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...

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Bibliographic Details
Published in:Water Resources Research
Main Authors: Renard, Benjamin, Lall, U.
Other Authors: 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
Language:English
Published: HAL CCSD 2014
Subjects:
HYDROLOGY
CLIMATE
STATISTICAL UNCERTAINTY
BAYESIAN STATISTICS
CLIMAT
HYDROLOGIE
STATISTIQUE BAYESIENNE
INCERTITUDE
[SDE]Environmental Sciences
North Atlantic
North Atlantic oscillation
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
Description
Summary: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.

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