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Volume 12
Issue 14
10.3390/rs12142298
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Article

Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

by
Yunqian Wang
1,2,3,4,
Jing Yang
5,*,
Yaning Chen
2,
Zhicheng Su
6,
Baofu Li
1,
Hao Guo
1 and
Philippe De Maeyer
3,7
1
School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
2
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3
Department of Geography, Ghent University, 9000 Ghent, Belgium
4
Sino-Belgian Joint Laboratory of Geo-information, Urumqi 830011, China
5
National Institute of Water and Atmospheric Research, Christchurch 8000, New Zealand
6
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
7
Sino-Belgian Joint Laboratory of Geo-information, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2298; https://doi.org/10.3390/rs12142298
Submission received: 1 June 2020 / Revised: 14 July 2020 / Accepted: 15 July 2020 / Published: 17 July 2020
(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)
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Abstract

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.
Keywords: drought monitoring; drought prediction; multiple drought indicators; soil moisture; precipitation; arid drought monitoring; drought prediction; multiple drought indicators; soil moisture; precipitation; arid
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MDPI and ACS Style

Wang, Y.; Yang, J.; Chen, Y.; Su, Z.; Li, B.; Guo, H.; De Maeyer, P. Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China. Remote Sens. 2020, 12, 2298. https://doi.org/10.3390/rs12142298

AMA Style

Wang Y, Yang J, Chen Y, Su Z, Li B, Guo H, De Maeyer P. Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China. Remote Sensing. 2020; 12(14):2298. https://doi.org/10.3390/rs12142298

Chicago/Turabian Style

Wang, Yunqian, Jing Yang, Yaning Chen, Zhicheng Su, Baofu Li, Hao Guo, and Philippe De Maeyer. 2020. "Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China" Remote Sensing 12, no. 14: 2298. https://doi.org/10.3390/rs12142298

APA Style

Wang, Y., Yang, J., Chen, Y., Su, Z., Li, B., Guo, H., & De Maeyer, P. (2020). Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China. Remote Sensing, 12(14), 2298. https://doi.org/10.3390/rs12142298

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