrological water balance components using a flexible, physically and GIS based WetSpass water balance model for the Dura sub-catchment, northern Ethiopia. WetSpass model input data included grid files of soil types, topography, slope, land use, temperature, precipitation, potential evapotranspiration (PET), wind-speed and groundwater, and parameter tables (dbf file). Descriptive and Inverse Distance Weighted were used to analyze the different data. Model outputs included actual evapotranspiration (ET), surface runoff and groundwater recharge. The PET of the area was computed using different methods and performance assessment showed < 2% of error, indicating all are robust for supporting decision making. In the study sub-catchment, the mean annual PET and actual ET were found to be 1560 and 576 mm, respectively. The model-estimated annual actual ET was calculated to be 78.4% of the mean annual rainfall. However, there is spatial and temporal variability in ET across the sub-catchment. About 77.5% of the annual actual ET was estimated during the summer season. In the study sub-catchment, about 7.9% and 13.7% of the mean annual precipitation are effective in contributing to groundwater recharge and surface runoff, respectively. On the basis of the model results, the majority of the Dura sub-catchment is generally characterized by low groundwater recharge and high actual ET. The water balance components estimated using the WetSpass model are closely comparable to the observed data as their fitness was showed using R2 > 0.90 and D < 5%, indicating that the model outputs are important for supporting decision making. On the basis of the model results, it is thus suggested to introduce and implement appropriate site-specific integrated soil and water management schemes such as water conservation structures that enhance groundwater and reduce runoff in the study catchment conditions.

1. Introduction

could lead to economic, social and political volatility and also to over deterioration of existing water resources. As the demand for water is growing too fast, the pressure will be even more serious in the future in developing countries (Alene, 2006; Kahsay, 2008; Gebru and Tesfahunegn, 2018). In Sub-Saharan African countries groundwater is one of the key water resource as surface water is not often sufficient to fulfill water demands. Groundwater is thus becoming the principal source of clean domestic and irrigation water for most rural and urban communities (Foster et al., 2000; Alene, 2006; MacDonald et al., 2012). The problem of siltation of reservoirs before their lifetime could be part of the reason for giving due attention towards groundwater resources. Besides this, in

Water is a valuable resource of the earth which is crucial for sustaining life within the globe. As a result, the demand of freshwater has been alarmingly increased globally with population growth and civilization. In developing countries, increasing population pressure associated with change in lifestyle and economic growth has more amplified the pressure on water resources and becomes increasingly a precious resource as demand is rising radically (Getnet, 2009; Nedaw, 2010; MacDonald et al., 2012). In many areas in the developing world, shortage of water has been faced and is becoming one of the basic environmental concerns in the 21st century. Such shortage of water

Abbreviation: WetSpass, is an acronym for Water and Energy Transfer between Soil, Plants and Atmosphere under quasi-Steady State; R2, coefficient of determination; D, percent difference or percentage of relative error ⁎ Corresponding author. E-mail address: [email protected] (G.B. Tesfahunegn). https://doi.org/10.1016/j.still.2019.104514 Received 16 February 2019; Received in revised form 3 October 2019; Accepted 19 November 2019 0167-1987/ © 2019 Elsevier B.V. All rights reserved.

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areas without surface reservoirs the potential water source for irrigation and domestic water use is groundwater (Yirga, 2004; Gebru and Tesfahunegn, 2018). Understanding groundwater resource using modeling for sustainable and efficient utilization is vital to ensure economic development of a country as it provides quantitative information on water availability and potential. Sustainable water resources assessment and utilization systems are also crucial to design and introduce appropriate water management practices that contribute to the intended water demand and supply for a society (FAO, 1986; UNESCO, 1999; Yirga, 2004; Dogramaci et al., 2015). In Ethiopia, rainfall varies extremely spatially and temporally throughout the country, but such variability is more notified in the northern Ethiopia. This has a great impact on water resources that are vital to support the life of local communities as rainfall is inadequate to supply the water demanded for socioeconomic development. Unfortunately, the uneven distribution of the rainfall in space and time together with poor management and harvesting of water resources have led the country to face a repeated famine and drought (FAO, 2005; Yazew, 2005; Dogramaci et al., 2015). In northern Ethiopian conditions, the dry period is mainly sensitive to prolonged water resource supply. In response to such issue, there are attempts to introduce catchment based assessment project studies that encompasses the estimation of hydrological components at small scale. Most of the techniques described in the existing literature to quantify the water balance elements are lumped or empirical approaches. However, the processes of the water balance components are natural and are really difficult to manage and fully explain the hydrologic processes by such models (Alene, 2006; Julius, 2010; Aish, 2014). As a result, over the recent decades the advances in computing techniques combined with bigger and wider data manipulation efforts have permitted for the development of physical hydrological simulation models. Currently, the applicability of physically-based hydrological models together with Geographic Information Systems (GIS) to estimate water balance components at the catchment-scale has been improved. Such approaches consider the influence by human interference and the impact of climatic variability on hydrological processes (Alemaw and Chaoka, 2003). Accordingly, the long-term average water balance components such as surface runoff, evapotranspiration and groundwater recharge can be estimated using water balance models that are integrated in a GIS environment (Alemaw and Chaoka, 2003; Kahsay, 2008; Julius, 2010; Aish, 2014; Rwanga and Ndambuki, 2017). The water balance is defined considering all the inflows and outflows of a hydrologic system as the net change in water storage. In modern hydrology, WetSpass which is an acronym for Water and Energy Transfer between Soil, Plants and Atmosphere under quasiSteady State was developed as a spatially distributed water balance model (Batelaan and De Smedt, 2001). To use this model in a GIS environment, its features are utilized in hydrological studies by coupling them with hydrological models (Alene, 2006). As a result of the GIS technology which supports the integration with a hydrology model, it is now possible to obtain reliable information about the major hydrologic processes and their spatial distribution in a catchment. So, WetSpass model in GIS environment that integrates all hydrological processes has become an excellent choice to address the more complex hydrological factors within small catchments. Such integration can provide a mechanism for handling interactions effect of climate, human interference, water body, groundwater level fluctuation, slope, topography, vegetation, soil, and geology. In short, GIS is applied for organizing, processing and preparing the catchment’s spatial data in the form of a grid map for being utilized by hydrological models (Batelaan and De Smedt, 2001; 2007; Julius, 2010; Aish, 2014; Rwanga and Ndambuki, 2017). Despite the good understanding on the importance of the integration of hydrological models with GIS application by researchers and development workers, there are limited scientific sources on model options for better prediction and simulation of the water balance components under the conditions of semiarid areas as many models

were developed in temperate zone conditions. Such limited information is mainly arising from the lack of sufficient knowledge about the application of the different tools and from data scarcity for data intensive models. As a result, adoption of suitable models to estimate and quantify hydrological parameters in a GIS environment for both gauged and ungauged catchment is found to be the key approach for planning and introducing sustainable water resources management practices (Julius, 2010; Gupta et al., 2015). Other scholars (e.g., Kahsay, 2008; Getnet, 2009; Abbaspour et al., 2015) also stated that a better understanding of the effects of hydrological characteristics on water balance components at the catchment-scale is considerably essential for planning and developing sustainable water management strategies. The use of physically-based water balance models is the most appropriate method for simulating water balance components in areas where there is lack of available data and where the input parameters are limited to assess water resources in an entire catchment. Many water balance models exist such as the Blue Nile model (Conway, 1997), the Thornthwaite water balance approach (Thornthwaite and Mather, 1957; Conway, 1997) and the large scale hydrological modelling approaches (Vôrôsmarty et al. (1989). The problem of these and other models is that they are data intensive, not cost effective to apply for ungauged sub-catchments, and scarce daily data lead to propagated uncertainties from data and the type of model applied. The complex topography and diversified resources and activities together with lack of detail and intensive data in ungauged sub-catchments are the main constraints to the application of the above hydrological models as a decision supporting tools. The choice of a less complex grid-based flexible water balance model which requires limited data inputs and runs on seasonal and annual time steps, and also allows easy new definition of land use types as input parameters (e.g., WetSpass model) is, however, feasible for the Dura sub-catchment conditions. In the semiarid region of Tigray (northern Ethiopia), water balance studies have been conducted only in few large catchments. Such approaches have shown limitations with respect to interpolation accuracy to support decisions at sub-catchment or small catchment scale. As a result, large scale studies contribute less information for proper implementation of interventions that improve water resources potential at small scale catchment in the region. It is also difficult to develop appropriate site-specific management strategies based on previous research results to improve the existing water resource components problem in the region using the limited and broad scale study results at hand. In addition, diverse model performance among the previous studies was observed. In order to implement effective management practices for water resources improvement detailed understanding of local water balance components at small scale (sub-catchment level) using appropriate techniques is important. However, information on the distribution of water balance components at small catchment scales with diversified land use types and soil texture is scarce as there has never been conducted systematic and detailed investigation at ungauged sub-catchment scale in northern Ethiopia. The objective of this study was thus to estimate the water balance components (temporal and spatial variability) using WetSpass model in a GIS environment for the conditions of the Dura-sub catchment, northern Ethiopia. 2. Materials and methods 2.1. Study area description This study was conducted from 2015 to 2017 in the Dura subcatchment, an administrative unit of the Tigray region, northern Ethiopia (Fig. 1). The Dura sub-catchment is found within the Dura catchment. The area of the Dura catchment is 5000 ha, and that of the Dura sub-catchment is 1240 ha. In the sub-catchment, altitude ranges between 2060 and 2644 m above sea level (Fig. 2A) (Ethiopian Mapping Agency, EMA, 1997). The slope ranges from 0 to 76% with a mean value of 9.4% and standard deviation of 4%. Topography of the study 2

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Fig. 1. Location of the study area: Ethiopia (A), Tigray Region (B), Dura catchment (C) and Dura sub-catchment (D). Blue color is reservoir in the sub-catchment.

Fig. 2. The Dura sub-catchment in northern Ethiopia: (A) Elevation and (B) slope. 3

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sub-catchment is described by flat, gentle and steep slopes (Fig. 2B). The elevation and slope information of the Dura sub-catchment were produced from the DEM using the spatial analyst-surface analysis (slope) tool in Arc GIS. The DEM of the Dura sub-catchment (Fig. 2A) was prepared with a resolution of 10 m grid size after digitizing the topographic map (scale 1:50,000) with a contour spacing of 20 m (EMA, 1997) in Arc GIS 10.1. The vector DEM map was converted to raster and projected using the Universal Transverse Mercator 37 North (UTM-37 N) reference system. A grid size of 10 m was suggested as the best grid size for hydrological modeling (Martz and Garbrecht, 1992; Tesfahunegn, 2011). For this study, the Dura sub-catchment was selected purposely from the mid-highlands in the region as this represents the dominant agroecology, farming system, vegetation, terrain complexity, access to road for frequent field visits, soil degradation, and water-harvesting (reservoir) with high sedimentation risk. The study sub-catchment was also considered as part of the areas where human being practiced traditional farming systems for many years. In such conditions the authors of this paper believed that estimation of water balance components considering the influencing factors in a GIS environment can support the development of management options that enhance water potential. In the study sub-catchment, cropping is the dominant agricultural system. Rain-fed agriculture is dominated over irrigated systems. The study sub-catchment’s land use land cover types were classified as cultivated land, bush land, forest, water body, grazing land and rural settlements (Fig. 3A) from the Land sat image of November 2015 using ERDAS IMAGINE software. The classification accuracy was 85% as verified using the 80 geo-referenced ground truth points observed from the sub-catchment. The vegetation of the study sub-catchment is categorized as dense, scattered and little to no vegetation. The study sub-catchment is dominated by cultivated land followed by scattered woody and bushy

land. There are also few dense forests around churches and along the river banks of the study area. According to Fetter (2001), land cover/ land use type is one of the key influencing factors for catchment hydrology. Soils are mainly sandy loam (Leptosols) on the steep slopes, sandy clay loam (Eutric Cambisols) on the middle to steep slopes, sandy clay (Chromic Cambisols) on gentle to flatland and clay loam (Chromic Vertisols) on the flat areas of the Dura sub-catchment (Fig. 3B). The study sub-catchment is dominated by some geological units (Fig. 3C). The major geological units of the sub-catchment are alluvial deposits, basalt, phonelite and sandstone. Alluvial deposits are found covering the flatland areas of the sub-catchment. The sandstone occurs underlying the volcanic rocks mainly in the northeastern part of the sub-catchment. The sandstone is coarse grained in texture. In the study area, mean annual rainfall of 734.6 mm was recorded (average of 35 years data from 1982 to 2016). The study sub-catchment has two rainy seasons that extend from March to April (small rainfall) and June to September (main rainfall season). Most of the annual rainfall was recorded during July and August, which accounted for about 78% of the total annual (Fig. 4A). The mean monthly temperature of the sub-catchment is19 0C, but it is higher in May to June and lower in January to December (Fig. 4B). According to FAO (1998), temperature can govern the evapotranspiration rate by exerting heat on the nearby air and transfers energy of the vegetation. The monthly wind speeds of the Dura sub-catchment range from 1.7 m s−1 to 4.0 m s−1 (Fig. 4C). Wind speed is also one of the driving forces for water losses as vapor. Accordingly, it has a strong effect on evaporation and evapotranspiration rates of the study catchment, because wind can serve as a water lifting mechanism. The Dura subcatchment is also characterized by semi-arid climate which ranges its aridity index from 0.439 to 0.483, with a mean value of 0.455 (Fig. 5), which is described as the ratio of rainfall to potential evapotranspiration of the area (Crosbie et al., 2010).

Fig. 3. The Dura sub-catchment in northern Ethiopia: (A) Land use/land cover, B) Soil type and C) Geological units. 4

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field survey existing well sites and soil sample collection sites were identified so as to assess groundwater depths and soil attributes in the study sub-catchment, respectively. Even though the study sub-catchment is dominated by some geological units, the groundwater point measurements were collected from each geological unit that is shown in Fig. 3C. A detailed procedure about soil sampling is given in sub-section 2.4. Moreover, during the field survey, 80 geo-referenced ground truth points were observed with regard to land use/ land cover types. Such field survey was undertaken for verification of the satellite image analysis of land use/ land cover (Fig. 3A), using GIS (ERDAS IMAGINE) supervised classification technique. 2.4. Soil sample collection and analysis Composite soil samples were collected randomly from the seven landforms (Fig. 6). The landforms were developed by considered variations in the topography and geomorphologic features of the study subcatchment (Tesfahunegn et al., 2016). The seven landforms included in the soil sampling were the escarpment (covers 29% of the catchment area), central ridge (27%), valley (19%), plateau (8%), rolling hill (9%), mountain (6%) and reservoir (1.2%). Detailed information about the landforms and the different erosion-status sites (stable, eroding and aggrading sites), which were used as soil sampling sites can be found in Tesfahunegn et al. (2016). Accordingly, from each erosion-status with various soil texture and land use types found in a landform, four representative soil samples were taken from 1.0 m pit depth. The total soil samples were 84 which were found from three erosion-statuses X seven landforms X replicated four times. The purpose of stratifying the catchment area into different sampling groups as erosion-status was to ensure that the sampling points were well distributed across the study sub-catchment. The geographical locations of the soil sampling points in each sampling sites were geo-referenced using GPS (Appendix Table A1). The soil samples were collected in the dry month (May 2015) of the study period; because the dry season is more comfortable for soil samples collection and processing as compare to the wet season. In each soil sampling sites, sampling plot area ranging from 12 to 18 m2 was used for soil sample collection from the pits opened. The size of the plot varied with the size of homogeneous soil group (erosion-status) that was identified from the landforms. The soil samples were collected at the cereal crop root depth that considered the most disturbed soil depth (1.0 m). The soil samples collected from each pits were carefully mixed in a bucket, and a composite sub-sample of 500 g was taken for analysis. Soil samples were air dried, grind and sieved through a 2 mm sieve before analysis. The samples were analyzed for soil texture following the Bouyoucos hydrometer method (Gee and Bauder, 1986). For the undisturbed soil samples collected from each pit in the sampling site, the core method (Blake and Hartge, 1986) was used. These samples were analyzed for field capacity at a suction of −1/3 bar and permanent wilting point at −15 bar pressure using a pressure plate apparatus (Baruah and Barthakur, 1997; Dexter and Bird, 2001). The number of the core samples varied from 2 to 4, depending on the horizon layers in a given pit to a depth of 1 m. Plant available and residual water content of the soils were calculated following the equations reported in Baruah and Barthakur (1997). The soil parameters were analyzed following the standard laboratory procedures adopted by the Ethiopian National Soil Laboratory (Ministry of Natural Resources Development and Environmental Protection (MoNRDEP), 1990) in Mekelle Soil Laboratory Center, Ethiopia. The mean soil analysis results of the study sub-catchment are shown in Table 1. The U.S. Department of Agriculture (USDA) soil classification system was used to classify soil texture. According to Tesfahunegn et al. (2011, 2016), the study sub-catchment, bulk density of clay loam soil and sandy loam were reported as 1.37 and 1.63 Mg m−3, respectively. The lowest and highest bulk density values were found near the reservoir and the valley, and the central ridge, respectively. This study

Fig. 4. Climatic data using 35 years of data (1983–2017): (A) Mean monthly rainfall, (B) Mean monthly temperature and (C) Mean monthly wind speed of the Dura sub-catchment, northern Ethiopia. (Source: Ethiopian Meteorology Agency, Mekelle branch).

2.2. Data type and sources In this study, secondary and primary data types were collected. Secondary data included meteorological data (rainfall, temperature, wind speed, humidity and sunshine hour) from the Ethiopian Meteorology Agency (Mekelle branch), satellite image of land use/ land cover, Digital Elevation Model (DEM) and topographic map from Ethiopian Mapping Agency (EMA). Moreover, published and unpublished documents were also reviewed in the context of this study. Primary data such as ground truth data on land cover/ land use types, vegetation condition, soil parameters and groundwater depth were collected from the study sub-catchment. These data were gathered through field observations, field measurements and informal discussions with various stakeholders. Details of these points are described in the following sub-sections. 2.3. Field surveys and image processing Detailed and intensive field investigations were carried out throughout the Dura sub-catchment from 2015 to 2017. The intensive field surveys were performed to characterize the study area in terms of groundwater depth, vegetation type, land use/ land cover and soil attributes based on the information acquired from the local communities as well as researchers’ field observations and measurements. During the 5

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Fig. 5. Aridity index (AI) of the Dura sub-catchment, northern Ethiopia.

Fig. 6. Land forms and erosion-status sites in the Dura sub-catchment of Tigray, Ethiopia (Source: Tesfahunegn et al. (2016)).

indicates that a large part of the catchment (> 70%) shows high bulk density (> 1.60 Mg m−3), in which this decreases soil water-holding capacity and the circulation of water in the soil system implying that soil bulk density influences the water balance components.

Table 1 Soil parameter values used by WetSpass model, which were determined from Dura sub-catchment, northern Ethiopia. Soil texture

FC

WP

PAW

RWC

Sandy loam Sandy clay loam Clay loam Sandy clay

0.21 0.26 0.33 0.32

0.09 0.16 0.19 0.23

0.12 0.10 0.14 0.09

0.041 0.068 0.075 0.109

2.5. Potential evapotranspiration computation The potential evapotranspiration (PET) of the Dura sub-catchment was computed by the FAO Penman-Monteith method as it encompasses almost all parameters that affect the PET relative to the other methods (FAO, 1998). The PET was also computed using the regression equations developed for Tekeze and Mereb basins in Ethiopia by Yilma (2002) and such PET values were compared with the FAO method. The Yilma (2002) regression equations are estimated monthly potential

FC, Field Capacity; WP, Wilting Point; PAW, Plant Available Water; RWC, Residual Water Content.

6

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evapotranspiration as a function of altitude (Appendix Table A2). The PET results calculated using the above two methods showed similar values which differed by less than 2% of error. When considering the entire sub-catchment, the FAO Penman-Monteith method provided point values of PET only for the selected meteorological stations. This implies that the FAO method does not represent the spatial variability of PET in the entire study area. However, the regression equations resulted in spatially and temporally distributed PET values. The Yilma (2002) regression equations are thus assumed preferable to be used for ungauged sites. Thus, a well distributed contour grid map of potential evapotranspiration for the entire Dura sub-catchment was developed using the regression equations reported in Yilma (2002). Accordingly, the individual months PET grid maps of the study sub-catchment were prepared from its elevation map using the raster calculator tool in Arc GIS (version 10.1). Similarly, the winter and summer season PET maps of the study area were prepared by adding the eight months of winter (October to May) and the four months of summer (June to September), respectively. In the case of the FAO Penman-Monteith method, the daily PET of the study area was computed manually and CROPWAT 8.0 model was used for comparing the methods. The results from both methods showed almost the same PET value. The detailed CROPWAT 8.0 model method is extensively described in FAO (2009). The meteorological parameters that were used as input to calculate the PET of the Dura subcatchment using the FAO Penman-Monteith method were prepared using MS Excel from the daily recorded meteorological data of the nearby stations of Aksum and Selekleka for the year of 1982–2016 (Source: Ethiopian Meteorology Agency, Mekelle branch).

that ETv is evapotranspiration from the vegetated, Es is evaporation from the bare soil, Eo is evaporation from the open water, Ei is evaporation from the impervious, Sv is surface runoff from the vegetated, Ss is surface runoff from the bare soil, So is surface runoff from the open water, Si is surface runoff from the impervious, Rv is groundwater recharge from the vegetated, Rs is groundwater recharge from the bare soil, Ro is groundwater recharge from the open water, and Ri is groundwater recharge from the impervious. The actual evapotranspiration is estimated as a function of potential evapotranspiration (PET), considering vegetation coefficient and ground actual soil moisture content at field conditions. The annual actual evapotranspiration is thus calculated by WetSpass model as a sum of evaporation from bare soil, transpiration of the vegetated cover, interception loss by vegetation and evaporations of open water body. Details of the WetSpass model for water balance calculation equations are available in Batelaan and De Smedt (2001, 2007). WetSpass model was initiated to be applied in the temperate region by Batelaan and De Smedt (2001). Accordingly, since the temperate region has dissimilar climate and locations as compared to the tropics, modifications of input attributes relevant to the tropical region are important. In the temperate region, summer and winter seasons last for six months each whereas in Ethiopia summer has four months and winter eight. Not only the duration of the seasons, there is also dissimilarity in the rainfall season and land use/ land cover between the two regions. Thus, for applying the WetSpass model for the Dura subcatchment, the meteorological grid map input was prepared using eight months for the winter and four months for the summer season. Similarly, the winter and summer land use/ land cover parameters were modified for the study sub-catchment (tropical) conditions. Parameter tables (dbf data) and grid map inputs data are needed for the WetSpass model to calculate the water balance components. Arc GIS (version 10.1) spatial analyst extension was used to prepare the input parameters.

2.6. WetSpass modeling 2.6.1. WetSpass model description WetSpass stands for Water and Energy Transfer between Soil, Plants and Atmosphere under quasi-Steady State. WetSpass is a flexible set-up that allows easy new definition of land use types. It is a physically and GIS based model used to estimate the long-term average spatially varying annual and seasonal water balance components: surface runoff, actual evapotranspiration and groundwater recharge. The total water balance as a raster cell is split into independent water balances for vegetated, bare-soil, open-water and impervious parts of each cell. Such splitting is accounted for by the non-uniformity of the land use per cell, which is dependent on the resolution of the raster cell. Details of the water balance components of a raster cell were given in the previous reports (e.g., Batelaan and De Smedt, 2001; Rwanga, 2013; Aish, 2014). The water balance is represented as the net result of the inflow and outflow of water. The most significant inflow component of the water balance is precipitation (rainfall). The most important outflow components of the water balance are surface runoff, evapotranspiration and groundwater recharge (Abu-Saleem et al., 2010; Aish, 2014). The WetSpass model water balance computation is basically a numerical representation of the net results of the inflow and outflow of water at a raster cell level. The total water balance of a given area is thus calculated as the summation of the water balance of each raster cell (Ateawung, 2010). As described by Batelaan and Woldeamlak (2007), total water balance per raster cell and season as well as annual are computed using Eqs. (1)–(3). ETraster = avETv + asEs + aoEo + aiEi

(1)

Sraster = avSv + asSs + aoSo + aiSi

(2)

Rraster = avRv + asRs + aoRo + aiRi

(3)

2.6.2. WetSpass input files 2.6.2.1. Grid maps and parameter tables (dbf data). WetSpass requires a combination of grid files and tables (dbf files) as input data. The ArcInfo grid files included soil, elevation, slope, land use, temperature, precipitation, PET, wind-speed and groundwater depth. Summer and winter seasons were considered for the above input files, except for soil, elevation and slope attributes. The model also requires parameter data such as the runoff coefficient. Parameters related to land use and soil types are connected to the model as attribute tables of the land use and soil raster maps. The model allows for easy definition of new land use or soil types as well as changes to the parameter values (Batelaan and De Smedt, 2001). The parameters were added to the grid maps as attribute tables. The soil used for the WetSpass model is classified on the basis of the U.S. Department of Agriculture classification system (Soil Survey Staff, 1951; Batelaan and De Smedt, 2001). Generally, the approach of this model follows the three steps as adopted from literature (e.g., Batelaan and De Smedt, 2001; Rwanga, 2013; Aish, 2014). These are: i) collecting historical weather and catchment’s attribute data; ii) building grid maps of rainfall, wind-speed, temperature, land use, soil, slope, elevation and groundwater depth; and iii) running GIS based water balance model (WetSpass). Details of input parameters and grid maps are given below. Land use parameter tables: The modified winter and summer land use parameter tables were prepared as crop, forest, grass, bare soil and open water. Moreover, the tables encompassed values for rooting depth, leaf area index, vegetation height, minimum stomata opening, and interception percentage of the area from different sources. These values vary across each land use type and seasons. Such data were modified after Tewolde (2009) through field measurement, field observation, and interview of experts and local communities in the study sub-catchment. The land use parameter table was prepared using MS Excel as dbf data format.

Where, ETraster, Sraster, Rraster are the total evapotranspiration, surface runoff, and groundwater recharge of a raster cell, respectively. Each of the water balance components having vegetated, bare soil, open water and impervious area are denoted by av, as, ao, and ai respectively. Note 7

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Soil attribute parameter table: The soil attribute table of the study sub-catchment was prepared in MS Excel as dbf data format. Such attribute table consisted of soil type (texture) field capacity, permanent wilting point, plant available and residual water content of the soils as presented in Table 1. The values of soil attribute data varied with soil types. So, for each soil types its own corresponded mean values of attribute data was prepared from the soil sampling profile. Runoff characteristics parameter table: The Dura sub-catchment runoff characteristics parameter table was prepared in MS Excel as dbf data format. Such table consisted of the runoff coefficient for each slope, soil type and land use of the study sub-catchment. The GIS was applied to discretize the study sub-catchment into different sub-sites based on the nature of surface slope, topography, soils and land use/ land cover while computing the runoff coefficients. Grid maps: In this study, the spatial input grid maps included elevation, slope, land use, soil, potential evapotranspiration and groundwater depth (grid size of 10 m × 10 m). Temperature, precipitation (rainfall) and wind speed parameters were prepared using MS Excel from the daily available recorded meteorological data of the nearby Aksum and Selekleka stations for the period of 1982–2016. Similarly, the groundwater depth attribute was prepared using MS Excel for the recorded period of 2015-2017. These were converted to grid maps via grouping them into winter and summer seasons in Arc GIS (version 10.1) using the Inverse Distance Weighted (IDW) interpolation technique. IDW is widely and commonly used interpolation method, which has been applied to allocate spatially the meteorological data for the whole study area. The application of IDW technique is based on the principle of nearest values are more related than values far away. IDW was selected as the preferred method of interpolation over that of Kriging because it was showed more reliable values of mean squared error when compared the measured values with the predicted ones. Details about IDW can be found in Gotway et al. (1996) and Zeiler (2010).

Table 2 Comparison between WetSpass model simulated and measured surface runoff and base flow values for the study catchment. Year

2015 2016 2017 Mean

Surface runoff (million m³) (R2 = 0.83)

Base flow (million m³) (R2 = 0.98)

Measured

Simulated

Measured

Simulated

1.189 1.236 1.246 1.224

1.252 1.282 1.298 1.277

0.043 0.051 0.056 0.050

0.046 0.052 0.059 0.052

R2 = Coefficient of determination that shows the relation between measured and model simulated values.

less than 5%. The R2 of 0.83 and 0.98 for the relation between observed and predicted surface runoff and base flow, respectively, also shows values of such water components are close to each other (Table 2). The values of the groundwater recharge estimated using WetSpass model was evaluated using the output from the Water-Table Fluctuation (WTF) method (Healy and Cook, 2002). The WTF technique estimated mean groundwater recharge of 60.3 mm, representing 8.2% of the mean annual rainfall of the study area. The WetSpass model estimated mean annual groundwater recharge of 58.02 mm for the study sub-catchment. Comparison of such values between the two approaches showed less than 4% of error (variance) which showed nearly the same prediction capacity. The WetSpass model outputs were also compared with values which were reported in previous reports elsewhere and the D values showed less than 13%. The derived evapotranspiration values calculated using the different methods in sub-section 2.5 were also used to verify WetSpass model estimated actual evapotranspiration values and found to be within the range of acceptable values for supporting decision making (< 7% of errors). Such acceptable errors between estimated and observed values by the different methods indicate that the estimation of water balance components using the WetSpass model is good enough to support for decisionmaking in the Dura sub- catchment conditions.

2.6.3. Model simulation outputs The WetSpass model simulated on an annual basis and also for the two different seasons (summer and winter). The model simulations were based on long-term annual and seasonal climate data of 35 years. The WetSpass model outputs included spatial seasonal and annual hydrological parameters mainly groundwater recharge, actual evapotranspiration, surface runoff, transpiration, interception loss, and evaporation. In this paper, however, only the major water balance components like groundwater recharge, actual evapotranspiration and surface runoff were presented. The WetSpass model calculates the total actual evapotranspiration as a sum of evaporation of water intercepted by vegetation, from bare soil between vegetations and water transpiration from vegetative cover (Abu-Saleem et al., 2010).

3. Results and discussion 3.1. Potential evapotranspiration (PET) The annual PET values of the sub-catchment calculated using the regression equations developed by Yilma (2002) varied from 1473 mm to 1618 mm, with a mean value of 1560.5 mm (Fig. 7). The mean value of PET of the Dura sub-catchment is found to be consistent with the results reported in the previous published reports from the Tigray region in northern Ethiopia; for instance, with the report from the Geba basin (Tewolde, 2009) and the Illala catchment (Arefaine et al., 2012). However, the mean PET value of 1625 mm which was reported in the previous reports showed a slightly higher value (4%) than that of the present result of annual PET. PET is found to be higher than rainfall as PET estimated in the availability of sufficient soil water for vegetation. The southern part of the study sub-catchment indicated a higher value of PET than in the northern, western and some eastern parts of the sub-catchment. Such PET variability across the sub-catchment could be caused mainly due to elevation differences (Fig. 2A) that resulted in a disparity in soil and air temperature. In line with this view, Leul (1994) has reported that average temperature drops by 0.6 °C per 100 m increment in altitude. Others have also reported that the highest PET value for the lowest elevation and lowest PET value for the highest elevation areas of the Geba river basin in the northern Ethiopia (e.g., Alene, 2006; Tewolde, 2009). Temperature and PET are strongly decreasing with increasing in altitude in the case of Mkomazi River Basin in Tanzania (Mmbando and Kleyer, 2018). The study sub-catchment PET value showed variation between the

2.6.4. Model performance evaluation Simulated water balance components were compared with measured data using different statistical indicators (e.g., percent difference, D and coefficient of determination, R2). The D value measures the average difference between the simulated and measured value divided by the measured value. D value close to 0% is the best preferred, however, D value up-to 15% are acceptable if the accuracy of the input data and observed data are relatively poor (Tesfahunegn et al., 2014). The WetSpass model result was evaluated for the study sub-catchment conditions on the basis of the annual surface runoff and discharge measurement in the study area and their difference or error of variation is found to be less than 5%. The measured data showed an average annual surface runoff value of 98.7 mm and an average annual base flow of 4.03 mm. On the other hand, the average model results for the accumulated surface runoff and base flow for the study area are found to be 103 mm and 4.22 mm, respectively. Such model values indicated that the runoff and base flow results of the study sub-catchment are reasonably comparable with the observed data with an error value of 8

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Fig. 7. Annual reference potential evapotranspiration computed from the regression approach for the Dura sub-catchment, northern Ethiopia.

the basin’s annual precipitation). Similar actual evapotranspiration values to the present finding have reported for the Geba basin in northern Ethiopia by Alene (2006), Dire Dawa in eastern Ethiopia by Tilahun and Merkel (2009), the Jafr basin of Jordan by Al Kuisi and El-Naqa (2013) and for the northeast Iran by Zarei et al. (2016). The Dura sub-catchment annual spatial actual evapotranspiration values ranged from 417 to 1618 mm (Fig. 8A). The estimated annual spatial actual evapotranspiration is within the range of previous reports determined for the different areas in the northern Ethiopia including the Geba basin (Alene, 2006), GumSelassa and Laelay Wukro watersheds (Abdurahman et al., 2008), and Werii watershed (Haile and Kassa, 2017). Reports of these previous studies showed that actual evapotranspiration ranged from 361 to 1826 mm year_1. Accordingly, the comparable spatial actual evapotranspiration values between the present and previous results indicated that the present result is reliable and acceptable as their mean differences error values are reported to be less than 13%. In this study, the highest mean annual evapotranspiration was detected in areas where dominantly covered by water body, shrubs and forests while a less evapotranspiration rate was observed from areas with settlements and seasonal farming activities (Table 3). In support of the present result of actual evapotranspiration variability with land use/ land cover several researchers have reported including the reports for the Geba basin in northern Ethiopia by Tewolde (2009), for the central Oklahoma by Liu et al. (2010), Illala catchment in northern Ethiopia by Arefaine et al. (2012), and Colorado River Basin by Singh et al. (2014). In addition, the model result showed that there is seasonal variability in actual evaporation in the study sub-catchment (Fig. 8B and C). On average, about 77.5% (446.3 mm) of the annual actual evapotranspiration was escaped during the wet season (summer season) whereas the remaining 22.5% (129.6 mm) was lost during the dry season (winter season). Similarly, Haile and Kassa (2017) have presented that about 76% of the annual rainfall of the Werii’s watershed of northern Ethiopia has lost as an actual evapotranspiration in the summer season while the rest 24% in the winter season. Al Kuisi and El-Naqa (2013) have also reported that 79% of the annual rainfall of the Jafr basin in Jordan has lost as an actual evapotranspiration during the summer season while the remaining 21% during the winter season. The seasonal evapotranspiration difference is also due to the variation in rainfall, temperature and land use/ land cover types

two seasons, in which the highest PET value was observed in the winter (dry season) while the lowest value was recorded in the summer (wet season). So, temperature differences associated with elevation and seasonal variations are the main PET governing factors in the Dura subcatchment. Different scholars have confirmed that PET values varied mainly with season, elevation and temperature differences as reported by Nedaw (2010) for Koraro area and Arefaine et al. (2012) for Illala catchment in the northern Ethiopia, and in southeast Kenya by Maeda et al. (2011). Similarly, other researchers have reported that PET varies across seasons and elevation differences for China (Gao et al., 2006), in the continent of Africa (e.g., Ateawung, 2010), and for Kinaye watershed in India (e.g., Kumar et al., 2016). In addition, in the past decade, numerous studies have found that air temperature is the dominant factor for increasing PET (e.g., Wang and Dickinson, 2012; Liu and Zhang, 2013; Zhang et al., 2013; Guo et al., 2017). Hence, agricultural management practices should be considered the variability in PET with elevations and across seasons so that to use resources efficiently and maintain sustainable production. 3.2. Actual evapotranspiration Annually, 575.9 mm of water is escaped from the study sub-catchment due to actual evapotranspiration which represents about 78.4% of the annual rainfall in the study area. Such value differed from the result reported by Tadesse et al. (2010) who estimated actual annual evapotranspiration using Thornthwaite method to be 613.3 mm. The slight overestimation (6.5%) by Tadesse et al. (2010) using the Thornthwaite method could be associated with its various limitations. For instance, this method has the limitation to incorporate at least all the governing factors that influence actual evapotranspiration computation. Though both give over all similarity and comparable results, the WetSpass model is good enough to determine an average actual evapotranspiration as well as its temporal and spatial variability for the entire study sub-catchment. This is because WetSpass model incorporates most of the spatial and temporal factors that affect the computation of actual evapotranspiration. Moreover, the computed annual actual evapotranspiration of the sub-catchment is comparable with that of WetSpass model estimated for the Geba basin in northern Ethiopia by Tewolde (2009) who reported to be 476 mm (76% of 9

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Fig. 8. WetSpass simulated actual evapotranspiration: A) Annual actual evapotranspiration; B) Summer season actual evapotranspiration; and C) Winter season actual evapotranspiration of the Dura sub-catchment, northern Ethiopia.

Table 3 Average annual evapotranspiration and surface runoff (mm) across the different land use and soil texture types in Dura sub-catchment, north Ethiopia. Annual actual evapotranpiration (mm) Land use

Soil type (by texture) Sandy clay 1549.95 557.03 423.85 – 532.79 535.10 719.74

Water body Bush land Agriculture Forest Rural settlement Grazing land Average Annual surface runoff (mm) Land use type Sandy clay Water body 77.82 Bush land 123.55 Agriculture 163.58 Forest – Rural settlement 180.66 Grazing land 169.25 Average 142.97 Annual groundwater recharge (mm) Land use Sandy clay Water body 0 Bush land 29.20 Agriculture 122.17 Forest – Rural settlement 10.63 Grazing land 5.34 Average 33.47

Sandy clay loam 1436.03 574.16 453.84 615.96 559.63 549.13 698.13

Clay loam – 570.09 – – 559.45 553.76 561.1

Sandy loam – 568.79 481.25 610.15 553.32 530.03 548.71

Average 1492.99 567.52 452.98 613.06 551.30 542.01 703.31

St. dev 80 7.4 28.7 4.1 12.7 11.3 24.03

Sandy clay loam 77.82 69.21 97.15 53.71 119.66 101.54 86.52

Clay loam – 171.81 – – 139.37 129.01 146.73

Sandy loam – 12.48 24.73 11.08 46.36 17.50 22.43

Average 77.82 94.26 95.15 32.40 121.51 104.33 87.58

St. dev 0 68.8 69.5 30.1 56.2 64.2 48.13

Sandy clay loam 0 66.77 158.43 40.3806 31.05 59.49 59.35

Clay loam – 47.32 – – 11.12 27.22 28.55

Sandy loam – 130.29 205.59 89.460 112.01 164.04 140.28

Average 0 68.40 162.06 64.92 41.20 64.02 66.77

St. dev 0 44.0 41.8 34.7 48.2 70.3 39.83

-, nill (no value) as there is no such land use for a given soil texture.

10

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between the two seasons and across locations. In the Dura sub-catchment (study area), a higher rainfall is available in the summer (July–September) than the winter season in which this influences the vegetation coverage and density of the study sub-catchment and thereby on the rate of evapotranspiration. The present result also showed that land use/ land cover variability influences more the spatial variability of actual evapotranspiration as compared to the effect of soil texture (soil type) on actual evapotranspiratio (Table 3). The lower variability in average annual evapotranspiration across the different soil texture indicates that evapotranspiration rate is less dependent on soil type in the study sub-catchment In line with the present result of seasonal variability in actual evapotranspiration, Muhammed (2012); Al Kuisi and El-Naqa (2013); Pan et al. (2015) and Haile and Kassa (2017) have reported that increasing in rainfall amount would result in an increase in evapotranspiration as rainfall affects soil moisture, vegetation growth, diversity and type. Other researchers have reported that rainfall and temperature rise would be resulted in an increment in the corresponding evapotranspiration rate even at the end of the 21st century (e.g., Easterling et al., 1997; Molina, 2015; Pan et al. (2015).

(2006); Batelaan and DeSmedt (2007); Batelaan and Woldeamlak (2007); Abrha (2009); Tewolde (2009); Zeleke and Merkel (2009); Al Kuisi and ElNaqa (2013) and Gitika and Ranjan (2014). Thus, the present model runoff spatial and temporal distribution results can support to understand the main factors that govern runoff variability in the northern Ethiopia conditions. In addition, the magnitude of surface runoff in winter and summer period varied seasonally in the study sub-catchment (Figs. 9B and C). About 80.5% (80.9 mm) of the surface runoff was estimated to be generated in the summer months (June to September) whereas the rest 19.5% (19.6 mm) was during the winter months (October to May) from the study sub-catchment (Figs. 9B and C). Consistent with the present result, studies in the northern part of Ethiopia reported that 80% of the total runoff generally generated in the summer season (Water Works Design and Supervision Enterprise, WWDSE (Water Works Design and Supervision Enterprise), 2007; Abrha, 2009; Tewolde, 2009). This could be attributed to a higher rainfall during the summer season than the winter season in which rainfall amount exceeds the infiltration capability of the soil that results in a higher runoff. On the other hand, in the winter period (dry months), the amount of rainfall is lower than the soil infiltration capacity in which this generates zero to very low (negligible) surface runoff in the Dura sub-catchment. Hamad et al. (2012) have reported similar finding of runoff variability on seasonal basis for the Gaza strip area. Al Kuisi and El-Naqa (2013) also reported that about 93% of the surface runoff of the Jafr basin in Jordan reported during the wet seasons while the rest of 7% was during the dry season. Hence, the seasonal surface runoff variation of the Dura sub-catchment is mainly governed by the amount of precipitation which is too little in the winter season coupled with a higher temperature condition. The implementation of water conservation and water harvesting structures can play a significant role for runoff variability in time and space (Sultan et al., 2017). For the tributaries located in the lower parts of the Dura sub-catchment, that is adjacent to the reservoir the best management practices such as zero grazing, vegetative gully stabilization, have already introduced (Fig. 10). Other commonly available soil and water management technique practiced in that specific small portion of the landscape of the sub-catchment includes terracing and plantation of grasses and small size trees. Such management practices boost the site’s water and soil retention capability and enhance groundwater recharge along the reservoir bank. As a result of the shallow groundwater depth, incoming precipitation at this spot is thus exposed largely to both evapotranspiration and runoff (mainly as base flow). The existence of shallow groundwater depth could be the source of perennial base flow near to the outlet of the sub-catchment or towards the inlet of the reservoir. Therefore, implementing site-specific soil and water management interventions are suggested as long term solutions to reduce runoff loss and thereby maintain water flow along the driest and seasonal streams in the study sub-catchment conditions.

3.3. Surface runoff The average annual surface runoff in the Dura sub-catchment is 100.5 mm (13.7% of the annual average rainfall). Such runoff encompasses the sheet portion of water flowing to large streams and rivers (Ward, 1989). Similar reports on the magnitude of surface runoff are available in literature, for example, surface runoff of 13% of the annual rainfall in Koraro area in the northern Ethiopia (Nedaw, 2010); 12% of the annual precipitation in the Gaza Strip in Palestine (Hamad et al., 2012; Gharbia et al., 2015) and 14% of the yearly precipitation in the northeast Iran (Zarei et al., 2016). The WetSpass model computed average annual surface runoff (100.5 mm) from this study is almost similar with that of Tadesse et al. (2010) who reported average annual runoff (104.5 mm) estimated using runoff coefficient method in the northern Ethiopia. The runoff values between the two methods vary at an error value of 3.8%.. Despite of this, the runoff coefficient method is applicable only to calculate average runoff value for a small catchment. Accordingly, the WetSpass model seems to be the best and preferable to that of the runoff coefficient method as the model is capable to estimate spatial and temporal variability using all the major parameters that influence runoff in a catchment. The WetSpass model estimated spatial annual surface runoff values vary from 5 mm to 222 mm across the Dura sub-catchment (Fig. 9A). The present spatial result of runoff is within the range of other reports which have reported (0–412 mm) in different parts of Ethiopia. Example of such runoff reports have reported from the northern Ethiopia for the Geba basin (Alene, 2006), Illala catchment (Arefaine et al., 2012) and Werii Watershed and Tekeze River Basin (Haile and Kassa, 2017); and Dire Dawa in the eastern Ethiopia by Zeleke and Merkel (2009). From this study result of surface runoff, the WetSpass model has a good predictive potential for ungauged sub-catchments in the presence of diversified resources (soil type, DEM, land use types and management practices). Surface runoff spatial variability could be associated with variation in soil type, vegetation type, land use/ land cover, slope, groundwater depth, precipitation and other meteorological parameters. In the study area, clay soil dominated areas and rural settlement lands showed the largest amount of runoff. A higher runoff depth was also estimated from the arable land (cultivated land) during this study (Table 3). This is due to the lower infiltration capacity of clay soil and a higher compactness of the rural settlement land units which leads to have a lower rate of water percolation and recharging (Horn et al., 1995; Défossez and Richard, 2002). On the contrary, areas in the study sub-catchment dominated by sandy soil and with better vegetation (shrubs and forests) coverage have showed a lower amount of surface runoff (Table 3). In sandy dominated sites with a better permeable nature of the soil, water can evaporate and/infiltrate instead of exposing to runoff loss. Similar findings on the effects of slope, soil texture and vegetation on runoff loss spatial variability were reported by Alene

3.4. Groundwater recharge The annual groundwater recharge estimated using the WetSpass model in the Dura sub-catchment of northern Ethiopia varies between 0 and 266 mm, with a mean value of 58.2 mm (Fig. 11A). This mean recharge value accounts for 7.9%7.9 % of the sub-catchment’s annual rainfall. The present study mean groundwater recharge result is similar to a recharge reported from the northern Ethiopia such as in Aynalem catchment (57 mm) (Hussien, 2000), Koraro area (57 mm) (Nedaw, 2010), Illala catchment (55.4 mm) (Gebru and Tesfahunegn, 2018) and Raya basin (55 mm) (Kahsay et al., 2018). Alene (2006) also estimated groundwater recharge that ranged between 0 and 215 mm yr−1 for the Geba basin in the north Ethiopia. Ward (1989) has reported that the fraction of rainfall that infiltrates into groundwater depends upon the other factors such as rainfall characteristics, vegetation, geology, topography, slope and soils. This study thus underlined that rainfall amount can influence to a great extent the spatial variability of groundwater recharge between different catchments, besides to the effects of the other physical factors. 11

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Fig. 9. WetSpass model estimated surface runoff: A) Annual surface runoff, B) Summer (wet season) surface runoff, and C) Winter (dry season) surface runoff of Dura sub-catchment in northern Ethiopia.

On the other hand, the computed groundwater recharge showed significantly lower value as compared to the reports from the other regions in Ethiopia. For instance, Asmerom (2008) and Getnet (2009) estimated the recharge value of the upper Blue Nile and White Nile Basin to be 63.3 mm and 146 mm, respectively. Gee et al. (2005) estimated the average annual recharge at the Hanford site in southeast Washington of the United States to be 62 mm yr−1. Huang and Pang

(2010) have reported groundwater recharge for the Guyuan and Xifeng in the Loess Plateau of China to be 100 mm yr−1. Batelaan and De Smedt (2001) estimated a long-term mean annual groundwater recharge in the Grotenete basin in Belgium (temperate zone) which ranges from 384 to 461 mm yr−1, with an average of 282 mm yr−1. Abu-Saleem et al. (2010) estimated groundwater recharge of the Hasa basin in the southern parts of Jordan that ranged from 0 to 12.83 mm yr−1,

Fig. 10. Biological and physical soil-water management practices near to the reservoir inlet of the study sub-catchment, northern Ethiopia. 12

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Fig. 11. WetSpass model estimated spatial distribution of groundwater recharge: (A) Annual groundwater recharge, (B) Summer season groundwater recharge and (C) Winter season groundwater recharge of the Dura sub-catchment, northern Ethiopia.

with an average value of 0.976 mm yr−1 in which this is lower than the present result in the study sub-catchment. The results of groundwater recharge varied considerably as a function of spatial region, physical factors and geomorphic features (Scanlon et al., 2002; (Adegoke et al., 2003; Moore and Rojstaczer, 2002; Döll and Flörke, 2005; Keese et al., 2005; Niu et al., 2007; Dogramaci et al., 2015). In addition, there is variability in groundwater recharge across the two seasons in the Dura sub-catchment. For example, the study area receives its 98% (56.2 mm) of annual groundwater during the summer season (wet months) (Fig. 11B) whereas the remaining 2% (2 mm) in the winter months (dry season) (Fig. 11C). This study revealed that groundwater recharge depth has found to be influenced in a given season by both land use type and soil texture. Relatively, the largest amount of recharge was observed in areas where the predominant soil type is sandy while clay soils dominant sites yield the lowest rate of recharge. The highest average recharge (206 mm) was estimated from sandy loam with arable land use type followed by grazing land of 164 mm recharge. On the contrary, the lowest recharge (10.6 mm) was estimated from rural settlement areas dominated with sandy clay texture (Table 3). The highest spatial variability in groundwater recharge is found in most of the sites in the northern and some eastern parts of the Dura sub-catchment, owing to the presence of deeper groundwater depth, permeable soils and better vegetation cover. On the other hand, the southern parts (around the reservoir) in the study catchment showed a lower magnitude of groundwater recharge owing to the occurrence of shallow groundwater depth (Fig. 11). This could be attributed to the presence of thick alluvial deposits and highly weathered geological structures. The shallow groundwater depth intern leads to have less recharge associated with relatively higher runoff in the area. In line with this, Tesfaye and Gebrestadikan (1982); Gintamo (2010) and Shivanna and Musthafa (2015) have reported that the type of geological formation and extent of weathering can

influence the permeability and storage of water throughout the rock. For instance, geological units that consist of abundant pore spaces can serve as extensive conduits for moving and storing water (high groundwater yield). Structures that form discontinuities in rock unit are playing an important role in water storage and permeability (Wu, 2009; Saumya and Srivastava, 2011). Hence, the fractured zones and alluvial deposition areas are important features of groundwater occurrences in the Dura sub-catchment conditions. In addition, the various biological and physical soil-water management practices introduced in the upstream part of the reservoir could be contributed to have a shallow groundwater depth towards the lower parts of the study catchment (Fig. 10). Consistent with the present result, Rockström et al. (2007) have reported that recharge and water storage in the root zone can be improved via different water management and moisture conservation systems. Similarly, other researchers (e.g., FAO, 2013; Thierfelder et al., 2013; Corbeels et al., 2014) also added that agricultural conservation schemes can promote higher water infiltration in soils with sufficient porous media. It is obvious that during the initial periods of the implemented soil and water management interventions high amount of water is expected to recharge to the underground (Haghnazari et al., 2015; GopiChand et al., 2017). Recharge rate gradually decreases as a result of saturation of the pores space as reported elsewhere by Lal and Shukla (2004); Baudron et al. (2012); and Esser (2017). Sites near to the reservoir of the sub-catchment, the groundwater is close to the surface of the earth and so recharge value has set to be zero (Fig. 11A-C). This shows that such area is saturated with water up-to the surface which does not permit to percolate additional water into the soil. Many other researchers (e.g., Pandey et al., 2003; Awulachew et al., 2010; Ayele, 2014; Adeoti et al., 2015) have stated that implementation of site specific water management strategies by considering the hydrological and environmental situations can effectively enhance groundwater resources 13

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and such resources can utilize to contribute towards ensuring food security of the local community in the conditions of the Dura sub-catchment. Other scholars (e.g., Pagiola, 1992; Lal, 1998) have reported that introducing better management systems that matches with the respective ecosystem and climatic conditions at catchment-scale is crucial for sustaining groundwater recharge. Haguepodder (1998) also added that understandings of hydrologic facts are a prerequisite for successful water resources development efforts and implementation of the best management activities.

type /land cover, topography and soil are accountable for the variation of the water balance components across the entire parts of the Dura subcatchment. The performance of the WetSpass model to estimate the water balance components in the conditions of the study sub-catchment showed that the model has the capability to estimate comparable values to the observed data, with less than 5% error. WetSpass model was thus found to be a robust tool to estimate temporal and spatial variability of water balance components when integrated with GIS even for ungauged sub-catchments with diversified resources (soil, DEM, land use types and management practices). Such model outputs are so important for planning sustainable use of water resources to meet the various demands by the community in the conditions of the study sub-catchment. Hence, implementation of site-specific appropriate integrated soil and water management schemes such as zero grazing, plantations of grasses and small size trees, terracing and check dams are suggested to improve recharge, and decrease runoff and evaporations losses in the study subcatchment conditions.

4. Conclusion GIS assisted WetSpass model estimated values of water balance components showed temporal and spatial variability in the Dura subcatchment of northern Ethiopia. The model estimated actual evapotranspiration values vary from 417 to 1618 mm year−1 in which the mean annual actual evapotranspiration represents 78.4% of the subcatchment’s mean annual rainfall. The WetSpass model estimated mean annual surface runoff varies between 5 and 222 mm, with a mean value of 100.5 mm (13.7% of the annual precipitation). The seasonal variability of runoff is described by 80.5% of the mean annual surface runoff in the summer (wet) months (80.9 mm) whereas the remaining occurs in the winter (dry) months (19.6 mm). The model annual groundwater recharge estimated for the Dura sub-catchment ranges from 0 to 266 mm, with a mean value of 58.2 mm (7.9% of the total annual rainfall). Ultimately, this research revealed that the Dura sub-catchment in the northern Ethiopia is characterized by low groundwater recharge, moderate surface runoff and higher evapotranspiration in the majority of the sub-catchment. Generally, the irregular distributions of climatic factors connected with the uneven variations of slope, land use

Acknowledgements This research was conducted with the financial support provided by Aksum University (Ethiopia) under the terms of grant no. AKU/IG/ RCSD/1092/07. The authors gratefully acknowledged the financial support provided for this project by Aksum University. The authors also highly appreciated for the farmers who involved in this research for their valuable time they spent and the assistance offered by the village administration and development agents during all discussions and data collection of this study. The authors are also very grateful to the editor and anonymous reviewers for their comments and suggestions while improving this article.

Appendix A Table A1 Geographical locations of the soil sampling points in the Dura sub-catchment, northern Ethiopia. ID

Northing

Easting

Landform

Erosion status

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

461049 463949 463227 463592 464530 464155 461529 463029 462706 462008 464384 464217 461685 463842 462414 463602 464530 464217 461278 462133 460799 461747 464384 463300 461987 462070 463102 463425 464228 463561 461307 462800 462487

1562009 1564822 1562280 1561290 1561436 1563603 1561675 1563593 1562769 1561644 1561946 1562467 1561092 1564551 1563124 1561873 1560977 1563937 1561821 1563113 1562999 1562162 1562676 1563874 1560998 1563134 1561602 1561436 1562238 1563530 1561962 1563416 1561654

rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid

Stable stable stable stable stable stable stable stable stable stable stable stable eroded eroded eroded eroded eroded eroded eroded eroded eroded eroded eroded eroded deposition deposition deposition deposition deposition deposition deposition deposition deposition

(continued on next page) 14

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Table A1 (continued) ID

Northing

Easting

Landform

Erosion status

34 35 36 37 38 39 40 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

462456 464196 464572 463286 462966 463150 463157 463657 463383 463425 463592 463606 463734 462810 462795 463856 463615 463489 462538 463317 463363 462641 462745 464039 463248 463535 463168 463707 464124 464027 463913 462865 462556 462465 462635 463177 463621 463647 463330 463546 463898 463624 462751 463286 462966 463150 463157 463123 463320

1561248 1561238 1563770 1560980 1560950 1560800 1560705 1561482 1560411 1561193 1561250 1561338 1561459 1561314 1561152 1561592 1560503 1561466 1561454 1561763 1561500 1561614 1561718 1561523 1560503 1561614 1561832 1561912 1561328 1561202 1562153 1560830 1560880 1561109 1561145 1561080 1561321 1561481 1561333 1561404 1561592 1561371 1561271 1560980 1560950 1560800 1560705 1560603 1560658

valley plateau escarpment inlet NE Inlet NW inlet N center dam rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central rid valley plateau escarpment rolling hill mountain central ridge valley plateau escarpment reservoir reservoir reservoir reservoir reservoir reservor

deposition deposition deposition reservoir reservoir reservoir reservoir stable stable stable stable stable stable stable stable stable stable stable stable eroded eroded eroded eroded eroded eroded eroded eroded eroded eroded eroded eroded deposition deposition deposition deposition deposition deposition deposition deposition deposition deposition deposition deposition inlet NE Inlet NW inlet N center dam spillway outlet

Table A2 Regression equations developed for the Tekeze and Mereb basin to estimate monthly reference evapotranspiration (PET) as a function of altitude (Source: (Yilma, 2002).) Y = PET (mm/day), X = Altitude (m) (Yilma, 2002). Month

Regression equation (Y = PET, mm/day; X = Altitude, m)

January February March April May June July August September October November December

Y Y Y Y Y Y Y Y Y Y Y Y

= = = = = = = = = = = =

-0.0007X + 5.3209 -0.0009X + 6.2027 -0.0009X + 6.8341 -0.0009X + 7.3190 - 0.0007X + 6.8455 -0.0007X + 6.6180 -0.8656 ln (X) + 10.681 -0.5021 ln (X) + 7.3998 -0.0005X + 5.1666 -0.0007X + 6.1164 -0.0008X + 5.6112 -0.0008X + 5.2040

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