Predicting root zone soil moisture using observations at 2121 sites across China
文献类型：期刊论文
| 作者 | Tian, Jing2; Zhang, Yongqiang2; Guo, Jianping1; Zhang, Xuanze2; Ma, Ning2; Wei, Haoshan2,3; Tang, Zixuan2,3 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2022-11-15 |
| 卷号 | 847页码:10 |
| 关键词 | Exponential filter method Root zone soil moisture Random forest classifier China |
| ISSN号 | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2022.157425 |
| 通讯作者 | Zhang, Yongqiang(zhangyq@igsnrr.ac.cn) |
| 英文摘要 | Root zone soil moisture (RZSM) is particularly useful for understanding hydrological processes, plant-land-atmosphere exchanges, and agriculture- and climate-related research. This study aims to estimate RZSM across China by using a one-parameter (T) exponential filter method (EF method) together with a randomforest (RF) regionalization approach and by using a large dataset containing in situ observations collected at 2121 sites across China. First, at each site, T is optimized at each of four soil layers (10-20 cm, 20-30 cm, 30-40 cm and 40-50 cm) by using 0-10-cm soil layer observations and the corresponding calibration layers. Second, an RF classifier is built for each layer according to the calibrated T values and 14 soil, climate and vegetation parameters across 2121 sites. Third, the calibrated T at each soil layer is regionalized with an established RF classifier. Spatial T maps are given for each soil layer across China. Our results show that the EF method performs reasonably well in predicting RZSM at the 10-20-cm, 20-30-cm, 30-40-cm and 40-50-cm layers, with Nash-Sutcliffe efficiency (NSE) medians of 0.73, 0.52, 0.38 and 0.27, respectively, between the observations and estimations. The T parameter shows a spatial pattern in each soil layer and is largely controlled by climate regimes. This study offers an improved RZSM estimation method using a large dataset containing in situ observations; the proposed method also has the potential to be used in other parts of the world. |
| WOS关键词 | NEAR-SURFACE ; EXPONENTIAL FILTER ; LAND-SURFACE ; EVAPORATION ; DEPTHS |
| 资助项目 | National Science Foundation of China[42071327] ; National Science Foundation of China[41671354] ; CAS Pioneer Hundred Talent Program, IGSNRR Supporting Fund[YJRCPT2019-101] ; Science for a Better Development of Inner Mongolia Program of the Bureau of Science and Technology of the Inner Mongolia Province[KJXMEEDS-2020005] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000888035800004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Science Foundation of China ; CAS Pioneer Hundred Talent Program, IGSNRR Supporting Fund ; Science for a Better Development of Inner Mongolia Program of the Bureau of Science and Technology of the Inner Mongolia Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187315]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Yongqiang |
| 作者单位 | 1.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res IGSNRR, Key Lab Water Cycle & Related Land Surface Proces, A11 Datun Rd, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Jing,Zhang, Yongqiang,Guo, Jianping,et al. Predicting root zone soil moisture using observations at 2121 sites across China[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2022,847:10. |
| APA | Tian, Jing.,Zhang, Yongqiang.,Guo, Jianping.,Zhang, Xuanze.,Ma, Ning.,...&Tang, Zixuan.(2022).Predicting root zone soil moisture using observations at 2121 sites across China.SCIENCE OF THE TOTAL ENVIRONMENT,847,10. |
| MLA | Tian, Jing,et al."Predicting root zone soil moisture using observations at 2121 sites across China".SCIENCE OF THE TOTAL ENVIRONMENT 847(2022):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
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