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Detecting the spatial heterogeneity in the potential occurrence probability of water disasters is a foremost and critical issue for the prevention and mitigation of water disasters. However, it is also a challenging task due to the lack of effective approaches. In the article, the entropy index was employed and those daily rainfall data at 520 stations were used to investigate the occurrences of rainstorms in China. Results indicated that the entropy results were mainly determined by statistical characters (mean value and standard deviation) of rainfall data, and can categorically describe the spatial heterogeneity in the occurrence of rainstorms by considering both their occurrence frequencies and magnitudes. Smaller entropy values mean that rainstorm events with bigger magnitudes were more likely to occur. Moreover, the spatial distribution of entropy values kept a good relationship with the hydroclimate conditions, described by the aridity index. In China, rainstorms are more to likely occur in the Pearl River basin, Southeast River basin, lower-reach of the Yangtze River basin, Huai River basin, and southwest corner of China. In summary, the entropy index can be an effective alternative for quantifying the potential occurrence probability of rainstorms. Four thresholds of entropy value were given to distinguish the occurrence frequency of rainstorms as five levels: very high, high, mid, low and very low, which can be a helpful reference for the study of daily rainstorms in other basins and regions. [ABSTRACT FROM AUTHOR]

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 precipitation, thus causing difficulties in the assessment of actual complexity. In this study, we identified and chose the optimal entropy index from six entropy indexes based on the reliability and stability of entropy values, to assess the complexity of precipitation. The six entropy indexes considered are: information entropy (IE), fuzzy entropy (FE), approximate entropy (AE), sample entropy (SE), wavelet entropy (WE), and permutation entropy (PE). We implemented the approach on the monthly precipitation data observed over the period 1968–2017 at 58 meteorological stations in Beijing, China. The results indicated that the SE index was the optimal index with the best reliability and stability and, thus, was used to examine the complexity of the 58 monthly precipitation series in Beijing. The results also suggested that the complexity of precipitation can be viewed in terms of four sub-regions in Beijing. The precipitation in the 'Northern Mountain Area', with high complexity, was more affected by the interactions between the terrain and climate variability, compared with that in the 'Southern Mountain Area' with low complexity. The precipitation in the 'Central Urban Area' was more significantly impacted by the extensive urbanization than that in the 'Northeast Suburban Area', causing higher complexity in the former. At temporal scale, the complexity of precipitation in the 'Southern Mountain Area' exhibited the largest increasing rate due to the highest impacts of climate variability, followed by the 'Central Urban Area' with a moderate increasing rate of complexity of precipitation due to the impacts of human activities. However, the complexity of precipitation in the 'Northern Mountain Area' and 'Northeast Suburban Area' exhibited small changes due to relatively weak sensitivity to climate variability and human activities. [ABSTRACT FROM AUTHOR]