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Consistent time series rainfall datasets are important in performing climate trend analyses and agro-hydrological modeling. However, temporally consistent ground-based and long-term observed rainfall data are usually lacking for such analyses, especially in mountainous and developing countries. In the absence of such data, satellite-derived rainfall products, such as the Climate Hazard Infrared Precipitations with Stations (CHIRPS) and Global Precipitation Measurement Integrated Multi-SatellitE Retrieval (GPM-IMERG) can be used. However, as their performance varies from region to region, it is of interest to evaluate the accuracy of satellite-derived rainfall products at the basin scale using ground-based observations. In this study, we evaluated and demonstrated the performance of the three-run GPM-IMERG (early, late, and final) and CHIRPS rainfall datasets against the ground-based observations over the Ziway Lake Basin in Ethiopia. We performed the analysis at monthly and seasonal time scales from 2000 to 2014, using multiple statistical evaluation criteria and graphical methods. While both GPM-IMERG and CHIRPS showed good agreement with ground-observed rainfall data at monthly and seasonal time scales, the CHIRPS products slightly outperformed the GPM-IMERG products. The study thus concluded that CHIRPS or GPM-IMERG rainfall data can be used as a surrogate in the absence of ground-based observed rainfall data for monthly or seasonal agro-hydrological studies.

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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.