Complexity measure of regional seasonal precipitation series based on wavelet entropy

Abstract

The chaos characteristics for system complexity, e.g. randomness and fractality, are generally ignored when researchers study the complex behaviour of precipitation time series, which makes it difficult to elicit adequate information for such series. The main objective of this study was to diagnose the complexity of seasonal precipitation by using wavelet entropy and mean wavelet entropy, and to find the complex system of spatial variation in seasonal precipitation for the subareas of Jiansanjiang Administration in China as a case study. The results illustrate that the complexity characteristic of the seasonal precipitation series is greatest in the North sub-area, lowest in the Middle sub-area, and intermediate in the South sub-area; topography and agricultural development are the key driving factors of the complex dynamic variation in the local seasonal precipitation time series. This study provides a basis for analysing the trend in development of complex precipitation time series and realizing the sustainable use of regional water resources.

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

“…Under the conditions of climate change, precipitation is characterized by uneven fluctuations (Pendergrass, 2018; Schroeer and Kirchengast, 2018). The unsteady state and uncertainty of precipitation are defined as precipitation complexity (Liu et al ., 2017). Due to precipitation complexity, the difficulties of long‐term forecasting and water resource management have increased to a certain extent (Harnack and Lanzante, 1985; Yuan et al ., 2014).…”

“…In terms of the characteristics of precipitation time series, scholars pay more attention to trend analysis. However, the research on precipitation complexity is still insufficient (Liu et al ., 2017; Caloiero et al ., 2018). Different scholars have conducted many in‐depth studies of precipitation trends at different time and space scales.…”