Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model
Study Region: This study focuses on the Cape Fear and Tar-Pamlico watersheds in North Carolina, which are characterized by diverse hydrological conditions, varied land use, soil types, and hydrological characteristics. Study Focus: The primary goal of this study is to examine the combined effects of...
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| Format: | Article |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221458182400483X |
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| author | Mahesh R. Tapas Randall Etheridge Thanh-Nhan-Duc Tran Manh-Hung Le Brian Hinckley Van Tam Nguyen Venkataraman Lakshmi |
| author_facet | Mahesh R. Tapas Randall Etheridge Thanh-Nhan-Duc Tran Manh-Hung Le Brian Hinckley Van Tam Nguyen Venkataraman Lakshmi |
| author_sort | Mahesh R. Tapas |
| collection | DOAJ |
| description | Study Region: This study focuses on the Cape Fear and Tar-Pamlico watersheds in North Carolina, which are characterized by diverse hydrological conditions, varied land use, soil types, and hydrological characteristics. Study Focus: The primary goal of this study is to examine the combined effects of three satellite precipitation products (SPPs) — ERA-5, gridMET, and GPM IMERG — along with three autocalibration techniques — DDS, GLUE, and LHS — on SWAT+ river flow predictions. Flow accuracy was assessed using three evaluation metrics: NSE, KGE, and R². New Hydrological Insights for the Region: Key findings revealed that five SWAT+ parameters (cn2, revap_co, flo_min, revap_min, and awc) were consistently sensitive across all SPPs and watersheds, with rainfall products exerting a greater influence on simulated river flow than optimization techniques. Among the SPPs, GPM IMERG performed the best, followed by ERA-5 and gridMET, while NSE was more responsive to changes in SPPs and calibration methods than KGE and R². For the Cape Fear and Tar-Pamlico watersheds, the study highlighted SWAT+ 's challenges in predicting base flow for groundwater-driven systems and demonstrated the potential of optimization techniques to improve flow simulations despite poor satellite-gauge rainfall correlation. The combination of the GPM IMERG dataset and the GLUE method proved most effective, offering valuable guidance for selecting optimal datasets and methods to enhance prediction accuracy in complex watersheds. |
| format | Article |
| id | doaj-art-87478fc80adb42cf8fa3348735757593 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-87478fc80adb42cf8fa33487357575932025-01-22T05:42:10ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102134Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ modelMahesh R. Tapas0Randall Etheridge1Thanh-Nhan-Duc Tran2Manh-Hung Le3Brian Hinckley4Van Tam Nguyen5Venkataraman Lakshmi6Integrated Coastal Sciences Program, East Carolina University, Greenville, NC 27858, USA; Department of Food, Agricultural and Biological Engineering, Ohio State University, Columbus, OH 43210, USA; Correspondence to: 590 Woody Hayes Drive, Columbus, OH 43210, USA.Department of Engineering, Center for Sustainable Energy and Environmental Engineering, East Carolina University, Greenville, NC 27858, USADepartment of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USANASA Goddard Space Flight Center Hydrological Sciences Laboratory, MD 20771, USA; Science Applications International Corporation, Greenbelt, MD 20771, USADepartment of Engineering, Center for Sustainable Energy and Environmental Engineering, East Carolina University, Greenville, NC 27858, USAHelmholtz Centre for Environmental Research – UFZ, Saxony 04138, GermanyDepartment of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USAStudy Region: This study focuses on the Cape Fear and Tar-Pamlico watersheds in North Carolina, which are characterized by diverse hydrological conditions, varied land use, soil types, and hydrological characteristics. Study Focus: The primary goal of this study is to examine the combined effects of three satellite precipitation products (SPPs) — ERA-5, gridMET, and GPM IMERG — along with three autocalibration techniques — DDS, GLUE, and LHS — on SWAT+ river flow predictions. Flow accuracy was assessed using three evaluation metrics: NSE, KGE, and R². New Hydrological Insights for the Region: Key findings revealed that five SWAT+ parameters (cn2, revap_co, flo_min, revap_min, and awc) were consistently sensitive across all SPPs and watersheds, with rainfall products exerting a greater influence on simulated river flow than optimization techniques. Among the SPPs, GPM IMERG performed the best, followed by ERA-5 and gridMET, while NSE was more responsive to changes in SPPs and calibration methods than KGE and R². For the Cape Fear and Tar-Pamlico watersheds, the study highlighted SWAT+ 's challenges in predicting base flow for groundwater-driven systems and demonstrated the potential of optimization techniques to improve flow simulations despite poor satellite-gauge rainfall correlation. The combination of the GPM IMERG dataset and the GLUE method proved most effective, offering valuable guidance for selecting optimal datasets and methods to enhance prediction accuracy in complex watersheds.http://www.sciencedirect.com/science/article/pii/S221458182400483XHydrological ModelingSoil and Water Assessment Tool Plus (SWAT+)Satellite Precipitation Products (SPPs)Autocalibration TechniquesPerformance Indices |
| spellingShingle | Mahesh R. Tapas Randall Etheridge Thanh-Nhan-Duc Tran Manh-Hung Le Brian Hinckley Van Tam Nguyen Venkataraman Lakshmi Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model Journal of Hydrology: Regional Studies Hydrological Modeling Soil and Water Assessment Tool Plus (SWAT+) Satellite Precipitation Products (SPPs) Autocalibration Techniques Performance Indices |
| title | Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model |
| title_full | Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model |
| title_fullStr | Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model |
| title_full_unstemmed | Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model |
| title_short | Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model |
| title_sort | evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the swat model |
| topic | Hydrological Modeling Soil and Water Assessment Tool Plus (SWAT+) Satellite Precipitation Products (SPPs) Autocalibration Techniques Performance Indices |
| url | http://www.sciencedirect.com/science/article/pii/S221458182400483X |
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