Optimal Rain Gauge Network Design Aided by Multi-source Satellite Precipitation Observation

Optimized rain gauge networks minimize their input and maintenance costs, ensure the overall accuracy of precipitation data. Although information entropy-based methods of optimizing rain gauge networks have been extensively studied, little attention has been paid to their physical meanings. Satellite precipitation observations are particularly susceptible to the effects of terrain elevation, vegetation, and other topographical factors, resulting in large deviations between satellite precipitation data and ground-based precipitation data. Satellite precipitation observations are more inaccurate where the deviations change more drastically, indicating that rain gauge stations should be utilized at these locations. Based on this idea, satellite precipitation observation data were utilized to facilitate rain gauge network optimization in this study. The deviations between ground-based precipitation data and three types of satellite precipitation observation data were used for information entropy estimation. The rain gauge network in the Oujiang River Basin of China was optimally designed according to the principle of maximum joint entropy. Two optimization schemes of culling and supplementing 40 existing sites and 35 virtual sites were explored, and some interesting findings were obtained. First, the optimization and ranking of the rain gauge station network facilitated by the three types of satellite precipitation observation data showed good stability and consistency. In addition, the joint entropy of deviation was larger than that of ground-based precipitation data alone, leading to a higher degree of discrimination between rain gauge stations and enabling the use of deviation data instead of ground-based precipitation data to assist network optimization, with more reasonable and interpretable results.