A stochastic cellular automaton model to describe the evolution of the snow-covered area across a high-elevation mountain catchment

Variations in the extent and duration of snow cover impinge on surface albedo and snowmelt rate, influencing the energy and water budgets. Monitoring snow coverage is therefore crucial for both optimising the supply of snowpack-derived water and understanding how climate change could impact on this source, vital for sustaining human activities and the natural environment during the dry season. Mountainous sites can be characterised by complex morphologies, cloud cover and forests that can introduce errors into the estimates of snow cover obtained from remote sensing. Consequently, there is a need to develop simulation models capable of predicting how snow coverage evolves across a season. Cellular Automata models have previously been used to simulate snowmelt dynamics, but at a coarser scale that limits insight into the precise factors driving snowmelt at different stages. To address this information gap, we formulate a novel, fine-scale stochastic Cellular Automaton model that describes snow coverage across a high-elevation catchment. Exploiting its refinement, the model is used to explore the interplay between three factors proposed to play a critical role: terrain elevation, sun incidence angle, and the extent of nearby snow. We calibrate the model via a randomised parameter search, fitting simulation data against snow cover masks estimated from Sentinel-2 satellite images. Our analysis shows that: The three simple assumptions are sufficient to yield a close correspondence between model output and the estimated snow cover masks. Across the study area, elevation and neighbouring snow appear to be particularly influential, with incidence playing a relatively minor role for much of the process. Incidence, though, plays a significant role early in the process, and allows the identification of regions that receive sufficient solar energy to trigger snowmelt. Discrepancies between model output and satellite data indicate other potential factors at play, the identification of which will demand future attention.