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Comparison Study of Multiple Precipitation Forcing Data on Hydrological Modeling and Projection in the Qujiang River Basin
Water ( IF 3.0 ) Pub Date : 2020-09-19 , DOI: 10.3390/w12092626 Yongyu Song , Jing Zhang , Xianyong Meng , Yuyan Zhou , Yuequn Lai , Yang Cao
Water ( IF 3.0 ) Pub Date : 2020-09-19 , DOI: 10.3390/w12092626 Yongyu Song , Jing Zhang , Xianyong Meng , Yuyan Zhou , Yuequn Lai , Yang Cao
As a key factor in the water cycle and climate change, the quality of precipitation data directly affects the hydrological processes of the river basin. Although many precipitation products with high spatial and temporal resolutions are now widely used, it is meaningful and necessary to investigate and evaluate their merits and demerits in hydrological applications. In this study, two satellite-based precipitation products (Tropical Rainfall Measurement Mission, TRMM; Integrated Multi-satellite Retrievals for GPM, IMERG) and one reanalysis precipitation product (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model, CMADS) are studied to compare their streamflow simulation performance in the Qujiang River Basin, China, using the SWAT model with gauged rainfall data as a reference. The main conclusions are as follows: (1) CMADS has stronger precipitation detection capabilities compared to gauged rainfall, while TRMM results in the most obvious overestimation in the four sub-basins. (2) In daily and monthly streamflow simulations, CMADS + SWAT mode offers the best performance. CMADS and IMERG can provide high quality precipitation data for data-scarce areas, and IMERG can effectively avoid the overestimation of streamflow caused by TRMM, especially on a daily scale. (3) The runoff projections of the three modes under RCP (Representative Concentration Pathway) 4.5 was higher than that of RCP 8.5 on the whole. IMERG + SWAT overestimates the surface water resources of the basin compared to CMADS + SWAT, while TRMM + SWAT provides the most stable uncertainty. These findings contribute to the comparison of the differences among the three precipitation products and provides a reference for the selection of precipitation data in similar regions.
更新日期：2020-09-19
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