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

Ignazio Giuntoli,Ilaria Prosdocimi,David M Hannah

doi:10.1029/2020wr027897

Abstract

AbstractFuture 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.

Highlights

A warming climate is expected to intensify the global water cycle with changes in the occurrence and severity of extreme events like intense precipitations and floodings [Lavell et al, 2012; Abbott et al, 2019]
For the first step we propose an improved way to assess changes in flood magnitude using multi-model ensembles that goes beyond expressing changes through the ensemble mean, which cancels out information on model consensus and reduces the signal across multiple members to a single value
For the second step, using the ISIMIP multi-model ensemble – already employed in future high flows studies [Dankers et al, 2014; Giuntoli et al, 2015b; Dottori et al, 2018] – we focus on the eastern half of the United States where observed data are available in catchments large enough to be compared to corresponding model grid-cells

Summary

Introduction

A warming climate is expected to intensify the global water cycle with changes in the occurrence and severity of extreme events like intense precipitations and floodings [Lavell et al, 2012; Abbott et al, 2019]. The main components of flood risk [Crichton, 1999] are expected to increase: flood hazard (as a result of increased energy in the system and of an intensified water cycle), flood exposure of people and assets (owing to global population growth and cities becoming more urbanized) and flood vulnerability (especially in overpopulated regions with low preparedness and poor infrastructure) [Oppenheimer et al, 2014] In this context, assessing changes in future floods is crucial to inform adaptation and mitigation strategies aimed at protecting human life, vulnerable ecosystems, human wellbeing, agricultural land, homes and other socio-economic assets. Two practical examples are the likely increase in pluvial flooding, as a result of more frequent intense precipitation events under climate change [Pendergrass, 2018]; and the reduction and shift in time of the annual spring flood in snow dominated catchments, as a result of reduced snow pack [Musselman et al, 2018]

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