Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events
Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction meth...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tri, Doan Quang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 15(2022), 18 vom: Sept. |
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| Übergeordnetes Werk: |
volume:15 ; year:2022 ; number:18 ; month:09 |
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| DOI / URN: |
10.1007/s12517-022-10801-3 |
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| Katalog-ID: |
SPR048126209 |
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| 520 | |a Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. | ||
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| 700 | 1 | |a Thai, Tran Hong |4 aut | |
| 700 | 1 | |a Van Hoa, Vo |4 aut | |
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10.1007/s12517-022-10801-3 doi (DE-627)SPR048126209 (SPR)s12517-022-10801-3-e DE-627 ger DE-627 rakwb eng Tri, Doan Quang verfasserin (orcid)0000-0003-2376-3222 aut Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. Bias-correction (dpeaa)DE-He213 IFS rainfall forecasts (dpeaa)DE-He213 Couple MIKE SHE-MIKE 11 (dpeaa)DE-He213 Flood forecasting (dpeaa)DE-He213 Thach Han River basin (dpeaa)DE-He213 Thai, Tran Hong aut Van Hoa, Vo aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 18 vom: Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:18 month:09 https://dx.doi.org/10.1007/s12517-022-10801-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 18 09 |
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10.1007/s12517-022-10801-3 doi (DE-627)SPR048126209 (SPR)s12517-022-10801-3-e DE-627 ger DE-627 rakwb eng Tri, Doan Quang verfasserin (orcid)0000-0003-2376-3222 aut Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. Bias-correction (dpeaa)DE-He213 IFS rainfall forecasts (dpeaa)DE-He213 Couple MIKE SHE-MIKE 11 (dpeaa)DE-He213 Flood forecasting (dpeaa)DE-He213 Thach Han River basin (dpeaa)DE-He213 Thai, Tran Hong aut Van Hoa, Vo aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 18 vom: Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:18 month:09 https://dx.doi.org/10.1007/s12517-022-10801-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 18 09 |
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10.1007/s12517-022-10801-3 doi (DE-627)SPR048126209 (SPR)s12517-022-10801-3-e DE-627 ger DE-627 rakwb eng Tri, Doan Quang verfasserin (orcid)0000-0003-2376-3222 aut Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. Bias-correction (dpeaa)DE-He213 IFS rainfall forecasts (dpeaa)DE-He213 Couple MIKE SHE-MIKE 11 (dpeaa)DE-He213 Flood forecasting (dpeaa)DE-He213 Thach Han River basin (dpeaa)DE-He213 Thai, Tran Hong aut Van Hoa, Vo aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 18 vom: Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:18 month:09 https://dx.doi.org/10.1007/s12517-022-10801-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 18 09 |
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10.1007/s12517-022-10801-3 doi (DE-627)SPR048126209 (SPR)s12517-022-10801-3-e DE-627 ger DE-627 rakwb eng Tri, Doan Quang verfasserin (orcid)0000-0003-2376-3222 aut Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. Bias-correction (dpeaa)DE-He213 IFS rainfall forecasts (dpeaa)DE-He213 Couple MIKE SHE-MIKE 11 (dpeaa)DE-He213 Flood forecasting (dpeaa)DE-He213 Thach Han River basin (dpeaa)DE-He213 Thai, Tran Hong aut Van Hoa, Vo aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 18 vom: Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:18 month:09 https://dx.doi.org/10.1007/s12517-022-10801-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 18 09 |
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10.1007/s12517-022-10801-3 doi (DE-627)SPR048126209 (SPR)s12517-022-10801-3-e DE-627 ger DE-627 rakwb eng Tri, Doan Quang verfasserin (orcid)0000-0003-2376-3222 aut Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. Bias-correction (dpeaa)DE-He213 IFS rainfall forecasts (dpeaa)DE-He213 Couple MIKE SHE-MIKE 11 (dpeaa)DE-He213 Flood forecasting (dpeaa)DE-He213 Thach Han River basin (dpeaa)DE-He213 Thai, Tran Hong aut Van Hoa, Vo aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 18 vom: Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:18 month:09 https://dx.doi.org/10.1007/s12517-022-10801-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 18 09 |
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. 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Tri, Doan Quang |
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Tri, Doan Quang misc Bias-correction misc IFS rainfall forecasts misc Couple MIKE SHE-MIKE 11 misc Flood forecasting misc Thach Han River basin Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events |
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Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events Bias-correction (dpeaa)DE-He213 IFS rainfall forecasts (dpeaa)DE-He213 Couple MIKE SHE-MIKE 11 (dpeaa)DE-He213 Flood forecasting (dpeaa)DE-He213 Thach Han River basin (dpeaa)DE-He213 |
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bias-correction data of ifs rainfall forecasts for hydrological and hydraulic models to forecast flood events |
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Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events |
| abstract |
Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins. © Saudi Society for Geosciences 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events |
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https://dx.doi.org/10.1007/s12517-022-10801-3 |
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Thai, Tran Hong Van Hoa, Vo |
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10.1007/s12517-022-10801-3 |
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2024-07-03T17:10:58.559Z |
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|
| score |
7.401005 |