Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG
Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extrem...
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MDPI AG
2025-08-01
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| author | Tao Jin Yuliang Zhou Ping Zhou Ziling Zheng Rongxing Zhou Yanqi Wei Yuliang Zhang Juliang Jin |
| author_facet | Tao Jin Yuliang Zhou Ping Zhou Ziling Zheng Rongxing Zhou Yanqi Wei Yuliang Zhang Juliang Jin |
| author_sort | Tao Jin |
| collection | DOAJ |
| description | Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. |
| format | Article |
| id | doaj-art-1e4da75ab5824d778d6a156f504d770a |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-1e4da75ab5824d778d6a156f504d770a2025-08-20T03:36:30ZengMDPI AGRemote Sensing2072-42922025-08-011715273210.3390/rs17152732Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERGTao Jin0Yuliang Zhou1Ping Zhou2Ziling Zheng3Rongxing Zhou4Yanqi Wei5Yuliang Zhang6Juliang Jin7College of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaPrecipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates.https://www.mdpi.com/2072-4292/17/15/2732precipitation concentrationatmospheric teleconnectionsextreme heavy precipitationspatiotemporal variationsYangtze river basin |
| spellingShingle | Tao Jin Yuliang Zhou Ping Zhou Ziling Zheng Rongxing Zhou Yanqi Wei Yuliang Zhang Juliang Jin Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG Remote Sensing precipitation concentration atmospheric teleconnections extreme heavy precipitation spatiotemporal variations Yangtze river basin |
| title | Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG |
| title_full | Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG |
| title_fullStr | Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG |
| title_full_unstemmed | Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG |
| title_short | Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG |
| title_sort | spatiotemporal evolution of precipitation concentration in the yangtze river basin 1960 2019 associations with extreme heavy precipitation and validation using gpm imerg |
| topic | precipitation concentration atmospheric teleconnections extreme heavy precipitation spatiotemporal variations Yangtze river basin |
| url | https://www.mdpi.com/2072-4292/17/15/2732 |
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