Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.1.1. Study Area
2.1.2. Data Sources and Preprocessing
2.2. Precipitation Concentrate Index
- Step 1: Categorize rainfall values at 1 mm intervals, ranging from 0 to the maximum value;
- Step 2: Count the number of rainy days within each rainfall classification range;
- Step 3: Calculate the number of rainy days and the corresponding rainfall amount for each category;
- Step 4: Perform cumulative summation of the results from Step 3 to obtain the cumulative percentages of rainy days and the corresponding cumulative rainfall percentages;
- Step 5: Establish the relationship between the cumulative percentage of rainy days and the cumulative percentage of rainfall for each year.
2.3. Mann–Kendall Test and Hurst Index
2.4. Pettitt Test
2.5. Random Forest
2.6. Hot Spot Identification
2.7. Extreme Heavy Precipitation Indices
3. Results
3.1. Verification of Lorenz Curve
3.2. Spatial Distribution Characteristics of Precipitation Concentration Index
3.3. Temporal Trends of Precipitation Concentration Index
3.4. Variability Diagnosis of Precipitation Concentration Index
3.5. Identification of Hotspots and Coldspots
4. Discussion
4.1. Drivers of Precipitation Concentration Index
4.2. Relationship Between Precipitation Concentration Index and Extreme Heavy Precipitation
5. Conclusions
- (1)
- The PCI in the Yangtze River Basin shows distinct spatial patterns. In regions surrounding the Sichuan Basin, the PCID values exceed 0.67, indicating a high risk of extreme heavy precipitation events. Precipitation within the basin shows significant seasonal variations, with PCIM values greater than 11 and an overall increasing trend from southeast to northwest. In the Jinsha River Basin, SPCI values are elevated during spring, autumn, and winter, whereas during summer the SPCI values across the entire basin are generally below 12.
- (2)
- Annual precipitation shows a significant increasing trend on the eastern and western sides of the basin, while seasonal variations exhibit clear regional differences. In spring, precipitation increases notably over the Tibetan Plateau; in summer, it peaks in the middle and lower reaches, linked to enhanced East Asian monsoon activity and a prolonged Meiyu front; in winter, increases in the lower reaches help alleviate dry-season water shortages. Overall, the PCID declines systematically across the basin (Z-values below −2.58), faster in urbanized downstream areas than in natural regions. SPCI patterns, influenced by orographic uplift and local disturbances, decrease in the western basin during spring, increase there in summer, decline in the northwest during autumn, and show slight downstream decreases with anti-persistence in winter.
- (3)
- The PCIM is primarily associated with large-scale atmospheric teleconnections such as ENSO and PDO. Change points for the PCIM are concentrated between the late 1980s and early 1990s. In contrast, the PCID is closely linked to sub-seasonal circulation oscillations and local thermal forcing, exhibiting upstream sensitivity to AO and ENSO, while downstream regions are predominantly associated with PDO and ENSO. Its change points occur earlier, from the late 1970s to the mid-1980s. Spatially, changes in the upstream region lag behind those in the middle and lower reaches, where human activities have prompted earlier shifts.
- (4)
- Extreme heavy precipitation events in the Yangtze River Basin are closely coupled with the PCID, showing strong temporal synchrony but with clear regional differences. On an interannual scale, the fluctuation trends of the PCID are highly consistent with those of R95p and Rx5day. Spatially, due to orographic uplift effects, the PCID in the Jinsha River Basin is tightly coupled with short-duration heavy rainfall, whereas in the Min-Tuo River Basin, the persistent influence of the Meiyu front renders the marginal effect of R99p on the PCID more pronounced. In the downstream Taihu Basin, the anomalously high values of Rx1day and the corresponding PCID response exhibits a lag, suggesting that when the intensity of extreme heavy precipitation exceeds a critical threshold, the dilution effect of increased total rainfall or the impact of human activities may weaken the capacity of the concentration index to capture extreme events.
- (5)
- The comparison with GPM IMERG reveals its mixed performance in monitoring precipitation concentration. Although GPM provides valuable large-scale coverage and correctly captures PCIM patterns as well as the overall decreasing direction of PCID trends, it tends to overestimate the PCID. Furthermore, it fails to capture the observed intensity and statistical significance of the decreasing trend and exhibits complex seasonal biases, such as overestimating concentration in the high-altitude upstream regions during winter.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Index | Intensity | Definition | Unit |
---|---|---|---|
Rx1day | Max 1-day precipitation | Annual maximum 1-day precipitation | mm |
Rx5day | Max 5-day precipitation | Annual maximum 5-day precipitation | mm |
R95p | Annual contribution from very wet days | Annual sum of daily precipitation > 95th percentile (baseline period 1990–2019) | mm |
R99p | Annual contribution from extremely wet days | Annual sum of daily precipitation > 99th percentile (baseline period 1990–2019) | mm |
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Jin, T.; Zhou, Y.; Zhou, P.; Zheng, Z.; Zhou, R.; Wei, Y.; Zhang, Y.; Jin, J. Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG. Remote Sens. 2025, 17, 2732. https://doi.org/10.3390/rs17152732
Jin T, Zhou Y, Zhou P, Zheng Z, Zhou R, Wei Y, Zhang Y, Jin J. Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG. Remote Sensing. 2025; 17(15):2732. https://doi.org/10.3390/rs17152732
Chicago/Turabian StyleJin, Tao, Yuliang Zhou, Ping Zhou, Ziling Zheng, Rongxing Zhou, Yanqi Wei, Yuliang Zhang, and Juliang Jin. 2025. "Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG" Remote Sensing 17, no. 15: 2732. https://doi.org/10.3390/rs17152732
APA StyleJin, T., Zhou, Y., Zhou, P., Zheng, Z., Zhou, R., Wei, Y., Zhang, Y., & Jin, J. (2025). Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG. Remote Sensing, 17(15), 2732. https://doi.org/10.3390/rs17152732