Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields
International audience Many studies report that hydrologic regimes are modulated by large-scale modes of climate variability such as the El Niño Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). Climate-informed frequency analysis models have therefore been proposed to condition t...
Published in: | Water Resources Research |
---|---|
Main Authors: | , |
Other Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2014
|
Subjects: | |
Online Access: | https://hal.archives-ouvertes.fr/hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599/document https://hal.archives-ouvertes.fr/hal-01192599/file/ly2014-pub00043038.pdf https://doi.org/10.1002/2014WR016277 |
id |
ftccsdartic:oai:HAL:hal-01192599v1 |
---|---|
record_format |
openpolar |
spelling |
ftccsdartic:oai:HAL:hal-01192599v1 2023-05-15T17:31:31+02:00 Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields Renard, Benjamin Lall, U. Hydrologie-Hydraulique (UR HHLY) Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) Columbia University New York 2014 https://hal.archives-ouvertes.fr/hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599/document https://hal.archives-ouvertes.fr/hal-01192599/file/ly2014-pub00043038.pdf https://doi.org/10.1002/2014WR016277 en eng HAL CCSD American Geophysical Union info:eu-repo/semantics/altIdentifier/doi/10.1002/2014WR016277 hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599/document https://hal.archives-ouvertes.fr/hal-01192599/file/ly2014-pub00043038.pdf doi:10.1002/2014WR016277 IRSTEA: PUB00043038 info:eu-repo/semantics/OpenAccess ISSN: 0043-1397 EISSN: 1944-7973 Water Resources Research https://hal.archives-ouvertes.fr/hal-01192599 Water Resources Research, American Geophysical Union, 2014, 50, 19 p. ⟨10.1002/2014WR016277⟩ HYDROLOGY CLIMATE STATISTICAL UNCERTAINTY BAYESIAN STATISTICS CLIMAT HYDROLOGIE STATISTIQUE BAYESIENNE INCERTITUDE [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2014 ftccsdartic https://doi.org/10.1002/2014WR016277 2021-03-21T00:05:58Z International audience Many studies report that hydrologic regimes are modulated by large-scale modes of climate variability such as the El Niño Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). Climate-informed frequency analysis models have therefore been proposed to condition the distribution of hydrologic variables on climate indices. However, standard climate indices may be poor predictors in some regions. This paper therefore describes a regional frequency analysis framework that conditions the distribution of hydrologic variables directly on atmospheric or oceanic fields, as opposed to predefined climate indices. This framework is based on a two-level probabilistic model describing both climate and hydrologic data. The climate data set (predictor) is typically a time series of atmospheric of oceanic fields defined on a grid over some area, while the hydrologic data set (predictand) is typically a regional data set of station data (e.g., annual average flow at several gauging stations). A Bayesian estimation framework is used, so that a natural quantification of uncertainties affecting hydrologic predictions is available. A case study aimed at predicting the number of autumn flood events in 16 catchments located in Mediterranean France using geopotential heights at 500 hPa over the North-Atlantic region is presented. The temporal variability of hydrologic data is shown to be associated with a particular spatial pattern in the geopotential heights. A cross-validation experiment indicates that the resulting probabilistic climate-informed predictions are skillful: their reliability is acceptable and they are much sharper than predictions based on standard climate indices and baseline predictions that ignore climate information. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Water Resources Research 50 12 9536 9554 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
HYDROLOGY CLIMATE STATISTICAL UNCERTAINTY BAYESIAN STATISTICS CLIMAT HYDROLOGIE STATISTIQUE BAYESIENNE INCERTITUDE [SDE]Environmental Sciences |
spellingShingle |
HYDROLOGY CLIMATE STATISTICAL UNCERTAINTY BAYESIAN STATISTICS CLIMAT HYDROLOGIE STATISTIQUE BAYESIENNE INCERTITUDE [SDE]Environmental Sciences Renard, Benjamin Lall, U. Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
topic_facet |
HYDROLOGY CLIMATE STATISTICAL UNCERTAINTY BAYESIAN STATISTICS CLIMAT HYDROLOGIE STATISTIQUE BAYESIENNE INCERTITUDE [SDE]Environmental Sciences |
description |
International audience Many studies report that hydrologic regimes are modulated by large-scale modes of climate variability such as the El Niño Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). Climate-informed frequency analysis models have therefore been proposed to condition the distribution of hydrologic variables on climate indices. However, standard climate indices may be poor predictors in some regions. This paper therefore describes a regional frequency analysis framework that conditions the distribution of hydrologic variables directly on atmospheric or oceanic fields, as opposed to predefined climate indices. This framework is based on a two-level probabilistic model describing both climate and hydrologic data. The climate data set (predictor) is typically a time series of atmospheric of oceanic fields defined on a grid over some area, while the hydrologic data set (predictand) is typically a regional data set of station data (e.g., annual average flow at several gauging stations). A Bayesian estimation framework is used, so that a natural quantification of uncertainties affecting hydrologic predictions is available. A case study aimed at predicting the number of autumn flood events in 16 catchments located in Mediterranean France using geopotential heights at 500 hPa over the North-Atlantic region is presented. The temporal variability of hydrologic data is shown to be associated with a particular spatial pattern in the geopotential heights. A cross-validation experiment indicates that the resulting probabilistic climate-informed predictions are skillful: their reliability is acceptable and they are much sharper than predictions based on standard climate indices and baseline predictions that ignore climate information. |
author2 |
Hydrologie-Hydraulique (UR HHLY) Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) Columbia University New York |
format |
Article in Journal/Newspaper |
author |
Renard, Benjamin Lall, U. |
author_facet |
Renard, Benjamin Lall, U. |
author_sort |
Renard, Benjamin |
title |
Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
title_short |
Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
title_full |
Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
title_fullStr |
Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
title_full_unstemmed |
Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
title_sort |
regional frequency analysis conditioned on large-scale atmospheric or oceanic fields |
publisher |
HAL CCSD |
publishDate |
2014 |
url |
https://hal.archives-ouvertes.fr/hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599/document https://hal.archives-ouvertes.fr/hal-01192599/file/ly2014-pub00043038.pdf https://doi.org/10.1002/2014WR016277 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
ISSN: 0043-1397 EISSN: 1944-7973 Water Resources Research https://hal.archives-ouvertes.fr/hal-01192599 Water Resources Research, American Geophysical Union, 2014, 50, 19 p. ⟨10.1002/2014WR016277⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1002/2014WR016277 hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599 https://hal.archives-ouvertes.fr/hal-01192599/document https://hal.archives-ouvertes.fr/hal-01192599/file/ly2014-pub00043038.pdf doi:10.1002/2014WR016277 IRSTEA: PUB00043038 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1002/2014WR016277 |
container_title |
Water Resources Research |
container_volume |
50 |
container_issue |
12 |
container_start_page |
9536 |
op_container_end_page |
9554 |
_version_ |
1766129152927203328 |