Abstract
The capacity of combined gauge-satellite precipitation estimates largely depends on the characteristics of the input data such as the number, location and reliability of rain gauges, and satellite-derived precipitation quality. The objective of this study is to examine the influence of rain gauge network configuration including density and spatial distribution on the performance of the gauge-satellite merging estimation at monthly and ten-day temporal scales. Dense rain gauge observations and satellite-derived precipitation data (i.e., TMPA 3B42 Version 7 and Version 06 IMERG Final Run) in two provinces of China are used. A two-stage downscaling-integration approach is applied in the gauge-satellite precipitation estimation. Various scenarios of rain gauge density and combination are designed and their corresponding merged precipitation estimates are evaluated using statistical indices. The merged results using the TMPA and IMERG precipitation product, respectively, are compared. The results show that: 1) the influence of rain gauge network configuration on the gauge-satellite merged precipitation estimates gradually decreases with the increase in rain gauge density, and the gauge-satellite merged precipitation estimates are more sensitive to the rain gauge network density in wet season and ten-day temporal scale than in dry season and monthly scale, respectively and 2) the merged precipitation estimation using the IMERG precipitation data generally outperforms the estimation using TMPA precipitation data in the low gauge density scenarios, and the gap decreases with the increase in the rain gauge network density. In the areas with sparse rain gauges, improving the quality of satellite precipitation data would significantly improve the performance of the gauge-satellite merging estimation.