Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models
Study region: Varahi River originating from the Western Ghats of India. Study focus: We developed a hybrid model that integrates process-based hydrological model (PHM) and data-driven (DD) techniques to generate streamflow simulations precisely. The hybrid modeling framework is practical as it respe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581822002038 |
| _version_ | 1798003077318443008 |
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| author | Sharannya Thalli Mani Venkatesh Kolluru Mahesha Amai Tri Dev Acharya |
| author_facet | Sharannya Thalli Mani Venkatesh Kolluru Mahesha Amai Tri Dev Acharya |
| author_sort | Sharannya Thalli Mani |
| collection | DOAJ |
| description | Study region: Varahi River originating from the Western Ghats of India. Study focus: We developed a hybrid model that integrates process-based hydrological model (PHM) and data-driven (DD) techniques to generate streamflow simulations precisely. The hybrid modeling framework is practical as it respects hydrological processes through the PHM while considering the advantage of the DD model's ability to simulate the complex relationship between residuals and input variables. Further, we have proposed an optimization-based nudging scheme for post-processing the hybrid model simulated streamflow to overcome the limitations in PHM and DD. New hydrological insights for the region: We formulated two approaches for simulating streamflow ensembles using DD and PHM models. In approach− 1, DD models are initially used to ensemble meteorological variables and then use the ensembles in a PHM to simulate streamflows. In approach− 2, PHM is forced with different sets of meteorological variables to simulate multiple streamflow sets and then use DD models to ensemble the PHM-derived streamflows. Random forest exhibited better performance for ensembling precipitation, temperature, and streamflow datasets compared to the other five DD algorithms in the study. Streamflows generated using approach− 2 showed reliable estimates when compared against observed streamflow values. However, post-processing the hybrid streamflows using an optimization-based nudging scheme outperformed the streamflows generated in approach− 1 and approach− 2 with better model fit statistics (R2 and NSE of 0.69 and 0.66). The output from the nudging scheme was further utilized for streamflow predictions under the combined impact of land use/cover (LULC) and climate change (CC) under the Representative Concentration Pathway 4.5 scenario. It depicted a decrease in monthly and seasonal stream flows with − 22.65 %, − 31.77 %, − 11.81 % for winter, summer, and monsoon seasons, respectively. These results suggest that water availability will decline, and water scarcity will increase in the study region. These variations in streamflow might negatively impact agriculture and natural ecosystems and even lead to water restrictions in the region. |
| first_indexed | 2024-04-11T12:03:18Z |
| format | Article |
| id | doaj.art-c3bc1d3a9c7c46c6860f830177d35362 |
| institution | Directory Open Access Journal |
| issn | 2214-5818 |
| language | English |
| last_indexed | 2024-04-11T12:03:18Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj.art-c3bc1d3a9c7c46c6860f830177d353622022-12-22T04:24:48ZengElsevierJournal of Hydrology: Regional Studies2214-58182022-10-0143101190Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological modelsSharannya Thalli Mani0Venkatesh Kolluru1Mahesha Amai2Tri Dev Acharya3Department of Water Resources & Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru 575025, India; Centre for Water Resources Development and Management, Kozhikode, Kerala 673571, IndiaDepartment of Sustainability and Environment, University of South Dakota, SD 57069, USA; Corresponding authors.Department of Water Resources & Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru 575025, IndiaInstitute of Transportation Studies, University of California Davis, Davis, CA 95616, USA; Corresponding authors.Study region: Varahi River originating from the Western Ghats of India. Study focus: We developed a hybrid model that integrates process-based hydrological model (PHM) and data-driven (DD) techniques to generate streamflow simulations precisely. The hybrid modeling framework is practical as it respects hydrological processes through the PHM while considering the advantage of the DD model's ability to simulate the complex relationship between residuals and input variables. Further, we have proposed an optimization-based nudging scheme for post-processing the hybrid model simulated streamflow to overcome the limitations in PHM and DD. New hydrological insights for the region: We formulated two approaches for simulating streamflow ensembles using DD and PHM models. In approach− 1, DD models are initially used to ensemble meteorological variables and then use the ensembles in a PHM to simulate streamflows. In approach− 2, PHM is forced with different sets of meteorological variables to simulate multiple streamflow sets and then use DD models to ensemble the PHM-derived streamflows. Random forest exhibited better performance for ensembling precipitation, temperature, and streamflow datasets compared to the other five DD algorithms in the study. Streamflows generated using approach− 2 showed reliable estimates when compared against observed streamflow values. However, post-processing the hybrid streamflows using an optimization-based nudging scheme outperformed the streamflows generated in approach− 1 and approach− 2 with better model fit statistics (R2 and NSE of 0.69 and 0.66). The output from the nudging scheme was further utilized for streamflow predictions under the combined impact of land use/cover (LULC) and climate change (CC) under the Representative Concentration Pathway 4.5 scenario. It depicted a decrease in monthly and seasonal stream flows with − 22.65 %, − 31.77 %, − 11.81 % for winter, summer, and monsoon seasons, respectively. These results suggest that water availability will decline, and water scarcity will increase in the study region. These variations in streamflow might negatively impact agriculture and natural ecosystems and even lead to water restrictions in the region.http://www.sciencedirect.com/science/article/pii/S2214581822002038Data-Driven techniquesEnsembleGlobal Circulation ModelNudgingProcess-based modelStreamflow simulation |
| spellingShingle | Sharannya Thalli Mani Venkatesh Kolluru Mahesha Amai Tri Dev Acharya Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models Journal of Hydrology: Regional Studies Data-Driven techniques Ensemble Global Circulation Model Nudging Process-based model Streamflow simulation |
| title | Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models |
| title_full | Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models |
| title_fullStr | Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models |
| title_full_unstemmed | Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models |
| title_short | Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models |
| title_sort | enhanced streamflow simulations using nudging based optimization coupled with data driven and hydrological models |
| topic | Data-Driven techniques Ensemble Global Circulation Model Nudging Process-based model Streamflow simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2214581822002038 |
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