This study was conducted to identify and map the surface irrigation potential in the data-scarce Jewuha watershed of the Awash Basin, Ethiopia. The suitability of the land, based on the soil characteristics and slope, was first assessed by the parametric evaluation technique. The overall suitability of the land was then evaluated considering additional factors including land use land cover, proximity to a water source and road using weighted overlay analysis through the analytic hierarchical process (AHP). Water diversion sites as the source of water supply points were selected based on theoretical site selection criteria with the help of a geographic information system (GIS) and physical observation. The surface water available at the diversion sites was estimated using the Soil and Water Assessment Tool (SWAT) model combined with the spatial proximity regionalization technique. The land suitability analysis revealed that 16.7% (11,359 ha) of the study area is suitable for surface irrigation. Five diversion sites were identified as sources of water supply and the total flows at these sites were 12.92 Mm3. It was found that only 27.3% (3,098 ha) of the suitable land, 5% of the total area of the watershed, can be effectively developed by surface irrigation.

  • To find the irrigation potential area of the watershed for both gauged and ugauged watersheds.

  • The surface water potential was estimated using the SWAT model and spatial proximity regionalization technique.

  • The suitability of the land was evaluated using the parametric evaluation technique.

Irrigation development and mapping of an irrigation potential area could provide opportunities to cope with the impact of climatic variability, enhance productivity per unit of land and increase the annual crop production volume significantly (Awulachew & Merrey 2005). Irrigation potential refers to the total suitability of the land and the suitability or capacity of water available for irrigation in the given watershed without affecting the crop growth (FAO 1997). It is about applying water to crops from surface and groundwater sources when natural rainfall is not enough to reliably produce desired crop yields and quality (Makin 2016).

In Ethiopia, only 4–5% of the potential area has been utilized for irrigation (Awulachew et al. 2007; Worqlul et al. 2015) so far even though there is no precise figure for the potential and actual irrigated area. This is due to a lack of consistent, reliable inventory and well-studied and documented data (Shiferaw 2007; Awulachew et al. 2008; MoWIE 2019). Thus, the assessment of irrigation potential is important to utilize the available water resources efficiently for the sustainable production of crops and to ensure the food security of the rapidly increasing population in the country (Haile 2015; Chandrasekharan et al. 2021).

Irrigation potential assessment requires the evaluation of both the land suitability and the adequacy of water resources for the cultivation of the major crops grown in the area (MoWIE 2019; Bushira & Abdule 2020; Shitu & Berhanu 2020; Ben-Gal et al. 2022). Land suitability assessment refers to evaluating the parcel of land for irrigation or agricultural development (Makin 2016; Nigussie et al. 2019; Rabhi et al. 2021). The suitability of the land for surface irrigation can be assessed by methods such as the standard guide prepared by the Food and Agriculture Organization of the United Nations (FAO (1976, 2007)) and the parametric evaluation technique as suggested by Sys et al. (1991). There exist a wide range of land evaluation systems, from the simplest single-factor correlation to very complicated and complex formulas or models. Among these, the multi-criteria evaluation (MCE) technique and parametric evaluation technique are widely used (Dengiz et al. 2010; Worqlul et al. 2015; Mathewos et al. 2018; Nigussie et al. 2019). The parametric evaluation approach by Sys et al. (1991) is a method of land evaluation for irrigation purposes based on the physio-chemical characteristics rating of a soil profile. Ayalew (2014) conducted a study on land suitability evaluation for surface and sprinkler irrigation using GIS in the Guang watershed, highlands of Ethiopia using a parametric evaluation approach. The result reveals that 990.25 ha of the watershed was highly suitable (S1) for surface irrigation methods and 2,370 ha of the watershed was unsuitable for sprinkler irrigation methods due to soil salinity, drainage and PH. Herein, the land is more suitable for surface irrigation than a sprinkler irrigation system. Kassaye et al. (2019) studied GIS-based multi-criteria land suitability analysis for surface irrigation along the Erer watershed, eastern Hararghe zone, Ethiopia. The objective of this study was to estimate the suitability of the land for surface irrigation in the GIS environment with a multi-criteria approach. The result revealed that 11.7% of the watershed is under the highly suitable category while 36.3% of the watershed is unsuitable. The drawback of the study is that it estimates only the suitability of the land but not the potential of the land based on the suitable land and available water in the river.

Water resource assessment relies on measured water flows in the catchment under consideration. If the catchment is, however, ungauged, hydrological models are commonly used to quantify runoff generated in the catchment (Griensven & Meixner 2006; Andualem et al. 2020). Several models are available for assessing the water resource in a catchment including the rational method (Fattahi et al. 2022), HBV (Mohammadi et al. 2021), HEC-HMS (Merwade 2012) and SWAT (Allen et al. 1997). However, SWAT performs better than the other models (Sisay et al. 2018; Guug et al. 2020; Singh & Saravanan 2020; Yifru et al. 2020; Fattahi et al. 2022). The flows simulated by the SWAT model can then be transferred to any point of interest in the catchment using regionalization techniques such as spatial proximity (SP) and physical similarity methods (Parajka et al. 2005; Oudin et al. 2008).

Rediet et al. (2020)’s land suitability evaluation is for surface irrigation using spatial information technology in the Omo-Gibe river basin, Southern Ethiopia investigates that from the total area of the land, 7% is highly suitable (S1), 64% is moderately suitable (S2) and the remaining are slightly suitable (S3) and not suitable (N). The study factors, considered for assessing the suitability of the land including LULC, slope, soil and proximity to the river are weighted overlay in the GIS environment to explore the overall suitability of the land. However, the study does not consider socio-economic factors like market and road in addition to water resources to know the physical irrigation potential of the land for irrigation development. The study area is the Jewuha catchment which is located in the middle Awash River Basin. The catchment drains to the Jewuha River. The river is one of the major tributaries of the Awash River. Despite the availability of this resource, it is not significantly used for irrigation development. Farmers in the area widely practise rainfed agriculture. For judicious use of the available land and water resources in the area, a good understanding of the irrigation potential is required. Therefore, this study was conducted to assess the irrigation potential for surface irrigation systems in the Jewuha watershed.

Description of the study area

Jewuha watershed is located in the middle Awash River Basin, between 10°00′3″ and 10°21′10″N latitude and 39°44′55″ and 40°10′4″E longitude (Figure 1). The river originates in the north of Shewa Robit town with a watershed area of 679.6 km2 from the outlet. The watershed is 240 km away from the capital city of Addis Ababa in the northeast direction.
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

The topography of the Jewuha watershed is characterized by diverse topographic conditions. The upper part of the watershed is characterized by mountainous and highly separated terrain with steep slopes and the downstream part is a gentle slope that is suitable for agricultural activity, with elevation ranges from 3,555 m above mean sea level (a.m.s.l) in the mountainous area to 1,101 m a.m.s.l in the lowland. Most of Ethiopia is characterized by a tropical climate which is affected by altitude. The eastern lowlands are much drier and have a hot semi-arid to desert climate. However, the southwestern and highland of the country obtains high rainfall distribution greater than 1,000 mm annually. As one part of the country, the climate of the Jewuha watershed is characterized by high rainfall with low temperatures in the highland area and low rainfall with high temperatures in the lowland area.

Data collection

In this study, data were collected from various sources such as meteorological, hydrological, spatial data and crop data (Table 1).

Table 1

Summary of data types and source

Data typeDerived dataSource
DEM Slope MoWIE 
LULC Land use land cover map WLRC 
Soil chemical properties CaCO3 and EC HWSD 
Soil physical properties Soil depth, texture, drainage MoWIE 
Road map Road network DIVA-GIS website (https://www.diva-gis.org
Meteorological HMD, PCP, SLR, TMP, WND. MoWIE 
Hydrological Streamflow MoWIE 
Crop Crop type and growing periods MoA 
Data typeDerived dataSource
DEM Slope MoWIE 
LULC Land use land cover map WLRC 
Soil chemical properties CaCO3 and EC HWSD 
Soil physical properties Soil depth, texture, drainage MoWIE 
Road map Road network DIVA-GIS website (https://www.diva-gis.org
Meteorological HMD, PCP, SLR, TMP, WND. MoWIE 
Hydrological Streamflow MoWIE 
Crop Crop type and growing periods MoA 

DEM, digital elevation model; LULC, land use/land cover; MoWIE, Ministry of Water, Irrigation, and Energy of Ethiopia; HWSD, Harmonized World Soil Database Version 1.2; WLRC, Water and Land Resource Center, Addis Ababa; HMD, relative humidity; PCP, precipitation; SLR, solar radiation; TMP, temperature; WND, wind speed; MoA, Ministry of Agriculture of Ethiopia.

Meteorological data

Meteorological data were collected from the National Metrological Agency of Ethiopia (NMAE). They were collected from two meteorological stations (Shewa Robit and Majeta) in the area for the years 1987–2019. The data include relative humidity (HMD), precipitation (PCP), solar radiation (SLR), maximum and minimum temperature (TMP) and wind speed (WND). The continuity and consistency of the meteorological data were checked by the normal ratio method and double mass curve, respectively. These data were used for WGEN statistic calculation for SWAT using the SWAT weather database.

Hydrological data

The daily streamflow data were collected at three gauging stations from the Ministry of Water, Irrigation and Energy (MoWIE) at Jewuha for 1985–2003, at Ataye for 1988–2001 and at Robi for 1995–2011. Jewuha station is used for the calibration and validation of the SWAT model in the watershed; however, flows at Ataye and Robi rivers were used for the validation of the regionalization technique during model parameter transfer to ungauged diversion points using the SP regionalization technique.

Spatial data

The spatial data include a digital elevation model (DEM), land use land cover (LULC), soil map and road map of the area. The DEM was obtained from the United States Geological Survey (USGS) website. The soil chemical parameters, covering the study area, were obtained from the Harmonized World Soil Database (HWSD) on the FAO website and the soil physical properties were obtained from the GIS and remote sensing department of the Ministry of Water, Irrigation and Energy (MoWIE). LULC data were obtained from the Water and Land Resource Center, Addis Ababa. The road map is another spatial dataset that was used to extract road proximity to assess the suitability of the study area for irrigation and it was obtained from the DIVA-GIS website.

Crop data

The major crops grown in the area and approximate growing periods were obtained from the Ministry of Agriculture.

Land suitability analysis

The land suitability for irrigation was assessed by weighing the factors of soil characteristics, slope, LULC, distance from the water source and distance from the road (Nigussie et al. 2019; Rediet et al. 2020; Shitu & Berhanu 2020).

Soil and slope suitability assessment

In this study, the land suitability assessment, based on the soil characteristics and slope, was made using the parametric evaluation method. The method requires the soil's physical (soil depth, texture, drainage) and chemical properties (CaCO3, EC) and topography (slope) of the area. As per the FAO recommendation, the land area with a slope up to 8% was considered for surface irrigation (Sys et al. 1991).

The parametric method allocates numerical ratings to each of the above factors depending on their relevance to the land use considerations. The ratings were obtained from the tables prepared by Sys et al. (1991). The rates were then used to calculate the capability index (CI) using Equation (1), which was rated based on the value ranges shown in Table 2.
(1)
where CI is the capability index for irrigation, A is the soil texture rating, B is the soil depth rating, C is the CaCO3 rating, D is the EC rating, E is the drainage rating and F is the slope rating.
Table 2

Capability indices (CI) class for soil and slope suitability

CI valueDefinitionSymbol used
>80 Highly suitable S1 
60–80 Moderately suitable S2 
45–59 Marginally suitable S3 
30–44 Currently not suitable N1 
<29 Permanently not suitable N2 
CI valueDefinitionSymbol used
>80 Highly suitable S1 
60–80 Moderately suitable S2 
45–59 Marginally suitable S3 
30–44 Currently not suitable N1 
<29 Permanently not suitable N2 

LULC suitability assessment

The LULC of the study was reclassified based on the classification system of FAO (1976) using the reclassification tool, which is an attribute generalization technique in ArcGIS, as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N) (Table 3).

Table 3

Land use land cover suitability rating

LULC typeDefinitionLULC rating (r)Class
Cultivated land Highly suitable S1 
Grassland/bare land Moderately suitable S2 
Shrub/bush/woodland Marginally suitable S3 
Settlement/forest/wetland Not suitable 
LULC typeDefinitionLULC rating (r)Class
Cultivated land Highly suitable S1 
Grassland/bare land Moderately suitable S2 
Shrub/bush/woodland Marginally suitable S3 
Settlement/forest/wetland Not suitable 

Distance from the water source (river) suitability assessment

The identification of land that is close to the water supply (river) was done by calculating the straight-line (Euclidean) distance from the streams. This was generated from a 20 m × 20 m cell size DEM in a GIS tool and the lands were then reclassified for their suitability as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N) for their distance from the river (Table 4). To do this, go to: ArcToolbox Spatial Analyst Tools > Distance > Euclidean Distance. In the Euclidean distance window, give an input raster or feature source as the river shape file and also give the output distance raster as the desired output location and file name.

Table 4

Distance from water source suitability rating

ClassDefinitionRating (r)Distance from a river (km)
S1 Highly suitable 0–2 
S2 Moderately suitable 2–4 
S3 Marginally suitable 4–5 
Not suitable >5 
ClassDefinitionRating (r)Distance from a river (km)
S1 Highly suitable 0–2 
S2 Moderately suitable 2–4 
S3 Marginally suitable 4–5 
Not suitable >5 

The land nearest to the stream was considered the most suitable for irrigation development and the land far from the stream is marginally suitable (Worqlul et al. 2015; Birhanu et al. 2019).

Distance from road suitability assessment

Similarly, the road map obtained from the DIVA-GIS website was used to reclassify lands for their suitability based on the classification system of FAO (1976) using the reclassification tool in the GIS (Table 5).

Table 5

Distance from road suitability rating

ClassDefinitionRating (r)Distance from road (km)
S1 Highly suitable 0–3 
S2 Moderately suitable 3–5 
S3 Marginally suitable 5–8 
Not suitable >8 
ClassDefinitionRating (r)Distance from road (km)
S1 Highly suitable 0–3 
S2 Moderately suitable 3–5 
S3 Marginally suitable 5–8 
Not suitable >8 

Weighted overlay analysis of land suitability

To find the overall suitability of the land for irrigation, a suitability model was created using the model builder in the Arc GIS toolbox. The weight assigned for each factor was overlaid in GIS to undertake an MCE (Khongnawang & Williams 2015). In an MCE, an attempt is made to combine a set of criteria to achieve a single composite basis for a decision according to a specific objective. The relative importance/weight of criteria and sub-criteria was estimated using MCE through an analytic hierarchical process (AHP) applied by using a pairwise comparison of each suitability factor (Saaty 1977).

In pairwise comparison, each factor was matched head-to-head (one-to-one) with the other and a pairwise or comparison matrix was prepared to express the relative importance (Table 6) (Worqlul et al. 2015; Kassaye et al. 2019; Shitu & Berhanu 2020). The diagonal elements of the pairwise comparison matrix were assigned the value of unity since the diagonal of the matrix value was obtained by the compared value of itself.

Table 6

Pairwise comparison matrix of factors

FactorSoil and slopeLULCDistance from riverDistance from road
Soil and slope 
LULC 0.5 
Distance from river 0.333 0.5 
Distance from road 0.25 0.333 0.5 
Sum 2.08 3.833 6.5 10 
FactorSoil and slopeLULCDistance from riverDistance from road
Soil and slope 
LULC 0.5 
Distance from river 0.333 0.5 
Distance from road 0.25 0.333 0.5 
Sum 2.08 3.833 6.5 10 

To fill the matrix, ratings were given for all factors on a 9-point continuous scale. For example, if one feels that land suitability based on soil and slope is very strongly more important than LULC suitability in determining suitability for irrigation, one will enter 7 on this scale. However, if the reverse is true, one will give the value of 1/7. The value was given based on expert judgement and related literature reviews.

From the pairwise comparison matrix, the normalized decision matrix of the irrigation suitability factors was then obtained by dividing all the elements in each column by the sum of the columns (Equation (2)). The normalized values are shown in Table 7.
(2)
where N is the normalized value, Aij is the rating of the factor in the ith row and jth column and Cj is the sum of the rating values of the factors in the j column in Table 6.
Table 7

Normalized value of factors

FactorSoilLULCDistance from riverDistance from road
Land 0.48 0.52 0.46 0.4 
LULC 0.24 0.26 0.31 0.3 
Distance from river 0.16 0.13 0.15 0.2 
Distance from road 0.12 0.09 0.08 0.1 
FactorSoilLULCDistance from riverDistance from road
Land 0.48 0.52 0.46 0.4 
LULC 0.24 0.26 0.31 0.3 
Distance from river 0.16 0.13 0.15 0.2 
Distance from road 0.12 0.09 0.08 0.1 

The eigenvectors and weights of the criteria were calculated from the normalized matrix through summation and the average of the values of each element in the row, respectively (Table 8).

Table 8

Eigenvector value of criteria

FactorEigenvectorsWeight, Wi
Land 1.86 0.465 
LULC 1.11 0.278 
Distance from river 0.64 0.160 
Distance from road 0.39 0.098 
FactorEigenvectorsWeight, Wi
Land 1.86 0.465 
LULC 1.11 0.278 
Distance from river 0.64 0.160 
Distance from road 0.39 0.098 

With a weighted linear combination, factors are combined by applying weight or per cent of influence to the suitability of the irrigation obtained by the pairwise comparison technique. The multiplication was based on the following equation.
(3)
where S is the suitability rate, Wi is the weight of the factor, and Xi is the criteria score of the factor.

The map obtained after overlaying was irrigation suitability which was again reclassified based on their degree of suitability as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N) (Table 9).

Table 9

Irrigation suitability rating

Suitability rating rangeClassDefinition
S1 Highly suitable 
S2 Moderately suitable 
S3 Marginally suitable 
Not suitable 
Suitability rating rangeClassDefinition
S1 Highly suitable 
S2 Moderately suitable 
S3 Marginally suitable 
Not suitable 

Estimation of water availability

SWAT model description

The water availability in the study area was assessed using the SWAT hydrological model. SWAT (Arnold et al. 2002) can simulate hydrological cycles with a daily time step by disaggregating a river basin into sub-basins and hydrologic response units (HRUs). SWAT uses the water balance equation to simulate the hydrologic cycle within a watershed (Equation (4)).
(4)
where SWt is the final water content (mm H2O), SWO is the initial soil water content on the day i (mm H2O), t is the time, days, Rday is the amount of precipitation on the day i (mm H2O), Qsurf is the amount of surface runoff on the day i (mm H2O), Ea is the actual evapotranspiration on the day i (mm H2O), Wseep is the amount of water entering the vadose (unsaturated) zone from the soil profile on the day i (mm H2O) and Qgw is the amount of return flow on the day i (mm H2O).

To give a better physical description of the water balance, the model reflects the difference in evapotranspiration for various land use and soil types in the subdivision of the watershed (Griensven & Meixner 2006; Gayathri et al. 2015; Andualem et al. 2020; Su et al. 2020). The runoff from each HRU was then predicted and routed to obtain the total yield for the watershed. In the first step, the SWAT model parameter was analyzed for its sensitivity analysis and then based on its selected value the model was calibrated and validated to check the performance of the model using statistical model parameter checker (Arnold et al. 2002; Moriasi et al. 2007; Rathjens & Oppelt 2012).

Regionalization technique for ungauged watersheds

Regionalization is the process of transferring hydrological information (parameters) of a model from a gauged watershed to an ungauged watershed. Among the different regionalization techniques, SP and physical similarity methods are widely used (Parajka et al. 2005; Oudin et al. 2008). In this study, SP with inverse distance weighting (IDW) was used to transfer the calibrated model parameter of the gauged watershed to the ungauged watershed. IDW was used to estimate the weight of the ungauged watershed. The distance between the two watersheds was determined using GIS. This regionalization technique was verified using leave-one-out cross-validation, in which a single gauged site is considered ungauged and the transferred parameters to that site are entered into the SWAT-CUP to validate with the observed flow.

Appropriate diversion sites (water points) were selected in the studied watershed using theoretical diversion site selection criteria and physical observation. These water points are ungauged. Observed flow at Jewuha and the two neighboring gauged watersheds (Robit and Ataye) were used to estimate the flow at the ungauged water points. The SP regionalization technique is based on the distance between the gauged and ungauged watersheds. While measuring the distance, the selected ungauged water points were found near the Robit and Jewuha gauged watersheds. Due to this, the calibrated parameters of the Jewuha and Robit watersheds were transferred to the ungauged water points. The parameters transferred by this technique were added into the SWAT model using a manual calibration helper and then the SWAT model was run to obtain the flow at each water point. The general formula for SP with the IDW method to regionalize the calibrated parameter of the gauged watershed is given in the following equation.
(5)
where Zug is the estimated model parameter at the ungauged watershed; n is the total number of observed points (gauges); zi is the calibrated parameter value at the gauged watershed and wi is the weight contributing to the interpolation which was found by the following equation.
(6)
where is the distance between the centroids of gauged and ungauged watersheds.

Estimation of irrigation water demand

Irrigation water demand is estimated from the water requirement of the major crops in the area of interest. The major crops considered in the study area for water demand estimation are maize, cabbage and onion. The CROPWAT computer program was used for the calculation of the crop water requirements based on the following equation.
(7)
where ETc is the crop evapotranspiration in mm; Kc is the crop coefficient and ETo is the reference evapotranspiration in mm.
The irrigation water demand was then calculated using Equation (8) based on ETc and effective rainfall.
(8)
where NIWD is the net irrigation water demand in mm and Peff is the effective rainfall in mm, which was calculated using the FAO dependable rainfall method which is given by the following equation.
(9)
where ER is the effective rainfall and P is the Precipitation/Rainfall.
The gross irrigation water demand of the crop was calculated using Equation (10) by considering the loss of water during the application of water to the irrigation field, and loss in the canal through seepage and evaporation. Thus, to compensate for this loss, irrigation efficiency was introduced. The irrigation project efficiency is commonly between 0.45 and 0.7 for surface irrigation (Luo et al. 2011). Thus, for this study, an irrigation efficiency of 0.50 was considered for the calculation of the gross irrigation water demand of crops.
(10)
where GIWD is the gross irrigation water demand in mm and η is the irrigation project efficiency.

Identification of irrigation potential

The availability of suitable land is not sufficient for estimating the irrigation potential of an area but also the availability of adequate water resources. For the estimation and delineation of the potential irrigable area, it is further required to determine the exact location of the water points from which water can be transported to the command area by gravity. The actual irrigable area or actual irrigation potential of an area shall be obtained by checking the suitable land against the adequacy of available surface water at a selected diversion site to satisfy the seasonal water requirements of the selected crops.

In this study, diversion sites were identified as water supply points by considering different factors. Where possible, the water sources is located above the command area so that the entire field can be irrigated by gravity. Other considerations are the accessibility of the site to the road, nearness to the command area and straight channel and narrow river cross-section at the site (Dai 2016). In this study, diversion site locations were obtained using GIS by considering the above factors.

After identifying the diversion sites, the irrigation potential commanded by the flows available at the selected sites was evaluated using the following equation.
(11)
where IP is the irrigation potential in ha; AW is the available water in the river at the selected diversion site in (m3) and GIWD is the gross irrigation water demand of the crops in m3/ha or mm (1 mm = 10 m3/ha).
The irrigation potential areas which are estimated for each diversion site might not be found along the river and therefore, its delineation is difficult during mapping. In such a case, manual delineation through trial and error was done by following the contour line generated. The general structure of the study is shown in Figure 2.
Figure 2

Flow diagram for the identification and mapping of surface irrigation potential.

Figure 2

Flow diagram for the identification and mapping of surface irrigation potential.

Close modal

Soil and slope suitability for irrigation

In this study, the parametric evaluation technique was employed for a combined analysis of the soil characteristics and slope to estimate the suitability of the study area for surface irrigation methods. The range of values of land capability indices (CI) was between 17 and 70. The result reveals that 12,373 ha of land was found moderately (S2) to marginally (S3) suitable for surface irrigation (Figure 3).
Figure 3

Soil and slope suitability for surface irrigation.

Figure 3

Soil and slope suitability for surface irrigation.

Close modal

LULC suitability

The different LULC types in the study area were rated according to their importance for irrigation and their suitability was reclassified based on the FAO (1976) LULC suitability classification (Table 10). It was found that 31,492 ha is highly suitable (S1), 5,756 ha is moderately suitable (S2), 30,599 ha is marginally suitable (S3) and 116 ha is not suitable (N) (Figure 4). The area currently cultivated is much higher than the land area which was found suitable based on the soil characteristics and slope. This is due to the uncontrolled expansion of land for cultivation on steep lands.
Table 10

Land use land cover suitability for irrigation

Suitability classLULCArea (ha)Percentage area (%)
S1 Agriculture 31,492 46.3 
S2 Grassland 5,756 8.5 
S3 Woodland/shrubland/bare land 30,599 45.0 
Settlement/wetland/forest 116 0.2 
Suitability classLULCArea (ha)Percentage area (%)
S1 Agriculture 31,492 46.3 
S2 Grassland 5,756 8.5 
S3 Woodland/shrubland/bare land 30,599 45.0 
Settlement/wetland/forest 116 0.2 
Figure 4

Land use land cover suitability.

Figure 4

Land use land cover suitability.

Close modal

Proximity to river and road suitability

The water source should be near the irrigated area to minimize the length of the delivery channels and pipelines and thereby develop an economical irrigation system. It also helps to make sure that there is no lack of irrigation water supply due to the distance from the river. The result of the suitability analysis of the land for irrigation due to distance from the river revealed that 27,992 ha of land is highly suitable, 18,780 ha is moderately suitable, 12,412 ha of land is marginally suitable and 8,779 ha is not suitable (Figure 5(a)).
Figure 5

Distance from source (river) and road suitability: (a) Distance from source (river), (b) Distance from the road.

Figure 5

Distance from source (river) and road suitability: (a) Distance from source (river), (b) Distance from the road.

Close modal

Similarly, it is desirable if the command area is near and accessible to a major road to transport the products from farmland to market and again farm inputs from market to farmland. The result of access to road suitability analysis revealed that 21,620 ha of land is highly suitable, 17,256 ha is moderately suitable, 18,257 ha is marginally suitable and 10,830 ha is not suitable for irrigation (Figure 5(b)). Similar studies were also reported by Worqlul et al. (2015) and Nigussie et al. (2019).

Weighted overlay analysis of the land suitability for surface irrigation

To find the overall suitability of the land for irrigation, the suitability maps from parametric evaluation analysis, LULC suitability map, proximity to river suitability map and distance from road suitability map were overlaid in a GIS environment using a weighted overlay analysis in the spatial analysis tool. To overlay these maps, the factors were given a weight based on the pairwise comparison. Soil and slope suitability was given the highest weight and distance from road suitability was given the least weight (Table 11). A similar study was done by Worqlul et al. (2015) in the Lake Tana basin of Ethiopia to assess the potential for surface irrigation.

Table 11

Weight developed for factors

FactorsWj (Weight (%)
Soil & slope suitability 47 
Land use land cover suitability 27 
Distance from source suitability 16 
Distance from road suitability 10 
FactorsWj (Weight (%)
Soil & slope suitability 47 
Land use land cover suitability 27 
Distance from source suitability 16 
Distance from road suitability 10 

Based on the weighted overlay analysis, 1.1% of the total catchment area (581 ha) was found highly suitable, 21% (10,778 ha) was moderately suitable, 77.9% (40,013 ha) was marginally suitable and 24.4% (16,591 ha) was not suitable. As shown in Figure 4, a high proportion of the cultivated area was found in the middle lowland area and is deemed suitable for surface irrigation due to its high soil depth and flat slope of less than 8%. The settlement areas and reserved forest (orange color in Figure 6) were considered unsuitable for irrigation. When cautiously looking at the result, the land area, which is steeper than 8% but is currently used by the farmers for cultivation, is categorized by the combined analysis as marginally suitable. Given that water availability permits development, putting this land for surface irrigation requires the implementation of appropriate soil and water conservation measures. However, as water is usually the limiting factor and marginal lands need special treatment to put into irrigated agriculture which will not be affordable by the small farmers in the area, only the highly and moderately suitable lands were considered as suitable lands for surface irrigation.
Figure 6

Land suitable for surface irrigation.

Figure 6

Land suitable for surface irrigation.

Close modal

Water availability analysis

The availability of surface water in the study area was assessed using the SWAT hydrological model. The model outputs and their performance are presented in the subsequent sections below.

SWAT model sensitivity analysis

Twenty-one model parameters were selected for sensitivity analysis. Among these, only five parameters were the most sensitive with a p-value less than 0.5. These are curve number (R_CN2), saturated hydraulic conductivity (R_SOL_K), groundwater delay (days) (V_GW_DELAY), Manning's ‘n’ values for overland flow (R_OV_N) and available water capacity of the soil layer (R_SOL_AWC).

Model calibration and validation

Using the measured river discharge data, the SWAT model was calibrated and validated at a monthly time scale. As an illustration for Jewuha station, the monthly flow data for the periods from 1990 to 1997 were used for calibration and from 1998 to 2003 were used for validation. The simulation of the model during calibration and validation is shown in Figure 7.
Figure 7

Simulation of Jewuha River flow during calibration and validation.

Figure 7

Simulation of Jewuha River flow during calibration and validation.

Close modal

Model performance evaluation

The performance of the model was evaluated using statistical measures such as R2, NSE, percent of bias (PBIAS) and RSR. As an illustration, the performance result of the simulation of flows at the Jewuha gauged station is presented in Table 12. There is a very good agreement between observed and simulated monthly flow. In all the periods, the model overestimates during the calibration time. This is because the model was affected by small traditional diversion structures which do not have sufficient data to enter the value into the SWAT model.

Table 12

Model performance

Statistical measureCalibrationValidation
R2 (Coefficient of determination) 0.74 0.71 
NSE (Nash–Sutcliffe efficiency) 0.73 0.7 
PBIAS (percent of bias) −0.8 7.9 
RSR 0.51 0.54 
Statistical measureCalibrationValidation
R2 (Coefficient of determination) 0.74 0.71 
NSE (Nash–Sutcliffe efficiency) 0.73 0.7 
PBIAS (percent of bias) −0.8 7.9 
RSR 0.51 0.54 

The p-factor is a good measure of the strength of calibration results. The P-factor is the percentage of measured data bracketed by the 95 PPU band and its value ranges between 0 and 1. When its value ranges between 0.7 and 1, the percentage of uncertainty is very good. As shown in Table 12, the p-factor was 0.75 which shows that the 95 PPU band is within acceptable ranges.

Flow regionalization

The flow from the gauged watershed to the ungauged diversion points was estimated through parameter regionalization by the SP technique using the IDW method. Five diversion sites were selected as appropriate water supply points. These points were found near the outlets of the five sub-watersheds of the study area and they are named after them. The stream flows obtained at these sites by the regionalization technique are shown in Table 13.

Table 13

Mean monthly stream flow (m3/s) of ungauged sites using SP regionalization

Ungauged sitesJanFebMarAprMayJunJulAugSepOctNovDec
Gida 0.5 0.97 0.87 0.83 0.8 0.4 1.06 2.62 1.83 1.13 0.8 0.7 
Lomi 4.1 4.62 5.75 6.92 4.1 1.8 8.00 21.5 14.54 8.14 5.22 4.3 
Gundifit 1.1 2.13 2.52 3.54 2.7 1.6 5.43 8.29 6.24 3.61 2.4 1.6 
Ashmaq 0.1 0.42 0.50 0.74 0.5 0.3 1.24 1.77 1.09 0.46 0.3 0.2 
Samet 0.4 0.78 0.90 1.30 0.9 0.6 2.35 2.99 1.77 0.90 0.7 0.5 
Ungauged sitesJanFebMarAprMayJunJulAugSepOctNovDec
Gida 0.5 0.97 0.87 0.83 0.8 0.4 1.06 2.62 1.83 1.13 0.8 0.7 
Lomi 4.1 4.62 5.75 6.92 4.1 1.8 8.00 21.5 14.54 8.14 5.22 4.3 
Gundifit 1.1 2.13 2.52 3.54 2.7 1.6 5.43 8.29 6.24 3.61 2.4 1.6 
Ashmaq 0.1 0.42 0.50 0.74 0.5 0.3 1.24 1.77 1.09 0.46 0.3 0.2 
Samet 0.4 0.78 0.90 1.30 0.9 0.6 2.35 2.99 1.77 0.90 0.7 0.5 

Irrigation water demand

CROPWAT model results include crop evapotranspiration (ETc) and effective rainfall to estimate the irrigation water requirement of the crop. As shown in Table 14, the monthly gross irrigation water requirement of the major crops grown in the area (maize, cabbage and onion) was estimated. The average seasonal water requirements of the crops were 417 mm.

Table 14

Gross irrigation water demand (mm/month)

Crop typeJanFebMarAprMay
Onion 135.15 59.3 89.85 37 
Maize 69.36 50.66 120.57 144 119.94 
Cabbage 123.74 83.41 144.21 74.6 
Average 109.42 64.46 118.21 85.2 39.78 
Crop typeJanFebMarAprMay
Onion 135.15 59.3 89.85 37 
Maize 69.36 50.66 120.57 144 119.94 
Cabbage 123.74 83.41 144.21 74.6 
Average 109.42 64.46 118.21 85.2 39.78 

Irrigation potential mapping

The irrigation potential of the watershed means the suitable land area that could be irrigated with the available water resource at the selected source. In this study, the potential area that can be developed by the different irrigation methods was found from the combined analysis of the available surface water which was estimated at selected diversion sites and the calculated gross irrigation water demands by CROPWAT (Table 15). The potential area was then mapped with due consideration of the area at an elevation below the diversion point to enable water transportation by gravity (Figure 8). Since the available flows at the selected diversion sites are the limiting factors, the actual irrigation potential of the Jewuha watershed was only 3,098 ha for surface irrigation which is only 27% of the suitable land and 5% of the study area.
Table 15

Surface water available at selected diversion sites and estimated irrigable area

Diversion site atWater potential (Mm3)GIWD (m3/ha)Irrigation potential (ha)
Gida 1.06 4,173 254 
Lomi 8.4 4,173 2,014 
Gundifit 2.34 4,173 560 
Ashmaq 0.34 4,173 83 
Samet 0.78 4,173 187 
Total 12.92  3,098 
Diversion site atWater potential (Mm3)GIWD (m3/ha)Irrigation potential (ha)
Gida 1.06 4,173 254 
Lomi 8.4 4,173 2,014 
Gundifit 2.34 4,173 560 
Ashmaq 0.34 4,173 83 
Samet 0.78 4,173 187 
Total 12.92  3,098 
Figure 8

Irrigable area map and diversion site location.

Figure 8

Irrigable area map and diversion site location.

Close modal

The surface irrigation potential in the data-scarce Jewuha watershed of the Awash River Basin was estimated and mapped. The land suitability for surface irrigation was first analyzed using the parametric evaluation technique by considering topography and the soil's physical and chemical characteristics. The overall suitability of the land was assessed by including additional factors such as LULC, proximity to a water source and access to road using weighted overlay analysis in a GIS environment. The result showed that 16.7% (11,359 ha) of land is suitable for surface irrigation. Five diversion sites near the outlets of five sub-watersheds of the study area were identified based on theoretical site selection criteria with the help of GIS. The availability of water at these sites was estimated by transferring hydrologic parameters from gauged sites by SP regionalization technique and flow simulated using the SWAT model. The total water potentials found at the diversion sites were 12.92 Mm3. The gross irrigation water demand was then estimated using three major crops grown in the area, viz. maize, cabbage and onion. The average seasonal demands of the crops were 417 mm. The irrigable area in the watershed was finally estimated and mapped based on the available water at the selected sites and gross irrigation water demand. It was found that 27% (3,098 ha) of the suitable land or 5% study area can be effectively developed by surface irrigation. Based on this result, we recommend that to obtain a high potential area for surface irrigation, provide a storage area at the selected diversion site but to obtain a high potential irrigable area, develop a pressurized irrigation system.

The authors would like to thank the Ministry of Water, Irrigation, and Energy (MoWIE) of Ethiopia for providing the necessary data for the research work

M.B.D. developed the study's conception and design, performed the analytic calculations and performed the model simulations. Material preparation, data collection and analysis were performed by H.H.K. Both M.B.D. and H.H.K. contributed to the final version of the manuscript. Both authors read and approved the final manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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