Abstract
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.
Highlights
Extreme precipitation events are associated with natural flooding disasters that have devastating impacts on the infrastructure, local economies, and human lives [1,2]
mean absolute error (MAE) values for R99p, R95PTOT, and R99PTOT are shown in Figures 10d–l and 11d–l, indicating again that China Meteorological Forcing Dataset (CMFD) was the most accurate, followed by APHRODITE and CHIRPS; errors for all three datasets peaked for the Zangnan (IX), Dawang-chayu (VI) and QinlianQinghai Lake (I) of the Qinghai-Tibet Plateau (QTP)
This study aimed to examine the capability of three gridded precipitation products namely, CMFD, APHRODITE, and CHIRPS, for the detection of spatiotemporal patterning of extreme precipitation events over the QTP
Summary
Extreme precipitation events are associated with natural flooding disasters that have devastating impacts on the infrastructure, local economies, and human lives [1,2]. As a region sensitive to climate change, the Qinghai-Tibet Plateau (QTP) is prone to natural hazards, such as debris flow from landslides, flash floods, and glacial lake outburst floods [3,4]. High quality and gridded precipitation datasets are vital for the concerted effort on drought monitoring, extreme climate analyses, and natural hazard risk assessments [5,6,7]. Extensive studies have been conducted to evaluate the performance of precipitation products at the local and regional scales. The assessment of gridded precipitation products such as Multi-Source Weighted-Ensemble Precipitation
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