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Abstract Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961–2100 for two watersheds located in Quebec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions.
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This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.