TY - JOUR A1 - Stuenzi, Simone Maria A1 - Kruse, Stefan A1 - Boike, Julia A1 - Herzschuh, Ulrike A1 - Oehme, Alexander A1 - Pestryakova, Luidmila A. A1 - Westermann, Sebastian A1 - Langer, Moritz T1 - Thermohydrological impact of forest disturbances on ecosystem-protected permafrost JF - Journal of geophysical research : Biogeosciences N2 - Boreal forests cover over half of the global permafrost area and protect underlying permafrost. Boreal forest development, therefore, has an impact on permafrost evolution, especially under a warming climate. Forest disturbances and changing climate conditions cause vegetation shifts and potentially destabilize the carbon stored within the vegetation and permafrost. Disturbed permafrost-forest ecosystems can develop into a dry or swampy bush- or grasslands, shift toward broadleaf- or evergreen needleleaf-dominated forests, or recover to the pre-disturbance state. An increase in the number and intensity of fires, as well as intensified logging activities, could lead to a partial or complete ecosystem and permafrost degradation. We study the impact of forest disturbances (logging, surface, and canopy fires) on the thermal and hydrological permafrost conditions and ecosystem resilience. We use a dynamic multilayer canopy-permafrost model to simulate different scenarios at a study site in eastern Siberia. We implement expected mortality, defoliation, and ground surface changes and analyze the interplay between forest recovery and permafrost. We find that forest loss induces soil drying of up to 44%, leading to lower active layer thicknesses and abrupt or steady decline of a larch forest, depending on disturbance intensity. Only after surface fires, the most common disturbances, inducing low mortality rates, forests can recover and overpass pre-disturbance leaf area index values. We find that the trajectory of larch forests after surface fires is dependent on the precipitation conditions in the years after the disturbance. Dryer years can drastically change the direction of the larch forest development within the studied period. KW - permafrost KW - boreal forest KW - periglacial process KW - Siberia KW - larch forest KW - disturbance Y1 - 2022 U6 - https://doi.org/10.1029/2021JG006630 SN - 2169-8953 SN - 2169-8961 VL - 127 IS - 5 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Kaiser, Soraya A1 - Grosse, Guido A1 - Boike, Julia A1 - Langer, Moritz T1 - Monitoring the transformation of Arctic landscapes BT - automated shoreline change detection of lakes using very high resolution imagery JF - Remote sensing / Molecular Diversity Preservation International (MDPI) N2 - Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations. KW - change detection KW - shoreline movement rate KW - shoreline movement direction KW - arctic water bodies KW - permafrost lowlands KW - automated monitoring KW - North KW - Slope KW - very high resolution imagery Y1 - 2021 U6 - https://doi.org/10.3390/rs13142802 SN - 2072-4292 VL - 13 IS - 14 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kruse, Stefan A1 - Stünzi, Simone Maria A1 - Boike, Julia A1 - Langer, Moritz A1 - Gloy, Josias A1 - Herzschuh, Ulrike T1 - Novel coupled permafrost-forest model (LAVESI-CryoGrid v1.0) revealing the interplay between permafrost, vegetation, and climate across eastern Siberia JF - Geoscientific model development : GMD ; an interactive open access journal of the European Geosciences Union N2 - Boreal forests of Siberia play a relevant role in the global carbon cycle. However, global warming threatens the existence of summergreen larch-dominated ecosystems, likely enabling a transition to evergreen tree taxa with deeper active layers. Complex permafrost-vegetation interactions make it uncertain whether these ecosystems could develop into a carbon source rather than continuing atmospheric carbon sequestration under global warming. Consequently, shedding light on the role of current and future active layer dynamics and the feedbacks with the apparent tree species is crucial to predict boreal forest transition dynamics and thus for aboveground forest biomass and carbon stock developments. Hence, we established a coupled model version amalgamating a one-dimensional permafrost multilayer forest land-surface model (CryoGrid) with LAVESI, an individual-based and spatially explicit forest model for larch species (Larix Mill.), extended for this study by including other relevant Siberian forest species and explicit terrain.
Following parameterization, we ran simulations with the coupled version to the near future to 2030 with a mild climate-warming scenario. We focus on three regions covering a gradient of summergreen forests in the east at Spasskaya Pad, mixed summergreen-evergreen forests close to Nyurba, and the warmest area at Lake Khamra in the southeast of Yakutia, Russia. Coupled simulations were run with the newly implemented boreal forest species and compared to runs allowing only one species at a time, as well as to simulations using just LAVESI. Results reveal that the coupled version corrects for overestimation of active layer thickness (ALT) and soil moisture, and large differences in established forests are simulated. We conclude that the coupled version can simulate the complex environment of eastern Siberia by reproducing vegetation patterns, making it an excellent tool to disentangle processes driving boreal forest dynamics. Y1 - 2022 U6 - https://doi.org/10.5194/gmd-15-2395-2022 SN - 1991-959X SN - 1991-9603 VL - 15 IS - 6 SP - 2395 EP - 2422 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Rolph, Rebecca A1 - Overduin, Pier Paul A1 - Ravens, Thomas A1 - Lantuit, Hugues A1 - Langer, Moritz T1 - ArcticBeach v1.0 BT - a physics-based parameterization of pan-Arctic coastline erosion JF - Frontiers in Earth Science N2 - In the Arctic, air temperatures are increasing and sea ice is declining, resulting in larger waves and a longer open water season, all of which intensify the thaw and erosion of ice-rich coasts. Climate change has been shown to increase the rate of Arctic coastal erosion, causing problems for Arctic cultural heritage, existing industrial, military, and civil infrastructure, as well as changes in nearshore biogeochemistry. Numerical models that reproduce historical and project future Arctic erosion rates are necessary to understand how further climate change will affect these problems, and no such model yet exists to simulate the physics of erosion on a pan-Arctic scale. We have coupled a bathystrophic storm surge model to a simplified physical erosion model of a permafrost coastline. This Arctic erosion model, called ArcticBeach v1.0, is a first step toward a physical parameterization of Arctic shoreline erosion for larger-scale models. It is forced by wind speed and direction, wave period and height, sea surface temperature, all of which are masked during times of sea ice cover near the coastline. Model tuning requires observed historical retreat rates (at least one value), as well as rough nearshore bathymetry. These parameters are already available on a pan-Arctic scale. The model is validated at three study sites at 1) Drew Point (DP), Alaska, 2) Mamontovy Khayata (MK), Siberia, and 3) Veslebogen Cliffs, Svalbard. Simulated cumulative retreat rates for DP and MK respectively (169 and 170 m) over the time periods studied at each site (2007-2016, and 1995-2018) are found to the same order of magnitude as observed cumulative retreat (172 and 120 m). The rocky Veslebogen cliffs have small observed cumulative retreat rates (0.05 m over 2014-2016), and our model was also able to reproduce this same order of magnitude of retreat (0.08 m). Given the large differences in geomorphology between the study sites, this study provides a proof-of-concept that ArcticBeach v1.0 can be applied on very different permafrost coastlines. ArcticBeach v1.0 provides a promising starting point to project retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations. KW - permafrost KW - erosion KW - modelling KW - arctic KW - climate change Y1 - 2022 U6 - https://doi.org/10.3389/feart.2022.962208 SN - 2296-6463 VL - 10 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Muster, Sina A1 - Riley, William J. A1 - Roth, Kurt A1 - Langer, Moritz A1 - Aleina, Fabio Cresto A1 - Koven, Charles D. A1 - Lange, Stephan A1 - Bartsch, Annett A1 - Grosse, Guido A1 - Wilson, Cathy J. A1 - Jones, Benjamin M. A1 - Boike, Julia T1 - Size distributions of arctic waterbodies reveal consistent relations in their statistical moments in space and time JF - Frontiers in Earth Science N2 - Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km(2) (100 m(2)) to 1 km(2). We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R-2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic. KW - permafrost KW - hydrology KW - waterbodies KW - size distribution KW - thermokarst KW - statistical moments KW - ponds KW - lakes Y1 - 2019 U6 - https://doi.org/10.3389/feart.2019.00005 SN - 2296-6463 VL - 7 PB - Frontiers Research Foundation CY - Lausanne ER -