Evaluation of the TRMM multisatellite precipitation analysis and its applicability in supporting reservoir operation and water resources management in Hanjiang basin, China

In this study, we first evaluated a satellite-based precipitation product (3B42V7) using gauge observations and then investigated its utility in supporting reservoir operation and water resources management in Hanjiang basin from January 1998 to December 2013. Direct comparison of 3B42V7 with gauge observations shows that it can well capture the spatial and temporal characteristics of precipitation over the study basin. However, the 3B42V7 estimates generally show slight underestimation of precipitation, especially for extreme precipitation events, which need be considered in the future algorithm development. Next, we conducted the long-term (2008-2013) hydrologic evaluation of the 3B42V7 product using a calibrated monthly hydrologic model. The results show that the performance of the monthly hydrologic model driven by 3B42V7 is compatible to the results driven by gauge-based simulations according to high values of Nash-Sutcliffe coefficients (0.83 and 0.66 for observation-driven and 3B47V7 driven simulations, respectively) and small values of biases (-8.16% and -3.98%). We further evaluated the applicability of 3B42V7 in reservoir operation through a set of operation experiments, in which modeled inflow series were used to make decisions. The results indicate that reservoir operations based on modeled streamflow using the 3B42V7 estimates perform well in water allocation decision-making and strongly agree with actual inflow based operations. Despite that 3B42V7 tends to slightly underestimate precipitation, the resultant operations do not impact the functions and benefits of reservoir operation much. This suggests that the 3B42V7 precipitation estimates are valuable and useful for monthly stream flow simulation and long-term reservoir operation in Hanjiang basin. This study provides a new insight on the evaluation and utility of the remote sensing based precipitation estimates. (C) 2017 Elsevier B.V. All rights reserved.