An overview of soil moisture drought research in China: Progress and perspective

被引:4
|
作者
Wang, Aihui [1 ]
Ma, Xin [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Atmospher Phys, Nansen Zhu Int Res Ctr, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
关键词
Soil moisture drought; Identification; Prediction; Perspective; CLIMATE-CHANGE; LAND-SURFACE; CHALLENGES; DATASET; INDEX;
D O I
10.1016/j.aosl.2022.100297
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Soil moisture drought (SMD) directly affects agricultural yield and land water resources. Understanding and predicting the occurrences and evolution of SMD are of great importance for a largely agricultural country such as China. Compared to other drought categories, SMD receives less attention due to the lack of long-term soil moisture datasets. In recent decades, SMD research has been greatly developed in China, benefiting from increased ground and satellite measurements along with state-of-the-art land surface models. Here, the authors provide a brief overview of the recent progress in SMD research in China, focus on historical drought identification and its prediction, and then raise some future perspectives. Based on historical SMD studies, drought frequency has increased overall and drought duration has been prolonged since the 1950s for China as a whole, but they both show substantial temporal variations at the regional scale. Research on SMD prediction has mainly relied on the statistical relationship between soil moisture and climate variables. Few studies based on the dynamical approach in seasonal drought prediction have highlighted the importance of initial conditions and atmospheric forcing datasets. Given the importance of SMD in agricultural practice and water resource management in China, it is necessary to emphasize the following: 1) conducting research on multiple time scales (e.g., from days to the centurial time scale) and cross-regional drought identification research; and 2) developing a SMD prediction system that takes advantage of climate prediction systems, land surface models, and multisource soil moisture datasets. .
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页数:6
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