期刊
REMOTE SENSING
卷 13, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/rs13153010
关键词
CMFD; APHRODITE; CHIRPS; extreme precipitation; Qinghai-Tibet Plateau
类别
资金
- National Key Research and Development Program of China [2016YFA0602404]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDA23060704]
This study evaluated the performance of three gridded datasets in detecting extreme precipitation events over the Qinghai-Tibet Plateau from 1981 to 2014, finding that all three datasets showed acceptable representation of extreme precipitation events in the region. CMFD outperformed the other datasets in capturing the spatiotemporal patterns of most extreme precipitation indices.
Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling-Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products' to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.
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