Estimation of hidden climate indices controlling flood occurrence
Describing the space-time variability of hydrologic extremes and its relation to climate variability is important for several scientific and operational purposes. For instance, quantifying the natural variability of extremes is useful for detecting and attributing changes; identifying relevant clima...
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ftcemoa:oai:irsteadoc.irstea.fr:PUB00059830 2023-05-15T17:36:48+02:00 Estimation of hidden climate indices controlling flood occurrence Renard, B. IRSTEA LYON UR RIVERLY FRA 2018 application/pdf https://irsteadoc.irstea.fr/cemoa/PUB00059830 Français fre https://irsteadoc.irstea.fr/cemoa/PUB00059830 Date de dépôt: 2019-01-23 - Tous les documents et informations contenus dans la base CemOA Publications sont protégés en vertu du droit de propriété intellectuelle, en particulier par le droit d'auteur. La personne consultant la base CemOA Publications peut visualiser, reproduire, ou stocker des copies des publications, à condition que l'information soit seulement pour son usage personnel et non commercial. L'utilisation des travaux universitaires est soumise à autorisation préalable de leurs auteurs. Toute information relative au signalement d'une publication contenue dans CemOA Publications doit inclure la citation bibliographique usuelle : Nom du ou des auteurs, titre et source du document, date et URL de la notice (dc_identifier). 52672 CLIMAT INONDATION EVENEMENT EXTREME STATISTIQUE BAYESIENNE INCERTITUDE climate flooding extreme event bayesian statistics statistical uncertainty Conférence invitée 2018 ftcemoa 2021-06-29T12:23:20Z Describing the space-time variability of hydrologic extremes and its relation to climate variability is important for several scientific and operational purposes. For instance, quantifying the natural variability of extremes is useful for detecting and attributing changes; identifying relevant climate drivers opens the way for practical applications including seasonal forecasting, future projections or past reconstructions. Many studies have reported that hydrologic extremes are modulated by large-scale modes of climate variability such as the El Nino Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO), amongst many others. Consequently, climate indices have been frequently used as predictors in probabilistic models describing hydrological extremes. However, standard climate indices such as ENSO/NAO turn out to be very poor predictors in some regions. This does not imply that hydrologic extremes are unrelated to climate variability in such regions, but rather that this relation cannot be expressed through standard ENSO/NAO climate indices. Consequently, this presentation describes an innovative method to avoid relying on standard climate indices, based on the following idea: the relevant climate indices are effectively unknown (they are hidden), and they should therefore be estimated directly from hydrologic data. In statistical terms, this corresponds to a Bayesian hierarchical model describing hydrologic extremes, with hidden climates indices treated as latent variables. Once these hidden climate indices have been estimated, it is possible to assess whether they are linked with specific patterns in atmospheric or oceanic variables, and if so to make predictions conditioned on these variables. The hidden climate indices approach is illustrated using a flood occurrence dataset at 207 hydrometric stations in France. This case study first shows that extracting hidden climate indices from occurrence data alone is feasible. Moreover, hidden climate indices yield a reliable description of flood occurrence data, in particular their tendency to cluster in space. Lastly, some of the hidden climate indices are linked with specific patterns in atmospheric variables, making them interpretable in terms of climate variability and opening the way for predictive applications. Other/Unknown Material North Atlantic North Atlantic oscillation Irstea Publications et Bases documentaires (Irstea@doc/CemOA) |
institution |
Open Polar |
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Irstea Publications et Bases documentaires (Irstea@doc/CemOA) |
op_collection_id |
ftcemoa |
language |
French |
topic |
CLIMAT INONDATION EVENEMENT EXTREME STATISTIQUE BAYESIENNE INCERTITUDE climate flooding extreme event bayesian statistics statistical uncertainty |
spellingShingle |
CLIMAT INONDATION EVENEMENT EXTREME STATISTIQUE BAYESIENNE INCERTITUDE climate flooding extreme event bayesian statistics statistical uncertainty Renard, B. Estimation of hidden climate indices controlling flood occurrence |
topic_facet |
CLIMAT INONDATION EVENEMENT EXTREME STATISTIQUE BAYESIENNE INCERTITUDE climate flooding extreme event bayesian statistics statistical uncertainty |
description |
Describing the space-time variability of hydrologic extremes and its relation to climate variability is important for several scientific and operational purposes. For instance, quantifying the natural variability of extremes is useful for detecting and attributing changes; identifying relevant climate drivers opens the way for practical applications including seasonal forecasting, future projections or past reconstructions. Many studies have reported that hydrologic extremes are modulated by large-scale modes of climate variability such as the El Nino Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO), amongst many others. Consequently, climate indices have been frequently used as predictors in probabilistic models describing hydrological extremes. However, standard climate indices such as ENSO/NAO turn out to be very poor predictors in some regions. This does not imply that hydrologic extremes are unrelated to climate variability in such regions, but rather that this relation cannot be expressed through standard ENSO/NAO climate indices. Consequently, this presentation describes an innovative method to avoid relying on standard climate indices, based on the following idea: the relevant climate indices are effectively unknown (they are hidden), and they should therefore be estimated directly from hydrologic data. In statistical terms, this corresponds to a Bayesian hierarchical model describing hydrologic extremes, with hidden climates indices treated as latent variables. Once these hidden climate indices have been estimated, it is possible to assess whether they are linked with specific patterns in atmospheric or oceanic variables, and if so to make predictions conditioned on these variables. The hidden climate indices approach is illustrated using a flood occurrence dataset at 207 hydrometric stations in France. This case study first shows that extracting hidden climate indices from occurrence data alone is feasible. Moreover, hidden climate indices yield a reliable description of flood occurrence data, in particular their tendency to cluster in space. Lastly, some of the hidden climate indices are linked with specific patterns in atmospheric variables, making them interpretable in terms of climate variability and opening the way for predictive applications. |
author2 |
IRSTEA LYON UR RIVERLY FRA |
format |
Other/Unknown Material |
author |
Renard, B. |
author_facet |
Renard, B. |
author_sort |
Renard, B. |
title |
Estimation of hidden climate indices controlling flood occurrence |
title_short |
Estimation of hidden climate indices controlling flood occurrence |
title_full |
Estimation of hidden climate indices controlling flood occurrence |
title_fullStr |
Estimation of hidden climate indices controlling flood occurrence |
title_full_unstemmed |
Estimation of hidden climate indices controlling flood occurrence |
title_sort |
estimation of hidden climate indices controlling flood occurrence |
publishDate |
2018 |
url |
https://irsteadoc.irstea.fr/cemoa/PUB00059830 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
52672 |
op_relation |
https://irsteadoc.irstea.fr/cemoa/PUB00059830 |
op_rights |
Date de dépôt: 2019-01-23 - Tous les documents et informations contenus dans la base CemOA Publications sont protégés en vertu du droit de propriété intellectuelle, en particulier par le droit d'auteur. La personne consultant la base CemOA Publications peut visualiser, reproduire, ou stocker des copies des publications, à condition que l'information soit seulement pour son usage personnel et non commercial. L'utilisation des travaux universitaires est soumise à autorisation préalable de leurs auteurs. Toute information relative au signalement d'une publication contenue dans CemOA Publications doit inclure la citation bibliographique usuelle : Nom du ou des auteurs, titre et source du document, date et URL de la notice (dc_identifier). |
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1766136405960949760 |