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This study aims to evaluate the effects of the Canadian Regional Climate Model’s (CRCM) spatial resolution on summer-fall floods simulation. Seven different climate simulations issued from the fourth and the fifth version of the CRCM are employed. Four different climate simulations issued from the fourth version of the CRCM (CRCM4) are compared. They are composed of two simulations driven by the Canadian General Circulation Model (CGCM) and two driven by the ERA-40c reanalysis using grid meshes of 15 km and 45 km resolutions for each driver. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim at 0.44° (≈ 48 km), 0.22° (≈ 24 km) and 0.11° (≈ 12 km) spatial resolutions are used. All comparisons are evaluated on a daily time-step for the 1961-1990 period (for CRCM4) and for the 1981-2010 period (for CRCM5). These seven simulations (four from CRCM4 and three from CRCM5) are used as input for two hydrological models of varying complexity (HSAMI and MOHYSE). Each model is calibrated using three different objective functions based on the Kling-Gupta Efficiency criteria (KGE) to target the summer-fall floods. Three seasonal indices are used to evaluate the CRCM outputs: bias (temperature), relative bias (precipitation) and variances ratio (temperature and precipitation). In an attempt to evaluate the effects of the spatial resolution on the hydrological modelling of summer-fall floods, streamflow simulations are generated using the seven climate datasets. The generated climate-driven streamflow simulations are analysed by two performance statistics: the seasonal values of KGE and the seasonal relative biases. Summer-fall floods are evaluated through the use of four flood indicators, the 2-year, 5-year, 10-year and 20-year return periods. The results revealed an impact of spatial resolution on climate model outputs (temperature and precipitation) and on summer-fall floods simulation by the two hydrological models and the three different calibration approaches, although this can be due to other elements such as domain size and climate model driver. The flood indicators demonstrate an increase on the summer-fall floods return periods with increasing resolution from both hydrological models. On the other hand the hydrological models structure and the calibration approaches did not show significant impacts on the summer-fall floods. The results highlight the need for further research to assess the additional uncertainty due to the impacts of the climate simulations spatial resolution on hydrological studies.
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The potential impacts of floods are of significant concern to our modern society raising the need to identify and quantify all the uncertainties that can impact their simulations. Climate simulations at finer spatial resolutions are expected to bring more confidence in these hydrological simulations. However, the impact of the increasing spatial resolutions of climate simulations on floods simulations has to be evaluated. To address this issue, this paper assesses the sensitivity of summer–fall flood simulations to the Canadian Regional Climate Model (CRCM) grid resolution. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim reanalysis at 0.44°, 0.22° and 0.11° resolutions are analysed at a daily time step for the 1981–2010 period. Raw CRCM5 precipitation and temperature outputs are used as inputs in the simple lumped conceptual hydrological model MOHYSE to simulate streamflows over 50 Quebec (Canada) basins. Summer–fall flooding is analysed by estimating four flood indicators: the 2-year, 5-year, 10-year and 20-year return periods from the CRCM5-driven streamflows. The results show systematic impacts of spatial resolution on CRCM5 outputs and seasonal flood simulations. Floods simulated with coarser climate datasets present smaller peak discharges than those simulated with the finer climate outputs. Smaller catchments show larger sensitivity to spatial resolution as more detail can be obtained from the finer grids. Overall, this work contributes to understanding the sensitivity of streamflow modelling to the climate model’s resolution, highlighting yet another uncertainty source to consider in hydrological climate change impact studies.