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SatPerm – Satellite-based Permafrost Modeling across a Range of Scales

Permafrost is found in about a quarter of the land area in the northern hemisphere. Unlike snow and ice cannot permafrost be "observed" with remote sensing techniques. However satellites collect data regarding permafrost from sensors in field, and permafrost can be modelled numerically using above-ground data sets of temperature and snow depth. We aim in SatPerm to see if such data sets can be used for modelling of permafrost.

About the project

Permafrost is permanently frozen ground of which a shallow surface layer thaws every summer and refreezes again in fall. Permafrost is found in about a quarter of the land area in the northern hemisphere which makes it an important element of the Earth's Cryosphere. Unlike for other elements of the Cryosphere, such as glaciers and sea ice, Remote Sensing techniques have remained of limited use in permafrost science since satellite sensors are not able to "see" the state of the ground below the surface.

The physical variable which scientists use to characterize the thermal state of the permafrost is the temperature of the ground. It can be measured directly in boreholes drilled in the frozen ground, but also modeled numerically using above-ground data sets of temperature and snow depth.

These variables, however, are operationally measured by satellite sensors even on global scale, and SatPerm will investigate possibilities of using these data sets as input for permafrost modeling. We will explore different methods and models to achieve this goal.

Objectives

Simple and fast methods can be applied to map ground temperatures and permafrost for large regions, while computationally demanding approaches will facilitate a higher accuracy for single points. For the latter, Ensemble Kalman Filter methods which have been successfully used in many science fields will be introduced to permafrost science.

Outcomes

For the Lena River Delta in North-East Siberia, a cold permafrost area with ground temperatures around -10°C, we implemented a modeling scheme based on satellite data and the CryoGrid permafrost model developed at UiO. The scheme could very well reproduce measured ground temperatures and thaw depths, and the study is published in the journal “The Cryosphere” (http://www.the-cryosphere-discuss.net/tc-2016-130/)  

In addition, we have conducted field work e.g. on Svalbard and in Mongolia to better understand the variability of ground temperatures, and relate it to external factors such as the distribution of winter snow cover. For the area around Ny-Ålesund on Svalbard, we are currently testing a novel modeling scheme using Ensemble Kalman Filter methods which uses satellite data sets to estimate first the snow depth distribution and subsequently the ground temperature distribution.

The novel techniques developed by the University of Oslo have been recognized internationally: the European Space Agency has included us in its new “GlobPermafrost” project in which satellite-based modeling of permafrost extent will be applied on the pan-arctic scale.

Background

SatPerm will focus on five field sites and regions. The special focus areas are located in Norway, Svalbard, Greenland, North-East Siberia and Mongolia which are hotspots of permafrost research.

At these sites, we will test and benchmark the SatPerm results in close collaboration with partners from Denmark, Germany, Poland, Japan and Mongolia.

Financing

'SatPerm – Satellite-based Permafrost Modeling across a Range of Scales' is funded through the Norwegian Research Council FRINATEK program, project number 239918. The project is given in the category "Unge forskertalent".

The SatPerm-project started up in 2015, and will continue out 2019.

Cooperation

This project is carried out in cooperation with researchers from different institutions (below). See links in right column for participating researchers.

Publications

  • Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment. ISSN 0034-4257. 239. doi: 10.1016/j.rse.2019.111618. Full text in Research Archive
  • Obu, Jaroslav; Westermann, Sebastian; Bartsch, Annett; Berdnikov, Nikolai M.; Christiansen, Hanne H & Dashtseren, Avirmed [Show all 21 contributors for this article] (2019). Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth-Science Reviews. ISSN 0012-8252. 193, p. 299–316. doi: 10.1016/j.earscirev.2019.04.023. Full text in Research Archive
  • Fiddes, Joel; Aalstad, Kristoffer & Westermann, Sebastian (2019). Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering. Hydrology and Earth System Sciences. ISSN 1027-5606. 23(11), p. 4717–4736. doi: 10.5194/hess-23-4717-2019. Full text in Research Archive
  • Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere. ISSN 1994-0416. 12(1), p. 247–270. doi: 10.5194/tc-12-247-2018. Full text in Research Archive
  • Kepski, Daniel; Luks, Bartek; Migala, K.; Wawrzyniak, Tomasz; Westermann, Sebastian & Wojtun, B. (2017). Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery. Remote Sensing. ISSN 2072-4292. 9(7). doi: 10.3390/rs9070733. Full text in Research Archive
  • Trofaier, Anna Maria; Westermann, Sebastian & Bartsch, Annett (2017). Progress in space-borne studies of permafrost for climate science: towards a multi-ECV approach. Remote Sensing of Environment. ISSN 0034-4257. 203, p. 55–70. doi: 10.1016/j.rse.2017.05.021. Full text in Research Archive
  • Westermann, Sebastian; Peter, Maria; Langer, Moritz; Schwamborn, Georg; Schirrmeister, Lutz & Etzelmüller, Bernd [Show all 7 contributors for this article] (2017). Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia. The Cryosphere. ISSN 1994-0416. 11(3), p. 1441–1463. doi: 10.5194/tc-11-1441-2017. Full text in Research Archive
  • Borge, Amund Frogner; Westermann, Sebastian; Solheim, Ingvild & Etzelmüller, Bernd (2017). Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years. The Cryosphere. ISSN 1994-0416. 11(1), p. 1–16. doi: 10.5194/tc-11-1-2017. Full text in Research Archive

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  • Salzano, Roberto; Aalstad, Kristoffer; Boldrini, Enrico; Gallet, Jean-Charles; Kȩpski, Daniel & Luks, Bartlomiej [Show all 9 contributors for this article] (2021). Terrestrial photography applications on snow cover in Svalbard (PASSES), The State of Environmental Science in Svalbard. Svalbard Integrated Arctic Earth Observing System. ISSN 978-82-93871-00-2. p. 236–251. doi: 10.5281/zenodo.4294084.
  • Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel; McCreight, James & Bertino, Laurent (2020). Retrieving, validating, and assimilating fractional snow-covered area from emerging optical satellites for snow reanalysis.
  • Aalstad, Kristoffer & Westermann, Sebastian (2020). ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway).
  • Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel; Karsten, Logan; McCreight, James & Bertino, Laurent (2020). Testing ensemble-based snow reanalysis in the Lakes basin using the ASO.
  • Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel & Bertino, Laurent (2019). Retrieving the depletion of snow-covered area from multiple optical satellite sensors with applications for SWE reanalysis.
  • Aalstad, Kristoffer; Westermann, Sebastian; Bertino, Laurent; Schuler, Thomas; Boike, Julia & Karsten, Logan (2018). Towards high-resolution Bayesian snow reconstruction in permafrost regions.
  • Aalstad, Kristoffer; Westermann, Sebastian; Karsten, Logan; Fiddes, Joel & Bertino, Laurent (2018). Snow history matching in mountainous terrain.
  • Aalstad, Kristoffer; Westermann, Sebastian; Karsten, Logan; Gutmann, Ethan; McCreight, James & Fiddes, Joel [Show all 7 contributors for this article] (2018). Ensemble-based reanalysis of the seasonal montane snowpack: Lessons from the ASO.
  • Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2017). Towards High-Resolution SWE Mapping in Permafrost Regions.
  • Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2017). Ensemble-based subgrid snow data assimilation.
  • Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2017). Towards High-Resolution SWE Mapping in Permafrost Regions.
  • Aalstad, Kristoffer; Westermann, Sebastian; Boike, Julia; Bertino, Laurent & Aas, Kjetil Schanke (2016). An ensemble-based snow data assimilation framework with applications to permafrost modeling.
  • Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2016). An ensemble-based snow data assimilation framework.
  • Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2016). An ensemble-based subgrid snow data assimilation framework applied to the southern Swiss alps.
  • Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2016). An ensemble-based subgrid snow data assimilation framework.
  • Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2019). Ensemble-based retrospective analysis of the seasonal snowpack. University of Oslo. ISSN 1501-7710.

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Tags: Permafrost, Remote sensing, Europe, Japan
Published Nov. 17, 2015 3:09 PM - Last modified Feb. 22, 2022 4:09 PM