Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions
This study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Hydrology X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589915524000282 |
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| author | Abhinav Gupta Sean A. McKenna |
| author_facet | Abhinav Gupta Sean A. McKenna |
| author_sort | Abhinav Gupta |
| collection | DOAJ |
| description | This study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL) modeling strategy, where the global model outputs are fed as input to a locally trained model, with the hypothesis that the local model can leverage watershed-specific information that the global model may miss. The GL models demonstrate enhanced accuracy in recession flow prediction for 20-30% of the watersheds compared to the global and local models. However, considering the entire hydrograph, the GL models often perform worse than the global model. Further, the DL models were trained on two different objective functions. The performance of the global model in a watershed depended strongly upon the objective function used. These results suggest that the performance of global models is affected by watershed uniqueness, suggesting that even a global DL model should be tailored to individual watersheds for optimal performance. |
| format | Article |
| id | doaj-art-5ba24535108c4dcaa26072b7b98d9aa8 |
| institution | OA Journals |
| issn | 2589-9155 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology X |
| spelling | doaj-art-5ba24535108c4dcaa26072b7b98d9aa82025-08-20T01:58:28ZengElsevierJournal of Hydrology X2589-91552025-01-012610019810.1016/j.hydroa.2024.100198Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functionsAbhinav Gupta0Sean A. McKenna1Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH, USA; Corresponding author at: Department of Chemical and Environmental Engineering, 2600 Clifton Ave., University of Cincinnati, Cincinnati, OH 45221, USA.Division of Hydrologic Sciences, Desert Research Institute, Reno, NV, USAThis study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL) modeling strategy, where the global model outputs are fed as input to a locally trained model, with the hypothesis that the local model can leverage watershed-specific information that the global model may miss. The GL models demonstrate enhanced accuracy in recession flow prediction for 20-30% of the watersheds compared to the global and local models. However, considering the entire hydrograph, the GL models often perform worse than the global model. Further, the DL models were trained on two different objective functions. The performance of the global model in a watershed depended strongly upon the objective function used. These results suggest that the performance of global models is affected by watershed uniqueness, suggesting that even a global DL model should be tailored to individual watersheds for optimal performance.http://www.sciencedirect.com/science/article/pii/S2589915524000282StreamflowRecession flowsHydrological modelingDeep learningWatershed uniquenessUncertainty |
| spellingShingle | Abhinav Gupta Sean A. McKenna Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions Journal of Hydrology X Streamflow Recession flows Hydrological modeling Deep learning Watershed uniqueness Uncertainty |
| title | Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions |
| title_full | Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions |
| title_fullStr | Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions |
| title_full_unstemmed | Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions |
| title_short | Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions |
| title_sort | hydrograph and recession flows simulations using deep learning watershed uniqueness and objective functions |
| topic | Streamflow Recession flows Hydrological modeling Deep learning Watershed uniqueness Uncertainty |
| url | http://www.sciencedirect.com/science/article/pii/S2589915524000282 |
| work_keys_str_mv | AT abhinavgupta hydrographandrecessionflowssimulationsusingdeeplearningwatersheduniquenessandobjectivefunctions AT seanamckenna hydrographandrecessionflowssimulationsusingdeeplearningwatersheduniquenessandobjectivefunctions |