Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting
Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale fo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/10/2860 |
| _version_ | 1797550987219566592 |
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| author | Andrew R. Goenner Kristie J. Franz William A. Gallus Jr Brett Roberts |
| author_facet | Andrew R. Goenner Kristie J. Franz William A. Gallus Jr Brett Roberts |
| author_sort | Andrew R. Goenner |
| collection | DOAJ |
| description | Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil Moisture Accounting hydrologic model to generate probabilistic streamflow predictions. The technique was tested using both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast version 2.0 (HREF) for 11 river basins across the upper Midwest for 109 cases. The resulting discharges associated with low probability of exceedance values were too large; no events were observed having discharges above the 10% exceedance value predicted from the technique applied to both ensembles, and no events were observed having discharges above the 25% exceedance value from the HREF-based forecast. The large differences are due to using the same precipitation exceedance value at all points; it is unlikely that all watershed points would experience the heavy rainfall associated with the 5% probability of exceedance. The technique likely can be improved through calibration of the basin-average precipitation forecasts based on typical distributions of precipitation within the convective systems that dominate warm-season precipitation events or calibration of the resulting probabilistic discharge forecasts. |
| first_indexed | 2024-03-10T15:38:22Z |
| format | Article |
| id | doaj.art-43456671101a4992a35da0b4157c47dd |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T15:38:22Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-43456671101a4992a35da0b4157c47dd2023-11-20T17:03:17ZengMDPI AGWater2073-44412020-10-011210286010.3390/w12102860Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood ForecastingAndrew R. Goenner0Kristie J. Franz1William A. Gallus Jr2Brett Roberts3Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USADepartment of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USADepartment of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USACooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 73019, USAProbabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil Moisture Accounting hydrologic model to generate probabilistic streamflow predictions. The technique was tested using both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast version 2.0 (HREF) for 11 river basins across the upper Midwest for 109 cases. The resulting discharges associated with low probability of exceedance values were too large; no events were observed having discharges above the 10% exceedance value predicted from the technique applied to both ensembles, and no events were observed having discharges above the 25% exceedance value from the HREF-based forecast. The large differences are due to using the same precipitation exceedance value at all points; it is unlikely that all watershed points would experience the heavy rainfall associated with the 5% probability of exceedance. The technique likely can be improved through calibration of the basin-average precipitation forecasts based on typical distributions of precipitation within the convective systems that dominate warm-season precipitation events or calibration of the resulting probabilistic discharge forecasts.https://www.mdpi.com/2073-4441/12/10/2860probabilistic forecastingflood forecastingquantitative precipitation forecastsensemble prediction systemsforecast verification |
| spellingShingle | Andrew R. Goenner Kristie J. Franz William A. Gallus Jr Brett Roberts Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting Water probabilistic forecasting flood forecasting quantitative precipitation forecasts ensemble prediction systems forecast verification |
| title | Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting |
| title_full | Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting |
| title_fullStr | Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting |
| title_full_unstemmed | Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting |
| title_short | Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting |
| title_sort | evaluation of an application of probabilistic quantitative precipitation forecasts for flood forecasting |
| topic | probabilistic forecasting flood forecasting quantitative precipitation forecasts ensemble prediction systems forecast verification |
| url | https://www.mdpi.com/2073-4441/12/10/2860 |
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