Hydrological processes in mountainous settings depend on snow distribution, whose prediction accuracy is a function of model spatial scale. Although model accuracy is expected to improve with finer spatial resolution, an increase in resolution comes with modelling costs related to increased computational time and greater input data and parameter information. This computational and data collection expense is still a limiting factor for many large watersheds. Thus, this work's main objective is to question which physical processes lead to loss in model accuracy with regard to input spatial resolution under different climatic conditions and elevation ranges. To address this objective, a spatially distributed snow model, iSnobal, was run with inputs distributed at 50‐m—our benchmark for comparison—and 100‐m resolutions and with aggregated (averaged from the fine to the large resolution) inputs from the 50‐m model to 100‐, 250‐, 500‐, and 750‐m resolution for wet, average, and dry years over the Upper Boise River Basin (6,963 km2), which spans four elevation bands: rain dominated, rain–snow transition, and snow dominated below treeline and above treeline. Residuals, defined as differences between values quantified with high resolution (>50 m) models minus the benchmark model (50 m), of simulated snow‐covered area (SCA) and snow water equivalent (SWE) were generally slight in the aggregated scenarios. This was due to transferring the effects of topography on meteorological variables from the 50‐m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100‐m simulation were greater than those of the aggregated 750 m. Topographic features such as slope and aspect were simplified, and their gradient was reduced due to coarsening the topography from the 50‐ to 100‐m resolution. Therefore, solar radiation was overestimated, and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100‐m model relative to the 50‐m model. Large residuals were observed in the wet year and at the highest elevation band when and where snow mass was large. These results support that model accuracy is substantially reduced with model scales coarser than 50 m.
Sohrabi, Mohammad M., Daniele Tonina, Rohan Benjankar, Mukesh Kumar, Patrick Kormos, Danny Marks, Charlie Luce (2019): On the role of spatial resolution on snow estimates using a process‐based snow model across a range of climatology and elevation. Hydrological Processes 33: 1260-1275. DOI: 10.1002/hyp.13397
This Paper/Book acknowledges NSF CZO grant support.