Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir
Abstract Flood forecasting for reservoir operation is a complex and challenging subject. It is, however, fundamental for minimizing damage and maximizing economic efficiency in reservoir management. Currently, real-time flood forecasting represents an essential trend on a global scale. This study in...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | Applied Water Science |
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| Online Access: | https://doi.org/10.1007/s13201-025-02503-4 |
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| _version_ | 1849764440225873920 |
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| author | Chau Kim Tran Nguyen Dong Dang Dang Mai Nguyen Bac Thi Ngoc Nguyen Binh Thi Hoa Le Hoang Cong Vo Hien Phu La |
| author_facet | Chau Kim Tran Nguyen Dong Dang Dang Mai Nguyen Bac Thi Ngoc Nguyen Binh Thi Hoa Le Hoang Cong Vo Hien Phu La |
| author_sort | Chau Kim Tran |
| collection | DOAJ |
| description | Abstract Flood forecasting for reservoir operation is a complex and challenging subject. It is, however, fundamental for minimizing damage and maximizing economic efficiency in reservoir management. Currently, real-time flood forecasting represents an essential trend on a global scale. This study introduces a real-time flood forecasting approach using a time-varying parameter hydrological model, applied to forecast inflows to Ta Trach reservoir in the historical flood season in 2020. The model dynamically updates parameters to reflect basin conditions in every time steps. Notably, the study’s method achieves high accuracy with Nash–Sutcliffe Efficiency values of 99.32, 95.7, and 89.14% for 1-h, 3-h, and 6-h lead times, respectively. Results surpass traditional fixed parameter and artificial intelligence models. Moreover, requiring only updated rainfall and inflow data, the model is computationally efficient, compatible with existing infrastructure in research area. With these advantages, the method presented in the study has opened a new approach and is suitable for broader applications in flood flow forecasting. |
| format | Article |
| id | doaj-art-68c81782f0c142cd846d6fe7f1dafc0d |
| institution | DOAJ |
| issn | 2190-5487 2190-5495 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj-art-68c81782f0c142cd846d6fe7f1dafc0d2025-08-20T03:05:09ZengSpringerOpenApplied Water Science2190-54872190-54952025-05-0115711310.1007/s13201-025-02503-4Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoirChau Kim Tran0Nguyen Dong Dang1Dang Mai Nguyen2Bac Thi Ngoc Nguyen3Binh Thi Hoa Le4Hoang Cong Vo5Hien Phu La6GIS and Remote Sensing Application Group, Thuyloi UniversityGIS and Remote Sensing Application Group, Thuyloi UniversitySchool of International Education, Thuyloi UniversityGIS and Remote Sensing Application Group, Thuyloi UniversityGIS and Remote Sensing Application Group, Thuyloi UniversityGIS and Remote Sensing Application Group, Thuyloi UniversityGIS and Remote Sensing Application Group, Thuyloi UniversityAbstract Flood forecasting for reservoir operation is a complex and challenging subject. It is, however, fundamental for minimizing damage and maximizing economic efficiency in reservoir management. Currently, real-time flood forecasting represents an essential trend on a global scale. This study introduces a real-time flood forecasting approach using a time-varying parameter hydrological model, applied to forecast inflows to Ta Trach reservoir in the historical flood season in 2020. The model dynamically updates parameters to reflect basin conditions in every time steps. Notably, the study’s method achieves high accuracy with Nash–Sutcliffe Efficiency values of 99.32, 95.7, and 89.14% for 1-h, 3-h, and 6-h lead times, respectively. Results surpass traditional fixed parameter and artificial intelligence models. Moreover, requiring only updated rainfall and inflow data, the model is computationally efficient, compatible with existing infrastructure in research area. With these advantages, the method presented in the study has opened a new approach and is suitable for broader applications in flood flow forecasting.https://doi.org/10.1007/s13201-025-02503-4Real-time flood forecastingTime-varying parametersInflow dischargeHydrological modelTa Trach reservoir |
| spellingShingle | Chau Kim Tran Nguyen Dong Dang Dang Mai Nguyen Bac Thi Ngoc Nguyen Binh Thi Hoa Le Hoang Cong Vo Hien Phu La Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir Applied Water Science Real-time flood forecasting Time-varying parameters Inflow discharge Hydrological model Ta Trach reservoir |
| title | Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir |
| title_full | Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir |
| title_fullStr | Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir |
| title_full_unstemmed | Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir |
| title_short | Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir |
| title_sort | real time flood forecasting using time varying parameter hydrological model case study for ta trach reservoir |
| topic | Real-time flood forecasting Time-varying parameters Inflow discharge Hydrological model Ta Trach reservoir |
| url | https://doi.org/10.1007/s13201-025-02503-4 |
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