Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models

Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the rea...
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Autor*in:	Nikhil Teja, Kalakuntla [verfasserIn] Manikanta, Velpuri [verfasserIn] Das, Jew [verfasserIn] Umamahesh, N.V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification

Übergeordnetes Werk:	Enthalten in: Journal of hydrology - Amsterdam [u.a.] : Elsevier, 1963, 625
Übergeordnetes Werk:	volume:625

DOI / URN:	10.1016/j.jhydrol.2023.130176

Katalog-ID:	ELV064885054

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520			\|a Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.
650		4	\|a Ensemble Prediction Systems
650		4	\|a Grand Ensemble
650		4	\|a Post-processing
650		4	\|a Multiple Hydrologic models
650		4	\|a Ensemble flood forecasting
650		4	\|a Verification
700	1		\|a Manikanta, Velpuri \|e verfasserin \|4 aut
700	1		\|a Das, Jew \|e verfasserin \|0 (orcid)0000-0002-0728-7708 \|4 aut
700	1		\|a Umamahesh, N.V. \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.jhydrol.2023.130176 doi (DE-627)ELV064885054 (ELSEVIER)S0022-1694(23)01118-6 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Nikhil Teja, Kalakuntla verfasserin aut Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead. Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification Manikanta, Velpuri verfasserin aut Das, Jew verfasserin (orcid)0000-0002-0728-7708 aut Umamahesh, N.V. verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 625 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 625
spelling	10.1016/j.jhydrol.2023.130176 doi (DE-627)ELV064885054 (ELSEVIER)S0022-1694(23)01118-6 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Nikhil Teja, Kalakuntla verfasserin aut Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead. Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification Manikanta, Velpuri verfasserin aut Das, Jew verfasserin (orcid)0000-0002-0728-7708 aut Umamahesh, N.V. verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 625 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 625
allfields_unstemmed	10.1016/j.jhydrol.2023.130176 doi (DE-627)ELV064885054 (ELSEVIER)S0022-1694(23)01118-6 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Nikhil Teja, Kalakuntla verfasserin aut Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead. Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification Manikanta, Velpuri verfasserin aut Das, Jew verfasserin (orcid)0000-0002-0728-7708 aut Umamahesh, N.V. verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 625 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 625
allfieldsGer	10.1016/j.jhydrol.2023.130176 doi (DE-627)ELV064885054 (ELSEVIER)S0022-1694(23)01118-6 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Nikhil Teja, Kalakuntla verfasserin aut Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead. Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification Manikanta, Velpuri verfasserin aut Das, Jew verfasserin (orcid)0000-0002-0728-7708 aut Umamahesh, N.V. verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 625 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 625
allfieldsSound	10.1016/j.jhydrol.2023.130176 doi (DE-627)ELV064885054 (ELSEVIER)S0022-1694(23)01118-6 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Nikhil Teja, Kalakuntla verfasserin aut Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead. Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification Manikanta, Velpuri verfasserin aut Das, Jew verfasserin (orcid)0000-0002-0728-7708 aut Umamahesh, N.V. verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 625 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 625
language	English
source	Enthalten in Journal of hydrology 625 volume:625
sourceStr	Enthalten in Journal of hydrology 625 volume:625
format_phy_str_mv	Article
bklname	Hydrologie: Allgemeines
institution	findex.gbv.de
topic_facet	Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification
dewey-raw	690
isfreeaccess_bool	false
container_title	Journal of hydrology
authorswithroles_txt_mv	Nikhil Teja, Kalakuntla @@aut@@ Manikanta, Velpuri @@aut@@ Das, Jew @@aut@@ Umamahesh, N.V. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	268761817
dewey-sort	3690
id	ELV064885054
language_de	englisch
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author	Nikhil Teja, Kalakuntla
spellingShingle	Nikhil Teja, Kalakuntla ddc 690 bkl 38.85 misc Ensemble Prediction Systems misc Grand Ensemble misc Post-processing misc Multiple Hydrologic models misc Ensemble flood forecasting misc Verification Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models
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topic_title	690 VZ 38.85 bkl Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models Ensemble Prediction Systems Grand Ensemble Post-processing Multiple Hydrologic models Ensemble flood forecasting Verification
topic	ddc 690 bkl 38.85 misc Ensemble Prediction Systems misc Grand Ensemble misc Post-processing misc Multiple Hydrologic models misc Ensemble flood forecasting misc Verification
topic_unstemmed	ddc 690 bkl 38.85 misc Ensemble Prediction Systems misc Grand Ensemble misc Post-processing misc Multiple Hydrologic models misc Ensemble flood forecasting misc Verification
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title	Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models
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title_full	Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models
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title_sort	enhancing the predictability of flood forecasts by combining numerical weather prediction ensembles with multiple hydrological models
title_auth	Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models
abstract	Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.
abstractGer	Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.
abstract_unstemmed	Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.
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title_short	Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models
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author2	Manikanta, Velpuri Das, Jew Umamahesh, N.V.
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doi_str	10.1016/j.jhydrol.2023.130176
up_date	2024-07-06T21:04:52.145Z
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Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models

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