Abstract
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.
HIGHLIGHTS
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.
INTRODUCTION
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.
MATERIALS AND METHODS
Description of the study area
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).
Data type . | Derived data . | Source . |
---|---|---|
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 type . | Derived data . | Source . |
---|---|---|
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).
CI value . | Definition . | Symbol 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 value . | Definition . | Symbol 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).
LULC type . | Definition . | LULC rating (r) . | Class . |
---|---|---|---|
Cultivated land | Highly suitable | 4 | S1 |
Grassland/bare land | Moderately suitable | 3 | S2 |
Shrub/bush/woodland | Marginally suitable | 2 | S3 |
Settlement/forest/wetland | Not suitable | 1 | N |
LULC type . | Definition . | LULC rating (r) . | Class . |
---|---|---|---|
Cultivated land | Highly suitable | 4 | S1 |
Grassland/bare land | Moderately suitable | 3 | S2 |
Shrub/bush/woodland | Marginally suitable | 2 | S3 |
Settlement/forest/wetland | Not suitable | 1 | N |
Source: Gurara (2020) and Rediet et al. (2020).
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.
Class . | Definition . | Rating (r) . | Distance from a river (km) . |
---|---|---|---|
S1 | Highly suitable | 4 | 0–2 |
S2 | Moderately suitable | 3 | 2–4 |
S3 | Marginally suitable | 2 | 4–5 |
N | Not suitable | 1 | >5 |
Class . | Definition . | Rating (r) . | Distance from a river (km) . |
---|---|---|---|
S1 | Highly suitable | 4 | 0–2 |
S2 | Moderately suitable | 3 | 2–4 |
S3 | Marginally suitable | 2 | 4–5 |
N | Not suitable | 1 | >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).
Class . | Definition . | Rating (r) . | Distance from road (km) . |
---|---|---|---|
S1 | Highly suitable | 4 | 0–3 |
S2 | Moderately suitable | 3 | 3–5 |
S3 | Marginally suitable | 2 | 5–8 |
N | Not suitable | 1 | >8 |
Class . | Definition . | Rating (r) . | Distance from road (km) . |
---|---|---|---|
S1 | Highly suitable | 4 | 0–3 |
S2 | Moderately suitable | 3 | 3–5 |
S3 | Marginally suitable | 2 | 5–8 |
N | Not suitable | 1 | >8 |
Source:Worqlul et al. (2015).
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.
Factor . | Soil and slope . | LULC . | Distance from river . | Distance from road . |
---|---|---|---|---|
Soil and slope | 1 | 2 | 3 | 4 |
LULC | 0.5 | 1 | 2 | 3 |
Distance from river | 0.333 | 0.5 | 1 | 2 |
Distance from road | 0.25 | 0.333 | 0.5 | 1 |
Sum | 2.08 | 3.833 | 6.5 | 10 |
Factor . | Soil and slope . | LULC . | Distance from river . | Distance from road . |
---|---|---|---|---|
Soil and slope | 1 | 2 | 3 | 4 |
LULC | 0.5 | 1 | 2 | 3 |
Distance from river | 0.333 | 0.5 | 1 | 2 |
Distance from road | 0.25 | 0.333 | 0.5 | 1 |
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.
Factor . | Soil . | LULC . | Distance from river . | Distance 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 |
Factor . | Soil . | LULC . | Distance from river . | Distance 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).
Factor . | Eigenvectors . | Weight, 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 |
Factor . | Eigenvectors . | Weight, 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 |
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).
Suitability rating range . | Class . | Definition . |
---|---|---|
4 | S1 | Highly suitable |
3 | S2 | Moderately suitable |
2 | S3 | Marginally suitable |
1 | N | Not suitable |
Suitability rating range . | Class . | Definition . |
---|---|---|
4 | S1 | Highly suitable |
3 | S2 | Moderately suitable |
2 | S3 | Marginally suitable |
1 | N | Not suitable |
Estimation of water availability
SWAT model description
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.
Estimation of irrigation water demand
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.
RESULTS AND DISCUSSION
Soil and slope suitability for irrigation
LULC suitability
Suitability class . | LULC . | Area (ha) . | Percentage area (%) . |
---|---|---|---|
S1 | Agriculture | 31,492 | 46.3 |
S2 | Grassland | 5,756 | 8.5 |
S3 | Woodland/shrubland/bare land | 30,599 | 45.0 |
N | Settlement/wetland/forest | 116 | 0.2 |
Suitability class . | LULC . | Area (ha) . | Percentage area (%) . |
---|---|---|---|
S1 | Agriculture | 31,492 | 46.3 |
S2 | Grassland | 5,756 | 8.5 |
S3 | Woodland/shrubland/bare land | 30,599 | 45.0 |
N | Settlement/wetland/forest | 116 | 0.2 |
Proximity to river and road suitability
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.
Factors . | Wj (Weight (%) . |
---|---|
Soil & slope suitability | 47 |
Land use land cover suitability | 27 |
Distance from source suitability | 16 |
Distance from road suitability | 10 |
Factors . | Wj (Weight (%) . |
---|---|
Soil & slope suitability | 47 |
Land use land cover suitability | 27 |
Distance from source suitability | 16 |
Distance from road suitability | 10 |
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
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.
Statistical measure . | Calibration . | Validation . |
---|---|---|
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 measure . | Calibration . | Validation . |
---|---|---|
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.
Ungauged sites . | Jan . | Feb . | Mar . | Apr . | May . | Jun . | Jul . | Aug . | Sep . | Oct . | Nov . | Dec . |
---|---|---|---|---|---|---|---|---|---|---|---|---|
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 sites . | Jan . | Feb . | Mar . | Apr . | May . | Jun . | Jul . | Aug . | Sep . | Oct . | Nov . | Dec . |
---|---|---|---|---|---|---|---|---|---|---|---|---|
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.
Crop type . | Jan . | Feb . | Mar . | Apr . | May . |
---|---|---|---|---|---|
Onion | 135.15 | 59.3 | 89.85 | 37 | 0 |
Maize | 69.36 | 50.66 | 120.57 | 144 | 119.94 |
Cabbage | 123.74 | 83.41 | 144.21 | 74.6 | 0 |
Average | 109.42 | 64.46 | 118.21 | 85.2 | 39.78 |
Crop type . | Jan . | Feb . | Mar . | Apr . | May . |
---|---|---|---|---|---|
Onion | 135.15 | 59.3 | 89.85 | 37 | 0 |
Maize | 69.36 | 50.66 | 120.57 | 144 | 119.94 |
Cabbage | 123.74 | 83.41 | 144.21 | 74.6 | 0 |
Average | 109.42 | 64.46 | 118.21 | 85.2 | 39.78 |
Irrigation potential mapping
Diversion site at . | Water 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 at . | Water 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 |
CONCLUSION
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.
ACKNOWLEDGEMENTS
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
AUTHOR'S CONTRIBUTIONS
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.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.