Assessment of spatiotemporal variability of precipitation using entropy indexes: a case study of Beijing, China

• Published in: Stochastic environmental research and risk assessment Vol. 36; no. 4; pp. 939 - 953
• Main Authors: Du, Longgang, Li, Xinxin, Yang, Moyuan, Sivakumar, Bellie, Zhu, Yanxin, Pan, Xingyao, Li, Zhijia, Sang, Yan-Fang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022; Springer Nature B.V
• Subjects: Aquatic Pollution; Chemistry and Earth Sciences; Climate; Climate change; Climate variability; Complexity; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Entropy; Entropy (Information theory); Environment; Hydrologic data; Math. Appl. in Environmental Science; Mountain regions; Mountains; Original Paper; Permutations; Physics; Precipitation; Probability Theory and Stochastic Processes; Reliability analysis; Stability analysis; Statistics for Engineering; Suburban areas; Urban areas; Urbanization; Variability; Waste Water Technology; Water Management; Water Pollution Control; Weather stations; Beijing China; China; Precipitation; Entropy; Spatial pattern; Climate variability; Complexity

Entropy indexes are widely used to measure the complexity of precipitation and its spatiotemporal variability, due to their advantages in quantifying the complex and nonlinear interactions of climate variables. However, different entropy indexes may provide different outcomes on the complexity of pr...