Remote Sensing-Based Analysis of Precipitation Events: Spatiotemporal Characterization across China

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

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Publication Details

Journal
Water
Published
2024-08-21
DOI
https://doi.org/10.3390/w16162345
Citations
1
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
0.38

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article

Remote Sensing-Based Analysis of Precipitation Events: Spatiotemporal Characterization across China

Jinxing Chen, Ruihao Zhang, Baolin Jiang, Xuejun Dai et al.
1 citations
Water
Precipitation Measurement and Analysis
0.38
article

Remote Sensing-Based Analysis of Precipitation Events: Spatiotemporal Characterization across China

Jinxing Chen, Ruihao Zhang, Baolin Jiang, Xuejun Dai, Xue Li, Zhihua Zhu, Chutong Peng
article en
1 citations

Abstract

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

WaterVol. 16(16)
Guangdong University of Technology (CN), Huizhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Huizhou University, Basic and Applied Basic Research Foundation of Guangdong Province
Climate action
Openalex Percentile: Top 41%
Precipitation Measurement and Analysis
0.38
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