Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya

Abstract

Abstract Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.