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Article

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

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR) Muenchener Strasse 20, D-82234 Wessling, Germany
2
Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Am Hubland, D-97074 Wuerzburg, Germany
3
Arctic Space Center, Finnish Meteorological Institute (FMI), Erik Palmenin Aukio 1, FI-00560 Helsinki, Finland
4
Department of Physical Geography, Stockholm University, Svante Arrheniusväg 8, SE-10691 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Bartłomiej Luks and Jesús Martínez-Frías
Geosciences 2021, 11(3), 130; https://doi.org/10.3390/geosciences11030130
Received: 22 December 2020 / Revised: 26 February 2021 / Accepted: 7 March 2021 / Published: 12 March 2021
(This article belongs to the Special Issue Monitoring of the Seasonal Snow Cover)
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). View Full-Text
Keywords: remote sensing; snow parameters; snow variability; MODIS; snow hydrology; spring flood; Sápmi; Mann-Kendall test; snowmelt runoff model remote sensing; snow parameters; snow variability; MODIS; snow hydrology; spring flood; Sápmi; Mann-Kendall test; snowmelt runoff model
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MDPI and ACS Style

Rößler, S.; Witt, M.S.; Ikonen, J.; Brown, I.A.; Dietz, A.J. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 2021, 11, 130. https://doi.org/10.3390/geosciences11030130

AMA Style

Rößler S, Witt MS, Ikonen J, Brown IA, Dietz AJ. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences. 2021; 11(3):130. https://doi.org/10.3390/geosciences11030130

Chicago/Turabian Style

Rößler, Sebastian, Marius S. Witt, Jaakko Ikonen, Ian A. Brown, and Andreas J. Dietz. 2021. "Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers" Geosciences 11, no. 3: 130. https://doi.org/10.3390/geosciences11030130

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