Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information

We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the G...

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Published in:Scientific Reports
Main Authors: Steirou, Eva, Gerlitz, Lars, Sun, Xun, Apel, Heiko, Agarwal, Ankit, Totz, Sonja, Merz, Bruno
Format: Text
Language:English
Published: Nature Publishing Group UK 2022
Subjects:
Article
Sea ice
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357046/
http://www.ncbi.nlm.nih.gov/pubmed/35933510
https://doi.org/10.1038/s41598-022-16633-1
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spelling ftpubmed:oai:pubmedcentral.nih.gov:9357046 2023-05-15T18:18:20+02:00 Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information Steirou, Eva Gerlitz, Lars Sun, Xun Apel, Heiko Agarwal, Ankit Totz, Sonja Merz, Bruno 2022-08-06 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357046/ http://www.ncbi.nlm.nih.gov/pubmed/35933510 https://doi.org/10.1038/s41598-022-16633-1 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357046/ http://www.ncbi.nlm.nih.gov/pubmed/35933510 http://dx.doi.org/10.1038/s41598-022-16633-1 © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . CC-BY Sci Rep Article Text 2022 ftpubmed https://doi.org/10.1038/s41598-022-16633-1 2022-08-14T00:39:36Z We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the GEV location and scale parameters on 14 indices, which represent the season-ahead climate or catchment state. The comparison of these climate-informed models with the classical GEV distribution, with time-constant parameters, suggests that there is a substantial potential for seasonal forecasting of flood probabilities. The potential varies between seasons and regions. Overall, the season-ahead catchment wetness shows the highest potential, although climate indices based on large-scale atmospheric circulation, sea surface temperature or sea ice concentration also show some skill for certain regions and seasons. Spatially coherent patterns and a substantial fraction of climate-informed models are promising signs towards early alerts to increase flood preparedness already a season ahead. Text Sea ice PubMed Central (PMC) Scientific Reports 12 1
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Article
spellingShingle Article
Steirou, Eva
Gerlitz, Lars
Sun, Xun
Apel, Heiko
Agarwal, Ankit
Totz, Sonja
Merz, Bruno
Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information
topic_facet Article
description We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the GEV location and scale parameters on 14 indices, which represent the season-ahead climate or catchment state. The comparison of these climate-informed models with the classical GEV distribution, with time-constant parameters, suggests that there is a substantial potential for seasonal forecasting of flood probabilities. The potential varies between seasons and regions. Overall, the season-ahead catchment wetness shows the highest potential, although climate indices based on large-scale atmospheric circulation, sea surface temperature or sea ice concentration also show some skill for certain regions and seasons. Spatially coherent patterns and a substantial fraction of climate-informed models are promising signs towards early alerts to increase flood preparedness already a season ahead.
format Text
author Steirou, Eva
Gerlitz, Lars
Sun, Xun
Apel, Heiko
Agarwal, Ankit
Totz, Sonja
Merz, Bruno
author_facet Steirou, Eva
Gerlitz, Lars
Sun, Xun
Apel, Heiko
Agarwal, Ankit
Totz, Sonja
Merz, Bruno
author_sort Steirou, Eva
title Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information
title_short Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information
title_full Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information
title_fullStr Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information
title_full_unstemmed Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information
title_sort towards seasonal forecasting of flood probabilities in europe using climate and catchment information
publisher Nature Publishing Group UK
publishDate 2022
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357046/
http://www.ncbi.nlm.nih.gov/pubmed/35933510
https://doi.org/10.1038/s41598-022-16633-1
genre Sea ice
genre_facet Sea ice
op_source Sci Rep
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357046/
http://www.ncbi.nlm.nih.gov/pubmed/35933510
http://dx.doi.org/10.1038/s41598-022-16633-1
op_rights © The Author(s) 2022
https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
op_rightsnorm CC-BY
op_doi https://doi.org/10.1038/s41598-022-16633-1
container_title Scientific Reports
container_volume 12
container_issue 1
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