Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China

This study evaluated the performance and utility of the Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset for monitoring drought over Yunnan Province, China. First, several statistical metrics were used to evaluate the CHIRPS data against 122 gauge observations acquired duri...

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Published inGeomatics, natural hazards and risk Vol. 10; no. 1; pp. 2145 - 2162
Main Authors Wu, Wenqi, Li, Yungang, Luo, Xian, Zhang, Yueyuan, Ji, Xuan, Li, Xue
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.01.2019
Taylor & Francis Ltd
Taylor & Francis Group
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Summary:This study evaluated the performance and utility of the Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset for monitoring drought over Yunnan Province, China. First, several statistical metrics were used to evaluate the CHIRPS data against 122 gauge observations acquired during 1981-2015. Then, the variation of drought was investigated further based on the Standardized Precipitation Index (SPI) over multiple timescales. The results showed the following: (1) the CHIRPS data performed well in terms of monthly precipitation estimation (CC = 0.89, RMSE = 44.26, and MAE = 27.89), albeit with slight overestimation of total precipitation (Bias = 2.9%). Moreover, the CHIRPS data were found adequate in capturing the spatial distribution of precipitation. (2) The CHIRPS-based SPI was shown to be able to capture the occurrence and characteristics of drought events, suggesting that the CHIRPS dataset could be used as an alternative precipitation source for monitoring drought. (3) On the annual scale, it was found that the regions affected most frequently by drought are areas in central, western, and southern parts of Yunnan Province. High frequency of drought occurrence was observed in both spring and winter. Moreover, the results revealed a widespread drying trend over Yunnan Province during 1981-2015, especially in autumn and winter.
ISSN:1947-5705
1947-5713
DOI:10.1080/19475705.2019.1683082