Going Beyond the Ensemble Mean: Assessment of Future Floods From Global Multi‐Models

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Going Beyond the Ensemble Mean: Assessment of Future Floods From Global Multi‐Models. / Giuntoli, Ignazio; Prosdocimi, Ilaria; Hannah, David M.

In: Water Resources Research, Vol. 57, No. 3, e2020WR027897, 01.03.2021.

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@article{fbc10aecf7c149bda6ff56d6ab64a2c8,
title = "Going Beyond the Ensemble Mean: Assessment of Future Floods From Global Multi‐Models",
abstract = "Future changes in the occurrence of flood events can be estimated using multi‐model ensembles to inform adaption and mitigation strategies. In the near future, these estimates could be used to guide the updating of exceedance probabilities for flood control design and water resources management. However, the estimate of return levels from ensemble experiments represents a challenge: model runs are affected by biases and uncertainties and by inconsistencies in simulated peak flows when compared with observed data. Moreover, extreme value distributions are generally fit to ensemble members individually and then averaged to obtain the ensemble fit with loss of information. To overcome these limitations, we propose a Bayesian hierarchical model for assessing changes in future peak flows, and the uncertainty coming from global climate, global impact models and their interaction. The model we propose allows use of all members of the ensemble at once for estimating changes in the parameters of an extreme value distribution from historical to future peak flows. The approach is applied to a set of grid‐cells in the eastern United States to the full and to a constrained version of the ensemble. We find that, while the dominant source of uncertainty in the changes varies across the domain, there is a consensus on a decrease in flood magnitudes toward the south. We conclude that projecting future flood magnitude under climate change remains elusive due to large uncertainty mostly coming from global models and from the intrinsic uncertain nature of extreme values.",
keywords = "Bayesian hierarchical models, future floods, multimodel ensembles, uncertainty",
author = "Ignazio Giuntoli and Ilaria Prosdocimi and Hannah, {David M.}",
year = "2021",
month = mar,
day = "1",
doi = "10.1029/2020WR027897",
language = "English",
volume = "57",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "American Geophysical Union",
number = "3",

}

RIS

TY - JOUR

T1 - Going Beyond the Ensemble Mean: Assessment of Future Floods From Global Multi‐Models

AU - Giuntoli, Ignazio

AU - Prosdocimi, Ilaria

AU - Hannah, David M.

PY - 2021/3/1

Y1 - 2021/3/1

N2 - Future changes in the occurrence of flood events can be estimated using multi‐model ensembles to inform adaption and mitigation strategies. In the near future, these estimates could be used to guide the updating of exceedance probabilities for flood control design and water resources management. However, the estimate of return levels from ensemble experiments represents a challenge: model runs are affected by biases and uncertainties and by inconsistencies in simulated peak flows when compared with observed data. Moreover, extreme value distributions are generally fit to ensemble members individually and then averaged to obtain the ensemble fit with loss of information. To overcome these limitations, we propose a Bayesian hierarchical model for assessing changes in future peak flows, and the uncertainty coming from global climate, global impact models and their interaction. The model we propose allows use of all members of the ensemble at once for estimating changes in the parameters of an extreme value distribution from historical to future peak flows. The approach is applied to a set of grid‐cells in the eastern United States to the full and to a constrained version of the ensemble. We find that, while the dominant source of uncertainty in the changes varies across the domain, there is a consensus on a decrease in flood magnitudes toward the south. We conclude that projecting future flood magnitude under climate change remains elusive due to large uncertainty mostly coming from global models and from the intrinsic uncertain nature of extreme values.

AB - Future changes in the occurrence of flood events can be estimated using multi‐model ensembles to inform adaption and mitigation strategies. In the near future, these estimates could be used to guide the updating of exceedance probabilities for flood control design and water resources management. However, the estimate of return levels from ensemble experiments represents a challenge: model runs are affected by biases and uncertainties and by inconsistencies in simulated peak flows when compared with observed data. Moreover, extreme value distributions are generally fit to ensemble members individually and then averaged to obtain the ensemble fit with loss of information. To overcome these limitations, we propose a Bayesian hierarchical model for assessing changes in future peak flows, and the uncertainty coming from global climate, global impact models and their interaction. The model we propose allows use of all members of the ensemble at once for estimating changes in the parameters of an extreme value distribution from historical to future peak flows. The approach is applied to a set of grid‐cells in the eastern United States to the full and to a constrained version of the ensemble. We find that, while the dominant source of uncertainty in the changes varies across the domain, there is a consensus on a decrease in flood magnitudes toward the south. We conclude that projecting future flood magnitude under climate change remains elusive due to large uncertainty mostly coming from global models and from the intrinsic uncertain nature of extreme values.

KW - Bayesian hierarchical models

KW - future floods

KW - multimodel ensembles

KW - uncertainty

U2 - 10.1029/2020WR027897

DO - 10.1029/2020WR027897

M3 - Article

VL - 57

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 3

M1 - e2020WR027897

ER -