Generation of daily high-spatial resolution snow depth maps from in-situ measurement and time-lapse photographs

Abstract

Acquiring information on snow depth distribution at high spatial and temporal resolution in mountain areas is time consuming and generally these acquisitions are subjected to meteorological constrains. This work presents a simple approach to assess snow depth distribution from automatically observed snow variables and a pre-existing database of snow depth maps. By combining daily observations of in-situ snow depth, georectified time-lapse photography (snow presence or absence) and information on snowpack distribution during annual snow peaks determined with a Terrestrial Laser Scanner (TLS), a method was developed to simulate snow depth distribution on day-by-day basis. This method was tested is Izas Experimental Catchment, in the Central Spanish Pyrenees, a site with a large database of TLS observations, time-lapse images and nivo-meteorological variables for six snow seasons (from 2011 to 2017). The contrasted snow climatic characteristics among the snow seasons allowed analysis of the transferability of snowpack distribution patterns observed during particular seasons to periods without spatialized snow depth observations, by TLS or other procedures. The method i) determines snow depth ratio among the observed maximum snow depths and all other snow map pixels at the TLS yearly snow peak accumulation, ii ) rescales these ratios on a daily basis with time-lapse images information and iii) calculates the snow depth distribution with; the rescaled ratios and the snow depth observed at the automatic weather station. The average of the six TLS observed peaks was the combination showing optimal overall applicability. Despite its simplicity, these simulated values showed encouraging results when compared with snow depth distribution observed on particular dates. This was due primarily to the strong topographic control of small scale snow depth distribution on heterogeneous mountain areas, which has high inter- and intra-annual consistencies.