The assessment of climate change impacts on water resources and flood
risk is typically underpinned by hydrological models calibrated and
selected based on observed streamflow records. Yet, changes in climate
are rarely accounted for when selecting hydrological models, which
compromises their ability to robustly represent future changes in
catchment hydrology. In this paper, we test a simple framework for
selecting an ensemble of calibrated hydrological model structures in
catchments where changing climatic conditions have been observed. We
start by considering 78 model structures produced using the FUSE modular
modelling framework and rely on a Pareto scheme to select model
structures maximizing model efficiency in both wet and dry periods. The
application of this approach in three case study basins in Peru enables
the identification of structures with good robustness, but also good
performance according to hydrological signatures not used for model
selection. We also highlight that some model structures that perform
well according to traditional efficiency metrics have low performance in
contrasting climates or suspicious internal states and fluxes.
Importantly, the model selection approach followed here helps to reduce
the spread in precipitation elasticities and temperature sensitivities,
providing a clearer picture of future hydrological changes. Overall,
this work demonstrates the potential of using contrasting climatic
conditions in a multi-objective framework to produce robust and credible
simulations, and to constrain structural uncertainties in hydrological
projections.