Abstract
Estimating snowmelt in semi-arid mountain ranges is an important but challenging task, due to the large spatial variability of the 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. This study examines the added value of including different treatments of solar radiation within empirical snowmelt models and benchmarks their performance against MODIS snow cover area (SCA) maps over the 2003-2016 period. Three spatially distributed, enhanced temperature index models that, respectively, include the potential clear-sky direct radiation, the incoming solar radiation and net solar radiation were compared with a classical temperature-index (TI) model to simulate snowmelt, SWE and SCA within the Rheraya basin in the Moroccan High Atlas Range. Enhanced models, particularly that which includes net solar radiation, were found to better explain the observed SCA variability compared to the TI model. However, differences in model performance in simulating basin wide SWE and SCA were small. This occurs because topographically induced variations in melt rates simulated by the enhanced models tend to average out, a situation favored by the rather uniform distribution of slope aspects in the basin. While the enhanced models simulated more heterogeneous snow cover conditions, aggregating the 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. Our findings call for caution when using MODIS for calibration and validation of spatially distributed snow models.
Highlights
Snow constitutes a key element in determining water availability in mountainous catchments, especially in arid and semiarid regions, so that a good understanding of snowpack processes is crucial to support water management strategies (Barnett et al, 2005; Viviroli and Weingartner, 2008; De Jong et al, 2009; Vicuña et al, 2011; Mankin et al, 2015; Qin et al, 2020)
The model performance increases with model complexity, i.e., the Heidke Skill Score (HSS) is lowest for TI and highest for ETIB
A slight increase in performance is even noted for ETIB, which suggests that the model-specific values may be slightly overfitted and less transferable compared to the multi-model average parameter set
Summary
Snow constitutes a key element in determining water availability in mountainous catchments, especially in arid and semiarid regions, so that a good understanding of snowpack processes is crucial to support water management strategies (Barnett et al, 2005; Viviroli and Weingartner, 2008; De Jong et al, 2009; Vicuña et al, 2011; Mankin et al, 2015; Qin et al, 2020). To take into account sublimation losses, a constant average mean daily sublimation rates was used over the entire basin (Table 3), based on the energy balance study at the Oukaimeden-SM site by Boudhar et al (2016) While this approach is admittedly simple, it allows correcting for first order sublimation losses (Jost et al, 2012) and as such avoid compensating these losses by artificially reducing precipitation during spatial interpolation and/or overestimating melt, as can be the case when sublimation losses are completely ignored. It is crucial in hydrological modeling to limit the vertical extrapolation of precipitation to avoid artificial snow build up at high elevations (Freudiger et al, 2017) In this sense, Liston and Elder (2006) limited the difference between the actual (Zx) and interpolated (Z0) station elevation ( Z = Zx − Z0) to a default maximum value ( Zmax) of 1800 m.
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