An evaluation of terrain‐based downscaling of fractional snow covered area data sets based on LiDAR‐derived snow data and orthoimagery

Abstract

Reliable maps of snow‐covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) that provide daily ∼500 m data, to derive higher‐resolution snow presence/absence grids. The method uses a composite index combining both the topographic position index (TPI) to represent accumulation effects and the diurnal anisotropic heat (DAH, sun exposure) index to represent ablation effects. The procedure is evaluated and calibrated using airborne‐derived high‐resolution data sets across the Tuolumne watershed, CA using 11 scenes in 2014 to downscale to 30 m resolution. The average matching F score was 0.83. We then tested our method's transferability in time and space by comparing against the Tuolumne watershed in water years 2013 and 2015, and over an entirely different site, Mt. Rainier, WA in 2009 and 2011, to assess applicability to other topographic and climatic conditions. For application to sites without validation data, we recommend equal weights for the TPI and DAH indices and close TPI neighborhoods (60 and 27 m for downscaling to 30 and 3 m, respectively), which worked well in both our study areas. The method is less effective in forested areas, which still requires site‐specific treatment. We demonstrate that the procedure can even be applied to downscale to 3 m resolution, a very fine scale relevant to alpine ecohydrology research.

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

“…The goal of the present study was to assess specifically the added-value of accounting for topography-driven radiative effects (topographical shadowing), which the distributed approach allows, in contrast to the semi-distributed approach. Further studies will explore in more detail how more sophisticated model approaches could further improve the performance of distributed simulations, which is deliberately beyond the scope of the present study [31,80,81].…”

Assessing Mountains as Natural Reservoirs With a Multimetric Framework

“…Evaluating the credibility of simulated climate and snow dynamics through a decision‐relevant lens and attributing sources of bias across multiple processes, scales of analysis, and models poses a significant challenge to the scientific community. The WUS is a well‐studied region with a vast literature in observed snowpack dynamics (Bales et al, ; Cristea et al, ; Henn et al, ; Kapnick & Hall, ; Margulis, Cortés, Girotto, & Durand, ; Mote, ; Mote et al, ; Painter et al, ; Serreze et al, ; Trujillo & Molotch, ) and modeling approaches (Ashfaq et al, , ; Giorgi et al, ; Huang et al, ; Liu et al, ; McCrary et al, ; Minder et al, ; Naz et al, ; Pierce & Cayan, ; Rasmussen et al, ; Rhoades et al, , , ; Sun et al, ; Wu et al, ) dating back decades (Anderson, ; Palmer, ). Consequently, a staggering growth in publicly available observational and regional climate data sets have been produced, yet a lag in expert direction regarding their applicability for regional planning (Hall, ).…”

Response Reviewer 1

“…However, most of the works which studied the snow covered area over large domains use a threshold for considering a pixel as snow covered or not , regardless the spatial scale(e.g. Cristea et al, 2017 at the very high resolution of 3 m, Gascoin et al, 2015 at the resolution of 500 m). Furthermore, we demonstrate in our paper that the strict validity of this relationship is secondary in the context of the evaluations performed in this paper.…”