Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China
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Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China Qian Zhu1 · Yulin Luo1 · Dongyang Zhou1 · Yue‑Ping Xu2 · Guoqing Wang3 · Ye Tian4 Received: 6 July 2020 / Accepted: 17 October 2020 © Springer Nature B.V. 2020
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
1 Introduction Drought is a complex and devastating natural disaster that has profound negative impacts on agriculture, ecological environment and economy (Rong et al. 2019). Extreme drought events caused by global warming have occurred more frequently and severely in the past two decades (Rong et al. 2019). Many countries have suffered different types of drought, and these severe droughts might be further aggravated around the world (Park et al. 2016). Nowadays, effective and accurate prediction and early warning of drought are particular * Qian Zhu [email protected] Extended author information available on the last page of the article
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important for the prevention of drought damages and economic loss. Therefore, it is urgent and necessary to predict and monitor drought through a simple and effective way. Soil moisture plays an important role in drought monitoring and the estimation of drought indices (Zhan et al. 2016; Park et al. 2017). Soil moisture data are usually derived from in situ observations with different depths and various densities (Mishra et al. 2017). However, the unevenly distributed and even unavailable in situ networks limit the availability of soil moisture (Liu et al. 2016). The su
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