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Monitoring and predicting drought based on multiple indicators in an arid area, China

(2020) REMOTE SENSING. 12(14).
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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
SOIL-MOISTURE, METEOROLOGICAL DROUGHT, SEASONAL PREDICTION, PRECIPITATION, INDEX, XINJIANG, DYNAMICS, MODEL, WATER, LAND, drought monitoring, drought prediction, multiple drought indicators, soil moisture, precipitation, arid

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MLA
Wang, Yunqian, et al. “Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China.” REMOTE SENSING, vol. 12, no. 14, Mdpi, 2020, doi:10.3390/rs12142298.
APA
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). https://doi.org/10.3390/rs12142298
Chicago author-date
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 (14). https://doi.org/10.3390/rs12142298.
Chicago author-date (all authors)
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 (14). doi:10.3390/rs12142298.
Vancouver
1.
Wang Y, Yang J, Chen Y, Su Z, Li B, Guo H, et al. Monitoring and predicting drought based on multiple indicators in an arid area, China. REMOTE SENSING. 2020;12(14).
IEEE
[1]
Y. Wang et al., “Monitoring and predicting drought based on multiple indicators in an arid area, China,” REMOTE SENSING, vol. 12, no. 14, 2020.
@article{8689214,
  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.},
  articleno    = {2298},
  author       = {Wang, Yunqian and Yang, Jing and Chen, Yaning and Su, Zhicheng and Li, Baofu and Guo, Hao and De Maeyer, Philippe},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keywords     = {SOIL-MOISTURE,METEOROLOGICAL DROUGHT,SEASONAL PREDICTION,PRECIPITATION,INDEX,XINJIANG,DYNAMICS,MODEL,WATER,LAND,drought monitoring,drought prediction,multiple drought indicators,soil moisture,precipitation,arid},
  language     = {eng},
  number       = {14},
  pages        = {18},
  publisher    = {Mdpi},
  title        = {Monitoring and predicting drought based on multiple indicators in an arid area, China},
  url          = {http://dx.doi.org/10.3390/rs12142298},
  volume       = {12},
  year         = {2020},
}

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