Tirthankar Roy1, Juan B. Valdes1, Aleix Serrat Capdevila2, Hoshin V. Gupta1, Bradfield Lyon3, Matej Durcik1
1Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
2Water Global Practice, The World Bank, Washington, DC
3The University of Maine, Orono, Maine
In this study, we compare two bias correction schemes in the context of climate change impacts assessment, namely power law transformation (PLT) and distribution-free adjusted quantile mapping (AQM-DF). The monthly biases in the high-resolution climate dataset, Agricultural Modern-Era Retrospective Analysis for Research and Application (AgMERRA), are corrected using the fine-resolution (0.05°) satellite and in-situ observation-based merged dataset, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The study is carried out for the Mara River basin in Africa, which faces several difficult water resources management challenges. Results from the preliminary assessment of the hydrologic forecasts generated using the bias corrected forcings are also presented.