Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions and the choice of predictor variables. In this study we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in twelve snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume, snowmelt and rainfall intensity, and soil moisture were estimated during the pre-flood period. Stepwise multivariate linear regression analysis was used to identify the most relevant predictors and assess their relative contribution to the interannual variability of flood characteristics. Results show that interannual variations in spring peak flow are controlled differently between basins. Overall, interannual variations in peak flow were mainly governed, in order of importance, by snowmelt intensity, rainfall intensity, snowmelt volume, rainfall volume, peak SWE, and soil moisture. Variations in the timing of peak flow were controlled in most basins by rainfall volume and rainfall and snowmelt intensity. In the northernmost, snow-dominated basins, pre-flood rainfall amount and intensity mostly controlled peak flow variability, whereas in the southern, rainier basins snowpack conditions and melt dynamics controlled this variability. Snowpack interannual variations were found to be less important than variations in rainfall in forested basins, where snowmelt is more gradual. Conversely, peak flow was more sensitive to snowpack conditions in agricultural basins where snowmelt occurs faster. These results highlight the impact of land cover and use on spring flood generation mechanism, and the limited predictability potential of spring floods using simple methods and antecedent hydrological factors.

Estimating snow water equivalent (SWE) and snowmelt in semi-arid mountain ranges is an important but challenging task, due to the large spatial variability of the seasonal snow cover and scarcity of field observations. Adding solar radiation as snowmelt predictor within empirical snow models is often done to account for topographically induced variations in melt rates, at the cost of increasing model complexity. This study examines the added value of including different treatments of solar radiation within empirical snowmelt equations. Three spatially-distributed, enhanced temperature index models that respectively include the potential clear-sky direct radiation (HTI), the incoming solar radiation (ETIA) and net solar radiation (ETIB) were compared with a classical temperature-index model (TI) to simulate SWE within the Rheraya basin in the Moroccan High Atlas Range. Extensive model validation of simulated snow cover area (SCA) was carried out using blended MODIS snow cover products over the 2003-2016 period. We found that models enhanced with a radiation term, particularly ETIB which includes net solar radiation, better explain the observed SCA variability compared to the TI model. However, differences in model performance were overall small, as were the differences in basin averaged simulated SWE and melt rates. SCA variability was found to be dominated by elevation, which is well captured by the TI model, while the ETIB model was found to best explain additional SCA variability. The small differences in model performance for predicting spatiotemporal SCA variations is interpreted to results from the averaging out of topographically-induced variations in melt rates simulated by the enhanced models, a situation favored by the rather uniform distribution of slope aspects in the basin. Moreover, the aggregation of simulated SCA from the 100 m model resolution towards the MODIS resolution (500 m) suppresses key spatial variability related to solar radiation, which attenuates the differences between the TI and the radiative models.