Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations
Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite in some locations global...
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ftuniveneziairis:oai:iris.unive.it:10278/3748307 2024-04-14T08:16:05+00:00 Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations Giuliani M. Zaniolo M. Castelletti A. Davoli G. Block P. Giuliani, M. Zaniolo, M. Castelletti, A. Davoli, G. Block, P. 2019 http://hdl.handle.net/10278/3748307 https://doi.org/10.1029/2019WR025035 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000496649400001 volume:55 issue:11 firstpage:9133 lastpage:9147 numberofpages:15 journal:WATER RESOURCES RESEARCH http://hdl.handle.net/10278/3748307 doi:10.1029/2019WR025035 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075188705 artificial intelligence climate teleconnection optimal reservoir operation seasonal forecast Settore FIS/06 - Fisica per il Sistema Terra e Il Mezzo Circumterrestre info:eu-repo/semantics/article 2019 ftuniveneziairis https://doi.org/10.1029/2019WR025035 2024-03-21T18:20:11Z Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected, and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance. We apply the framework to the Lake Como basin, a regulated lake in northern Italy mainly operated for flood control and irrigation supply. Numerical results show the existence of notable teleconnection patterns dependent on both ENSO and the North Atlantic Oscillation over the Alpine region, which contribute in generating skilful seasonal precipitation and hydrologic forecasts. The use of this information for conditioning the lake operations produces an average 44% improvement in system performance with respect to a baseline solution not informed by any forecast, with this gain that further increases during extreme drought episodes. Our results also suggest that observed preseason sea surface temperature anomalies appear more valuable than hydrologic-based seasonal forecasts, producing an average 59% improvement in system performance. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Università Ca’ Foscari Venezia: ARCA (Archivio Istituzionale della Ricerca) Water Resources Research 55 11 9133 9147 |
institution |
Open Polar |
collection |
Università Ca’ Foscari Venezia: ARCA (Archivio Istituzionale della Ricerca) |
op_collection_id |
ftuniveneziairis |
language |
English |
topic |
artificial intelligence climate teleconnection optimal reservoir operation seasonal forecast Settore FIS/06 - Fisica per il Sistema Terra e Il Mezzo Circumterrestre |
spellingShingle |
artificial intelligence climate teleconnection optimal reservoir operation seasonal forecast Settore FIS/06 - Fisica per il Sistema Terra e Il Mezzo Circumterrestre Giuliani M. Zaniolo M. Castelletti A. Davoli G. Block P. Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
topic_facet |
artificial intelligence climate teleconnection optimal reservoir operation seasonal forecast Settore FIS/06 - Fisica per il Sistema Terra e Il Mezzo Circumterrestre |
description |
Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected, and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance. We apply the framework to the Lake Como basin, a regulated lake in northern Italy mainly operated for flood control and irrigation supply. Numerical results show the existence of notable teleconnection patterns dependent on both ENSO and the North Atlantic Oscillation over the Alpine region, which contribute in generating skilful seasonal precipitation and hydrologic forecasts. The use of this information for conditioning the lake operations produces an average 44% improvement in system performance with respect to a baseline solution not informed by any forecast, with this gain that further increases during extreme drought episodes. Our results also suggest that observed preseason sea surface temperature anomalies appear more valuable than hydrologic-based seasonal forecasts, producing an average 59% improvement in system performance. |
author2 |
Giuliani, M. Zaniolo, M. Castelletti, A. Davoli, G. Block, P. |
format |
Article in Journal/Newspaper |
author |
Giuliani M. Zaniolo M. Castelletti A. Davoli G. Block P. |
author_facet |
Giuliani M. Zaniolo M. Castelletti A. Davoli G. Block P. |
author_sort |
Giuliani M. |
title |
Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
title_short |
Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
title_full |
Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
title_fullStr |
Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
title_full_unstemmed |
Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
title_sort |
detecting the state of the climate system via artificial intelligence to improve seasonal forecasts and inform reservoir operations |
publishDate |
2019 |
url |
http://hdl.handle.net/10278/3748307 https://doi.org/10.1029/2019WR025035 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000496649400001 volume:55 issue:11 firstpage:9133 lastpage:9147 numberofpages:15 journal:WATER RESOURCES RESEARCH http://hdl.handle.net/10278/3748307 doi:10.1029/2019WR025035 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075188705 |
op_doi |
https://doi.org/10.1029/2019WR025035 |
container_title |
Water Resources Research |
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55 |
container_issue |
11 |
container_start_page |
9133 |
op_container_end_page |
9147 |
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