Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin

Abstract

Gridded precipitation products have the advantages of wide spatial coverage and high spatiotemporal resolution compared to conventional rain-gauge data, and have been extensively applied in hydrology-related research. As one of the most representative of the gridded precipitation products, APHRODITE performs well in most regions over Asia except around the Tibetan Plateau. This study implemented four bias correction methods including both mean-based (LS, LOCI) and distribution-based approaches (CDF, LS_CDF) for APHRODITE data over the Yarluzangbu-Brahmaputra River Basin based on different climate zones, and the correction methods were assessed according to the performance of both statistical indices and hydrological simulation. First, it was found that the APHRODITE data underestimated the precipitation amount and overestimated the number of raindays for the entire study region; and the bias is relatively small in upstream (Tibetan Plateau) and large in downstream (floodplains). Second, the result showed that all four correction methods could effectively improve the precipitation estimates of the APHRODITE data and the combined LS_CDF method performed the best because of its greater spatial consistency advantages of bias and closer matching of the wet-day event and cumulative frequency. Hydrological performance also supports that the LS_CDF method is the best due to the fine driving simulation result for all three hydrological stations during long periods and the ability to better force simulation of extreme runoff. Third, this study also found that even though distribution-based approaches performed well in precipitation correction, especially for extreme precipitation, their correction effects also depend on the spatial consistency of the bias, which is even more important than frequency matching. These findings have certain reference values for the evaluation, correction, and application of grid precipitation data, not only for APHRODITE data, but also satellite and reanalysis precipitation data.