Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model
文献类型：期刊论文
| 作者 | Liu, Yonghao; Li, Taohui; Zhang, Wenxiang; Lv, Aifeng1 |
| 刊名 | WATER
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 15期号:23页码:4133 |
| 关键词 | SMAR genetic algorithm parameter regionalization root-zone soil moisture |
| DOI | 10.3390/w15234133 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water-heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of detection is limited. RZSM can be estimated on a large scale using the soil moisture analytical relationship (SMAR) and surface soil moisture (SSM). However, the applicability of the SMAR to different deep-root zones and covariate sources is unclear. This paper investigates the applicability of the SMAR in the Shandian River Basin, upstream of the Luan River in China, by combining site and regional soil moisture, soil properties, and meteorological data. In particular, we first compared the estimation results of the SMAR at different depths (10-20 cm; 10-50 cm) and using covariates from different sources (dataset, SMAR-P1; literature, SMAR-P2) at the site in order to generate SMAR calibration parameters. The parameters were then regionalized based on multiple linear regression by combining the SMAR-P1, SMAR-P2, and SMAR calibration parameters in the 10-50 cm root zone. Finally, the Shandian River RZSM was estimated using regional surface soil moisture and the aforementioned regionalized parameters. At the site scale, diffusion coefficient b obtained in the 10-20 cm root zone at the same depth as the surface layer exceeded the upper limit of the SMAR by one. This is not fit an environment within the site context, and thus the SMAR is not applicable at this particular depth. The opposite is observed for the 10-50 cm root zone. In addition, SMAR-P1 (RMSE = 0.02) outperformed SMAR-P2 (RMSE = 0.04) in the estimation of the RZSM at 10-50 cm. Parameter regionalization analysis revealed the failure of SMAR-P2 to pass the significance test (p > 0.05) for building a multivariate linear model, while SMAR-P1 successfully passed the significance test (p < 0.05) and finished the parameter regionalization process. The median RMSE and median R-adj(2) of the regional RZSM results were determined as 0.12 and 0.3, respectively. The regional RZSM agrees with the spatial trend of the Shandian River. This study examines the suitability of the SMAR model in varying deep-root zones and with diverse covariate sources. The results provide a crucial basis for future utilization of the SMAR. |
| WOS关键词 | NEAR-SURFACE ; ASSIMILATION ; RETRIEVAL ; PATTERN ; TRENDS ; FILTER ; WATER |
| WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
| 出版者 | MDPI |
| WOS记录号 | WOS:001117475900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200980]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Yunnan Normal Univ, Fac Geog, Key Lab Plateau Geog Proc & Environm Change Yunnan, Kunming 650500, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yonghao,Li, Taohui,Zhang, Wenxiang,et al. Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model[J]. WATER,2023,15(23):4133. |
| APA | Liu, Yonghao,Li, Taohui,Zhang, Wenxiang,&Lv, Aifeng.(2023).Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model.WATER,15(23),4133. |
| MLA | Liu, Yonghao,et al."Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model".WATER 15.23(2023):4133. |
入库方式： OAI收割
来源：地理科学与资源研究所
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