An integrated index developed for measuring precipitation complexity: a case study of Jinsha River basin, China.

Accurate quantification of precipitation complexity is vital for assessing the impact of changing environments on precipitation processes and guiding precipitation forecasting. However, previous research mostly quantified precipitation complexity from different perspectives, resulting in differences in complexity results. In this study, the multifractal detrended fluctuation analysis (MF-DFA; derived from fractal), Lyapunov exponent (derived from Chao), and sample entropy (derived from entropy) were used for investigating the complexity of regional precipitation. Then, the integrated complexity index was established by using the intercriteria correlation (CRITIC) method and the simple linear weighting (SWA) method. Finally, the proposed method is applied to China's Jinsha River basin (JRB). The research indicates that (1) the discriminability of the integrated complexity index is higher than that of MF-DFA, Lyapunov exponent, and sample entropy, which can better distinguish the precipitation complexity in the Jinsha River basin; (2) the higher complexity of monthly precipitation was mainly concentrated in the southeast, and the lower complexity was mainly located in the northwest; moreover, the monthly precipitation complexity for the selected study area is the highest at 0.854 at Weixi station and the lowest at 0.152 at Batang station; (3) the superimposed effects of the southwest monsoon, terrain, and reservoir construction have become the main factors that influence the spatial variation of complexity for precipitation. This study provides a new idea for developing an integrated complexity index, and the results are of great significance for regional precipitation disaster prevention and water resources management.