Developing an entropy and copula-based approach for precipitation monitoring network expansion

Abstract

Extreme precipitation events have been increasing in recent decades and have been causing impacts to human life and property, which requires expansion of existing networks to provide essential information for hydrometeorological analysis. This study developed an entropy and copula-based approach for precipitation monitoring network expansion, aiming at adding stations in ungauged areas with high value of monitoring (VOM), which was estimated through information content and redundancy. The approach was applied to a network in the Taihu Lake basin, China. We grouped the adjacent stations based on Thiessen polygons, computed VOM for each group and identified candidate locations for additional stations. The entropy measures, e.g., joint entropy and total correlation, required to compute VOM were estimated through the principle of maximum entropy (POME) and copula-based joint distribution fitting. We evaluated the performance of five copula families and adopted the Student t-copula as the optimal copula model. Results showed that orographic effect and network density were the main factors that influence VOM variation, i.e., high VOM appeared in the southwest mountain area and the east area with sparsely distributed stations. We also compared the results with those derived from three data quantization methods, among which the rounding technique produced consistent results. The final network schemes were generated by adding stations following a strategy that supports decision preference on the positioning scheme and station number, and were evaluated through the Kriging interpolation error. The proposed approach provides an efficient and a practical way for strengthening a precipitation monitoring system.