Simulated surface and shallow groundwater resources in the Abaya-Chamo Lake basin, Ethiopia using a spatially-distributed water balance model

Journal of Hydrology: Regional Studies 24 (2019) 100615

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Simulated surface and shallow groundwater resources in the Abaya-Chamo Lake basin, Ethiopia using a spatially-distributed water balance model

T

⁎

Dagnachew Daniel Mollaa,b,c, , Tenalem Ayenew Tegayea, Christopher G. Fletcherc a

Geological Engineering (Hydrogeology) Graduate Program, School of Earth Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia Faculty of Meteorology and Hydrology, Water Technology Institute, Arba Minch University, P.O. Box 21, Arba Minch, Ethiopia c Department of Geography and Environmental Management, University of Waterloo 200 University Ave West, Waterloo N2L 3G1, Canada b

A R T IC LE I N F O

ABS TRA CT

Keywords: Surface water Groundwater Water balance WetSpass Abaya-Chamo Lake basin

Study region: The volcano-tectonic lakes basin of Abaya-Chamo is part of the Main Ethiopian Rift system and exhibits large variations in geomorphology, physiography and climate between the rift floor and the plateau. Study focus: Despite the importance of streamflow for water resources management and planning in the basin, many of the rivers there are ungauged. To make quantitative estimates of streamflow for spatially resolved water availability in such a highly heterogeneous environment, therefore, requires numerical modeling. This study is the first to quantify the surface and shallow groundwater resources in Abaya-Chamo, and to validate the physically fully distributed hydrologic model WetSpass under highly data-limited conditions, in a complex two-lake environment. New hydrological insights: Simulated total river flow and estimated baseflow were verified at 15 gauging stations, with a good agreement. The WetSpass model is shown to be suitable for such a complex setting with a correlation coefficient of 0.95 and 0.97 for total flow and baseflow respectively at a statistically significant level (p-value < 0.05). The simulated annual water budget reveals that 74.6% of the 22.1 billion lit/yr in total precipitation in the basin is lost through evapotranspiration, 15.7% through surface runoff, and only 9.7% recharges the groundwater system. The simulations also revealed the surface runoff and groundwater recharge are the most sensitive to soil textural class, while evapotranspiration depends more strongly on land use.

1. Introduction The complex geological processes of the intra-rift faulting and associated volcanic activities led to the formation of volcanotectonic structural depressions, which became born sites and creation of several largest lakes in East Africa, as well as large parts of its topography (Le Turdu et al., 1999, https://psugeo.org/Africa/African%20UNEP%20Atlas/Africa_Atlas_English_Chapter_1.pdf). It forms a unique rift system including, the Main Ethiopian Rift (MER) which make up various lake basins in the region. Where, the southern section of the MER occupies lakes such as Awassa, Abaya, Chamo and Chew Bahir. The present study area, Abaya-Chamo lakes basin as a part is thought to be hydrologically separate units, but due to the subsurface interconnection of NE-SW aligned regional faults, and exhibits large variations in geomorphology, physiography and climate between the rift floor across the ⁎ Corresponding author at: Faculty of Meteorology and Hydrology, Water Technology Institute, Arba Minch University, P.O. Box 21, Arba Minch, Ethiopia E-mail addresses: [email protected], [email protected], [email protected] (D.D. Molla).

https://doi.org/10.1016/j.ejrh.2019.100615 Received 4 December 2018; Received in revised form 27 June 2019; Accepted 28 June 2019 2214-5818/ © 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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escarpment and the plateau, (Alemayehu et al., 2005). Understanding surface and groundwater systems over such a complex rift margin zone are difficult and a key issue for the sustainable management, planning, and development in rapidly developing countries such as Ethiopia, which experiences frequent water scarcity associated with its arid/semi-arid climate (Molla and Tegaye, 2019; Halcrow, 2008; JICA, 2012). The key challenges in the lakes basin stem from inadequate development of water resources to support the region’s rapid population growth, including poor sanitation and water delivery, underdeveloped management and deployment of irrigation for agriculture and food production (Bekele, 2001). The results of these challenges include poor health of the region’s population, flooding of river areas, and loss of infrastructure and damage. In general, it has been recognized that there is a severe lack of coordinated research efforts to address the water resources problems in the ACL basin. Despite the importance of streamflow for water resources management and planning in the basin, many of the rivers are ungauged. Only a few previous studies have assessed the hydrology and hydrogeology of the region, with the main objective of quantity and quality assessment of surface water and groundwater separately, often using empirical and simple modeling approaches (Molla and Tegaye, 2019; Bekele, 2001; Halcrow, 2008; JICA, 2012 and others). Lake systems play an important role in surface watergroundwater interaction and are especially important for the use and demand of unsustainable water resources. Indeed, the groundwater contribution (as a baseflow component) is a highly desirable entity to know about water management in general, as the part of total discharge that originates from delayed storages in a river catchment. This parameter can also be used possibly to calibrate and validate hydrological models as well (Arnold et al., 2000; Stewart et al., 2007; Batelaan and De Smedt, 2007; Combalicer et al., 2008; Eckhardt, 2008; Risser et al., 2009). Quantification and visualization of spatial and temporal relations of hydrological processes, occurrence and pattern across the basin are difficult to figure out because of their high dynamic and continuous or discontinuous characteristics. Indeed, selection of the appropriate input parameter and physical boundary conditions based on defining variables are far important for model creation, on which there can be a variation in the results (Soczyńska, 1989; Ponce, Shetty 1995; Fiedler, Ramirez 2000; Batelaan, De Smedt 2001; Dąbrowski et al., 2011). Methods for determining groundwater recharge are, in particular, of fundamental importance, since groundwater, resources are typically limited, and their computational difficulties have been intensively discussed in previous works as either point based or lumped areal approaches (Simmers, 1988; Lerner et al., 1990; de Vries and Simmers, 2002; Scanlon et al., 2002). The point estimates were used as a base of extrapolation or rationalization over a large area; however, the outputs are not such evident as described in. Most studies suggest the physically distributed approaches are a more viable method to improve spatial estimates (Batelaan and De Smedt, 2007; Sophocleous, 1992; and Jochen, 2002). To make quantitative estimates of surface and shallow groundwater resource in a highly heterogeneous environment like the ACL basin, therefore, requires numerical modeling. Physically based numerical hydrologic modeling has become the gold standard in modern water science for evaluation of water resources at the basin scale. However, the most available physically distributed models require high temporal resolution data such as; SWAT (Arnold et al., 2000), DREAM (Manfreda et al., 2005), WetSpa (Wang et al., 1996), HBV (Losjo et al., 1999) and SVAT (Armbruster and Leibundgut, 2001). These models are continuous, data-intensive and use more complex water balance calculations, that questioning the applicability in developing countries (like Ethiopia) where data availability at hourly or daily time scales is typically unavailable. In spite of that, there is a strong demand for a hydrologic model that can able to estimate the spatially varying water balance components over long-term averages. Inordinately, the Water and Energy Transfer between Soil, Plants, and Atmosphere under quasi-Steady State condition (WetSpass) model can be run using a seasonal or annual time step, which dramatically reduces the requirements for input data (Batelaan and De Smedt, 2001 and 2007; Xu and Singh, 1998). Even so, WetSpass model was calibrated for temperate conditions in Belgium (Al Kuisi et al., 2013) and needs parameter modifications as it has not been calibrated or validated in a complex surface-groundwater zone such as the ACL basin in the tropical setting. This study is the first to quantify the surface/shallow groundwater resources in the complex two-lake environment of the AbayaChamo basin. We have deployed the physically distributed hydrologic model, WetSpass under highly data-limited conditions, which constitutes a stringent test for the simulation of spatially varying surface and shallow groundwater resources. We were, therefore, assessed the applicability of the WetSpass model to understand the spatial hydrologic pattern and the physical drivers of the annual water budget in this complex rift margin zone. The results will help to build scientific and technical capacity developing nations, and to generate increased connections between the scientific and policy communities. In the next Section (2), we describe the study region in more detail and the WetSpass model configuration. In Section 3, we present the modeling results, and Section 4 contains the main conclusions and discussion. 2. Data and methodology 2.1. Description of the study area and hydro-climate The Main Ethiopian Rift (MER) as part of the great East African rift system is seismically and volcanically active and “virtually the only places worldwide where the transition from continental to oceanic rifting is exposed on land” (Keir et al., 2006). The Abaya-Chamo lakes (ACL) basin is located in southern Ethiopia, in the central section of the Main Ethiopian Rift (MER). It is bounded by the limits from UTM 307113 m to 468468 m, and 598747 m to 897222 m Easting and Northing respectively (Molla and Tegaye, 2019) (Fig. 1). The region’s altitude varies between 3,430 m and 1,107 m above sea level, and the lake basin covers an area of about 18,900 km2. Topographically, the basin is a fault-bounded valley with a wide flat valley bottom bounded by volcanic mountains and hills that define the border of the Lake’s basin. The rift margins are not clearly defined everywhere, and the highest 2

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Fig. 1. Location, elevation, major river basin meteorological and river gauging stations of the Study area.

elevations of the surface of flanking plateaux are up to 3,430 m in the west (Chencha towards Fonko and Butajira highlands) and 2,500 m in the east (Hagere Mariam towards Hagere Selam), where large upthrust mountains descend steeply toward the lakes (Molla and Tegaye, 2019). 3

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Fig. 2. represents the monthly variability of the standardized mean deviation of an average rainfall and temperature for the whole basin.

The climate of the area can be classified as semi-humid to semi-arid subtropical climate, with an average annual temperature around 20 °C in the rift floor, with slightly lower values in the adjacent highlands and the average annual temperature is 13 °C to 24.2 °C with a very weak seasonal cycle. Mean annual rainfall varies from 951 mm to 1,653 mm in the river basins, depending on local topography and position. As shown in Fig. 2, the seasonal cycle of rainfall is strongly bimodal, with two distinct wet seasons controlled by the seasonal migration of the Inter-Tropical Convergence Zone (ITCZ): from March to June (locally known as “Belg”) and from July to October (locally known as “Kiremt”, Molla, and Tegaye, 2019; Otterbach, 1995; Legesse et al., 2003). The dry season (locally known as “Bega”) runs from November to February. In this study, we, therefore, focus on the average hydro-climate over these three seasons’ (four-month each), these are denoted by the letters of the months they contain as MAMJ and JASO (the wet seasons), and NDJF (the dry season) Molla and Tegaye, 2019). The main features of the drainage basin are two low-lying lakes, Abaya (1,110 km2 ) to the North and Chamo (316 km2 ), located in the center of the rift floor with inflow from the rivers of the basin. The river network can be used to classify a series of sub-river basins on the basis of the lake distribution and topography. We are focusing here on six major rivers and their tributaries flow into Abaya and Chamo lakes (Fig. 1). These sub-basins are, in geographical order by basin size: Billate (1) (5,659 km2), Hamessa-Guracha (2) (1,007 km2), Gidabo (3) (4,199 km2), Kulfo-Gina (4) (1,369 km2), Gelana (5) (3,866 km2); and Sife-Chamo (6) (1,379 km2) (Molla & Tegaye, 2019; JICA, 2012; Halcrow et al., 2008). The geology of the basin as shown in Fig. 3 is highly complicated and extremely faulted, as described many previous studies (Molla & Tegaye, 2019; Kazmin et al., 1980; Halcrow, 2008; Woldegebriel et al., 1990; JICA, 2012). The southern part of the study area is covered by high and low-grade metamorphic rocks of the Mozambican belt. The pre-rift Tertiary volcanic succession consisting of basaltic and silicic lava plateaus covers the escarpment on both sides of the rift (Molla & Tegaye, 2019). With the formation of the Wonji Fault Belt because of, tectonic movements and volcanic activity; the rhyolite, trachyte lava flows, pumice, pyroclastic, unwelded tuffs, ignimbrites, obsidians, and pitchstones are being distributed throughout the entire study area /chiefly in the rift floor to the north and around the like Abaya. The dominant parts of the rift floor, in particular, the surroundings of Abaya Lake, are covered by Pleistocene–Holocene volcano-sedimentary. As one goes from the north through the rift to the south of Lake Chamo, the age of the rock generally gets older (Molla & Tegaye, 2019). As a general approach, the estimation procedure for a given hydrological process involves an assessment of the distribution of the geophysical characteristics of the basin areas. Fig. 4 shows the flow chart of the particular steps in this study. On which the WetSpass model links from the raw data through processing, and encoding of spatiotemporal information to modeling, and finally to a water balance simulation. This study was employed to estimate the water balance components such as actual evapotranspiration, surface runoff, and groundwater recharge, which are calibrated to historical gauged river flow and estimated baseflow data. 2.2. WetSpass model WetSpass is a spatially distributed, quasi-steady state water balance numerical model to simulate and predict long-term average hydrological processes at seasonal and annual basin scale (Batelaan and De Smedt, 2001, 2007). This seasonal graphical user version of the model has the ability to simulate evapotranspiration, interception, runoff, soil water balance, and recharge (Batelaan and De Smedt, 2007). The WetSpass model has been widely shown to be suitable for explaining the impact of changes in land use and soil texture on water balance over long periods of time in mid-latitudes (Batelaan et al., 2003; Wang et al., 2012), and more recently in a tropical setting over the Ethiopian Geba basin (Gebreyohannes et al., 2013). By default, in a humid climate setting, the WetSpass model calculates water balances for winter (wet) and summer (dry) over 6-month seasonal averages. Previous studies carried out by Gebreyohannes et al. (2013), Abu-Saleem et al. (2010), Wang et al. (2011) highlighted the importance of the clear separation of hydrological seasons (length and type) across different climatic regions. We, therefore, applied the model in this study for three 4

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Fig. 3. Simplified geological map of the Abaya-Chamo Lakes basin. Source: (JICA, 2012; Kefale et al., 2013; Thomas et al., 2015; Molla & Tegaye, 2019).

seasons (four-month each) as defined above, which is appropriate because the necessary input data are available with a relatively high temporal resolution. The model performs the water balance computation at a raster cell level, as given in Eqs. (1)–(3). The total water balance is thus calculated as the summation of each raster cell’s water balance for independent vegetated, bare soil, open water, and an impervious fraction’s of a raster cell as described in (Batelaan and De Smedt, 2007).

ETraster = a v ETv + as Es + ao ETo + ai ETi

(1)

Sraster = a v S v + as Ss + ao So + ai Si

(2) 5

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Fig. 4. Flow charts and integration of data in the WetSpass water balance model. Adopted and modified from Batelaan and De Smedt (2001).

(3)

Rraster = a v R v + as R s + ao R o + ai Ri

Where, ETraster , Sraster , and Rraster are the total evapotranspiration, surface runoff, and groundwater recharge of a raster cell respectively, each having a vegetated, bare-soil, open-water and impervious area component denoted by a v , as , a o , and ai , respectively. To separate the precipitation into surface runoff, evapotranspiration, an interception, and groundwater recharge, the model uses digital data. In the seasonal version of the WetSpass GRAPHICAL USER INTERFACE manual (Batelaan and De Smedt, 2007), the detailed methodology is provided. The water balance of a basin (as given in Eq. (4)), is therefore, depends on the average annual/seasonal precipitation (P), interception fraction (I), surface runoff (S), actual transpiration (AET) and groundwater recharge (R), all with the unit of length per time [LT−1], which are estimated from the vegetation type, soil type, slope, groundwater depth, and climatic variables (such as: precipitation, potential evapotranspiration, temperature and wind-speed). (4)

P= AET+ S+ I+ R

In order to validate the total flow simulation from the WetSpass model (which comprises both surface runoff and groundwater recharge), observational flow data from 15 gauging stations were collected from the hydrology section of the Ministry of Water, 6

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Irrigation and Energy (MoWIE), Ethiopia. Besides, the base flow estimates and results of the various previous regional and fragmented study were used here as a supplementary confirmation approach to the total flow calibration. As it is supported by many previous studies (such as; Arnold et al., 2000; Stewart et al., 2007; Batelaan and De Smedt, 2007; Combalicer et al., 2008; Eckhardt, 2008; Risser et al., 2009), which were examined under long-term mean conditions where the contributions of groundwater as a baseflow flux. Thus, the groundwater discharge can be used as a proxy way to compare the basin mean groundwater recharge. Here, the basic assumptions are relayed on the equivalency of baseflow and the groundwater discharge of a basin and that implies the groundwater discharge to rivers is approximately equal to recharge (Piggott et al., 2005; Risser et al., 2005). So that the gauged river flow records were partitioned into the surface and baseflow components using the Time Plot baseflow separation program (Time Plot) / Gabriel periodic filter algorithm (equation (5)). This method is based on a recursive filter commonly used in signal analysis as discussed in Nathan and McMahon (1990) and Lyne and Hollick (1979), Molla & Tegaye, 2019). The formulation of the filter is given by;

fk = αfk − 1 +

(1 + α ) *(yk − yk − 1 ) 2

(5)

sampling time, yk is the total flow, and α is a filter parameter. Lastly, the filtered Where, fk is the filtered “fast” response at the baseflow is given by yk − fk . The observed daily total flow and baseflow component were then averaged over each season, and compared to the model simulation output that estimates seasonal mean flow and groundwater recharge of the area. The location and record period of the stations are shown in Fig. 1 and supplementary 3.

k th

2.3. Input data for WetSpass The model requires two types of input data, i.e. Geo-spatially referenced grid maps and parameter tables (Batelaan and De Smedt, 2007). The grid maps with a cell size of 500*500 m2 ; consist of slope angle, land-use, soil texture, groundwater depth, and seasonal/ annual meteorological maps of precipitation, potential evapotranspiration, temperature and wind speed. Digital Elevation Model (DEM) (NASA JPL, 2013) of the study area is used with a cell size of 30 m. The DEM is processed to prepare a topographic elevation map, a slope angle map (Fig. 5a) and river network of the Abaya-Chamo lakes basin (Fig. 1), and shows that slope angles range from 0° to 27° with a mean of 3.6°. The south-western part and eastern highlands have the highest degree of slope. The land-use map of the basin was derived from cloud-free Land sat ETM + satellite images of February (path 168, rows 55 and 56) and November/January (path 169, rows 54, 55 and 56), 2000 (NASA JPL;, 2013) using the standard ENVI supervised image classification. The land-use map (Fig. 5b) of the basin, agriculture (43.6%), wood/bush land (28.1%), forest (7.6%), grass (10.7%), swamp/marsh (2.4%) and open water (7.4%) were identified in seven land-use classes. A soil map of the lakes basin of Abaya-Chamo was extracted from the harmonized FAO database (FAO, 1998). Using the topsoil percentages of particle size fractions, soil type classes were translated into USGS soil texture classes in order to make the data compatible with a model requirement. The textural map of the converted soil in Fig. 5c shows the texture of the soil classes, i.e. clay (29.5%), clay loam (15.8%), sandy clay (6.7%), sandy clay loam (35.7%), and Sandy loam (12.2%) of the Abaya-Chamo lake basin. Piezometric map obtained from the groundwater depth in relative to the surface elevation, which is used to include the seepage fluxes in the model’s calculations of the water balance. Therefore, the study area’s groundwater depth was deduced from the spatially available wells, lakes level, and spring points. Fig. 5d shows the potentiometric surface elevation (contour of groundwater above means sea level). The meteorological conditions of a given wide and independent area are known to affect the formation of water resources. So that, the annual and seasonal meteorological parameters such as precipitation, temperature, potential evapotranspiration, and wind speed were prepared from the available meteorological stations in order to grasp the hydro-meteorological characteristics of the AbayaChamo lakes Basin along with other sets of data, to analyze water balance of the basin (Fig. 6 and Supplementary 1). The available 46 meteorological stations as indicated in Fig. 1 were checked for reliability through data quality and consistency among each other and against the climatological history as well within the Abaya-Chamo Basin. Meteorological data are available for the period from the early 1980s for some stations, though, the range varies from station to station. All available 46 stations have daily precipitation recordings, but 23 stations have only been recorded temperature and only 10 stations for wind speed. The spatial maps and the information on meteorological parameters within the study area are shown in the supplementary material (1).The parameters for land use, soil type, and runoff must be specified in four lookup tables required to run the WetSpass model. The attribute tables involve parameters related to the land-use type and soil type. The former land use table contains parameters such as rooting depth, leaf area index and vegetation height (Batelaan and De Smedt, 2007) and which were calibrated for temperate conditions in Belgium (Al Kuisi et al., 2013). Indeed, the application of this model as described in Abu-Saleem et al. (2010); Al Kuisi et al. (2013), requires parameters modifications of in the lookup tables for other regions that are characterized by different climatological regions (such as semi-arid, arid areas). For instance, in Ethiopian condition, Gebreyohannes et al. (2013) made some parameter adjustments to leaf area index, root depth and bareness for Geba basin. Accordingly, in this study, the relative differences between Geba basin and the environmental setup of Abaya-Chamo lakes basin are taken into account through professional suggestion and selected field observations. Therefore, the modifications of seasonal land use lookup table parameters include: (a) decrease in values of leaf area index; (b) a substantial decrease in the depth of the root, since, as described in detail by Jochen et al. (2002), Pierret, Alain et al 7

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Fig. 5. Maps used as input for the WetSpass model: (a) slope angle, (b) land-use, (c) soil type (d) hydrogeological map and water level head elevation contour (in the figure, H/M and H/L stands for aquifer productivity from High to Moderate and High to Low respectively).

(2016). The depth of rooting in humid to arid environments is likely to be shallower, as water tends to be available throughout the growing season in the upper soil horizons. The deeper roots in the arid area are potentially less important, and so, rooting depths are not strongly linked to potential evapotranspiration (PET), because water infiltration depths will be more limited than evaporative requirements, and (c) 20% increase of the bareness of all land cover’s proportions from each land-use class.

8

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Fig. 6. The Seasonal and Annual variations of Meteorological parameters used as input for WetSpass model.

3. Numerical simulation of the steady-state hydro-climate of the Abaya-Chamo lakes Basin 3.1. Model validation The results of the WetSpass model simulation were verified against observations of river flow at 15 river gauge stations of subbasins in the Abaya-Chamo Lake’s basin (Fig. 1). River measurements account for direct runoff flow (surface runoff) and baseflow (sub-surface flow) that recharge the groundwater reservoir. Model simulation results for mean total flows were compared to mean

Fig. 7. (a): Comparison of observed total annual average discharge with WetSpass simulation results (surface runoff plus recharge) and (b) Comparison of estimated baseflow with WetSpass simulated recharge at the 15 gauging stations shown in Fig. 1. The dashed lines (green) shows 95% confidence interval for the regression fits (p-value < 0.05). 9

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annual flows at 15 gauging stations of the sub-basins, and also the simulated groundwater recharge are compared against estimated baseflow as given in Fig. 7(a)–(b).The estimated results of baseflow yield deduced from daily river flows time series for sub-basins of 15 river gauging station within Abaya-Chamo basin, using the Time plot technique for baseflow separation (Nathan and McMahon, 1990a) is given in the Supplementary 2. This was used only for validation of groundwater recharge simulated by the spatially distributed WetSpass model. In the analyses period, the observed average annual total river flows are between 0.32 m3/s and 17.59 m3/s for the 15 river gauging station, while the estimated average annual baseflow is between 0.13 m3/s and 9.25 m3/s . Next, we compare the observed total discharge versus simulated at measuring stations, and additionally the estimated baseflow versus simulated groundwater recharge respectively in the study area. The scatter plots in Fig. 7(a)–(b), show a good agreement at 95% confidence level with a correlation coefficient of 0.95 and 0.97 respectively with the standard error of 0.12, and 0.20. These figures reflect a degree of accuracy in the application of the WetSpass model in the study area, which shows a strong correlation between the simulated and the observed ground truth or estimated values. The tendencies of high and low peak values at the stations are quite well simulated. The simulated streamflow values are generally underestimated in particular for most of the gauging stations, with the exception of small overestimations at Billate and Hamessa-Guracha, just north of Lake Abaya (gauging station of Bilate-Tena and HamessaHumbo). The underestimated values are mainly observed in the upper stream portions of the Abaya-Chamo lakes basin (gauging station of Weira, Kulfo, Sala and U-Gelana) (see locations on Supplementary 3). The areas of these stations are also associated with the major geological structures and fissured aquifers that can promote deep groundwater flow. The small discrepancies are shown in Fig. 7(a)–(b). This can also be caused by the natural variability of hydrological processes, the complex physical characteristics of the lakes basin, and the uncertainty induced by the parameterization and a representation of the realistic models. In addition, the river flow record and the estimation of baseflow can also be a cause of error since it is never completely free of errors.

3.2. Simulated hydrologic components We begin by describing the mean simulated hydrologic cycle averaged over the entire basin, and the associated antecedent hydrological processes: precipitation rate, actual evapotranspiration, and surface runoff. These parameter values are then combined in the water balance equation (Table 5) to estimate the rate of groundwater recharge and distribution. Fig. 8 summarizes the seasonal and annual variability of each parameter: the bars indicate the 25th (Q25), 50th (Q50 ) and 75th (Q75 ) percentiles of each distribution,

Fig. 8. the variation of simulated seasonal and annual water balance components. 10

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Fig. 9. Maps of water balance components simulated by WetSpass model: (a) total annual evapotranspiration (mm), (b) total annual surface runoff (mm), (c) total annual Interception (mm), and (d) total Annual groundwater recharge (mm).

while the whiskers represent the extreme minimum and the maximum values. The spatial distribution of the long-term annual components of the simulated water balance is given in Fig. 9. 3.2.1. Actual evapotranspiration Actual Evapotranspiration is given as a summation of evaporation from bare soil and open water bodies, vegetation transpiration, and evaporation of precipitation intercepted by the vegetation (Batelaan and De Smedt, 2001). The model result in Fig. 9(a) shows 11

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Table 1 Mean annual evapotranspiration amount for different land use and soil class combinations. AET (mm/yr)

Clay

Clay loam

Sandy clay

Sandy clay loam

Sandy loam

Mean

STD

Agriculture Forest Grass land Open water Swamp/Mersh Wood/Bush land Mean STD

670.9 800.3 824.6 1094.5 671.6 763.4 830.9 158.4

752.4 818.0 876.2 1094.5 706.6 826.1 864.3 142.8

633.9 737.0 782.6 1094.5 623.7 699.4 787.4 181.3

989.3 961.8 1087.0 1094.5 974.6 1058.2 1035.2 62.8

800.7 836.9 809.7 1094.5 830.2 832.7 880.8 119.9

769.4 830.8 876.0 1094.5 761.3 836.0 879.7 127.0

139.4 82.3 122.8 0.00 141.6 135.5 96.4

that the central axis from the northeast toward the south-west, including lakes of Abaya and Chamo, have high average actual evapotranspiration, while the northern, eastern, southeastern edges of the basin have a lower value. The high values are shown in Table 1, which are mainly due to the availability of relatively excess precipitation, surface water, and the favorable combination of land use-soil type. From this Table 1, the highest values of evapotranspiration are found in the open water, forest or Grassland land use classes with sandy clay loam and sandy loam soil combinations. Obviously, forests are known for their relatively high evapotranspiration and the coarser textures of the soil that can hold more soil water. While agricultural land, swamp/marsh along with sandy clay, clay or clay loam soils have the lowest values. Besides, the average and standard deviation values in Table 1 can be used to identify the dominating factor between land cover and soil texture. It implies the factor with a lower standard deviation value could have affected more than the factor with a higher value. Accordingly, the land cover class can amplify evapotranspiration more than the soil textural type’s in Abaya-Chamo lake basin. The annual value of evapotranspiration varies between 541.9 and 1307.3 mm/yr , with a mean of 873.26 mm/yr . Whereas; the seasonal values show significant temporal differences in the basins as given in Fig. 7. During the wet season (Kiremt and Belg), about 29.5% and 32.5% of the evapotranspiration takes place, while the remaining 12.5% occurs during the dry season (Bega). The seasonal variation is caused by uneven seasonal distribution of precipitation and changes in vegetation cover during the dry period. For example, during the dry period, the energy required for evaporation is not the limiting factor, but the availability of water. This explains why the actual rate of evaporation is lower in wet seasons than the dry season.

3.2.2. Surface runoff and interception High spatial surface runoff found in the East and west of Lake Abaya and Chamo than those in the north and south of Lake Abaya and Chamo (Fig. 9(b)). The spatial and temporal variation of the surface runoff generation can be explained primarily by the distribution of precipitation rate, the combination of land cover- soil texture over the physiographical arrangement as given in Table 2. The largest surface runoff occurs on clay soil combinations with land use types such as agricultural land, swamp/marsh or wood/ bush land. In contrast, the lowest values are happening to sandy loam and sandy clay loam soils with grassland and forest. The smaller standard deviation values of the runoff for different types of soil in Table 2 indicate that surface runoff is more influenced by the texture of soil than land-use types. The mean temporal variation of surface runoff values is shown in Fig. 8 and Supplement 2(b). The annual surface runoff ranges from 1 to 637.8 mm/yr with an average of 183.4 m/yr . During the wet season (Kiremt and Belg) about 6.8% and 7.4% of the surface runoff occur respectively, while the remaining 1.6% occurs during the dry season (Bega). There is a small seasonal variation in the surface runoff as compared to actual evapotranspiration throughout the year, even though due to the significant changes in vegetated surfaces in a wet season. The interception is given as a fraction of precipitation value, which depends on the type and distribution of vegetation (de Jong and Jetten, 2007; Roberts, 1983; Calder, 1979; Nonhebel, 1987). Total annual interception in the basin ranges from 0 to 14.2 m/yr ; with an average value of 3.09 mm/yr . Seasonal interception ranges from 0 to 6.12%; 0 to 1.28% and 0 to 6.24 respectively, for Kiremt, Bega and Belg (Fig. 7). In the north of Lake Abaya, the relatively highest interceptions occur (Supplementary(c)).

Table 2 Mean annual surface runoff amount for different combinations of land use and soil class. S. Runoff (mm/yr)

Clay

Clay loam

Sandy clay

Sandy clay loam

Sandy loam

Mean

STD

Agriculture Forest Grass land Swamp/Marsh Wood/Bush land Mean STD

473.9 322 308.7 392.3 374.6 374.3 65.7

392.5 265.4 236.7 338 302.7 307.1 61.2

447.7 305.6 297.7 360.9 358.5 354.1 59.9

4.5 2.8 2.4 5.6 3.3 3.7 1.3

4.0 2.1 2.0 4.0 2.8 3.0 1.0

264.5 179.6 169.5 220.2 208.4 208.4 37.8

239.4 163.0 155.2 197.5 189.3 188.9

12

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Table 3 Mean annual groundwater recharge amount for different land use and soil class combinations. Recharge (mm/yr)

Clay

Clay loam

Sandy clay

Sandy clay loam

Sandy loam

Mean

STD

Agriculture Forest Grass land Swamp/Marsh Wood/Bush land Mean STD

37.2 69.5 27.6 145.7 32.2 62.4 49.4

68.6 155.3 56.9 155.2 68 100.8 49.6

45 93.9 33.7 143.2 57.3 74.6 44.5

173.4 227.2 62.4 198.5 103.4 153.0 68.3

337.3 316.9 302.6 315 291.6 312.7 17.1

132.3 172.5 96.7 191.5 110.5 140.7 40.3

126.9 101.2 116.1 72.5 104.4 104.2

3.2.3. Groundwater recharge The estimation of groundwater recharge is regarded as a highly challenging parameter in hydrogeology. Although the simulation results using spatially distributed multivariate gives, the eastern highland and northern part of the basin generally have a higher groundwater recharge than those in the central basin floor and the western part of the basin. This is likely due to a combination of favorable conditions such as high precipitation, permeable soils, gentle topography and land use cover in Fig. 9(d). As indicated in Table 3, the average annual groundwater recharge values for different combinations of land use-soil classes reveal that the largest groundwater recharge is observed in soil textural classes of sandy loam and sandy clay loam along with agricultural and forest land use type. This is basically because of the high permeability of these soils, but it could also be partly due to lower evaporation rates and less runoff on the relatively gentle slopes of agricultural land. On the other hand, grassland and wood/bush land yield less groundwater recharge on any type of soil due to their high potential of transpiration and interception processes especially the clay textural types. This is evidently linked to a high transpiration rate, particularly in the case of wood/bush land, which was confirmed by the research conducted by Venkatraman and Ashwath (2016), Sophocleous (2000), Batelaan (2006) as well as Okoński and Miller (2006). As indicated in Table.3, the highest spatial variation of recharge is found by the soil textural classes, which have a smaller standard deviation than land use type. As a result, this signifies that the recharge increases distinctly with coarse soil texture regardless of the land cover classes. The annual groundwater recharge in the Lake basin of the Abaya-Chamo ranges from 0 to 540.8 m/yr , with an average value of 113.74 mm/yr (Figs. 8 and 9(d)). In regard to seasonal variation about 4.97% and 5.1% of the recharge occurs during the rainy seasons (Kiremt and Belg respectively), But here, negative recharge values are observed during the dry season (Bega) by indicating no recharge, rather it signifies the groundwater contributions to surface water as discharge, that is in the range between 0 to −63.6 mm/yr . Vegetation covers, therefore, can maintain a higher ET through groundwater osmosis in these areas. Besides, the spatial surface runoff map in Fig. 9 shows that the Eastern and western parts of Lake Abaya and Chamo have a large potential surface water resource compared to the northern and southern parts of Lake Abaya and Chamo. In particular, the western part of the lake as described by Molla and Tegaye (2019), here are also characterized by a high degree of slope and highly subjected to episodes of flash flooding, that plays a major role in the transport of sediments in the lakes. While the Bilate river basin was generally identified as the recharge zone in the basin with their highly productive intercalated volcano sedimentary aquifers, especially the eastern plateaus and north of Lake Abaya

3.3. Surface water balance 3.3.1. Surface Water balances of major sub-river basins As given in Table 4, the magnitude of the largest annual inflow source to the lake basin occurs in Gidabo (about 1257.7 mm/yr ) and Gelana (1191.2 mm/yr ) sub-river basin located in the east of the lakes due to the high rate of the mean annual rainfall. While the Chamo-Sife sub-river basin to south and west of Lake-Chamo exhibit the smallest magnitude of annual rainfall. Consequently, due to the distribution of the annual rainfall and land coverage particularly the grassland creates the highest total evapotranspiration as an outflow, which is estimated 928.2 mm/yr and 882.3 mm/yr respectively for Gidabo and Billate sub-river basins. In contrast, the Kulfo-Gina sub-river basin has a relatively lowest evaporation loss. The slope steepness of Kulfo-Gina and Hamessa-Guracha sub-basin, unlike others, are yield the highest annual surface runoff about 334.5 mm/yr and 319.6 mm/yr respectively while Billate river basin has 89.8 mm/yr which may be associated with the soil textural classes. The water balances of sub-river basins show that the groundwater recharge is largely dominated by the rate and spatial distribution of rainfall and soil textural properties. Indeed, the distributions of relatively high permeable quaternary volcano-sedimentary aquifers units in the Bilate and Gidabo river basin have recharged the groundwater about 157.5 mm/yr and 144.9 mm/yr respectively. While, the estimated annual recharge yield were small in the west sides of both lakes (Abaya and Chamo), where the sloppy sub-rivers basins such as Hamessa-Guracha, Kulfo-Gina, and Chamo-Sife yields only 68.5 m/yr , 78.8 mm/yr and 67.4 mm/yr respectively. Therefore, the difference in the water balances of each sub-river basins can be explained by the rate and distribution of rainfall, geomorphometric, physiographical characteristics (land use, soil, elevation, slope, area) as given by Molla and Tegaye (2019), and the associated response of the hydro-climatic parameters. 13

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Table 4 The annual water balance of the major sub-river basins in the study area simulated with the WetSpass model. Sub River-Basin

Water balance components

Min Billate

Gidabo

Hamessa-Guracha

Kulfo-Gina

Gelana

Sife-Chamo

Precipitation(P) Evapotranspiration Interception(I) Surface runoff (S) Recharge (R) Input - Output Precipitation(P) Evapotranspiration Interception(I) Surface runoff (S) Recharge (R) Input - Output Precipitation(P) Evapotranspiration Interception(I) Surface runoff (S) Recharge (R) Input - Output Precipitation(P) Evapotranspiration Interception(I) Surface runoff (S) Recharge (R) Input - Output Precipitation(P) Evapotranspiration Interception(I) Surface runoff (S) Recharge (R) Input - Output Precipitation(P) Evapotranspiration Interception(I) Surface runoff (S) Recharge (R) Input - Output

Fractions of mean P. (%)

Volume (Billion lit/yr.)

33.7 130.0 1.3 151.5 103.1

100.0 78.1 0.3 7.9 13.9

6.39 4.99 0.02 0.51 0.89

75.9 150.7 2.7 213.1 119.3

100.0 73.8 0.3 14.6 11.5

5.28 3.90 0.02 0.77 0.61

24.8 116.4 1.9 158.8 72.5

100.0 65.4 0.3 28.5 6.1

1.13 0.74 0.00 0.32 0.07

37.6 122.1 2.7 152.3 63.3

100.0 63.5 0.3 29.6 7.0

1.55 0.98 0.00 0.46 0.11

57.6 125.7 1.3 193.1 61.4

100.0 70.7 0.3 21.2 8.1

4.60 3.26 0.01 0.98 0.37

14.2 165.7 1.9 194.0 41.3

100.0 76.1 0.2 17.8 6.2

1.50 1.14 0.00 0.27 0.09

Annual values ((mm/yr) ) Max

Mean

1026.1 1217.3 1129.6 549.9 1184.9 882.3 0.0 12.4 3.3 1.0 529.8 89.8 0.0 410.1 157.5 P – (ETO + S + R) = 0.0 1074.1 1392.1 1257.7 646.9 1307.3 928.2 0 14.1 3.6 2.1 637.7 183.7 0 540.8 144.9 P – (ETO + S + R) = - 0.9 1078.5 1219.4 1121.5 549.4 1130.5 733.1 0.0 12.5 3.0 1.0 525.8 319.6 6.4 362.6 68.5 P – (ETO + S + R) = - 0.3 1075.1 1207.9 1131.1 554.0 1194.0 718.0 0.0 12.5 3.5 1.1 522.5 334.5 0.0 337.3 78.8 P – (ETO + S + R) = - 0.2 1094.8 1372.0 1191.2 584.4 1183.2 842.3 0.0 14.2 3.3 1.2 606.8 252.7 0.0 387.4 96.2 P – (ETO + S + R) = - 0.0 1060.1 1127.4 1086.2 541.9 1123.9 826.6 0.0 11.6 2.6 1.2 524.0 192.9 0.0 213.0 67.4 P – (ETO + S + R) = - 0.7

(ETO)

(ETO)

(ETO)

(ETO)

(ETO)

(ETO)

Std.

Table 5 The annual water balance of the Abaya-Chamo lakes basin simulated with the WetSpass model. Water balance component

Precipitation(P) Evapotranspiration (ETO) Interception(I) Surface runoff (S) Recharge (R) Input - Output

Annual values (mm/yr) Min

Max

Mean

Std.

1026.1 541.8 0.0 1.0 0.0 P – (ETO + S +

1392 1307.4 14.2 637.7 541.1 R) = - 0.8

1169.5 873.1 3.1 183.4 113.9

75.2 152.9 2.1 188.5 99.3

Fractions of mean rainfall (%)

Volume (Billion lit/yr.)

100.0 74.6 0.3 15.7 9.7

22.1 16.5 0.06 3.5 2.1

3.3.2. The total surface water balances of Abaya-Chamo Lakes basin The overall water balance of the Abaya-Chamo lakes basin is given in Table 5. The simulation of the WetSpass model is presented using long-term average annual condition. The water balance structure is dominated by evapotranspiration, which constituted 74.6% of the precipitation and is the largest water balance component in the Abaya-Chamo Lake basin. About 15.7% of the annual precipitation in the Abaya-Chamo basin accounts for the surface runoff value only a small fraction of the annual precipitation remains to be intercepted (0.3%) and the rest part is groundwater recharge. This makes up only 9.7% of the total annual precipitation in the lake basin. These results, confirmed by previous studies, for example, in the north of the study area in the Central Ethiopian Rift Valley by Zenaw (2003) estimated that approximately 5% of the rainfall in the Awassa basin was recharged. As stated by WABCO (1990) in the Master Plan of the RVLB for Water Resources Development in Ethiopia computed the recharge of more than 5% of rainfall. Recharge calculated from the values of average base flow shows the possibility of recharge variability in Dila Sheet as part of the Abaya Chamo 14

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D.D. Molla, et al.

lakes basin, in which . Thomas et al. (2015) estimated between 7% and 20% of the precipitation. Additionally, The Southern nation nationalist people representative (SNNPR), water resource development bureau report on a hydrogeological mapping of Gidabo basin (Yirgalem/ Kilissa Topo Sheets) estimated evapotranspiration and groundwater recharge of 806 mm/yr and 135 mm/yr (10% of precipitation) respectively (Shiferaw & Abebe, 2004). Halcrow (2008) were also quantified the surface runoff from the total annual average river flow into the lake systems about 3,018 million cubic meters. The small error in the water balance is about a 0.3% deficit due to the assumption that water bodies can unlimitedly evaporate at PET rate. 4. Conclusions and recommendation The physically distributed hydrological model, WetSpass was used to simulate the hydrology and water balance of the AbayaChamo lakes basin in the Main rift valley of Southern Ethiopian. The model uses detailed basin characteristics and considers the effects of highly variable topography, soil type, and land use cover on the lake basin hydrologic characteristics. Comparisons of observed and simulated river flow at sub-basins show; the model results correspond reasonably and realistically, and there was also a good agreement between the simulated recharge and the estimated baseflow at 15 gauging stations in the basin. In general, the model has a great potential to determine the influence on hydrological behavior of changes in land use or soil type. Accordingly, in the case of spatial digital data limitations, parameterization and regionalization of an input map needs great care and employing appropriate techniques to estimation and interpolation each of the input variables. Thus, the WetSpass model is also verified in this study for its applicability in semi-arid/arid with highly variable physical characteristic over complex rift margin that involves lakes system for the evaluation of the long-term regional water resources. The model can, therefore, be applied to other similar basins, in particular in developing regions with limited time series and spatial data at high-resolution. Indeed, the calibrations of the simulated water balance components using parameters in the lookup tables of the land use and soil type are better adjusted using either predetermined values for a particular environment (i.e. Humid, semi-humid/semi-arid, arid) or needs field measurements along with professionals suggestion. The total flow from the WetSpass model is given by the summation of surface runoff and groundwater recharge. The mean ratio of groundwater recharge to the total flow (groundwater recharge/total flow) is about 0.38. This means, the groundwater recharge is about 38.3% of the total flow, which is nearly comparable to the baseflow index (BFI). In comparison, the BFI is estimated at an average of about 0.42 across the entire basin using baseflow separation techniques as estimated by Molla and Tegaye (2019). This confirms the importance of both techniques to estimate the water resources available in the study area (i.e., baseflow separation and modeling approach). The water resource potential or availability summary is given in Table 5 from model output shows the lake basin as a whole has a total of 22.1 billion lit/yr rainfall with a mean value of 1169.5 mm/year. About 16.5 billion lit/yr of this total is lost due to evapotranspiration, with the remainder lost due to surface runoff (3.5 billion lit/yr) and groundwater recharge (2.1 billion lit/yr). Our results indicate that the potential for flash flooding in the basin is significantly east-west asymmetry episodes. We, therefore, recommend that land use management should focus on the western side of Lake Abaya, since its steep slopes give rise to high surface runoff potential, and can lead to land degradation and transport of sediments into the lake. Areas in the eastern plateaus and north of Lake Abaya, in particular, the Bilate and Gidabo river basin, were identified as predominant recharge zones, so, development activities in this area should be managed through consideration of possible contamination and pollution of lake water. According to the respective potential, both the surface and shallow groundwater resources in the lake basin can support the current water demands of both the potential irrigation and another development strategy. However, climate change is expected to bring significant changes in the region’s hydro-climate, and thus the future studies should examine how the projected future changes in temperature and precipitation can affect the availability of water resources. Acknowledgments The authors are grateful to the School of Earth Sciences; Addis Ababa University, and Faculty of Meteorology and Hydrology, Water Technology Institute, Arba Minch University, Ethiopia for study support. I acknowledge Dr.ing. Abdollahi Bashir from the Department of Hydrology and Hydraulic engineering, Vrije Universities Brussel, Belgium for providing me the Seasonal WetSpass GRAPHICAL USER INTERFACE Model. Additionally, we are kindly thankful for the following data provided institutions for this research: The National Meteorological Agency, Ministry of Water Resources and Ethiopian Geological Survey. Sida project through Addis Ababa University, Ethiopia funding for visit to the University of Waterloo in Canada Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ejrh.2019. 100615. References Abu-Saleem, A., Al-Zubi, Y., Rimawi, O., Al-Zubi, J., Alouran, N., 2010. 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