The research was conducted to assess the climate change impact on crop water requirements (CWRs) in the Woybo catchment of southern Ethiopia. The impact of climate change was assessed through climate models under RCP4.5 and RCP8.5 emission scenarios for the 2050s compared to 1971–2005. During the 2050s, the annual mean minimum temperature increased by 1.9–2.3 °C, while the maximum temperature over the catchment increased by 1.4–2.0 °C. The projected rainfall is expected to increase in autumn, summer, and annually by 3.7–16.8 and 8.7–12.2% in the 2050s under RCP4.5 and 8.5, respectively. But it is likely to decrease in winter and spring by 1.5–10.2% and 8.7–2.8%, respectively. The average annual potential evapotranspiration will rise by 0.1–6.8%. Under both RCPs, CWRs may increase from 6.2 to 17.2% during the growing season, while irrigation water requirements (IWRs) may rise from 4.6 to 16.4%. Future period projection results show that potential evapotranspiration and IWRs will increase for maize crops compared to the baseline period. The study will help water managers, users, and agricultural developers in preparing new water-saving strategies and achieving agricultural sustainability.

  • Due to continuous change in climatic conditions, farmers are abruptly facing a huge loss due to erratic rainfall, increase in temperature, insufficient irrigated water, and many other causes.

  • An attempt has been made in this research to study the impact of climate change on maize production and, with different scenarios, the effects have been calculated and assessed.

  • The RCPs are well augmented.

Climate change refers to long-term shifts in temperature and weather patterns. These shifts may be a natural result of the solar cycles. They will likely impact the hydrological cycle and, consequently, affect the available water resources and agricultural water demand (Rannow & Neubert 2014). Climate change substantially affects the productivity of major staple food crops such as maize because the growth and development of crops are mainly dependent on sunlight, temperature, and water (Araya et al. 2013). It has detrimental effects on crop water requirements (CWRs). Due to the rise in temperature on the earth's surface, the demand for irrigation water can also increase despite limited resources due to climate variability.

Agriculture is the backbone of the Ethiopian economy, which accounts for half of GDP, 83.9% of exports, and 85% of total employment (UNDP 2011). It is greatly dependent on weather conditions. Climate change impacts on agricultural crop production vary from place to place and from crop to crop. Higher temperatures can reduce crop production in parts of the world while crop yield could increase with warm-wet climate change in some areas. Changes in climatological parameters lead to an increase in evapotranspiration, which might be a result of the widening of plant leaf stomata at high temperatures that allow the escape of water vapor (Gurara et al. 2021; Onyutha 2021). Hence, an increase in temperature, evapotranspiration, and variable rainfall patterns might have negative effects on the availability of water for crop and crop water requirements (Saadi et al. 2015; Salman et al. 2020). Maize is the major food, feed, and industrial crop globally, and the leading staple food crop in many developing countries. Southern central Rift Valley of Ethiopia is one of the most vulnerable regions to climate risks, where increased temperatures and changes in rainfall patterns have been altering the evapotranspiration rate and reducing water availability for maize crops (Markos et al. 2023). Mehari (2019) reviews the impact of climate change on CWRs in Ethiopia. The author stated that the demand for CWR and irrigation requirements will increase in the future due to increasing temperatures. Although higher temperatures shorten the life cycle of grain crops, plants produce a smaller amount of grains which reduces the yield of crop production.

General circulation models (GCMs) can simulate future climate changes in large-scale climate studies but they have limitations to their application on small-scale studies due to their low spatial resolution. GCM data can be used on the regional scale and in small-scale studies after downscaling it. The RCMs' data is freely available on the CORDEX database used to evaluate the effects of potential climate change on water resources (Majone et al. 2012; Bayissa et al. 2021). Representative concentration pathways (RCPs) are defined by their total radiative forcing for climate change studies (van Vuuren et al. 2011). RCP4.5 is the moderate scenario in which emissions peak around 2040 and then decline and the radiative forcing is 4.5 W/m2. RCP8.5 is the highest baseline emissions scenario in which emissions continue to rise throughout the twenty-first century and the radiative forcing pathway leads to 8.5 W/m2.

Woyibo is one of the main tributaries of Omo-Gibe, which is found in the southwestern part of the basin. It is used for irrigation, domestic water supply, and livestock to the communities in the watershed. However, the existing water resources management system of the catchment is adversely affected by the rapid growth of population, deforestation, and poor agricultural practices leading to adverse impacts of climate change (Fekadu et al. 2017).

Few studies have been conducted to investigate the impact of climate change in the Woybo catchment. The general limitation of these studies is that they did not study climate change's impact on CWRs and used only a single regional climate model. Lambebo (2018) evaluated surface water availability and demand for the current and future climate conditions in the Woybo catchment using the RCA4 climate model using RCP2.6, 4.5, and 8.5 scenarios. The finding indicated that the future climate condition (2016–2045) relative to the current climate (1986–2015) shows increased temperatures and decreased rainfall for the mentioned periods in all scenarios. Similarly, the streamflow is likely to decrease due to climate change. The author also explained that the agricultural production in the catchment is highly sensitive to climate change. However, this reviewed literature did not study climate change's impact on CWRs in the study area. It is important to recognize that a single GCM model which makes the result is doubtful. Uncertainty in climate model outputs for climate change impact assessment can be reduced using multiple GCM outputs with robust downscaling methods (USAID 2014). The investigation of the CWRs is primarily determined by the soil's water supply and the crop's demand for water. The main goal of this research is to use the CROPWAT model and Multi-RCM models to evaluate the impact of climate change on maize crop production in Woybo Catchment under RCP4.5 and 8.5 scenarios (2041–2070) relative to the observed period (1992–2015). Hence, this study has been used to determine the impacts of climate change on CWRs and to support farming under anticipated climate change to sustain agricultural production.

Study area

The Woybo River is one of the main tributaries of the Omo-Gibe basin and drains from the eastern part of the Omo River in south Ethiopia. The catchment is geographically located between 37°51′40″ and 37°31′0″ E longitude and 6°55′200″ and 7°2′40″ N latitude, covering an area of about 506.21 km2. The Woybo River originates from a small spring located near Mount Damot in Wolaita (Figure 1).
Figure 1

Location map of the study area. (a) Ethiopian river basins, (b) the Omo-Gibe river basin, and (c) the Woybo catchment.

Figure 1

Location map of the study area. (a) Ethiopian river basins, (b) the Omo-Gibe river basin, and (c) the Woybo catchment.

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Climate

The study area has a tropical climate regime. Climatic variations are primarily a function of both altitude and temperature. The amount of rainfall in the catchment strongly varies due to seasonal and elevation differences. The amount of rainfall decreases throughout the catchments with a decrease in elevation. The study area is characterized by a bimodal rainfall type, a short rainy season, which extends between March and May and is locally known as ‘Belg’ and the long rainy season summer locally known as ‘Kiremt’ which extends from June to October. Fekadu et al. (2017) also stated that the rainfall distribution of the study area is largely controlled by the South-North movement of the Inter-Tropical Convergence Zone (ITCZ). The average maximum temperature varies between 17.8 and 26.5 °C and the range of minimum temperature is 10.1–16.3°C. November–February is the hottest month with rare rainfall while March–October is the coldest month in the study area. Analysis of 24-year data from 1992 to 2015 was made by using four meteorological stations and the average annual rainfall of the study area was found to be 1,404.25 mm. Annual rainfall ranges from 963.5 to 1,708.4 mm at the lowland and highland, respectively (Figure 2).
Figure 2

Mean monthly rainfall, maximum and minimum temperatures of the study area.

Figure 2

Mean monthly rainfall, maximum and minimum temperatures of the study area.

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Meteorological data

Historical climate data like rainfall, wind speed, sunshine hours, maximum and minimum temperature, and relative humidity of the study area were obtained from the National Meteorology Agency (NMA) of Ethiopia. The observational periods of these data were recorded from 1992 to 2015.

Climate data

CORDEX is a globally coordinated program to improve the framework for the global development of regional-scale climate forecasts for impact assessment and adaptation studies (Giorgi et al. 2009). The climate data were downloaded from the Earth System Grid Federation (ESGF) site of Coordinated Regional Climate Downscaling Experiment (CORDEX) program archives http://pcmdi9.llnl.gov/esgf-web-fe/. For this study, only rainfall, maximum, and minimum temperature data were used datasets which are dynamically downscaled from RCM by nesting GCMs for the reference period (1975–2005) and future period (2006–2100) under RCP4.5 and RCP8.5 scenarios were used. Because RCP6.0 is located between RCP4.5 and RCP8.5, I selected the RCP4.5 scenario, which is an intermediate scenario, and the RCP8.5 scenario, which is a high-emission scenario. Additionally, some climate models do not have precipitation, maximum, and minimum temperature data in the RCP6.0 scenario.

RCP4.5 is developed by the Pacific Northwest National Laboratory in the US. Here the radioactive forcing is 4.5 W/m2 (≈650 ppm CO2 equiv.) stabilized shortly after the year 2100, consistent with a future with relatively ambitious emissions reductions (Clarke et al. 2007). The RCP8.5 assumes no policy changes to reduce emissions in the future. It was created in Austria by the International Institute for Applied System Analysis and is characterized by rising greenhouse gas emissions that result in high greenhouse gas concentrations over time (Riahi et al. 2007). A rising radiative forcing pathway leads to 8.5 W/m2 (≈1,370 ppm CO2 equiv.) by 2100. For this research, outputs from six models were used, namely MPI-CCLM4, ICHEC-HIRAM5, ICHEC-RACMO22T, CNRM-RCA4, CanESM2-CanRCM4, and ICHEC-REMO2009 and their ensemble model in Table 1. An ensemble of model simulations defined by several climate models are used together in one scenario to improve the accuracy and reliability of forecasts. So the selected climate models were based on the availability of the model output data at the time of analysis, the validity of the model, and also based on their spatial resolution. The spatial resolution of the analyzed RCMs was approximately 50 × 50 km (0.44° by 0.44°) with a temporal resolution of one day. A popular climatological baseline period or reference period is a 30-year ‘normal’ period from 1975 to 2005, as defined by the World Meteorological Organization (WMO), and from 2006 to 2100 future time climate.

Table 1

General description of CORDEX-RCMs and their driving GCMs

RCM modelDriving GCMSimulating periodInstituteSpatial resolution
CanRCM4 CanESM2 1975–2100 Canadian Centre for Climate Modeling and Analysis (CCCMA) 0.44 × 0.44 
 CCLM4  MPI 1975–2100 Climate Limited Area Modelling (CLM) Community 0.44 × 0.44 
HIRAM5 ICHEC 1975–2100 Danmarks meteorologiske, institute (DMI), Denmark 0.44 × 0.44 
RACMO22T ICHEC 1975–2100 Koninklijk Nederlands Meteorologisch Instituut, (KNMI), Netherlands 0.44 × 0.44 
RCA4 CNRM 1975–2100 Och Hydrologiska, Institut (SMHI), Sweden 0.44 × 0.44 
REMO ICHEC 1975–2100 Helmholtz-Zentrum Geesthacht, Climate Service Center, Max Planck Institute for Meteorology 0.44 × 0.44 
RCM modelDriving GCMSimulating periodInstituteSpatial resolution
CanRCM4 CanESM2 1975–2100 Canadian Centre for Climate Modeling and Analysis (CCCMA) 0.44 × 0.44 
 CCLM4  MPI 1975–2100 Climate Limited Area Modelling (CLM) Community 0.44 × 0.44 
HIRAM5 ICHEC 1975–2100 Danmarks meteorologiske, institute (DMI), Denmark 0.44 × 0.44 
RACMO22T ICHEC 1975–2100 Koninklijk Nederlands Meteorologisch Instituut, (KNMI), Netherlands 0.44 × 0.44 
RCA4 CNRM 1975–2100 Och Hydrologiska, Institut (SMHI), Sweden 0.44 × 0.44 
REMO ICHEC 1975–2100 Helmholtz-Zentrum Geesthacht, Climate Service Center, Max Planck Institute for Meteorology 0.44 × 0.44 

Soil and land use/cover

Soil and land use/cover data were obtained from the Water, Irrigation, and Energy (MoWIE) office for the year 2008. The amount of water that can penetrate the soil is controlled by the soil types in a given area. Based on the FAO international soil classification method (1998), dystric nitosols (55.3%), eutric vertisols (31.94%), chromic luvisols (10.54%), and lithic leptosols (1.69%) are the major soils in the Woybo watershed. The land cover classification map showed that agricultural land, woodland, shrubs, and grassland accounted for 82.545, 0.7, 1.167, and 0.3%, respectively (Fekadu et al. 2017). Likewise, the dominant land use/land cover type in the Woybo catchment was agricultural land.

Crop data

Wheat, sorghum, maize, teff, barley, and vegetables are the most common crops in the Woybo catchment. The maize crop was selected to estimate CWRs because it is widely grown as a food crop in the study area and needs a higher amount of irrigation water than other crops. The CWRs are usually determined based on the reference evapotranspiration (ETo) and Kc tabulated by the Food and Agriculture Organization (FAO) for different growing conditions (Allen et al. 1998). The indicative values of crop water needs for maize are 500–800 mm/total growing period.

Data quality tests were performed on the datasets for the years 1992–2015 to check for missing, homogeneous, and inconsistent data. Analysis of climate change trends and their impact on crop productivity simulation needs long-term time series filling of missed data. The first step of any hydrological or meteorological study is accessing reliable data. For this research, the second-degree inverse distance weighted (IDW) method was used to fill the gaps in the missing records of daily rainfall data for stations in which the missing data record is more than 10% of the total observed period (Teegavarapu & Chandramouli 2005).

After all hydrometeorological data had been filled in, its quality and consistency were evaluated to improve the model's simulation output using a double mass curve. It was done by comparing the data of a single station with that of several other stations in the area. The homogeneity of the selected stations was tested using the nondimensional parameter, which is the ratio of average monthly rainfall to the average annual record of the station.

Mann-Kendall trend test

The trend analysis was carried out using the nonparametric Mann-Kendall (MK) test. The nonparametric Mann-Kendall trend test (MK) is widely used in hydro-climatic time-series trend estimation (Yue & Wang 2004). The Mann-Kendall Statistic (S) (Equation (1)) is represented by
formula
(1)
formula
where Yj and Yi represent the data points at time i and j, respectively, where j > i and n are the last periods in the time series. If S > 0, then the earlier observation in the time series is less than the latter observation in the time series and it is an indicator of an increasing (upward) trend, while S < 0 indicates a decreasing (downward) trend. If n > 10, then the Statistic ‘S’ is approximately normally distributed with the mean of S equal to zero, the variance statistic VAR(S) is then represented by Equation (2).
formula
(2)
Kendall's tau (τ) is obtained by applying the MK test. It determines the strength of the association between two variables by measuring their correlation (Equation (3)).
formula
(3)
Here, S represents the MK statistic, and n shows the number of data pairs. The probability p is determined to know the confidence level in the hypothesis of the MK test (Equation (4)).
formula
(4)
Z is the standard normal test statistics which is computed by Equation (5).
formula
(5)
The value of p is essential for accepting and rejecting the hypothesis of the MK test. The critical value of Z1 − α/2 for a p value of 0.05 from the standard normal table is 1.96. If the calculated value of p is greater than the value of α, then Ho cannot be rejected. If the value of p is less than the value of Alpha, then Ha should be accepted.
Sen's estimator of the slope: Sen's estimator is another method of nonparametric test used to show the magnitude of the trend in the time series data (Equation (6)).
formula
(6)

In the above equation, Xj and Xk represent data values at the time j and k, respectively, where j > k. Sen's estimator of the slope is the median of these N values of Mi. The sign of Mi indicates the reflection of the trend of time series data. The positive sign of mi represents the increasing trend while the negative sign shows the decreasing trend. Addinsoft's XLSTAT 2015 was used for the MK test and Sen's estimator of a slope. The hypothesis was tested at a 95% confidence level for all variables.

Bias correction methods for climate model

RCM-simulated climate variables such as temperature and rainfall are used as input data for hydrological models to evaluate impact assessment in different catchments. However, the output of the RCM model often shows significant biases because of model uncertainty (due to the simplification of processes), input uncertainty (due to potential errors in input data), and parameter uncertainty (due to inherent nonuniqueness of parameters in inverse modeling). Bias correction is used to minimize this discrepancy and uncertainty between observed and simulated climate variables. It uses a transformation algorithm to adjust the GCM outputs. A CMhyd statistical downscaling tool that provided simulated climate data that can be considered representative of the location of the gauges was used in the watershed model setup and the climate model data was extracted and bias-corrected for each of the gauge locations (Rathjens et al. 2016). To reduce the discrepancy and uncertainty between observed and simulated climatic variables, bias correction is applied. The bias correction method is applied to each daily precipitation and temperature dataset derived from the African CORDEX before being used for any future climate change projection.

Power transformation

RCM data for precipitation was bias-corrected by using the power transformation method because it corrects the mean, variance, and coefficient of variation (CV) (Lafon et al. 2013). The main principle of this method is to display the mean and standard deviation of the daily rainfall data, followed by the bias correction factors calculated from the observed and simulated variables (Equation (7)).
formula
(7)
where P* is the corrected rainfall, and p is the simulated rainfall. Parameters a and b were found for every month of the year, including data from all years available. Parameter b was done iteratively using a solver in excel by certifying the CV of the corrected rainfall matching that of the observed. The parameter a, which depends on the value of b, was determined by matching the means of the corrected and observed rainfall. So ‘a’ and ‘b’ are the parameters obtained from calibration in the baseline period and subsequently applied to the projection period.

Variance scaling

The variance scaling bias correction approach was employed for temperature data as it is often approximately normally distributed. Hence, the bias correction for temperature involves only shifting and scaling to adjust the mean and the variance (Ho et al. 2012; Equation (8)).
formula
(8)
where is the corrected daily temperature. is the uncorrected daily temperature from the RCM model and is the observed daily temperature while is the mean observed temperature and is the mean simulated temperature.

Climate model performance

The statistical metrics test is used to evaluate the performance of RCM output. Thus, it includes the percent of bias, root mean square error (RMSE), CV, and correlation coefficient (Correl) (Kim et al. 2014). The term bias is used to describe the systematic error in rainfall amount. The positive value of bias indicates overestimation, while the negative bias shows underestimation. There is no systematic difference between the observed and simulated model outputs as the value of bias is close to zero (Equations (9)–(12)).
formula
(9)
formula
(10)
formula
(11)
formula
(12)

CROPWAT

CROPWAT software was used to examine metrological and agronomic data from the study area to determine the CWR, irrigation scheduling, and scheme design. For calculating the CWR, it is possible to do so by repeatedly pulling up the relevant climate and rainfall datasets, crop files, and matching planting dates. For the computation of CWR, CROPWAT requires multi-crop parameters, i.e., cropping pattern, planting, and harvesting data were collected from past research, and Kc values, crop rooting depth, and allowable depletion were taken from FAO (2008). The soil data was obtained from Water, Irrigation, and Energy (MoWIE) for the year 2008. The observed meteorological data, such as maximum and minimum temperature and rainfall data, was obtained from the National Meteorological Agency (NMA) from 1992 to 2015. Likewise, the future maximum and minimum temperature and rainfall meteorological data were downloaded from six RCM climate models from 2041 to 2070. After the appropriate data (temperature and precipitation) were processed and rearranged, the CROPWAT model was used to calculate the CWRs and irrigation water requirements (IWRs) for the period 2041–2070 under the RCP4.5 and RCP8.5 scenarios as well as for the current period from 1992 to 2015.

Reference evapotranspiration

The existing/baseline period of reference evapotranspiration (ETo) was calculated by using CROPWAT.8 software version and climate data, ETo was determined in mm/day (Equation (13)).
formula
(13)
where ETo is potential evapotranspiration (mm/day), Rn is net radiation flux (MJ m−2day−1), G is heat flux density in the soil, it is very small and can be neglected (MJ m−2 day−1), T is mean daily air temperature (°C), is psychometric constant (kPa °C−1), U is wind speed measured at 2 m height (ms−1), es is saturation vapor pressure ea = es × RH/100 , RH is relative humidity (%), esea is saturation vapor pressure deficit (kPa), Δ is slope of the saturation vapor pressure curve (kPa °C−1), and 900 is coefficient for reference crop (kJ/day).

Hargreaves potential evapotranspiration

Because the existing data are downscaled precipitation, minimum temperatures, and maximum temperatures, the Hargreaves method is used to calculate the potential evapotranspiration for the future time horizon. Hargreaves & Samani (1985) developed a simplified equation and it requires only the maximum and minimum temperature, day of the year, and latitude to compute ETo (Equation (14)).
formula
(14)
where EToHG is potential evapotranspiration as estimated by the Hargreaves method (mm/day); is the mean temperature; Tmax is the maximum temperature; Tmin is the minimum temperature; and Ra is extra-terrestrial radiation calculated from latitude and time of year.

Crop water requirement

CWR or crop evapotranspiration is the amount of water required to compensate for the cropped field's evapotranspiration loss. CWR was calculated in mm/month and for maize crops in mm/period. Equation (15) was used to calculate ETc.
formula
(15)
where ETc is the crop evapotranspiration, mm/day; Kc is the crop coefficient (dimensionless); and ETo is the reference crop evapotranspiration (mm/day).

Irrigation water requirement

IWR is the amount of water other than precipitation that crops need to meet water demand and can be calculated using Equation (16):
formula
(16)
where IR is the irrigation requirement; ETc is the crop evapotranspiration; and Peff is the effective precipitation over the crop growth period.

Crop coefficient (Kc) is defined as the ratio of the crop evapotranspiration (ETc) to the reference crop evapotranspiration (ETo) and obtained from FAO guidelines (No. 56) for each growth stage (Allen et al. 1998).

Effective rainfall refers to the portion of rainfall that can effectively be used by plants. Effective rainfall was computed by using the Soil Conservation Service method (USDA). It is the most widely used method and essential for the water-scarce area (Smith et al. 2002; Equations (17) and (18)).
formula
(17)
formula
(18)
where Pe is effective rainfall determined in mm/decade, and P is total rainfall that occurred in the crop growing season in the area in mm.

Climate change impact analysis

Seasonal and annual changes in rainfall and temperature greatly influence variations in CWRs. The application of the delta-change method was used to calculate the changes in the monthly, seasonal, and annual impact of climate change on different climate variables between baseline and future periods for all scenario intervals. The pattern of change was calculated by using delta change (Wilby & Dawson 2013) in Equation (19):
formula
(19)
where Qsim is simulated climate data of future data and Δ is percent changes and differences between the projected climate and baseline climate of monthly, seasonal, and yearly RCM data.

Evaluation of rainfall estimates from climate models

The CanESM2-CanRCM4 model had the least bias (6.6%), indicating that the systematic error between simulated and observed rainfall is lower and that of simulated rainfall data over the catchment. However, the ICHEC-REMO2009 model showed the largest bias of 29.5%, which explains that the RCM rainfall amount largely deviates from the observed rainfall amount. This is used to assess the RCMs' ability to predict the basin's mean annual rainfall. Based on the bias, RMSE, CV, and r results, the CanESM2-CanRCM4 models performed the best and were the most accurate compared to the simulations of other models, whereas the performance of the ICHEC-REMO2009 model performed the poorest (Table 2).

Table 2

Uncorrected RCP and bias-corrected RCP scenarios statistical metrics with that of observed data (1992–2005)

Observed and RCMMonthly rainfall (mm)Bias (%)
CV (%)
RMSE (mm/month)
Correlation (r)
CorrUncorrCorr.UncorrCorrUncorrCorrUncorr
Observed 120.4  – 54.4    – – 
CanESM2-CanRCM4 128.3 0.1 6.6 54.2 57.8 0.2 30 
ICHEC-REMO2009 72.7 −5.1 −29.5 55.4 81.3 9.4 52.7 0.9 0.8 
Ensemble 84.8 −0.6 −11.6 54 64.1 5.2 32.7 0.9 0.8 
Observed and RCMMonthly rainfall (mm)Bias (%)
CV (%)
RMSE (mm/month)
Correlation (r)
CorrUncorrCorr.UncorrCorrUncorrCorrUncorr
Observed 120.4  – 54.4    – – 
CanESM2-CanRCM4 128.3 0.1 6.6 54.2 57.8 0.2 30 
ICHEC-REMO2009 72.7 −5.1 −29.5 55.4 81.3 9.4 52.7 0.9 0.8 
Ensemble 84.8 −0.6 −11.6 54 64.1 5.2 32.7 0.9 0.8 

Trends of observed meteorological data

The nonparametric Kendall trend test was used to evaluate the long-term trends of observed rainfall, as well as maximum and minimum temperatures, for the period between 1992 and 2015 at a significance level of α equal to 0.05. The annual rate of rainfall was insignificantly decreased by 1.4 mm/year (Figure 3). The rate of change in mean annual maximum and minimum temperatures increased by 0.01 and 0.02°C, respectively, indicating statistically significant rising trends. Additionally, the rate of change of potential evapotranspiration showed a statistically significant positive trend, with an increasing rate of 1.7 mm/year. The summary of MK trend test results is shown in Table 3.
Table 3

Mann-Kendall test results for mean annual rainfall, maximum and minimum temperature, and potential evapotranspiration during 1992–2015

VariableKendall's tauS-valueP-valueSen's slopeTrend natureTrend of significant
Rainfall −0.05 −14 0.75 −1.41 Negative Not significant 
Tmax 0.43 88 0.00 0.01 Positive Significant 
Tmin 0.44 94 0.01 0.02 Positive Significant 
PET 0.35 97 0.03 1.68 Positive Significant 
VariableKendall's tauS-valueP-valueSen's slopeTrend natureTrend of significant
Rainfall −0.05 −14 0.75 −1.41 Negative Not significant 
Tmax 0.43 88 0.00 0.01 Positive Significant 
Tmin 0.44 94 0.01 0.02 Positive Significant 
PET 0.35 97 0.03 1.68 Positive Significant 
Figure 3

Mann-Kendall test results for mean annual rainfall and potential evapotranspiration (PET) during 1992–2015.

Figure 3

Mann-Kendall test results for mean annual rainfall and potential evapotranspiration (PET) during 1992–2015.

Close modal

Projected changes in seasonal and annual rainfall in the 2050s (2041–2070) period

Evaluating seasonal rainfall distribution is critical since agricultural activity is strongly dependent on rainfall distribution. The amount of seasonal rainfall is expected to increase by 1.48–8.08% for CanESM2-CanRCM4 and ensemble models throughout the winter (December to February) season under the RCP4.5 scenario. However, the ICHEC-REMO2009 model predicts a 10.19% decrease in rainfall amount. The ICHEC-REMO2009 and ensemble models' rainfall magnitudes will decrease by 12.2 and 1.5%, respectively, whereas the CanESM2-CanRCM4 model's rainfall amount will increase by 5.3% under the RCP8.5 scenario. Under the RCP4.5 scenario, the amount of seasonal rainfall will decrease by 8.7–5.5% throughout the spring (March to May) season for CanESM2-CanRCM4, ICHEC-REMO2009, and ensemble models. Under RCP8.5, the CanESM2-CanRCM4 model predicted an increase in mean seasonal rainfall of 6.32%. However, the ICHEC-REMO2009 and ensemble models predicted a 5.0 and 2.8% decrease in mean seasonal rainfall, respectively. For the ensemble and CanESM2-CanRCM4 models, the amount of mean annual rainfall will increase under the RCP4.5 scenario, ranging from 8.7 to 11.3%. The ICHEC-REMO2009 model, on the other hand, will be down 7.7%. Under RCP8.5, the magnitude of mean annual rainfall will increase by 10.2–12.2% for ensemble and CanESM2-CanRCM4 models. The ICHEC-REMO2009 model, on the other hand, predicts a 9.8% decrease (Figures 4 and 5). The projected seasonal rainfall change in the figures shows that the future rainfall distribution over the study area increased for summer (June to August) and autumn (September to November) with a range of 3.7–16.8% in mid-term under RCP4.5 and 8.5. For the ensemble and CanESM2-CanRCM4 models, the amount of mean annual rainfall will increase under the RCP4.5 scenario, ranging from 8.7 to 11.3%. The ICHEC-REMO2009 model, on the other hand, will be down 7.7%. Under RCP8.5, the magnitude of mean annual rainfall will increase by 10.2–12.8% for ensemble and CanESM2-CanRCM4 models. The ICHEC-REMO2009 model, on the other hand, predicts a 9.8% decrease (Figures 5 and 6). Almost all models predicted the greatest increase in mean seasonal rainfall in the summer and autumn seasons, but it shows a significant decline in the winter and spring seasons. The impact of climate change on surface hydrological processes in the Omo-Gibe basin has been studied using the RegCM3 model with the A1B scenario. Annual temperature and potential evapotranspiration showed an increasing trend. But rainfall showed a significant monthly and seasonal variation over the 1985–2005 periods. Based on the outputs of RegCM3, future minimum temperature varies between 0.40 and 0.14 °C and the maximum temperature also varies between 0.70 and 0.2 °C in the 2030s to 2090s. Similarly, surface water decreases in terms of mean monthly discharge in the dry season and increases in the wet season. The result of a projected change of rainfall showed a decreasing trend in the Belg and Bega (Summer) seasons while increasing in the Kiremt (Winter) season. The average annual evaporation and rainfall showed an increasing trend from −2.58 to 2.49% for rainfall in the 2030s to 2090s and 5.5–12.4% for evaporation in the 2030s to 2090s. The author concluded that the amount of water demand will be high due to increased population growth. However, the author did not use bias correction and it is also based on a single RegCM3 output and with an SRES A1B scenario which makes the result doubtful.
Figure 4

Mann-Kendall test results for mean annual maximum and minimum temperature during 1992–2015.

Figure 4

Mann-Kendall test results for mean annual maximum and minimum temperature during 1992–2015.

Close modal
Figure 5

Changes in mean seasonal and annual rainfall in the Woybo catchment under RCP4.5 (2041–2070) year.

Figure 5

Changes in mean seasonal and annual rainfall in the Woybo catchment under RCP4.5 (2041–2070) year.

Close modal
Figure 6

Changes in mean seasonal and annual rainfall in the Woybo catchment under RCP8.5 (2041–2070) year.

Figure 6

Changes in mean seasonal and annual rainfall in the Woybo catchment under RCP8.5 (2041–2070) year.

Close modal

Projected changes in maximum and minimum temperature in the 2050s (2041–2070) period

The projected maximum and minimum temperatures indicated that all selected RCM models showed an increasing magnitude in the mid-term under both RCPs in the catchment. The annual mean maximum temperature over the Woybo catchment will be increased by a range of 1.4–1.7 °C under the RCP4.5 emission scenario. At the same time, the annual mean minimum temperature will be increased by 1.9–2.1 °C compared to the baseline period. Under RCP8.5, the magnitude of the mean annual maximum temperature will increase by 1.6–2°C. Likewise, the mean annual minimum temperature will rise from 2.1 to 2.3 °C under RCP4.5 (2041–2070) (Figure 7) and RCP8.5 (2041–2070) (Figure 8). This occurs under natural causes of climate change including large eruptions of volcanoes (which can sporadically increase the concentration of atmospheric particles, blocking out more sunlight) and human activities. When the temperature rises, the availability of water decreases, resulting in a higher evaporation rate and a rise in water demand, which affects agriculture and water use in the catchment. Mohammed (2013) studied the impact of climate change on water resources in the Omo-Gibe Basin by using the REMO climate model under B1_923 scenarios in 2020–2050. The result for both maximum and minimum temperatures was expected to rise by 1.5°C under the B1–923 scenario and the rainfall also declined by 1.4%. So, climate change can have profound impacts on water availability that depend on hydrological variables like rainfall, temperature, and evapotranspiration.
Figure 7

Mean annual maximum and minimum temperature change over the Woybo catchment under RCP4.5 (2041–2070) year.

Figure 7

Mean annual maximum and minimum temperature change over the Woybo catchment under RCP4.5 (2041–2070) year.

Close modal
Figure 8

Mean annual maximum and minimum temperature change over the Woybo catchment under RCP8.5 (2041–2070) year.

Figure 8

Mean annual maximum and minimum temperature change over the Woybo catchment under RCP8.5 (2041–2070) year.

Close modal

Projected changes in potential evapotranspiration in the 2050s (2041–2070) period

The annual average potential evapotranspiration over the Woybo catchment in the 2041–2071 periods was projected to increase by 0.2–3.0% in all RCM models under the RCP4.5 scenario. In comparison to the reference, all climate model outputs projected an increase in potential evapotranspiration ranging from 1.8 to 6.8% under the RCP8.5 scenario. The results indicated that one of the biggest challenges related to climate change is the increment in temperature that leads to an increase in evapotranspiration and a higher loss of moisture from the soil. This enhances the demand for irrigation water, especially in the Woybo catchment. Overall, it is clear from the predictions of all the RCMs in this study that both temperature and potential evapotranspiration will rise significantly in the future (Table 4). Akirso (2017) analyzed the climate change impact on the frequency of flood flows for the Abelti catchment in the Upper Omo-Gibe basin using HadGEM2-ES model output under RCP2.6 and 4.5 in the 2021–2080 period. The author discussed that the average of projected evapotranspiration and temperature will be increased in monthly, seasonal, and annual time scales from 2021 to 2080. MK test also indicated that both temperature and evaporation show an increasing trend over the catchment, whereas rainfall exhibited decreases and increases in both scenarios. Streamflow is also likely to increase in the future due to increases in rainfall in both time horizons in the 2020s and 2050s. The main limitation of this study is that it is based on a single HadGEM2-ES output which makes the result questionable.

Table 4

Projected changes in annual potential evapotranspiration

Period2041–2070
RCM modelsAnnual RCP4.5Annual RCP8.5
CanESM2-CanRCM4 0.2 1.8 
ICHEC-REMO2009 3.0 6.8 
Ensemble 1.5 3.6 
Period2041–2070
RCM modelsAnnual RCP4.5Annual RCP8.5
CanESM2-CanRCM4 0.2 1.8 
ICHEC-REMO2009 3.0 6.8 
Ensemble 1.5 3.6 

Water requirements for maize under current conditions

The growing periods of the maize crop are mostly from January to May in the Woybo catchment. As indicated in Table 5, a maize crop with a growing period of 125 days to maturity would require a 454.7 mm depth of water, while 306.1 mm would be required as supplementary irrigation. For 306.1 mm of water, additional irrigation for maize crops grown in the area during dry times and drought is required for an individual farmer to offset the effect of water stress on maize yield. Consequently, the absence of supplementary irrigation during the dry season would result in a reduced maize yield. CWRs and IWRs are low at the initial stage because effective rainfall can satisfy water requirements and high at the mid-stage due to the minimum fall of effective rainfall.

Table 5

Crop water requirements and irrigation water requirement of maize crops for the period of 1992–2015 at each stage

StagesLength of growth stage (days)ETC in mm/growth stageEff rain (mm/dec)IWR in mm/growth stage
Initial (January) 20 24.8 22.1 2.7 
Development (January–February) 35 124.2 45.5 78.7 
Mid-season (February–Mar) 40 209 49.7 159.3 
Late (April–May) 30 96.7 31.3 65.4 
Total 125 454.7 148.6 306.1 
StagesLength of growth stage (days)ETC in mm/growth stageEff rain (mm/dec)IWR in mm/growth stage
Initial (January) 20 24.8 22.1 2.7 
Development (January–February) 35 124.2 45.5 78.7 
Mid-season (February–Mar) 40 209 49.7 159.3 
Late (April–May) 30 96.7 31.3 65.4 
Total 125 454.7 148.6 306.1 

When the plant canopy is small and partially shades the soil surface, the crop coefficient has a low value. From the start of the development and mid-season stages, the Leaf Area Index (LAI) and plant height increased rapidly. Due to leaf enlargement, which increases transpiration, the maximum LAI was achieved when the plants reached their maximum height at a mid-season stage with high crop evapotranspiration. Hence, the amount of water extracted increased with plant growth, which in turn increased the ETc. As the crop matures and the leaves begin to rise, the crop coefficient drops. As a result, crop coefficient (Kc) development can significantly enhance evapotranspiration (ETc) predictions in various crop growth stages. The peak irrigation water demand will occur in March when there is a need for full irrigation in the area. Zewdu et al. (2020) discussed that Ethiopian agriculture production is expected to decline by 6–32.5% due to climate change impacts over the 2030–2050 periods.

Water requirements for maize under future conditions in the 2050s period

From 2041 to 2070, simulations of the maize crop's evapotranspiration (ETc) and IWR under the RCP4.5 and 8.5 climate scenarios were performed (Table 6). Under the RCP4.5 scenario, the change in IWR may increase by 4.6–12.9% during the growing crop season, while the change in total CWR may increase by 6.2–9.4%. The crop water needed for maize reaches a maximum at the mid-stage, as seen in Table 6. Because of the low effective rainfall and significant relative water deficits, crops require additional water to meet their water needs. The effective rainfall was 206.9, 161.9, and 169.6 mm/month, while the total crop water needed was 480.7, 485.4, and 487.5 mm/month for CanRCM4, REMO2009, and ensemble models, respectively. The overall irrigation demand is 272.8, 339.3, and 317.9 mm/month for the CanRCM4, REMO2009, and ensemble models, respectively, to satisfy the necessary amount of water. Therefore, the overall irrigation requirement is extremely low at first. If crop water needs are minimal, irrigation is not necessary. Bekele et al. (2019) discussed the impact of climate change on surface water resources for estimating crop water demand on the Upper Blue Nile. Based on the dynamically downscaled HadGEM2-ES outputs, the researchers stated that the future climate projections showed that there is an increasing trend in average temperature and evapotranspiration during 2020–2079 for the three projected climate scenarios (RCP8.5, RCP4.5, and RCP2.6). Due to the increase in average temperature and evapotranspiration, irrigation water, and crop water demand may increase in all future periods compared to the baseline period in the coming 60 years.

Table 6

Crop water requirements and irrigation water requirement of maize crops for the period of 2041–2070 at each stage

StageCanRCM4 4.5
REMO2009 4.5
Ensemble 4.5
ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)
Initial 19.5 16.5 20.4 36.2 15.2 14.7 0.5 
Development 164.6 113.6 50 140.6 45.8 94.8 174.9 101.1 73.8 
Mid-season 204.9 39.5 165.4 222.8 51.9 170.9 218.6 42.9 175.7 
Late 91.7 37.3 54.4 101.6 28 73.6 78.8 10.9 67.9 
Total 480.7 206.9 272.8 485.4 161.9 339.3 487.5 169.6 317.9 
StageCanRCM4 4.5
REMO2009 4.5
Ensemble 4.5
ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)
Initial 19.5 16.5 20.4 36.2 15.2 14.7 0.5 
Development 164.6 113.6 50 140.6 45.8 94.8 174.9 101.1 73.8 
Mid-season 204.9 39.5 165.4 222.8 51.9 170.9 218.6 42.9 175.7 
Late 91.7 37.3 54.4 101.6 28 73.6 78.8 10.9 67.9 
Total 480.7 206.9 272.8 485.4 161.9 339.3 487.5 169.6 317.9 

Based on the projected RCP8.5 scenario, the magnitude of total CWR and irrigation water demand increased from 6.6 to 17.2% and 5.5 to 16.4%, respectively, in all selected models. The projected increase in average temperature also raises CWRs compared to the baseline for optimum production. Consequently, current and future CWRs to climate change and the dynamic nature of weather variables might alter the future CWRs in the study area. Under the RCP8.5 scenario, maize's future IWR and CWR could increase during the mid- and development stages. The high crop coefficient values during the growing stage can be important for this. Furthermore, under future climate scenarios, higher temperatures and thus higher ETc result in significant CWR and IWR of the maize crop (Table 7). Orkodjo et al. (2022) studied the impact of climate change on the future availability of water for irrigation and hydroelectric power generation in the Omo-Gibe basin fifteen RCM multi-model ensemble models under RCP4.5 and RCP8.5 scenarios over short-term (2017–2044) and medium-term (2045–2072).

Table 7

Crop water requirements and irrigation water requirement of maize crops for the period of 2041–2070 at each stage

StageCanRCM4 8.5
REMO2009 8.5
Ensemble 8.5
ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)
Initial 44.7 40.7 32.7 22.4 10.3 30.6 16.4 14.2 
Development 114.7 60.5 54.2 140.1 54.2 85.9 148.9 54.4 94.5 
Mid-season 228.1 21.8 206.3 230.2 19.9 210.3 252.2 72 180.2 
Late 95.1 41.5 53.6 108.1 56.5 51.6 98.8 25.2 73.6 
Total 482.6 164.5 330.3 511.1 153.0 358.1 530.5 168 362.5 
StageCanRCM4 8.5
REMO2009 8.5
Ensemble 8.5
ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)ETc (mm/dec)Eff rain (mm/dec)Irr. req. (mm/dec)
Initial 44.7 40.7 32.7 22.4 10.3 30.6 16.4 14.2 
Development 114.7 60.5 54.2 140.1 54.2 85.9 148.9 54.4 94.5 
Mid-season 228.1 21.8 206.3 230.2 19.9 210.3 252.2 72 180.2 
Late 95.1 41.5 53.6 108.1 56.5 51.6 98.8 25.2 73.6 
Total 482.6 164.5 330.3 511.1 153.0 358.1 530.5 168 362.5 

Maize is a crop that requires a considerable amount of water, as it grows in the hottest season of the year. However, compared to other field crops, maize is known to have high water use efficiency and can produce a significant quantity of dry matter relative to crop evapotranspiration (Şen 2023). The results of the study explained that maize phenology, CWR, and IWR were important to project climate scenarios. The crop duration would reduce due to increases in Tmax and Tmin. The results have stated that rising temperatures caused a shortening of crop growth duration. An increase in temperature caused a faster accumulation of degree days, which in turn reduced the number of days required to achieve a specific growth stage for the crop. The crop's water demand would increase. This may be due to increased evapotranspiration losses from maize crops. ETo losses were greatly influenced during sunny periods due to high temperature, wind speed, and low relative humidity.

The results of this study showed that IWR and CWR simulations directly depend on rainfall and temperature projections, while higher CO2 concentrations shortened growing periods in the catchment. According to the predicted RCP scenarios during high temperatures, the amplified evapotranspiration rates and altered rainfall patterns can substantially influence water availability for crop irrigation. The projected rainfall is expected to decrease in the winter and spring seasons by 1.5–10.2% and 8.7–2.8%, respectively, in the 2050s in the catchment. This means that maize production will require more irrigation water compared to the baseline for optimum production due to the shortage of rainfall. The results exhibited that the CWR and IWR were low at the initial stage because effective rainfall could satisfy the water requirement and high at the mid-stage due to the minimum fall in effective rainfall.

Orkodjo et al. (2022) stated that the temperature increases under RCP4.5 emission scenarios would range from 2.6 to 4.5 °C, whereas under RCP4.5, they would be between 2.4 and 3.3 °C. Under RCP4.5 emission scenarios, the predicted decrease in rainfall ranges from 10.7 to 13.6%, while under RCP8.5 emission scenarios, it ranges from 11.1 to 13.8%. Under RCP4.5 emission scenarios, the expected reduction in annual average streamflow is between 7.0 and 10.9%, while under RCP8.5 emission scenarios, the reduction is between 10.9 and 12.8%. Water availability for irrigation and the production of hydroelectric power will decrease over the study periods, with decreases of 15.5–25.4% and 10.5–20.2%, respectively. Between 7.9 and 30.6%, more water will be scarce due to the combined effects of climate change and rising water demand. The average air temperature is expected to rise under the RCP4.5 and RCP8.5 emission scenarios, but rainfall, streamflow, and water availability in the river basin are predicted to decrease. According to projections under the RCP4.5 and RCP8.5 emission scenarios, there will be a significant decrease in the amount of water available for irrigation and hydroelectric power production in the Omo-Gibe river basin.

In this study, the implications of climate change on maize CWRs in the Woybo catchment in South-Western Ethiopia were evaluated. The Woybo catchment's patterns of rainfall distribution were classified as bimodal. A bias correction was used to correct rainfall and temperature biases, checking the climate model's performance. The trends of observed meteorological data were assessed using the MK test under different climate change scenarios. The observed annual rainfall for 1992–2015 shows a decreasing trend in the Woybo catchment. However, there have been statistically significant increasing trends for maximum, minimum, and potential evapotranspiration. In the mid-term, annual rainfall is expected to decrease slightly under the RCP4.5 and RCP8.5 scenarios.

A greater change in evapotranspiration and a higher temperature increase were noted due to more water evaporating from the soil and plant canopy, which has an impact on plant development and yield. The reason cited is rapid socioeconomic and population growth, with limited climate change mitigation and adaptation. The future scenarios showed an increase in IWR and CWR for the maize crop. During the developing growth period, the highest incremental CWR and IWR are predicted. When compared with other growth stages, CWR may be slightly reduced in the mid-term periods of the end and initial stages, but it may greatly increase throughout the development and mid-stage growth phases. The rise in evapotranspiration and average temperature may be responsible for this increase in future CWR and IWR of maize crops. More water can be required for supplementary irrigation. So, current and future CWRs due to climate change and the dynamic nature of weather variables might alter the future crop water requirement study area. Therefore, it is recommended that different sectors and at the government level, climate change adaptation methods need to be implemented to maintain the future irrigation area. The farmers' immediate action will be required at this stage. These actions and strategies may include the use of drought-resistant varieties of crops, crop diversification, changes in cropping patterns and calendars of planting dates, and water harvesting to improve irrigation productivity. Additionally, this study utilized CanESM2, CanRCM4, ICHEC-REMO2009, and their ensemble models output with RCP4.5 and 8.5 scenarios using maize crops to analyze crop water demand. Hence, future studies should consider different climate model outputs with an RCP6.0 emission scenario corresponding to different crop types to minimize climate model uncertainty and improve agricultural production of the catchment rainfall.

Y. G. S. has collected the entire set of data, compiled them, and used as input for model analysis. E. T. E. worked on the model parameters with their calibration, validation, and bias correction before drawing any conclusions. T. K. L. has drawn the figures and tables and written the entire manuscript. All 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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