*2.1. Materials*

We used daily MODIS Terra (MOD10A1 V.006) and Aqua (MYD10A1 V.006) snow products (i), a digital elevation model (DEM) with 30 m spatial resolution (ii), Landsat-7, Landsat-8, and Sentinel-2A data (iii), a Landsat based land-cover classification map (iv), hydro-climatic data observed with high frequency (v), iButton surface temperature measurements distributed in the catchment (vi), and photographs from a time-lapse digital camera (vii). The datasets i–iv were used for reconstructing SCA and SCD whereas v–vii were used to validate the results.

MODIS snow-cover data: Daily MODIS snow products (Version. 006) from the Aqua and Terra satellites with 500 m spatial resolution were employed for the winters 2000/2001 to 2016/2017. These products contain normalized difference snow index (NDSI) values 0–100 as percentages, as well as other parameters, including cloud coverage. The MODIS snow products including the 8-day composite snow product are also widely used in many regions of the world [28–38]. However, the main drawbacks of these products are cloud coverage and coarse spatial resolution [39]. Therefore, we used the original daily data rather than using 8-day snow product to minimize the uncertainties that may have resulted from any cloud-filtering [15]. The data were obtained from the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS) Reverb platform [40].

Digital Elevation Model: We used a 30 m DEM derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [41]. The DEM was used to derive the altitude-dependent rate of SCD to aggregate MODIS data to 30 m resolution.

Landsat and Sentinel data: Optical remote sensing data from the Landsat-7, Landsat-8, and Sentinel-2A were used to generate the winter snow cover time series [42]. The Landsat-7/8 data have a spatial resolution of 30 m and a temporal resolution of 16 days (8 days when they are combined). The Sentinel-2 has a spatial resolution of 20 m and a temporal resolution of 5 days. In total, 40 clear-sky day images (Table S1, Supplementary Materials) between the beginning of October 2016 (mid-autumn) and end of May 2017 (late spring) were processed. The Sentinel images were aggregated to 30 m resolution to meet the same Landsat and DEM resolutions.

Land-cover: Land-cover classification for Sugnugur catchment was produced based on a Landsat-7 image of the year 2011. We believed that this map was appropriate because there was no significant change in land-cover since 2011. In total, five different land-covers were classified in the Sugnugur catchment, including: forest, burned forest, short grassland, shrub, and rock. Areas with forest and shrubs were used in this study for correcting snow distribution maps from the Landsat and Sentinel images because of the low snow detection rate under canopy closure.

In-situ measurements: Snow depth (SD) has been observed using the Campbell SR50A-L sensor since winter 2012/2013 in short grassland vegetation, as well as other climatic parameters such as precipitation, air temperature, surface temperature, and albedo at our hydro-climatic station site located near the entry of the catchment. The SD sensor measures the distance between the sensor and the underlying surface. These data contain continuous SD measurements including records on no-snow conditions with 1 h intervals. As the snow depth measurement at the hydro-climatic station site cannot represent the areal distribution of snow in the catchment, we also conducted a series of snow field measurements at 13 locations for two consecutive winters, 2016–2018, in order to understand the spatial variability of snow distribution and SD. In addition to the snow field measurements, we also observed surface temperatures for 2016–2017 using temperature iButtons at near surface (5 cm) depth with a 3 h interval. The iButtons were placed at all the snow field measurement locations with altitudes ranging from 1193 to 1612 m.a.s.l.
