Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China

Qian Zhu (), Yulin Luo, Dongyang Zhou, Yue-Ping Xu (), Guoqing Wang () and Ye Tian ()
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Qian Zhu: Southeast University
Yulin Luo: Southeast University
Dongyang Zhou: Southeast University
Yue-Ping Xu: Zhejiang University
Guoqing Wang: Nanjing Hydraulic Research Institute
Ye Tian: Nanjing University of Information Science & Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 105, issue 2, No 45, 2185 pages

Abstract: Abstract Droughts have caused many damages in many countries and might be aggravated around the world. Therefore, it is urgent to predict and monitor drought accurately. Soil moisture and its corresponding drought index (e.g., soil water deficit index, SWDI) are the key variables to define drought. However, in situ soil moisture observations are inaccessible in many areas. This study applies support vector machine (SVM) by using a new set of inputs to investigate the performance of in situ and remote sensing products (CMORPH-CRT, IMERG V05 and TRMM 3B42V7) for soil moisture and SWDI forecast over the Xiang River Basin. This study also assesses whether the addition of remote sensing soil moisture as input can improve the performance of SWDI prediction. The results are as follows: (1) the new set of inputs is suitable for drought prediction based on SVM; (2) using in situ precipitation as input to SVM shows the best performance for soil moisture prediction, which followed by TRMM 3B42V7, IMERG V05 and CMORPH-CRT; (3) in situ precipitation and IMERG V05 as input are more suitable for indirect SWDI prediction, while CMORPH-CRT and TRMM 3B42V7 are more suitable for direct SWDI prediction; (4) the addition of soil moisture with in situ precipitation or CMORPH-CRT both can improve the performance of direct SWDI prediction; (5) the lead time for drought prediction with SVM over the Xiang River Basin is about 2 weeks.

Keywords: Drought; Support vector machine (SVM); Soil moisture; SWDI; Remote sensing products (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-020-04394-x

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