Ensemble Methods for Parameter Estimation of WRF‐Hydro
Abstract The WRF‐Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open‐source PEST+...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR038048 |
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| author | Arezoo RafieeiNasab Michael N. Fienen Nina Omani Ishita Srivastava Aubrey L. Dugger |
| author_facet | Arezoo RafieeiNasab Michael N. Fienen Nina Omani Ishita Srivastava Aubrey L. Dugger |
| author_sort | Arezoo RafieeiNasab |
| collection | DOAJ |
| description | Abstract The WRF‐Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open‐source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF‐Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation. |
| format | Article |
| id | doaj-art-1e641cc4dcf7410399b8d05139877646 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-1e641cc4dcf7410399b8d051398776462025-08-20T02:36:42ZengWileyWater Resources Research0043-13971944-79732025-01-01611n/an/a10.1029/2024WR038048Ensemble Methods for Parameter Estimation of WRF‐HydroArezoo RafieeiNasab0Michael N. Fienen1Nina Omani2Ishita Srivastava3Aubrey L. Dugger4Research Applications Laboratory National Center for Atmospheric Research Boulder CO USAUS Geological Survey Upper Midwest Water Science Center Madison WI USAResearch Applications Laboratory National Center for Atmospheric Research Boulder CO USAResearch Applications Laboratory National Center for Atmospheric Research Boulder CO USAResearch Applications Laboratory National Center for Atmospheric Research Boulder CO USAAbstract The WRF‐Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open‐source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF‐Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.https://doi.org/10.1029/2024WR038048hydrologyparameter estimationWRF‐hydroPEST++ |
| spellingShingle | Arezoo RafieeiNasab Michael N. Fienen Nina Omani Ishita Srivastava Aubrey L. Dugger Ensemble Methods for Parameter Estimation of WRF‐Hydro Water Resources Research hydrology parameter estimation WRF‐hydro PEST++ |
| title | Ensemble Methods for Parameter Estimation of WRF‐Hydro |
| title_full | Ensemble Methods for Parameter Estimation of WRF‐Hydro |
| title_fullStr | Ensemble Methods for Parameter Estimation of WRF‐Hydro |
| title_full_unstemmed | Ensemble Methods for Parameter Estimation of WRF‐Hydro |
| title_short | Ensemble Methods for Parameter Estimation of WRF‐Hydro |
| title_sort | ensemble methods for parameter estimation of wrf hydro |
| topic | hydrology parameter estimation WRF‐hydro PEST++ |
| url | https://doi.org/10.1029/2024WR038048 |
| work_keys_str_mv | AT arezoorafieeinasab ensemblemethodsforparameterestimationofwrfhydro AT michaelnfienen ensemblemethodsforparameterestimationofwrfhydro AT ninaomani ensemblemethodsforparameterestimationofwrfhydro AT ishitasrivastava ensemblemethodsforparameterestimationofwrfhydro AT aubreyldugger ensemblemethodsforparameterestimationofwrfhydro |