Evaluating climate model ensembles design for hydrological impact assessment: uncertainty attribution, transferability, and weighting

Abstract

Understanding the impacts of climate change on water availability and hydrological extremes is critical for effective water resources planning. Hydrological impact assessments rely heavily on global climate model (GCM) ensembles to quantify future changes and their associated uncertainties. The use of multi-model ensembles (MMEs), however, presents several methodological challenges, including model selection, uncertainty attribution, and ensemble weighting. Selecting a reduced subset from an ever-growing pool of GCMs introduces methodological trade-offs between computational feasibility and ensemble representativeness. Similarly, weighting the individual GCMs by performance or by independence affects the outcome as well as its uncertainty limits. Yet, despite the critical role of these decisions, there is little consensus on best practices, and the influence of these design strategies on hydrological projections remains underexplored. To tackle these issues, three specific research objectives are pursued in this thesis: (1) to quantify the impact of GCM selection based on climate indices on uncertainty transferability to hydrological projections; (2) to examine the hydrological implications of including or excluding high-sensitivity climate models in multi-model ensembles; and (3) to compare the effects of different GCM weighting schemes on the uncertainty of future streamflow projections. Rather than promoting a single optimal strategy, the objective is to understand how different ensemble design choices affect the propagation of climate uncertainty into hydrological space. The first analysis investigates the transferability of climate uncertainty to hydrological outputs by applying sampling methods such as the KKZ algorithm to sub-select climate models based on temperature and precipitation indices. This experiment was conducted across 3,540 North American catchments using 20 CMIP5 GCMs, two bias correction methods and three conceptual hydrological models. Results show that when carefully designed, reduced ensembles can retain most of the spread observed in streamflow projections derived from the full ensemble. However, the translation of uncertainty is non-uniform and nonlinear, meaning small differences in climate inputs, particularly precipitation, may result in large variations in streamflow, especially for high and low flow regimes. Secondly, the thesis examines the effect of excluding high Equilibrium Climate Sensitivity (ECS) models, referred to as “hot” models, on projected streamflow. Exclusion of these models reduces the spread of projected streamflow changes in some regions such as Alaska, southwestern U.S., and parts of Canada, but increased it in others, highlighting the need to evaluate GCMs using region-specific, rather than global, criteria. Finally, the thesis assesses the performance of weighting schemes in GCMs through a pseudoreality experiment, where each of the GCMs is, in turn, simulated as the “true” future. This allows an objective comparison of weighting performance against a known target in future where the true reality is unknown. The analysis applies six weighting approaches to an ensemble of 22 CMIP6 GCMs, coupled with a hydrological model across 3,107 North American catchments. Results indicate that unequal weighting by historical temperature and precipitation improves climate variable projections' quality. But for streamflow, these improvements are blunted, particularly if bias correction has been applied to inputs. This thesis provides new insights into the design of climate model ensembles for hydrological impact assessments. It emphasizes that ensemble construction should not be based solely on climate performance metrics, but must incorporate impact-relevant behavior such as streamflow variability and seasonality. The findings advocate for a more pragmatic approach to ensemble design, balancing model diversity, computational efficiency, and relevance to the intended application.

Date	18 Nov 2025
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	François Brissette (Supervisor)