Transformer-based soil moisture simulation for understanding future drying trend globally
文献类型：期刊论文
| 作者 | Liu, Yangxiaoyue2,7; Tian, Yuan2,8; Xin, Ying1,9,10; Yang, Yizhuo3; Zeng, Jiangyuan4; Feng, Min5; Song, Chunqiao6,11,12 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 665页码:134709 |
| 关键词 | Soil moisture Simulation Global scale Transformer Trend analysis |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2025.134709 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | As a crucial element of the terrestrial water cycle, multiple future scenario soil moisture (SM) datasets are widely applied to investigating Earth surface processes using ensemble averages. However, they may run the risk of vague variation trend resulted from averaging multiple models, which are characterized by different land surface models on hydrological process simulation. To improve spatiotemporal pattern reliability, this study innovatively designs a Transformer SM Simulation Net (TSMSNet), to conduct global SM simulation of SSP1-2.6, SSP2-4.5, and SSP5-8.5 during 2016-2099. Nine qualified future SM datasets, along with their spatial distribution of error parameters, and geographic data are selected as model inputs. The learning target is calculated through merging merits from Soil Moisture Active Passive and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land SM. The TSMSNet SM (R = 0.68, ubRMSE = 0.045 m3/m3) achieves good matching degree against in situ measurements compared to the Convolutional Long Short Term Memory (CSMSNet) simulated SM (R = 0.65, ubRMSE = 0.047 m3/m3). The TSMSNet SM could favorably match the long-term trend of learning target, which exhibits advantage over ensemble averages and CSMSNet SM. TSMSNet SM presents an overwhelming drying trend. The decline magnitude rises accompanied by SSP changing from sustainable pathway to fossil-fueled development. In terms of land cover types, evident drying trends are found in cropland and forest. SM shows faster descent rate at habitable areas than inhabitable areas. This paper develops a reliable TSMSNet SM dataset, which is expected to be a valuable reference for understanding future SM variations. |
| URL标识 | 查看原文 |
| WOS关键词 | ALGORITHMS ; SATELLITE ; PRODUCTS ; ENSEMBLE ; DECLINE ; CYCLE ; LAND |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001633104600001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219760]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Tian, Yuan |
| 作者单位 | 1.State Ocean Adm, South China Sea Inst Planning & Environm Res, Guangzhou 510310, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 3.Xian Univ Finance & Econ, Xian 710100, Peoples R China; 4.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; 5.Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr, State Key Lab Tibetan Plateau Earth Syst Environm, Beijing 100101, Peoples R China; 6.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Lake & Watershed Sci Water Secur, Nanjing 210008, Peoples R China; 7.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 8.Key Lab Ecosyst Network Observat & Modeling, Lhasa Plateau Ecosyst Res Stn, Beijing 100101, Peoples R China; 9.Minist Nat Resources, Key Lab Marine Environm Survey Technol & Applicat, Guangzhou 510310, Peoples R China; 10.Minist Nat Resources, South China Sea Dev Res Inst, Technol Innovat Ctr South China Sea Remote Sensing, Guangzhou 510310, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Yangxiaoyue,Tian, Yuan,Xin, Ying,et al. Transformer-based soil moisture simulation for understanding future drying trend globally[J]. JOURNAL OF HYDROLOGY,2026,665:134709. |
| APA | Liu, Yangxiaoyue.,Tian, Yuan.,Xin, Ying.,Yang, Yizhuo.,Zeng, Jiangyuan.,...&Song, Chunqiao.(2026).Transformer-based soil moisture simulation for understanding future drying trend globally.JOURNAL OF HYDROLOGY,665,134709. |
| MLA | Liu, Yangxiaoyue,et al."Transformer-based soil moisture simulation for understanding future drying trend globally".JOURNAL OF HYDROLOGY 665(2026):134709. |
入库方式： OAI收割
来源：地理科学与资源研究所
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