Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam
The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-11-01
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| Series: | Water Practice and Technology |
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| Online Access: | http://wpt.iwaponline.com/content/18/11/2862 |
| _version_ | 1827614656939687936 |
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| author | Anant Patel S. M. Yadav |
| author_facet | Anant Patel S. M. Yadav |
| author_sort | Anant Patel |
| collection | DOAJ |
| description | The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's atmospheric-hydrologic ensemble flood forecasting model has been developed using TIGGE data. Precipitation can be reliably predicted by TIGGE's global ensemble numerical weather prediction (NWP) systems. By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts are produced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction together with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration and validation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneous extended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques. Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier's score performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82%, respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologic coupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood with reliability using peak time and flood warning probability distributions.
HIGHLIGHTS
Novel ensemble approach was proposed to predict reservoir inflow using the complex and dynamic rainfall–runoff model.;
Spatial, hydrological, and meteorological data were used as input for the semi-distributed hydrological model.;
Post-processing of ensemble data and combining the runoff results of the semi-distributed models to boost the overall efficiency.;
Improved flood forecasting and warning based on ensemble model.; |
| first_indexed | 2024-03-09T08:58:59Z |
| format | Article |
| id | doaj.art-332eb001a60940eabb8ebc08b8ebd991 |
| institution | Directory Open Access Journal |
| issn | 1751-231X |
| language | English |
| last_indexed | 2024-03-09T08:58:59Z |
| publishDate | 2023-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Practice and Technology |
| spelling | doaj.art-332eb001a60940eabb8ebc08b8ebd9912023-12-02T12:11:46ZengIWA PublishingWater Practice and Technology1751-231X2023-11-0118112862288310.2166/wpt.2023.178178Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi DamAnant Patel0S. M. Yadav1 Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, SVNIT-Surat, Gujarat, India Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, SVNIT-Surat, Gujarat, India The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's atmospheric-hydrologic ensemble flood forecasting model has been developed using TIGGE data. Precipitation can be reliably predicted by TIGGE's global ensemble numerical weather prediction (NWP) systems. By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts are produced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction together with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration and validation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneous extended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques. Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier's score performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82%, respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologic coupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood with reliability using peak time and flood warning probability distributions. HIGHLIGHTS Novel ensemble approach was proposed to predict reservoir inflow using the complex and dynamic rainfall–runoff model.; Spatial, hydrological, and meteorological data were used as input for the semi-distributed hydrological model.; Post-processing of ensemble data and combining the runoff results of the semi-distributed models to boost the overall efficiency.; Improved flood forecasting and warning based on ensemble model.;http://wpt.iwaponline.com/content/18/11/2862ecmwfensembleflood forecastinghydrological modelreservoir inflow |
| spellingShingle | Anant Patel S. M. Yadav Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam Water Practice and Technology ecmwf ensemble flood forecasting hydrological model reservoir inflow |
| title | Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam |
| title_full | Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam |
| title_fullStr | Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam |
| title_full_unstemmed | Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam |
| title_short | Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam |
| title_sort | development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for dharoi dam |
| topic | ecmwf ensemble flood forecasting hydrological model reservoir inflow |
| url | http://wpt.iwaponline.com/content/18/11/2862 |
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