Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS
During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This st...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2014/581756 |
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| author | Sheng-Chi Yang Tsun-Hua Yang |
| author_facet | Sheng-Chi Yang Tsun-Hua Yang |
| author_sort | Sheng-Chi Yang |
| collection | DOAJ |
| description | During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs. |
| format | Article |
| id | doaj-art-337b64c661fe44fba6b9cab6aac6d43a |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-337b64c661fe44fba6b9cab6aac6d43a2025-02-03T01:13:00ZengWileyAdvances in Meteorology1687-93091687-93172014-01-01201410.1155/2014/581756581756Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMSSheng-Chi Yang0Tsun-Hua Yang1Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, 11 F, No. 97, Section 1, Roosevelt Road, Zhongzheng District, Taipei 10093, TaiwanTaiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, 11 F, No. 97, Section 1, Roosevelt Road, Zhongzheng District, Taipei 10093, TaiwanDuring an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs.http://dx.doi.org/10.1155/2014/581756 |
| spellingShingle | Sheng-Chi Yang Tsun-Hua Yang Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS Advances in Meteorology |
| title | Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS |
| title_full | Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS |
| title_fullStr | Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS |
| title_full_unstemmed | Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS |
| title_short | Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS |
| title_sort | uncertainty assessment reservoir inflow forecasting with ensemble precipitation forecasts and hec hms |
| url | http://dx.doi.org/10.1155/2014/581756 |
| work_keys_str_mv | AT shengchiyang uncertaintyassessmentreservoirinflowforecastingwithensembleprecipitationforecastsandhechms AT tsunhuayang uncertaintyassessmentreservoirinflowforecastingwithensembleprecipitationforecastsandhechms |