Optimizing Precipitation Gauge Networks for Hydrological Modeling Using High-Quality Reanalysis Data: A Spatial Pattern-Based Approach

Precipitation is important for hydrological monitoring and modeling. The accuracy of Mean Areal Precipitation (MAP) estimation relies largely on the the configuration of precipitation network. This study proposes a novel framework for optimizing rain gauge networks by leveraging high-quality reanalysis precipitation data to evaluate MAP estimation. Using the Qingyi River Basin as a case study, we employ the CMA Multi-source Precipitation Analysis System (CMPAS) data to characterize precipitation spatial patterns and establish a benchmark for network optimization. The framework introduces two metrics, i.e., bias of mean areal precipitation (MB) and Kullback-Leibler divergence (KL), to quantify differences between gauge-derived and CMPAS-derived precipitation fields. Evaluation of the current network reveals significant MAP estimation discrepancies in sub-basins with high precipitation variability. Through importance analysis of candidate gauges and hierarchical optimization, we demonstrate that strategic gauge placement guided by precipitation patterns markedly improves MAP estimation accuracy. The optimized network reduces MAP estimation bias by over 5\% in critical sub-basins. This framework offers advantages over traditional methods by enabling preliminary analysis of proposed gauge locations and explicitly incorporating spatial distribution considerations. The methodology proves effective for both network expansion and rationalization while maintaining computational efficiency through its hierarchical optimization strategy