Preprints
https://doi.org/10.5194/hess-2020-606
https://doi.org/10.5194/hess-2020-606

  04 Dec 2020

04 Dec 2020

Review status: this preprint is currently under review for the journal HESS.

Uncertainties and their interaction in flood risk assessment with climate change

Hadush Meresa, Conor Murphy, Rowan Fealy, and Saeed Golian Hadush Meresa et al.
  • Irish Climate Analysis and Research UnitS (ICARUS), Dept. of Geography, Maynooth University, Maynooth, Ireland

Abstract. The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local scale changes. Understanding and quantifying this cascade is essential to developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four Irish catchments, incorporating within our modelling chain uncertainties associated with 12 Global Climate Models contained in the Coupled Model Inter-comparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood risk in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding related to changes in annual maximum precipitation. We use a sensitivity test based on the analysis of variance (ANOVA) to decompose uncertainties and their interactions in estimating selected flood quantiles in the 2080s for each catchment. We find that the dominant sources of uncertainty vary between catchments, calling into question the ability to generalise about the importance of different components of the cascade of uncertainty in future flood risk. For two of our catchments, uncertainties associated with bias correction methods and extreme value distributions outweigh the uncertainty associated with the ensemble of climate models. For all catchments and flood quantiles examined, hydrological model parameter uncertainty is the least important component of our modelling chain, while the uncertainties derived from the interaction of components are substantial (>20 percent of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall runoff processes. Future work that seeks to further explore the dominant components of uncertainty as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be included in climate change impact assessments.

Hadush Meresa et al.

 
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Hadush Meresa et al.

Hadush Meresa et al.

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