Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations
AbstractIncreasingly 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 longâ€term predictions to timely prompt anticipatory operations. Despite in some locatio...
Published in: | Water Resources Research |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://www.openaccessrepository.it/record/154406 https://doi.org/10.1029/2019wr025035 |
id |
ftopenaccessrep:oai:zenodo.org:154406 |
---|---|
record_format |
openpolar |
spelling |
ftopenaccessrep:oai:zenodo.org:154406 2024-05-12T08:08:12+00:00 Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations Giuliani, Matteo Zaniolo, Marta Castelletti, Andrea Davoli, Guido Block, Paul 2019-10-21 https://www.openaccessrepository.it/record/154406 https://doi.org/10.1029/2019wr025035 eng eng url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/154406 doi:10.1029/2019wr025035 info:eu-repo/semantics/openAccess Water Science and Technology info:eu-repo/semantics/article publication-article 2019 ftopenaccessrep https://doi.org/10.1029/2019wr025035 2024-04-17T15:10:33Z AbstractIncreasingly 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 longâ€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 hydrologicâ€based seasonal forecasts, producing an average 59% improvement in system performance. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository Water Resources Research 55 11 9133 9147 |
institution |
Open Polar |
collection |
Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository |
op_collection_id |
ftopenaccessrep |
language |
English |
topic |
Water Science and Technology |
spellingShingle |
Water Science and Technology Giuliani, Matteo Zaniolo, Marta Castelletti, Andrea Davoli, Guido Block, Paul Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations |
topic_facet |
Water Science and Technology |
description |
AbstractIncreasingly 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 longâ€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 hydrologicâ€based seasonal forecasts, producing an average 59% improvement in system performance. |
format |
Article in Journal/Newspaper |
author |
Giuliani, Matteo Zaniolo, Marta Castelletti, Andrea Davoli, Guido Block, Paul |
author_facet |
Giuliani, Matteo Zaniolo, Marta Castelletti, Andrea Davoli, Guido Block, Paul |
author_sort |
Giuliani, Matteo |
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 |
https://www.openaccessrepository.it/record/154406 https://doi.org/10.1029/2019wr025035 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/154406 doi:10.1029/2019wr025035 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1029/2019wr025035 |
container_title |
Water Resources Research |
container_volume |
55 |
container_issue |
11 |
container_start_page |
9133 |
op_container_end_page |
9147 |
_version_ |
1798851118469480448 |