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

Abstract

Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity.

Spatial and Temporal Variability of Precipitation Complexity in Northeast Brazil

“…Thus, the time series that contains more frequent and more similar sequences is more regular and displays lower values of SampEn. Sample entropy method is used in data analysis in physiology [43,44], geophysics [45], hydrology [28,32,37,38], engineering [46], and finances [47,48]. The algorithm that calculates sample entropy consists of the following steps [35]:…”

“…Originally developed for physiological applications, SampEn does not require any previous knowledge about the source generating the dataset, and is robust to noise and time series length, which facilitates its applicability in a wide variety of research fields [36]. Recently, the SampEn method was successfully applied in rainfall studies providing new information about the complexity of rainfall dynamics and its spatiotemporal variability [37][38][39][40].…”

Spatio‐temporal variations of wet and dry spells in Iran and their association with large‐scale climatic indices

“…The effects of natural and climatic variability on hydrological cycles and water resources can be better understood if we have a better grasp of precipitation's temporal and spatial variability. This is why there has been so much study of it in the last few decades (Hu et al ., 2017; Markonis et al ., 2017; Ongoma and Chen, 2017; Zubiate et al ., 2017; Panisset et al ., 2018; Talchabhadel et al ., 2018; Gebrechorkos et al ., 2019; Zhang et al ., 2020b). Wet and dry spells of length d are periods of d consecutive wet or dry days, respectively (Li et al ., 2016).…”