Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland
Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these f...
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American Meteorological Society
2023
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Online Access: | https://publikationen.bibliothek.kit.edu/1000163785 https://publikationen.bibliothek.kit.edu/1000163785/151620932 https://doi.org/10.5445/IR/1000163785 |
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ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000163785 2024-01-21T10:08:40+01:00 Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland Chang, Annie Y.-Y. Bogner, Konrad Grams, Christian M. Monhart, Samuel Domeisen, Daniela I. V. Zappa, Massimiliano 2023-11-03 application/pdf https://publikationen.bibliothek.kit.edu/1000163785 https://publikationen.bibliothek.kit.edu/1000163785/151620932 https://doi.org/10.5445/IR/1000163785 eng eng American Meteorological Society info:eu-repo/semantics/altIdentifier/wos/001106641700001 info:eu-repo/semantics/altIdentifier/doi/10.1175/JHM-D-21-0245.1 info:eu-repo/semantics/altIdentifier/issn/1525-755X info:eu-repo/semantics/altIdentifier/issn/1525-7541 https://publikationen.bibliothek.kit.edu/1000163785 https://publikationen.bibliothek.kit.edu/1000163785/151620932 https://doi.org/10.5445/IR/1000163785 https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess Journal of Hydrometeorology, 24 (10), 1597–1617 ISSN: 1525-755X, 1525-7541 Climate classification/regimes Hydrology Operational forecasting Machine learning Ensembles ddc:550 Earth sciences info:eu-repo/classification/ddc/550 doc-type:article Text info:eu-repo/semantics/article article info:eu-repo/semantics/publishedVersion 2023 ftubkarlsruhe https://doi.org/10.5445/IR/100016378510.1175/JHM-D-21-0245.1 2023-12-24T23:09:31Z Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode. Article in Journal/Newspaper North Atlantic KITopen (Karlsruhe Institute of Technologie) |
institution |
Open Polar |
collection |
KITopen (Karlsruhe Institute of Technologie) |
op_collection_id |
ftubkarlsruhe |
language |
English |
topic |
Climate classification/regimes Hydrology Operational forecasting Machine learning Ensembles ddc:550 Earth sciences info:eu-repo/classification/ddc/550 |
spellingShingle |
Climate classification/regimes Hydrology Operational forecasting Machine learning Ensembles ddc:550 Earth sciences info:eu-repo/classification/ddc/550 Chang, Annie Y.-Y. Bogner, Konrad Grams, Christian M. Monhart, Samuel Domeisen, Daniela I. V. Zappa, Massimiliano Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland |
topic_facet |
Climate classification/regimes Hydrology Operational forecasting Machine learning Ensembles ddc:550 Earth sciences info:eu-repo/classification/ddc/550 |
description |
Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode. |
format |
Article in Journal/Newspaper |
author |
Chang, Annie Y.-Y. Bogner, Konrad Grams, Christian M. Monhart, Samuel Domeisen, Daniela I. V. Zappa, Massimiliano |
author_facet |
Chang, Annie Y.-Y. Bogner, Konrad Grams, Christian M. Monhart, Samuel Domeisen, Daniela I. V. Zappa, Massimiliano |
author_sort |
Chang, Annie Y.-Y. |
title |
Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland |
title_short |
Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland |
title_full |
Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland |
title_fullStr |
Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland |
title_full_unstemmed |
Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland |
title_sort |
exploring the use of european weather regimes for improving user-relevant hydrological forecasts at the subseasonal scale in switzerland |
publisher |
American Meteorological Society |
publishDate |
2023 |
url |
https://publikationen.bibliothek.kit.edu/1000163785 https://publikationen.bibliothek.kit.edu/1000163785/151620932 https://doi.org/10.5445/IR/1000163785 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Journal of Hydrometeorology, 24 (10), 1597–1617 ISSN: 1525-755X, 1525-7541 |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/001106641700001 info:eu-repo/semantics/altIdentifier/doi/10.1175/JHM-D-21-0245.1 info:eu-repo/semantics/altIdentifier/issn/1525-755X info:eu-repo/semantics/altIdentifier/issn/1525-7541 https://publikationen.bibliothek.kit.edu/1000163785 https://publikationen.bibliothek.kit.edu/1000163785/151620932 https://doi.org/10.5445/IR/1000163785 |
op_rights |
https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5445/IR/100016378510.1175/JHM-D-21-0245.1 |
_version_ |
1788699467819515904 |