Impact of climate change on potential evapotranspiration and crop water requirement in Upper Wabe Bridge watershed, Wabe Shebele River Basin, Ethiopia

Gurara, Megersa Adugna; Jilo, Nura Boru; Tolche, Abebe Debele

doi:10.1016/j.jafrearsci.2021.104223

Cited by 28 publications

(7 citation statements)

References 70 publications

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“…The observed higher increase in crop ET under the RCP8.5 scenario aligns with similar predictions of ET under global temperature rise, caused by high greenhouse gas emissions, leading to greater loss via evapotranspiration [102]. Using an ensemble of three GCMs at three locations in Ethiopia, Gurara et al [103] projected that by the end of the 21st century, there will be an increase in potential evapotranspiration by an amount between the range of 21.1% and 41% compared to the historical time period under the RCP8.5 scenario. Overall, when compared with the historical data (Figure 11), the results of the ET predictions under RCP4.5 showed a 0.4% increase (mean = 22.1 mm) in the weekly ET in the near term; a 3.1% increase (mean = 22.7 mm) in the middle term; and a 3.8% increase (mean = 22.8 mm) at the end-of-century period.…”

Section: Projections Of Evapotranspiration In the Futuresupporting

confidence: 74%

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Igwe

Sharda

Hefley

2023

Land

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Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

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Section: Projections Of Evapotranspiration In the Futuresupporting

confidence: 74%

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Igwe

Sharda

Hefley

2023

Land

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“…The results of prior studies on hydrological responses to climate change also illustrated fluctuation in rainfall [20,21]. Such trends are expected to affect the agricultural production and irrigation water requirements in the basin [59][60][61].…”

Section: Spatial Distribution Of Historical Mean Annual Rainfall and Temperaturementioning

confidence: 97%

Spatiotemporal Trend Analysis of Temperature and Rainfall over Ziway Lake Basin, Ethiopia

et al. 2021

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Rainfall and temperature trends detection is vital for water resources management and decision support systems in agro-hydrology. This study assessed the historical (1983–2005) and future (2026–2100) rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) trends of the Ziway Lake Basin (Ethiopia). The daily observed rainfall and temperature data at eleven stations were obtained from the National Meteorological Agency (NMA) of Ethiopia, while simulated historical and future climate data were obtained from the Coupled Model Intercomparison Project 5 (CMIP5) datasets under Representative Concentration Pathways (RCP) of 4.5 and 8.5. The CMIP5 datasets were statistically downscaled by using the climate model data for hydrologic modeling (CMhyd) tool and bias corrected using the distribution mapping method available in the CMhyd tool. The performance of simulated rainfall, Tmax, and Tmin of the CMIP5 models were statistically evaluated using observation datasets at eleven stations. The results showed that the selected CMIP5 models can reasonably simulate the monthly rainfall, Tmax, and Tmin at the majority of the stations. Modified Mann–Kendall trend test were applied to estimate the trends of annual rainfall, Tmax, and Tmin in the historical and future periods. We found that rainfall experienced no clear trends, while Tmax, and Tmin showed consistently significant increasing trends under both RCP 4.5 and 8.5 scenarios. However, the warming is expected to be greater under RCP 8.5 than RCP 4.5 by the end of the 21st century, resulting in an increasing trend of Tmax and Tmin at all stations. The greatest warming occurred in the central part of the basin, with statistically significant increases largely seen by the end of the 21st century, which is expected to exacerbate the evapotranspiration demand of the area that could negatively affect the freshwater availability within the basin. This study increases our understanding of historic trends and projected future change effects on rainfall- and evapotranspiration-related climate variables, which can be used to inform adaptive water resource management strategies.

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“…is expansion of traditional agricultural activity has been causing land use dynamics, soil erosion, and sediment yield [10]. e Awash River basin is the most irrigated area in Ethiopia, which is why many farmers consider it their food security [11]. Climate change has recently been one of the causes of agricultural depletion and sediment yield in the region, affecting many farmers who are normally under-economically productive [12].…”

Section: Introductionmentioning

confidence: 99%

Statistical and SWAT Model-Based Performance Evaluation of RCMs in Modeling Streamflow and Sediment Yield at Upper Awash Sub-Basin, Ethiopia

Tumsa

2022

Applied and Environmental Soil Science

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The focus of this study was to evaluate the performance of the regional climate models with regard to simulating streamflow, sediment yield, precipitation, and temperatures. It is recognized that RCMs are not free of bias and uncertainty when simulating climate variables. The evaluation was about simulating annual climatology, annual cycles, and annual variability of climate variables by statistical tools and streamflow and sediment yield by SWAT model output. The study used observed and CORDEX Africa-44 meteorological data for RACMO22T, RCA4, CCLM4-8-17, and HIRHAM5 models using grid points. This analysis of the mean annual rainfall cycle in the summer season shows that all RCMs were underestimated. However, RACMO22T and RCA4 are better suited for simulating climate variables. The higher errors were associated with the simulations of maximum and minimum temperatures in the highest terrain area of the catchment. The statistical analysis with climatology indicates that all RCM was performed in much the same way, except for the seasonal perspective. In this case, RACMO22T was best able to simulate streamflow and sediment yield with PBIAS of 0.14, NSE of 0.91, R2 of 0.82, R2 of 0.72, NSE of 0.78, and PBIAS of −2.61%, respectively. RCA4 simulated streamflow better, but it underestimated the simulated sediment yield. The result proved that RACMO22T and RCA4 performed better in the upper floodplain area. The performance of the climate model varied with catchments, locations, and terrains. The output of this statistical and SWAT model shows that climate models do not accurately simulate hydro-climatological variables. Finally, this study showed that climate models were better at simulating the rainy season than the dry season. This integration of statistical tools and the SWAT model to analyze the RCM’s performance is a unique method to improve the quality of the output for its implementation in maintaining water balance and sediment load reduction.

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Impact of climate change on potential evapotranspiration and crop water requirement in Upper Wabe Bridge watershed, Wabe Shebele River Basin, Ethiopia

Cited by 28 publications

References 70 publications

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Spatiotemporal Trend Analysis of Temperature and Rainfall over Ziway Lake Basin, Ethiopia

Statistical and SWAT Model-Based Performance Evaluation of RCMs in Modeling Streamflow and Sediment Yield at Upper Awash Sub-Basin, Ethiopia

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