Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Abstract

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

“…The Poland coast showed a similar decrease for this period of −0.47 days/year, while the Neman river was almost twice as much at −0.85 days/year. The SCD increase in northern Fennoscandia in the boreal spring shown in Figure 9 confirms previous studies [26]. For the continental areas in Canada, the SCD increase found by [27] in the boreal spring (Figure 9) could be confirmed.…”

“…For the global mountain ranges, [22] found for 38 observed years (1982-2020) a mean SCE decrease of −3.6% ± 2.7%, and a Snow Cover Duration (SCD) decrease of −15.1 days ± 11.6 days. Our study will focus on changes in the SCD and also on different time periods-since drastic changes in the SCD can occur on a small scale and are limited to a few months, such as an increase in the SCD during the boreal spring in the Sápmi region (North Fennoscandia) [26]. A shift in the SCD was found particularly in continental areas; [27] analyzed snow data in Canada from 185 stations and found an SCD decrease of −1.68 days/decade in the boreal autumn and an increase of +0.28 days/decade in the boreal spring.…”

Multi-faceted analyses of seasonal trends and drivers of land surface variables in Indo-Gangetic river basins

“…The Global SnowPack is processed at the German Aerospace Center (DLR) and was used to perform analyses on SCA changes (Dietz et al, 2015). The DLR Global SnowPack is based on daily MODIS (M*D10A1.006) snow products at a spatial resolution of 500 m. In this context, MODIS snow data are widely used to characterize changes in snow cover (Nepal et al, 2021;Notarnicola, 2020;Rößler et al, 2021). Dietz et al (2015) implemented several processing steps to interpolate pixels obscured by cloud coverage.…”

A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables—A Case Study for Indo-Gangetic River Basins

“…This daily time series represents snow cover area as a binary mask with pixels classified as "snow" and "no-snow". GSP data were already employed in several studies to investigate snow cover phenology [57][58][59]. Details on validation of MODIS snow products are provided in Dietz et al [14] and Notarnicola [42].…”