On the role of spatial resolution on snow estimates using a process-based snow model across a range of climatology and elevation

Authors: SOHRABI, MOHAMMAD - UNIVERSITY OF CALIFORNIA; TONINA, DANIELE - UNIVERSITY OF IDAHO; BENJANKAR, ROHAN - SOUTHERN ILLINOIS UNIVERSITY; KUMAR, MUKESH - DUKE UNIVERSITY; KORMOS, PATRICK - NATIONAL WEATHER SERVICE; MARKS, DANIEL

Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/2/2019
Publication Date: 1/18/2019
Citation: Sohrabi, M.M., Tonina, D., Benjankar, R., Kumar, M., Kormos, P., Marks, D., Luce, C. 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(8):1260-1275. https://doi.org/10.1002/hyp.13397.
DOI: https://doi.org/10.1002/hyp.13397

Interpretive Summary: This paper describes the sensitivity of a physics-based snow model to a range of spatial averaging schemes for the forcing data. The analysis was conducted using the iSnobal snow model over the Boise River Basin (6,963 km2) in Idaho, USA. It shows that 50m data produce the best and most reliable results, while increasing the model scale to 500m causes the model to underestimate both the development and ablation of the seasonal snow cover. The analysis illustrates why fine resolution data are critical to modeling mountain snow covers.

Technical Abstract: 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, the reduction in scale comes with modeling costs. This computational expense is still a limiting factor for many large watersheds. Thus, this work’s main objective is to unveil what physical processes lead to loss in model accuracy with regard to inputs 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 50m – our benchmark for comparison – and 100m resolutions and with aggregated inputs from the 50m model to 100m, 250m, 500m and 750m resolution for wet, average and dry years over the Upper Boise River Basin (6,963 km2), which spans 4 elevation bands: rain dominated, rain-snow transition and snow dominated below treeline and above treeline. Residuals of simulated snow cover 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 50m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100m simulation were even larger than those of the aggregated 750m. Topographic features such as slope and aspect were flattened due to coarsening the topography from the 50 to 100m resolution. Therefore, net radiation was over-estimated and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100m model relative to the 50m 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 reduces substantially with model scales coarser than 50m.