Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States
Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. Wi...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-02-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004227 |
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| _version_ | 1849729552531587072 |
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| author | Hongxiang Yan Ning Sun Hisham Eldardiry Travis Thurber Patrick Reed Daniel Kennedy Sean Swenson Jennie Rice |
| author_facet | Hongxiang Yan Ning Sun Hisham Eldardiry Travis Thurber Patrick Reed Daniel Kennedy Sean Swenson Jennie Rice |
| author_sort | Hongxiang Yan |
| collection | DOAJ |
| description | Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications. |
| format | Article |
| id | doaj-art-62978cfebc6c4231978cb21d4cb059bd |
| institution | DOAJ |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-62978cfebc6c4231978cb21d4cb059bd2025-08-20T03:09:12ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-02-01172n/an/a10.1029/2024MS004227Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United StatesHongxiang Yan0Ning Sun1Hisham Eldardiry2Travis Thurber3Patrick Reed4Daniel Kennedy5Sean Swenson6Jennie Rice7Pacific Northwest National Laboratory Richland WA USAPacific Northwest National Laboratory Richland WA USAPacific Northwest National Laboratory Richland WA USAPacific Northwest National Laboratory Richland WA USADepartment of Civil and Environmental Engineering Cornell University Ithaca NY USANational Center for Atmospheric Research Boulder CO USANational Center for Atmospheric Research Boulder CO USAPacific Northwest National Laboratory Richland WA USAAbstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.https://doi.org/10.1029/2024MS004227CLM5parameter uncertaintyhydrologic predictionensembleCONUS |
| spellingShingle | Hongxiang Yan Ning Sun Hisham Eldardiry Travis Thurber Patrick Reed Daniel Kennedy Sean Swenson Jennie Rice Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States Journal of Advances in Modeling Earth Systems CLM5 parameter uncertainty hydrologic prediction ensemble CONUS |
| title | Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States |
| title_full | Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States |
| title_fullStr | Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States |
| title_full_unstemmed | Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States |
| title_short | Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States |
| title_sort | ensemble based spatially distributed clm5 hydrological parameter estimation for the continental united states |
| topic | CLM5 parameter uncertainty hydrologic prediction ensemble CONUS |
| url | https://doi.org/10.1029/2024MS004227 |
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