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Open AccessArticle

Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
University of Chinese Academy of Sciences, Beijing 100039, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USA
Faculty of Environmental Sciences, University of Lay Adventists of Kigali (UNILAK), P.O. 6392, Kigali, Rwanda
Author to whom correspondence should be addressed.
Academic Editors: Xuepeng Zhao, Wenze Yang, Hui Lu, Ken Knapp, Viju John, Clement Atzberger and Prasad S. Thenkabail
Remote Sens. 2016, 8(5), 379;
Received: 28 February 2016 / Revised: 13 April 2016 / Accepted: 27 April 2016 / Published: 4 May 2016
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5° spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Precipitation Index (SPI) at various time scales (1 month to 12 months) is calculated for detecting drought events. The results show that PERSIANN-CDR depicts similar drought behavior as the ground-based CPAP in terms of capturing the spatial and temporal patterns of drought events over eastern China, where the intensity of gauge networks and the frequency of droughts are high. 6-month SPI shows the best agreement with CPAP in identifying drought months. However, large differences between PERSIANN-CDR and CPAP in depicting drought patterns and identifying specific drought events are found over northwestern China, particularly in Xinjiang and Qinghai-Tibet Plateau region. Factors behind this may be due to the relatively sparse gauge networks, the complicated terrain and the performance of PERSIANN algorithm. View Full-Text
Keywords: drought monitoring; meteorological drought; PERSIANN-CDR precipitation; SPI drought monitoring; meteorological drought; PERSIANN-CDR precipitation; SPI
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Guo, H.; Bao, A.; Liu, T.; Chen, S.; Ndayisaba, F. Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China. Remote Sens. 2016, 8, 379.

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