Temporal Pattern Analysis of Local Rainstorm Events in China During the Flood Season Based on Time Series Clustering

Abstract

Similar to the rainfall depth, duration and intensity, the temporal pattern is also an important characteristic of rainstorm events. Studies have shown that temporal patterns will influence runoff modelling, flash flood warning thresholds as well as urban and infrastructure flood inundation simulations. In this study, a time series clustering method using dynamic time warping (DTW) as similarity measurement criteria is proposed to analyze rainfall temporal patterns. Compared with the existing approaches, it can better reflect the real rainfall processes. Based on this novel method, five representative temporal patterns were extracted from 13,299 rainstorm events during the flood season in China. Through the analysis of their statistical characteristics, the disaster-causing risks of each temporal pattern were compared. Furthermore, we found that for rainstorm events whose durations are less than 24 h, the rainfall is mainly concentrated in 3 to 6 h, which proposes higher requirements for the design of flood control and drainage projects compared with those using average intensities of 12 or 24 h as design standards. Finally, through regional analysis, we found that the rainfall depth, intensity and peak value are affected by the macroclimate. However, the temporal patterns are not strongly related to the macroclimate but are more likely to be affected by the local climate and topography, which needs further studies at smaller scales.

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

“…Hoshino and Yamada (2023) classify the spatial and temporal characteristics of rainstorms in a massive-ensemble climate data set by calculating the distance matrix between each rainstorm event and exploring the disaster scenarios corresponding to different rainstorm patterns. F. Wang (2020) proposes a DTW (dynamic time warping) method for clustering the time series of rainfall, to analyze the representative rainfall processes. There is still a need to improve the understanding of the spatiotemporal clustering of short-duration extreme rainfall in small-scale urban areas, which can enhance the response and prevention of unforeseen rainstorm measures.…”

Event‐based extreme precipitation variability analysis over a part of the Hindu Kush Himalayan region

“…Wu et al (2018a) used seven typical temporal modes to analyse multiday precipitation events based on the occurrence of extreme precipitation, and similarly, He et al (2022) recognized seven typical rainfall patterns but with hourly data. Also, Wang (2020) defined five temporal patterns using a time series clustering method named dynamic time wrapping with hourly precipitation data.…”

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

“…The value chosen depends on the study objectives and on the temporal resolution of the rainfall data. In order to analyze the temporal dynamics of rainfall events during the flood season, Wang [15] suggested a MIT of 6 h, a minimum event duration of 3 h, and a minimum accumulation of 10 mm. Sottile et al [16] considered an MIT of 1 h and a cumulative threshold of 1 mm.…”

“…The choice of an MIT is more related to hydrological considerations rather than to the physical and meteorological properties of rainfall [20]. However, the MIT method is simple to implement, hence its widespread use [14,15,[21][22][23][24][25] and its adoption in our study.…”