Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 1

Research Article

Groundwater-recharge estimation in Waja-Golesha Sub-basin, Northern Ethiopia:

An approach using WetSpass Model

Seyoum Bezabih Kidane

Department of Water resource Engineering and Management, Ethiopian Institute of Agricultural Research,

Ethiopia

Corresponding author: seyoumb18@gmail.com

Received: September 15, 2022; Received in revised form: January 23, 2023; Accepted: April 12, 2023

Abstract: Understanding the spatial variability of groundwater recharge in response to distributed Land-use, soil

texture, topography, groundwater level, and hydrometeorological parameters is significant when considering the

safety of groundwater resource development. Thus, this study aimed to estimate the groundwater recharge of the

Waja-Golesha watershed, in northern Ethiopia using the WetSpass (Water and Energy Transfer between, Soil,

Plants, and Atmosphere under quasi Steady State) hydrological model. The model inputs are prepared in the form of

20m grid size maps and attribute tables. The model parameters table was prepared using expert knowledge and

scientific literature. The modeling results of long-term spatial-temporal annual rainfall of 664.5 mm were

fractionated as 161.5 mm (24%) of runoff, 438.2 mm (71%) evapotranspiration, and the remaining 29 mm (5%) of

groundwater recharge. From the entire area of the sub-basin (532.6 km2), 1.6*105 cubic meters (m3) of water was

added to the groundwater through infiltration. The seasonal distribution of the recharge indicates that 72%

occurred in the wet season while the rest 28% resulted in the dry season. The evaluation of the modeled output

indicates that WetSpass works well to model the groundwater recharge of the Waja-Golesha sub-basin. To preserve

the resource's long-term viability, the balance between groundwater recharge and projected abstraction rates for

agriculture and domestic water supply must be considered in future groundwater resource development plans in the

watershed.

Keywords: Ethiopia, groundwater recharge, Waja-Golesha, WetSpass

This work is licensed under a Creative Commons Attribution 4.0 International License

1. Introduction

Groundwater is the largest source of fresh water and

one of the most curtail resources for human beings

(Mengistu et al., 2019). Due to its essential qualities

such as consistent temperature, wide distribution and

continuous availability, excellent natural quality,

limited vulnerability, low development cost, and

draught reliability, groundwater is a very critical and

dependable source of water supply in all climatic

regions. Sustainable utilization of groundwater

resources is crucial for the present and future

generations because groundwater is a finite and

vulnerable resource (Yenehun et al., 2017).

Consequently, Groundwater resource management

and sustainable use directly depend on knowledge of

groundwater recharge and potential (Fenta et al.,

2015). For efficient and sustainable groundwater

water resource management, evaluation of

groundwater recharge is a prerequisite and vital for

economic development (Arefayne and Abdi, 2015).

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 2

Depending on the source and mechanisms of

recharge, direct measurements, water balance

methods, tracer techniques, and empirical methods

are the techniques used to estimate groundwater

recharge (Simmers, 2017). As stated by Gidafie et al.,

(2016), space or time scale, range, and reliability of

recharge estimates are the factors considered during

selection of recharge estimation methods. WetSpass

(Water and Energy Transfer between Soil, Plants, and

Atmosphere under quasi Steady State) hydrological

model was used for this study by considering the

specially distributed factors such as land use, soil

texture, slope, and hydrometeorological parameters to

model long-term average spatial distribution of

groundwater recharge. This is because considering

groundwater resource safety is highly dependent on

understanding variability of recharge as a function of

variables such as land cover, soil type, slope,

groundwater depth, and hydrometeorological

parameters.

Rain-fed agriculture is the main activities in the case

of Ethiopian condition even if the rainfall variability

is high (Dereje and Nedaw, 2019). Less productivity

is the result of shortage and variability of rainfall

during the growing periods of crops. The mean

annual potential evapotranspiration which ranges

between 1900 and 2100 mm much greater than the

mean annual precipitation that ranges from 300 to

800 mm occurred in northern Ethiopia (Kahsay et al,

2018) and the area has a growing period of about 60

days and annual air temperature ranges from 16 to 27

°c. As a result, food security in the area is largely

dependent on supplementary irrigation.

Waja-Golesha watershed is one of the groundwater

based irrigation areas located in North Wollo zone of

Amhara national regional state. Local farmers fail to

keep soil moisture requirements for growing crops in

the area because of the erratic nature of rainfall (time

and space) distribution. Accordingly, groundwater

resource use in the area for agricultural development

is growing continuously. Therefore, groundwater

recharge estimation and groundwater potential

assessments were not supported adequately for

expanding irrigation in the area. Point estimate of

groundwater recharge for groundwater resource

development and potential site delineation was the

focus of previous studies (Ayenew et al., 2008;

Belay, 2015). However, groundwater recharge

estimation needs reliable methods (Rwanga, 2013).

Therefore, the objective of this study was to estimate

long-term seasonal/annual average spatial

groundwater recharge in the Waja-Golesha watershed

northern Ethiopia by adapting GIS-based WetSpass

model.

2. Materials and Methods

2.1. Description of the study area

Waja-Golesha watershed is located in northern part

of Ethiopia between latitude of 12°08′–12°19′N and

longitude of 39°23′–39°10′E (Figure 1). The study

area is bounded by the North-Western Ethiopian

Plateau in the west and the Afar Rift in the east with

an area of about 532.6 km

. It is among the

watersheds on the western edge of the Danakil basin.

The main physiographic features prevailing in the

area were north–south oriented mountains in the

west, a steep fault scarp in the east, a major graben

and isolated hills within the graben. The study area is

elongated intermountain graben filled with

quaternary sediments, bordered to the east and west

by rugged volcanic mountains with relatively high

elevation. The watershed is constituted of different

types of volcanic rocks and alluvial sediments. The

volcanic rocks (basalts, rhyolite, and granite) having

Tertiary age cover the majority of the area and are

found exposed in the surrounding mountains and

underlying the alluvium in the valley. The alluvium

sediments are mainly found occupying the valley

floor. Major and inferred faults, fractures, and

lineament having an alignment of N-S, NNW-SSE,

NW-SE, NNE-SSW, and NE-SW are the major

geological structures that are found in the catchment

(Tadesse et al., 2015).

The elevation of the watershed ranges from 1,352 m

within the valley floors to 3,032 m above sea level in

the western mountain ridges. The watershed is

characterized by an erratic, bimodal rainfall pattern

with the main rainy season lasting from late June to

early September. The highest rainfall record occurs in

July and August, whereas the short spring rainy

season extends from February to March. The average

monthly temperature of the Waja-Golesha watershed

varies from a minimum average of 4.7 °C in the Lasta

plateaus to a maximum average of 35.5 °C in the

Waja lowlands. The highest temperature is recorded

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 3

in June and the lowest value is in November. The

western mountainous part of the area is highly

dissected by streams and steep slope topography

which favors high runoff. As a result, the valley floor

is seasonally recharged via the incoming runoff from

the nearby hills (Kahsay et al, 2018). Thus rain-fed

agriculture by diverting seasonal flush floods is a

common practice (Fenta et al., 2015).

2.2. Dataset and sources

In this study, the main data sources were both

primary and secondary. The secondary source

includes remote sensing data such as the Digital

Elevation Model (DEM) from Alaska Satellite

Facility (ASF), land use/land cover, which was

collected from the Ethiopian Geospatial Institute

(EGI), soil map of FAO (1998), and meteorological

data collected from the national meteorological

agency of Ethiopian. Digital elevation model with

12.5m resolution was downloaded from Alaska

Satellite Facility (ASF) webpage

(https://asf.alaska.edu/) and processed in ArcGIS and

used to develop the elevation and slope map of the

watershed. The Waja-Golesha watershed soil texture

map was downloaded from the Food and Agriculture

Organization website (http://www.fao.org) (FAO,

1998). Using the United States Department of

Agriculture (USDA) textural classification standards,

the soil texture was determined. The land use/land

cover map of Ethiopia was prepared by Ethiopian

Geospatial Institute (GSI) for 2020 with a 20 m

resolution and the land use land cover map of the

Waja-Golesha watershed was modified and

developed from this map.

The primary source includes the groundwater depth

(groundwater table) data, which was directly

collected by measuring from the existing boreholes

using the deep meter.

Meteorological datas were collected from seven

meteorological stations which covered the data from

2001 to 2020 (20 years). Potential evapotranspiration

was calculated by using Enku simple temperature

method (Enku and Melesse, 2014) was used.

Figure 1: Location map of the Waja-Golesha watershed

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 4

2.3. Data analysis

2.3.1. WetSpass model description

WetSpass is an acronym for Water and Energy

Transfer between, Soil, Plants and Atmosphere under

quasi Steady State (Al Kuisi and El-Naqa, 2013). It is

a physically based model for the valuation of the

long-term average spatial patterns of groundwater

recharge, surface runoff and evapotranspiration from

long-term average meteorological data together with

land-use, soil, and groundwater level grid maps by

employing physical and empirical relationships

(Gebrerufael et al., 2018). WetSpass gives different

hydrologic outputs on a yearly and seasonal (summer

and winter) basis (Gebremeskel and Kebede, 2017).

The model is integrated with ArcGIS as a raster

model and coded in Avenue and Visual Basic.

Parameters such as land-use and related soil type are

connected to the model using attribute tables of the

land-use and soil raster maps. The attribute tables

also allow defining new land cover or soil types

easily, as well as changes in the parameter values,

which permits analysis of future land and water

management scenarios (Ghouili et al., 2017). The

WetSpass model treats a basin or region as a regular

pattern of raster cells (Al Kuisi and El-Naqa, 2013).

The total water balance for a given raster cell (Figure

2) is split into independent water balance components

for the vegetated, bare-soil, open-water and

impervious parts of each cell. This allows one to

account for the non-uniformity of the land-use per

cell, which is dependent on the resolution of the

raster cell. The processes in each part of a cell were

set in a cascading way. This means that an order of

occurrence of the processes, after the precipitation

event, is assumed. Defining such an order is a

prerequisite for the seasonal timescale with which the

processes will be quantified. The quantity determined

for each process is consequently limited by a number

of physical and hydro-meteorological constraints of

the given area under investigation (Esayas and

Gebeyehu, 2019).

Water balance components of vegetated, bare-soil,

open water and impervious surfaces are used to

calculate the total water balance of a raster cell using

the Equations [1-3].

        

        

        







Where

 Eta = Total evapotranspiration

 Sa = surface runoff

 Ra = Groundwater recharge

 av = vegetated area

 as = bare soil area

 ao = open water area

 ai = impervious area

 E = evaporation

Precipitation is taken as the starting point for the

computation of the water balance of each of the

above-mentioned components of a raster cell. The

rest of the processes, such as interception, surface

runoff, evapotranspiration, and recharge follow in an

orderly manner.

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 5

Figure 2: Schematic water balance of hypothetical raster cell (Batelaan and De Smedt, 2001)

2.3.2. WetSpass model data inputs preparation

GIS-based hydrological models such as the WetSpass

model was used for analyzing groundwater systems

in steady-state condition and needs long-term average

hydro-meteorological data and spatial patterns of

watershed-based biophysical layers as the main

inputs. WetSpass needs the parameters in seasonal

basis, as a result, four months of June, July, August,

and September are considered summer (main rainy

season) and the remaining 8 months are considered as

winter (dry season) in the case of Ethiopian condition

particularly at the study area. Grid maps and

parameter tables are required as inputs for the model

and were prepared with the help of ArcGIS tools.

These grid maps were a land-use land cover, soil

texture, slope, topography and groundwater levels,

rainfall, potential evapotranspiration and wind speed.

The input files prepared as parameter tables were also

prepared in a database file format (dbf); these are

summer and winter land use land cover, soil texture

and runoff coefficient.

Hydro-meteorological data

Meteorological data obtained from seven stations of

the Ethiopian Meteorological Agency (EMA) were

used for the preparation of metrological input data for

the WetSpass model. Missing meteorological data

record was a common problem in the stations of the

watershed. Each station data was analyzed for the

calculation of the seasonal and annual meteorological

values. Rainfall and temperature data were available

for all seven stations while wind speed was recorded

only at Kobo, Maichew and Chercher meteorological

stations. Enku simple temperature method (Enku and

Melesse, 2014), was used to calculate potential

evapotranspiration.

The seasonal and annual grid maps of the climatic

variables were developed using an interpolation

method to predict values from a limited number of

sample data points at unknown geographic point data.

Inverse Distance Weighted (IDW) interpolation

method was applied as it gives consistent result with

known values. The WetSpass input grid maps of

major meteorological parameters of Waja-Golesha

watershed (Figure. 3) indicated high spatial variation

as a function of topography. For example, there was a

significant spatial variation of rainfall in the

watershed which is strongly influenced by orographic

effect within the Lasta and Zoble highlands receives

high rainfall as compared to the valley bottoms of

Waja-Golesha watershed (Figure 3) shows high

spatial variation as a function of topography.

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 6

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 7

Figure 3: WetSpass input grid maps of major meteorological parameters in Waja-Golesha watershed: (A) winter rainfall,

(B) summer rainfall, (C) winter PET, (D) summer PET, (E) winter temperature, (F) summer temperature, (G) winter

wind speed, and (H) summer wind speed

Groundwater depth data was produced from the

elevation of static water level measurements in

boreholes and springs. Overall 67 static water level

measurements which were mostly concentrated in the

valley area were used for interpolation to produce the

groundwater depth grid map (Figure 4). The

groundwater depth grid map was prepared by

subtracting the static water level from the surface

elevation of the boreholes and springs found within

the watershed and interpolated by kriging Arc GIS

tool.

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 8

Figure 4: Groundwater depth map of Waja-Golesha watershed

Biophysical input data

The dominant land use of the Waja-Golesha

watershed was crop land which accounts for 49.5%

of the total area, followed by wood land 13.2 %,

shrubs 12.3%, bare land 11.6%, forest, and grassland

and settlement collectively cover 13.4 % (Figure 5a).

Using the United States Department of Agriculture

(USDA) textural classification standards, the soil

texture of the watershed was classified into three

classes: sandy loam, silty loam and clay (Figure 5b).

Based on the soil map of Waja-Golesha watershed,

Silty loam covers 25.5%, sandy loam 28.3% and clay

covers 46.2%. The Waja-Golesha watershed was

developed from land-use land cover map of Ethiopia.

The elevation (Figure 6a) and slope (Figure 6b) of the

Waja-Golesha watershed were developed from 12.5

m DEM. The slope is an important component

determining the watershed hydrological features. It is

categorized according to the degree of steepness,

which represents 0-5% level to gentle slope, 5-10%

moderate slope, 10-15% strong slope, 15-20% steep

slope, and slopes greater than represent very steep

slope.

Figure 5: Land use land cover (a) and soil map of Waja-Golesha watershed (b)

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 9

Figure 6: Elevation (a) and slope map of the Waja-Golesha watershed (b)

2.3.3. Parameter tables /lookup tables preparation

Parameter tables are also important for running the

WetSpass model. Therefore four parameter tables

were prepared such as summer and winter land use

land cover, soil texture and runoff coefficient

parameters in DBF (database file) format. Basically,

the model user guide and some other literature

reviews were used to adjust and develop the

parameter values to the watershed characteristics. In

this study excel (xls) file to dbf file format converter

software was used to prepare the lookup tables.

2.3.4. Adaptation of WetSpass to the case of Waja-

Golesha sub-basin

WetSpass is originally developed for conditions in

the temperate regions in general and Europe in

particular (Gebrerufael et al, 2018). Later the model

was applied all over the world under different

conditions by modifying its parameters (Fenta et al.,

2015; Meresa et al., 2019; Salem et al, 2019). The

modified WetSpass model was applied in semi-arid

region of Ethiopia to simulate the hydrological water

balance of the Upper Bilate Catchment (Dereje and

Nedaw, 2019), Birki watershed (Esayas and

Gebeyehu, 2019) and Werii watershed (Gebremeskel

and Kebede, 2017). The land-use classes and textural

composition and classification of soils for tropical

countries like Ethiopia are apparently different than

the case of temperate regions. Even though some

similar land-use classes literally exist in both

temperate and tropical regions, they are not the same

in characteristics. Summer and winter seasons of

temperate regions are not the same as those of

tropical regions. Taking the case of Ethiopia, winter

is the dry season while summer is the main rainy

season. Hence, before doing any watershed

simulation modification of the model is required so

as to adapt it in Ethiopian condition.

Thus, a modified WetSpass model was developed for

Waja-Golesha watershed where the land-use

parameter tables (summer and winter seasons) for

Waja-Golesha watershed were modified and adjusted

to represent the condition of Waja-Golesha watershed

using expert knowledge and scientific literature.

Land-use (summer and winter), soil and runoff

coefficient are the parameter tables used by

WetSpass. The land-use attribute table includes

parameters such as land-use type, rooting depth, leaf

area index, and vegetation height. The soil parameter

table contains soil parameters such as textural soil

class, plant available water contents and others.

Whereas, the runoff coefficient attribute table

contains parameters for runoff classes of various land

uses, slope, runoff coefficient etc.

Necessary modification was done on the land-use

parameters mainly for the leaf area index, crop

height, interception percentage, to fit the condition of

Waja-Golesha watershed. Moreover, the vegetative

area, bare area, impervious area, and open water area

proportions of each land-use class in Waja-Golesha

watershed have been modified (Tables 1 and 2). The

year was divided into two seasons‟ summer (June to

September) and winter (October to May) with their

respective input data (land use, precipitation,

potential evapotranspiration, temperature, wind speed

and groundwater depth).

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 10

Table 1: Summer land-use parameter table modified for the Waja-Golesha sub-basin

Number

Luse_type

Runoff_veg

Num_veg_ro

Num_imp_ro

Veg_area

Bare_area

Imp_area

Openw_area

Root_depth

Lai

Min_stom

Interc_per

Veg_height

Settl

Grass

0.5

0.2

0.3

0.0

0.3

0.20

100.0

10.0

0.12

Bare

Bare soil

0.0

0.7

0.3

0.0

0.05

0.00

110.0

0.0

0.001

Agri

Crop

0.8

0.1

0.0

0.4

0.20

180.0

35.0

0.7

Grass

1.0

0.0

0.3

2.00

100.0

10.0

0.2

Wood

Forest

0.8

0.0

0.2

0.0

2.0

5.00

250.0

25.0

15.0

Forest

0.80

0.0

0.20

0.0

2.50

3.50

310.0

50.0

10.00

Shrub

Grass

0.80

0.0

0.2

0.0

0.6

6.00

110.0

42.0

2.50

Table 2: Winter land-use parameter table modified for the Waja-Golesha sub-basin

Number

Luse_type

Runoff_veg

Num_veg_ro

Num_imp_ro

Veg_area

Bare_area

Imp_area

Openw_area

Root_depth

Lai

Min_stom

Interc_per

Veg_height

Settl

Grass

0.4

0.50

0.10

0.0

0.3

0.2

100.0

10.0

0.12

Barel

Bare soil

0.00

0.70

0.3

0.0

0.05

0.0

110.0

0.0

0.001

Agric

Crop

0.20

0.40

0.4

0.0

0.35

2.0

180.0

22.0

0.6

Grass

0.60

0.30

0.10

0.0

0.30

1.0

140.0

10.0

0.12

Wood

Forest

0.20

0.80

0.0

2.00

4.0

250.0

10.0

15.0

Forest

0.80

0.10

0.0

2.00

4.0

340.0

42.0

10.0

Shrub

Grass

0.65

0.30

0.05

0.0

0.60

3.0

110.0

30.0

2.0

Luse_type = land-use type, Runoff_veg = runoff vegetation, Num_veg_Ro = runoff class for vegetation type, Num_imp_Ro = impervious runoff class for

impervious area types, Veg_area = vegetated area, Bare_area = bare area, Imp_area = impervious area, Openw_area = open-water area, Root_depth = Root

Depth, Lai = Leaf Area Index, Min_stom = minimum stomata opening, Interc_per = interception percentage, Veg_height = vegetation height

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 11

2.3.5. Analysis and grid maps combination

WetSpass gives various hydrologic outputs on a

yearly and seasonal (summer and winter) basis

(Gidafie et al., 2016). The results from the WetSpass

model can be analyzed in various ways (Kahsay et al,

2018). The spatial variations of recharge and runoff

can be obtained as a function of land use and soil

type. As all output from the model are grid maps and

not tabular values, it would be helpful to combine

two or more grid maps. The ArcGIS function called

„combine‟ is used to combine different grids to

produce database files for further analysis and

graphical presentations. Accordingly, the land-use

and soil maps were combined with surface runoff,

recharge and actual evapotranspiration maps to

visualize the impact of different land covers and soil

texture on evapotranspiration, surface runoff, and

groundwater recharge.

2.3.6. Validation of WetSpass Mode

The validation of the WetSpass model was performed

by using stream flow data recorded at the Golina

river gauging station for the period 2012–2019

obtained from the Ministry of Water and

Energy of Ethiopia (MoWE) to perform the

hydrograph analysis. The automated Web-Based

Hydrograph Analysis Application (WHAT) was

applied to derive a base flow from stream-flow data.

WHAT has three separating filters: the Eckhardt

recursive digital filter method (RDF) (Eckhardt,

2005), the one-parameter digital filter method (OPM)

(Lyne and Hollick, 1979; Nathan and McMahon,

1990; Arnold and Allen, 1999; Arnold et al., 2000)

and the local-minimum method (LMM) (Lim et al.,

2005) where the Eckhardt recursive digital filter

method was applied in this study as indicated in the

equation [4] below.







  



      







  





Where, bt represents base flow at time step t

/s); bt−1 represents the filtered base flow at time

step t−1 (m

/s); BFImax presents the maximum long-

term ratio of base flow/total streamflow; Qt is the

total streamflow at time step t (m

/s) and α is the

filter parameter. Eckhardt (2005) suggested BFImax

values of 0.50 for ephemeral streams including

porous aquifers, 0.25 for perennial streams

containing hard rock aquifers, and 0.80 for perennial

streams containing porous aquifers. The proposed

values of 0.80 for BFImax and 0.98 for the filter

parameter, which correlates to the hydrogeological

characteristics of the watershed, were used in this

case.

3. Results and Discussion

3.1. Validation of WetSpass Mode

Conventionally, the validation processes of the

WetSpass distributed hydrologic water balance model

were implemented through manual adjusting or

modifying the model parameters existing in the

WetSpass model within a given range of values. The

objective function is typically the correlation of the

coefficient of determination R2 between the

simulated surface runoff and observed discharge. The

adjusted parameters include alfa coefficient, “a”

interception, Lp coefficient, and runoff delay factor

“x.” These parameters were held in reserve

optimizing up to the attainment of a final agreement

between the calculated against observed discharge

recorded at Hormat-Golina river and base flow

obtained from separating the observed discharge

using base flow separator techniques (Figure 7).

Figure 8 shows that the simulation analysis has

attained excellently with a correlation coefficient of

the “line of the goodness of fit” and Nash-Sutcliffe

efficiency (NSE) of 0.94 and 0.93, respectively, with

a standard error of 0.21. Evaluation of the WetSpass

model showed representative results for the total

discharge and good results for the base flows.

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 12

Figure 7: Compression between simulated and observed flow data

Figure 8: Model validation

3.2. Outputs of WetSpass Model

The main outputs of the WetSpass model are raster

maps of annual and seasonal groundwater recharge,

surface runoff and actual evapotranspiration for the

period 2001 to 2020. In these maps, every pixel

represents the magnitude of the water balance

component in mm.

3.2.1. Groundwater recharge

The seasonal and annual groundwater recharge of the

Waja-Golesha watershed changes spatially with the

basin characteristics and topography (Figure 9). The

WetSpass model evaluates the annual long-term

groundwater recharge of the Waja-Golesha watershed

to be 5 mm and 86 mm as minimum and maximum

values, respectively, with a mean value of 29 mm.

The Average recharge represents only 5% of the areal

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 13

average rainfall. The temporal variability of the

recharge was 72% and 28% for the wet and dry

periods respectively. About 1.6*105 m

of water was

added to the groundwater annually from the entire

area of the watershed (532.6 km

). In other research

areas, similar studies have been undertaken to

estimate average groundwater recharge using the

WetSpass model. Accordingly, average recharge was

found to be 28 mm (5% of annual precipitation) by

Esayas and Gebeyehu (2019), 37 mm (6%) by

Gidafie et al. (2016), 24.90 mm (7.4%); Meresa et al.

(2019), 30.06 mm (4.2%) by Gebremeskel and

Kebede (2017), 116 mm (9.4%) ; Dereje and Nedaw

(2019), 66 mm (12%); Haile (2015) and 55 mm (8%)

by Gebreruael et al (2018).

The mean annual spatial groundwater recharge is

highly variable depending on the factors that govern

groundwater recharge (Figure 9). The southern and

western highlands of the Waja-Golesha watershed

had high annual groundwater recharge due to the

presence of permeable soils, high rainfall and

vegetation cover. The western foothill side areas

were also characterized by high groundwater

recharge occurrence mainly due to the flat

topography and coarse permeable soils. On the

contrary, the lowlands and central southeastern of the

area had low groundwater recharge due to their being

discharge areas and the dominance of less permeable

fine-textured soils. Grass land and sandy-textured soil

class results high ground water recharge (Table 3)

due to high permeability of sandy soils, less runoff on

the relatively gentler slopes of grass lands. While

bare land with clay soils results low infiltration and

contributed to high surface runoff.

3.2.2. Surface runoff

Depending on slope and other catchment

characteristics, surface runoff resulting from Waja-

Golesha watershed was varied specially (Figure. 10).

The average surface runoff from Waja-Golesha

watershed was 131.5 mm with minimum and

maximum values of 22 and 361 mm respectively. The

surface runoff accounts 24% of annual rainfall which

is equivalent 2.2*10

cubic meters of water exit the

catchment as runoff. The seasonal variation of

surface runoff was 51% and 49% in the wet and dry

seasons respectively. This shows the bi-modal nature

of precipitation distribution in the watershed.

In different sub-catchments of Ethiopia, similar

results were presented: 20.8% of precipitation, Upper

Bilate Catchment, Southern Ethiopia (Dereje and

Nedaw, 2019),7.1% of precipitation, Birki

Watershed, Eastern Tigray, Northern Ethiopia

(Meresa et al., 2019), 6% of annual precipitation,

Werii watershed of the Tekeze River Basin, Ethiopia

(Gebremeskel and Kebede, 2017), 7.2% of annual

precipitation, Geba basin, Northern Ethiopia

(Yenehun et al., 2017) and 7% of precipitation, Illala

Catchment, Northern Ethiopia (Teklebirhan et al.,

2012).

The highest mean annual surface runoff of the Waja-

Golesha watershed was observed in western

highlands, which are characterized by clay and silty

loam soil which had low permeability, which

increases the surface runoff. On the other hand, the

lowest runoff occurs in the northern and central parts

of the watershed due to the presence of sandy loam

soils. Clay soil with settlement and bare land

contributes high runoff generation whereas grassland

and forest with sandy loam soil produce low runoff

(Table 4). Areas across the high lands which have

relatively high rainfall produce high runoff than the

valley floor which had low rainfall.

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 14

Table 3: Simulated mean annual recharge (mm) for the combinations of land-use and soil texture

Soil texture

Forest

Shrubs

Agriculture

Grass land

Bare land

Settlement

Mean

Sandy loam

47.5

36.5

45.8

52.2

28.1

27.1

38.7

Silty loam

20.2

28.5

22.5

24.7

Clay

6.5

12.9

4.8

5.2

17.5

3.7

8.7

Mean

27.7

25.5

23.6

28.6

22.7

17.8

Figure 9: Ground water recharge map of Waja-Golesha watershed

Table 4: Mean annual surface runoff for different combinations of land-use and soil texture

Soil texture

Forest

Shrubs

Agriculture

Grassland

Bare land

Settlement

Mean

Clay

92.2

173.1

173.7

97.1

315.5

322.2

195.6

Silt loam

85.3

163.5

164

299.3

301.3

183.6

Sandy loam

76.6

154.5

152.4

276.9

268.7

168.0

Mean

84.7

163.7

163.4

88.0

297.2

297.4

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 15

Figure 10: Surface runoff from Waja-Golesha watershed

3.2.3. Actual evapotranspiration

The WetSpass simulated mean annual

evapotranspiration of the Waja-Golesha watershed

was 474mm constituting about 71% of the annual

average rainfall of the area. This indicated that

evapotranspiration was the main process of water loss

in the watershed mainly due to the high rate of

radiation and existence of strong dry winds. The

higher evapotranspiration took place during the

summer season (63%) while the remaining 37% took

place during the winter season. The actual

evapotranspiration during the summer season was

26% higher than the simulated actual

evapotranspiration that took place during the winter

period which indicates the bimodal nature of the

precipitation in the study area. Low land parts of the

area were responsible for higher annual

evapotranspiration (Figure. 12). Similarly, areas with

high precipitation like Gaba basin have high

evapotranspiration (Gebreyohannes et al., 2013).

Similarly, Gebremeskel and Kebede, (2017), reported

that actual evapotranspiration is 90.7% of the average

rainfall in the Werii watershed of the Tekeze River

Basin, Ethiopia, Yenehun et al., (2017), reported

90.7% of the average rainfall in Geba basin, Northern

Ethiopia, Meresa et al., (2019), get 85.5% of average

rainfall in the Birki Watershed, Eastern Tigray,

Northern Ethiopia, Dereje and Nedaw, (2019), also

simulates 69.8% of average rainfall in the Upper

Bilate Catchment, Southern Ethiopia and Gebrerufael

et al, (2018), reported that 84% of average rainfall in

the Raya valley, Northern Ethiopia. As a result,

evapotranspiration removes the majority of average

rainfall. The spatial variation of evapotranspiration

was analyzed by combining average annual

evapotranspiration with different land-use and soil

classes. Because of water availability in the soil

texture and high transpiration from the vegetation

forest, grass land and shrubs with sandy loam and

clay soil texture have high evapotranspiration (Table

5).

3.2.4. Water balance components

The overall water balance analysis of the Waja-

Golesha watershed (Table 6) indicated that only a

small fraction of the annual rainfall remains to

recharge the groundwater reservoir of the watershed.

While the rest leaves the watershed mainly through

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 16

evapotranspiration and to a lesser extent via surface

runoff. The higher standard deviation value revealed

in the water balance component indicates high spatial

variation of the water balance element within the

basin. This is mainly in response to the uneven

distributions of the climatic parameters associated

with variations of Land-use/land cover, soil type,

topography and slope.

Table 5: Simulated mean annual evapotranspiration for combinations of land-use and soil texture

Soil texture

Forest

Shrubs

Agriculture

Grassland

Bare land

Settlement

Mean

Sandy loam

549.7

552.5

358.8

557.3

485.8

483.9

498.0

Silty loam

576.5

572.6

364.7

570.6

501.6

497.2

513.9

Clay

578.1

564.6

375.9

584.7

495.4

498.4

516.2

Mean

568.1

563.2

366.5

570.9

494.3

493.2

Figure 11: Actual evapotranspiration from Waja-Golesha watershed

Table 6: Water balance components of Waja-Golesha watershed

Water balance components

Annual values (mm/year)

minimum

maximum

Mean

Standard deviation

Precipitation (PCP)

577.2

726.3

664.5

13.6

Evapotranspiration (ET)

259

657

474

89.

Runoff (Ro)

361

161.5

Recharge (Re)

82.0

29.0

Water balance

PCP-ET-Ro-Re = 0.0

4. Conclusion and Recommendation

Specially distributed long-term average recharge of

Waja-Golesha sub-basin was estimated by distributed

water balance model (WetSpass). WetSpass model

output indicates that the mean annual recharge in the

basin was 29 mm and was estimated to represent

about 5% of the mean annual rainfall. From the entire

area of the Waja-Golesha watershed 1.6*10

/yr.

water was added annually. The seasonal recharge of

the area was 72% and 28% in wet and dry season

Kidane, S.B. J. Agri. Environ. Sci. 8(1), 2023

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 17

respectively. Sandy textured soils with grass land use

results high recharge while low recharge was

observed in clay soils with settlement and bare lands.

Wetspass was suitable model for analysis of land use

change on water balance of the watershed and

therefore wetSpass is good enough to simulate

groundwater recharge of the Waja-Golesha

watershed. 474 mm of evapotranspiration which is

71% of annual rainfall was simulated in the

catchment. Due to the presence of strong dry wind

and high radiation in the catchment,

evapotranspiration was the main process of water loss

in the area. From the water balance analysis result

most of the annual rainfall lost the sub-basin through

surface runoff and evapotranspiration while small

fraction of the rainfall was responsible for

groundwater recharge.

The groundwater recharge map along with other

thematic maps can serve as a source of information

database which can be updated from time to time by

adding new information. Therefore there should be

well organized data base system in different

governmental organization so as to provide accurate

data about the hydrogeological as well as

hydrological systems for feature studies.

Funding statement

This research did not receive any specific grant from

funding agencies in the public, commercial, or not-

for-profit sectors.

Data availability statement

Data will be made available on request.

Declaration of interest’s statement

The authors declare no competing interests.

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