@misc{LaraNitzeGrosseetal.2018, author = {Lara, Mark J. and Nitze, Ingmar and Grosse, Guido and Martin, Philip and McGuire, A. David}, title = {Reduced arctic tundra productivity linked with landform and climate change interactions}, series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, number = {550}, issn = {1866-8372}, doi = {10.25932/publishup-42313}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423132}, pages = {10}, year = {2018}, abstract = {Arctic tundra ecosystems have experienced unprecedented change associated with climate warming over recent decades. Across the Pan-Arctic, vegetation productivity and surface greenness have trended positively over the period of satellite observation. However, since 2011 these trends have slowed considerably, showing signs of browning in many regions. It is unclear what factors are driving this change and which regions/landforms will be most sensitive to future browning. Here we provide evidence linking decadal patterns in arctic greening and browning with regional climate change and local permafrost-driven landscape heterogeneity. We analyzed the spatial variability of decadal-scale trends in surface greenness across the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) using the Landsat archive (1999-2014), in combination with novel 30 m classifications of polygonal tundra and regional watersheds, finding landscape heterogeneity and regional climate change to be the most important factors controlling historical greenness trends. Browning was linked to increased temperature and precipitation, with the exception of young landforms (developed following lake drainage), which will likely continue to green. Spatiotemporal model forecasting suggests carbon uptake potential to be reduced in response to warmer and/or wetter climatic conditions, potentially increasing the net loss of carbon to the atmosphere, at a greater degree than previously expected.}, language = {en} } @article{HeeschenJanochaSpangenbergetal.2020, author = {Heeschen, Katja U. and Janocha, Julian and Spangenberg, Erik and Schicks, Judith Maria and Giese, Ronny}, title = {The impact of ice on the tensile strength of unconsolidated sand}, series = {Marine and petroleum geology}, volume = {122}, journal = {Marine and petroleum geology}, publisher = {Elsevier}, address = {Oxford}, issn = {0264-8172}, doi = {10.1016/j.marpetgeo.2020.104607}, pages = {9}, year = {2020}, abstract = {Tensile strength is an important parameter when it comes to predictions of potential fracturing of sediments by natural processes such as the emplacement of ice or gas hydrate lenses, as well as anthropogenic fracturing or else the stability of engineering constructions such as boreholes. Yet, tensile strength (sigma(tau)) measurements of unconsolidated ice-bearing or gas hydrate-bearing sands are scarce and affected by a large variability.
In the course of the SUGAR project we successfully used ice as a model for pore-filling and "load-bearing" gas hydrate in sand to determine compressional wave velocity. We were thus able to verify comparable formation characteristics and morphologies of ice and gas hydrate within the pore space. As these are important values for the tensile strength of ice/hydrate-bearing sands, ice was also used as a model for hydrate-bearing sands, despite differences in the mechanical behavior and strength of pure ice and gas hydrate. Water-saturated sand cores with ice saturations (S-ice) between 0 and 100\% were tested at -6.8 degrees C. The varying S-ice were a result of the freezing point depression caused by saline solutions of different concentrations. The sigma(tau) was directly determined using a sleeve-fracturing test with an internal pressure that was created within the frozen samples. The setup was also adapted to fit a pressure vessel for tests using confining pressure.
The correlation of S-ice - sigma(tau) shows an exponential increase of sigma(tau) with S-ice. Whereas at S-ice < 60\% the increase is small, it is large at S-ice > 80\%. In conjunction with the change in strength, the viscoelastic behavior changes. A clear peak strength occurs at S-ice > 80\%. We conclude that given 60\% < S-ice < 80\% the pore-filling morphology of the ice converts into a frame-building habitus and at S-ice > 80\% the frame gains strength while the amount of residual water decreases. Tensile failure and cracking now exceed grain boundary sliding as the prevailing failure mode. The ice morphology in the sand is non-cementing and comparable to a gas hydrate-sand mixture.}, language = {en} } @misc{LaraNitzeGrosseetal.2018, author = {Lara, Mark J. and Nitze, Ingmar and Große, Guido and McGuire, David}, title = {Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1035}, issn = {1866-8372}, doi = {10.25932/publishup-45987}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-459875}, pages = {12}, year = {2018}, abstract = {Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.}, language = {en} }