Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency?
Abstract Remote sensing (RS) soil moisture retrievals are frequently assimilated into land surface models (LSMs) to enhance model estimates. However, soil moisture data assimilation (DA) efficiency is highly model‐dependent, making it imperative to investigate whether current LSMs can achieve expect...
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| Format: | Article |
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Wiley
2025-04-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR038702 |
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| author | Jianhong Zhou Jianzhi Dong Huihui Feng Kun Yang Wade T. Crow Zhiyong Wu Xin Tian Jiaxin Tian Xiaogang Ma Yaozhi Jiang |
| author_facet | Jianhong Zhou Jianzhi Dong Huihui Feng Kun Yang Wade T. Crow Zhiyong Wu Xin Tian Jiaxin Tian Xiaogang Ma Yaozhi Jiang |
| author_sort | Jianhong Zhou |
| collection | DOAJ |
| description | Abstract Remote sensing (RS) soil moisture retrievals are frequently assimilated into land surface models (LSMs) to enhance model estimates. However, soil moisture data assimilation (DA) efficiency is highly model‐dependent, making it imperative to investigate whether current LSMs can achieve expected DA efficiencies and identify potential model limitations for DA. Here, we examine soil moisture DA efficiency based on a typical LSM by benchmarking it against a reference soil moisture merging scheme (i.e., assigning weights to combine multiple products into a single one). Both the merged and DA soil moisture estimates are comparable since they are based on identical error estimation theory and the same RS soil moisture data sets. In theory, the DA soil moisture estimates should be superior to the merged results—since DA can characterize the temporal variation of model error and propagate DA benefits into subsequent forecast steps. However, ground‐based validation results indicate that DA soil moisture performs worse than simply merged results in regions where the LSM is less precise than RS retrievals. Further combing synthetic experiment, we confirm that the unexpected DA results are primarily attributable to land parameterization uncertainty, which leads to an unrealistic representation of soil moisture events (e.g., dry‐downs) and significantly hampers the DA application. Given this, soil moisture DA is likely to remain suboptimal in achieving its desired goals. Therefore, this study emphasizes the urgency and necessity of reducing model parameterization uncertainty in land DA systems. |
| format | Article |
| id | doaj-art-d92f6eb807384e009c378668fc9f8b38 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-d92f6eb807384e009c378668fc9f8b382025-08-20T02:36:42ZengWileyWater Resources Research0043-13971944-79732025-04-01614n/an/a10.1029/2024WR038702Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency?Jianhong Zhou0Jianzhi Dong1Huihui Feng2Kun Yang3Wade T. Crow4Zhiyong Wu5Xin Tian6Jiaxin Tian7Xiaogang Ma8Yaozhi Jiang9School of Earth System Science Tianjin University Tianjin ChinaSchool of Earth System Science Tianjin University Tianjin ChinaCollege of Hydrology and Water Resources Hohai University Nanjing ChinaDepartment of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing ChinaUSDA Hydrology and Remote Sensing Laboratory Beltsville MD USACollege of Hydrology and Water Resources Hohai University Nanjing ChinaSchool of Earth System Science Tianjin University Tianjin ChinaDepartment of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing ChinaDepartment of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing ChinaSchool of Resources and Environment University of Electronic Science and Technology Chengdu ChinaAbstract Remote sensing (RS) soil moisture retrievals are frequently assimilated into land surface models (LSMs) to enhance model estimates. However, soil moisture data assimilation (DA) efficiency is highly model‐dependent, making it imperative to investigate whether current LSMs can achieve expected DA efficiencies and identify potential model limitations for DA. Here, we examine soil moisture DA efficiency based on a typical LSM by benchmarking it against a reference soil moisture merging scheme (i.e., assigning weights to combine multiple products into a single one). Both the merged and DA soil moisture estimates are comparable since they are based on identical error estimation theory and the same RS soil moisture data sets. In theory, the DA soil moisture estimates should be superior to the merged results—since DA can characterize the temporal variation of model error and propagate DA benefits into subsequent forecast steps. However, ground‐based validation results indicate that DA soil moisture performs worse than simply merged results in regions where the LSM is less precise than RS retrievals. Further combing synthetic experiment, we confirm that the unexpected DA results are primarily attributable to land parameterization uncertainty, which leads to an unrealistic representation of soil moisture events (e.g., dry‐downs) and significantly hampers the DA application. Given this, soil moisture DA is likely to remain suboptimal in achieving its desired goals. Therefore, this study emphasizes the urgency and necessity of reducing model parameterization uncertainty in land DA systems.https://doi.org/10.1029/2024WR038702land surface modeldata assimilationmodel parameterssoil moistureremote sensingtriple collocation |
| spellingShingle | Jianhong Zhou Jianzhi Dong Huihui Feng Kun Yang Wade T. Crow Zhiyong Wu Xin Tian Jiaxin Tian Xiaogang Ma Yaozhi Jiang Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? Water Resources Research land surface model data assimilation model parameters soil moisture remote sensing triple collocation |
| title | Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? |
| title_full | Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? |
| title_fullStr | Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? |
| title_full_unstemmed | Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? |
| title_short | Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? |
| title_sort | can typical land surface model parameterizations support the expected soil moisture assimilation efficiency |
| topic | land surface model data assimilation model parameters soil moisture remote sensing triple collocation |
| url | https://doi.org/10.1029/2024WR038702 |
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