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

Sebastian Rößler,Ian A Brown,Jaakko Ikonen,Andreas J Dietz,Marius S Witt

doi:10.3390/geosciences11030130

Sebastian Rößler, Ian A Brown + Show 3 more

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https://doi.org/10.3390/geosciences11030130

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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).

Highlights

The lakes are recognizable due to their relatively small number of snow-covered days. This is due to the fact that they still have an open water surface next to snow-covered land, or they are covered with lake ice, which forms a separate class in the MODerate resolution Imaging Spectroradiometer (MODIS) data
The abstractions can be seen when we compare the timing of SCDES or SCDLS derived from Global SnowPack (GSP) and snow cover start (SCS) or snow cover melt (SCM) from the air temperature developments
We presented how satellite-derived snow cover information can be linked to the occurrence of hydrological extreme events

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The hydroclimatic regime in the Arctic and sub-Arctic is predominantly nival and spring floods triggered by snowmelt occur every year [1,2,3,4]. Spring floods are defined as the characteristic high discharge events that occur after prolonged low flows over the winter. Characteristics of spring floods are discharge peaks demonstrating rapid acceleration in discharge followed by distinct kurtosis; these indicate the sudden onset of widespread snowmelt in spring [5]. As a major component of the cryosphere, the seasonal snow cover and its alteration is a clearly visible indication of changing climatic conditions

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