ALBERT

Description: Thematic Open Access data portals foster and support an open data culture in order to reduce knowledge gaps and data uncertainty. Here we present the Arctic Permafrost Geospatial Center (APGC), which provides open access, high quality spatial data in the field of permafrost research. The distribution and easy access of a wide range of permafrost-related data products supports multi-scale and interdisciplinary analysis of combined field, remote sensing and modelling data. The APGC mission is to provide data that is of high usability, significance and impact, and to facilitate data discovery, data view and supports metadata documentation and exchange via the APGC data catalogue at https://apgc.awi.de/. The catalogue structure can host data models of varying themes, formats, and spatial and temporal extents. Data can be searched by interactively selecting locations on a base map and by many predefined metadata filters. Data can be downloaded directly through a link to the publishing data repository such as PANGAEA. The Catalogue is based on the open source CKAN catalogue architecture, which allows on-the-fly access to catalogued data in QGIS. The APGC currently features over 200 selected datasets from projects such as ERC PETA-CARB, ESA GlobPermafrost, and others. Data products provide information about surface and subsurface permafrost characteristics in the Arctic, Antarctica, or mountain permafrost areas, e.g., soil temperatures, soil carbon, ground ice, land cover, vegetation, periglacial landforms, subsidence and more. Collections of datasets allow users to easily get an overview of the spatial distributions of datasets or their availability in different formats. An additional WebGIS application allows users to explore most of the data interactively (https://maps.awi.de). Data submissions are evaluated according to the following access criteria: permafrost focus, broader significance and impact, open access, high quality, and available metadata.

Description: Thematic Open Access data portals foster and support an open data culture in order to reduce knowledge gaps and data uncertainty. We here present the Arctic Permafrost Geospatial Center (APGC), which provides open access, high quality geospatial data in the field of permafrost research. The APGC mission is (i) to provide data that is of high usability, significance and impact, and (ii) to facilitate data discovery, data view and supports metadata documentation and exchange via a data catalogue (http://apgc.awi.de/). The Data Catalogue is based on the open source CKAN data catalogue architecture, which uses the metadata standard DCAT. The catalogue structure can host a variety of data models of varying themes, format, spatial and temporal extents. Data is documented according to the fair data principles. Each catalogue entry has a data abstract, data preview and extensive metadata that can be downloaded in RDF/XML-, JSON- or Turtle-format. Data can be searched by location – using spatial keywords or by interactively selection locations on a base map. Data can further be searched by product type, project, tags, keywords, license type, or data format. Data can be downloaded directly via link to the publishing data repository such as PANGAEA. APGC, initially supported by the ERC PETA-CARB and the ESA GlobPermafrost projects currently features over 100 selected datasets mainly from these projects. A WebGIS application is available for most of these data sets, which allows users to explore the data interactively (http://maps.awi.de). Data provide information about surface and subsurface permafrost characteristics in the Arctic, Antarctica, or mountain permafrost areas, e.g., soil temperatures, soil carbon, ground ice, land cover, vegetation, periglacial landforms, subsidence and more. Data include in-situ measurements, earth observation, and modelling and are provided in vector or raster format. New data submissions to the catalogue are evaluated according to the following access criteria: permafrost focus, significance and impact, access, quality, and metadata. APGC invites submissions from both individual users as well as project consortiums.

Description: It is important to know how surface evapotranspiration and change in inundated areas are correlated, especially in flat Arctic wetlands such as the tundra region near Barrow, Alaska, as their underlying frozen ground and low hydrological gradient due to flat relief confine the lateral runoff of their standing water. Moreover, knowledge regarding seasonal dynamics of inundated areas is expected to be an essential and controlling factor in modeling regional energy and hydrological balance, which are related closely to frozen ground stability, in Arctic wetlands. However, the seasonal change and spatial distribution of inundated areas have not yet been well explored and quantified. Here we’ve deployed high spatial resolution (WorldView2 and QuickBird) images of Barrow area on eight dates from 2006-2014, to investigate seasonal change of inundated areas for a 4700 ha wetland, including the Barrow Ecosystem Observatory. Inundation dynamics were measured in the field in 2014 using DGPS. These ground truth data was used to develop a classification algorithm for discriminating between open water, overgrown water (mixed vegetation and standing water), and dry surfaces in the high-resolution images. The inundation index is created by combining NIR band, NDVI, and stack mean of BGR and NIR bands, and shown to be capable for mapping the extent of open water, dry, and overgrown water surfaces. In order to explore the relationship between water balance and changes in the inundated area, the estimated seasonal change in the inundated areas was compared with the daily surface water balance (rainfall – evaporation) calculated using available micrometeorological data for the years 2006-2014. Our results suggest that inundation dynamics correlated with the surface water balance during mid-late summer (July-September), though this relationship was not valid in the early summer (June), when surface hydrology is governed mainly by surface runoff above the shallow thawing front of the ground. With the inundation index developed and relationship between inundation index and surface water balance quantified in this study, it will become possible to automatically estimate inundation dynamics, which will improve our understanding of Arctic wetlands hydrology and support the scaling of local measurements to regional scales.

Description: Arctic ponds, i. e. water bodies with a surface area equal to or smaller than 10⁴ m² (1 ha), are currently not inventoried on a circum-arctic scale. However, they are a key element of the water, energy, and carbon balance and abundant in Arctic permafrost lowlands. Ponds and lakes have been subject to both wetting and drying in a warming climate yet studies remain ambivalent regarding the causes of these changes. Goals of this study are to (i) investigate the variability of water body size distributions as a function of landscape characteristics, and (ii) assess the vulnerability of water bodies in different landscapes to scenarios of wetting and drying. Ponds and lakes were mapped from high-resolution aerial and satellite imagery with resolutions of 4 m or better in 14 regions in Alaska, Canada, and Siberia covering a total area of ca. 1.6*104 km². Whereas lake distributions are similar, pond distributions in our study regions vary significantly with the area-normalized number of ponds differing up to 3 orders of magnitude. Landscape characteristics that may explain the current water body distributions include climate (eg., precipitation, evapotranspiration, temperature), permafrost (eg., ground ice content, maximum thaw depth) and terrain characteristics (eg., topography, glaciation, landscape age) which we derive from in situ, remote sensing and modeling data sources. Multivariate regression analysis are used to relate landscape characteristics to distribution parameters. This study for the first time allows to quantify the circum-arctic variability of pond distribution. The current maps are the start of a high-resolution circum-arctic water body inventory and present a baseline for future surface inundation mapping and modelling. We present representative regional probability density functions (pdf) and assess the potential to upscale pdfs using spatial landscape characteristics. We then discuss the vulnerability of water bodies to wetting or drying based on the distribution parameters, their correlation with landscape characteristics and the likeliness of both to change in different future climate scenarios.

Description: Sub-grid and small scale processes occur in various ecosystems and landscapes (e.g., periglacial ecosystems, peatlands and vegetation patterns). These local heterogeneities are often important or even fundamental to better understand general and large scale properties of the system, but they are either ignored or poorly parameterized in regional and global models. Because of their small scale, the underlying generating processes can be well explained and resolved only by local mechanistic models, which, on the other hand, fail to consider the regional or global influences of those features. A challenging problem is then how to deal with these interactions across different spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in the northern peatlands, because of the huge amount of carbon stored in these regions. Land-atmosphere greenhouse gas fluxes vary dramatically within these environments. Therefore, to correctly estimate the fluxes a description of the small scale soil variability is needed. Applications of statistical physics methods could provide useful tools to upscale local features of the landscape, relating them to large-scale properties. To test this approach we considered a case study: the polygonal tundra. Cryogenic polygons, consisting mainly of elevated dry rims and wet low centers, pattern the terrain of many subartic regions and are generated by complex crack-and-growth processes. Methane, carbon dioxide and water vapor fluxes vary largely within the environment, as an effect of the small scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components, such as the water table level inside the polygon wet centers, or the depth at which frozen soil thaws. We developed a stochastic model for this environment using Poisson-Voronoi diagrams, which are able to upscale statistical large scale properties of the system taking into account the main processes within the single polygons. We then compare the results with available recent field studies and demonstrate that the model captures the main statistical characteristics of the landscape and describes its dynamical behavior under climatic forcings (e.g., precipitation and evapotranspiration). In particular, we model and analyze water table dynamics, which directly influences greenhouse gas emissions and changes in the system. Hydraulic interconnectivities and large-scale drainage may also be investigated through percolation properties and thresholds in the Voronoi-Deleaunay graph.

Description: Sub-grid and small scale processes occur in various ecosystems and landscapes (e.g., periglacial ecosystems, peatlands and vegetation patterns). These local heterogeneities are often important or even fundamental to better understand general and large scale properties of the system, but they are either ignored or poorly parameterized in regional and global models. Because of their small scale, the underlying generating processes can be well explained and resolved only by local mechanistic models, which, on the other hand, fail to consider the regional or global influences of those features. A challenging problem is then how to deal with these interactions across different spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in the northern peatlands, because of the huge amount of carbon stored in these regions. Land-atmosphere greenhouse gas fluxes vary dramatically within these environments. Therefore, to correctly estimate the fluxes a description of the small scale soil variability is needed. Applications of statistical physics methods could be useful tools to upscale local features of the landscape, relating them to large-scale properties. To test this approach we considered a case study: the polygonal tundra. Cryogenic polygons, consisting mainly of elevated dry rims and wet low centers, pattern the terrain of many subartic regions and are generated by complex crack-and-growth processes. Methane, carbon dioxide and water vapor fluxes vary largely within the environment, as an effect of the small scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components, such as the water table level inside the polygon wet centers, or the depth at which frozen soil thaws. We developed a stochastic model for this environment using Poisson-Voronoi diagrams, which is able to upscale statistical large scale properties of the system taking into account the main processes within the single polygons. We compare the results with available recent field studies and demonstrate that the model captures the main statistical characteristics of the landscape and describes its dynamical behavior under climatic forcings (e.g., precipitation and evapotranspiration). We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. We also investigate hydraulic interconnectivities and large-scale drainage through percolation properties and thresholds in the Voronoi-Deleaunay graph. The model captures the main statistical characteristics of the landscape topography, such as polygon area and surface properties as well as the water balance. This approach enables us to statistically relate large-scale properties of the system taking into account the main small-scale processes within the single polygons. Overall, the general agreement between field measurements and model results suggests that such statistical methods and simple parameterizations, if accurately tuned with field data, could be a powerful way to consider spatial scale interactions in such heterogenous and complex environments. http://www.earth-syst-dynam-discuss.net/3/453/2012/esdd-3-453-2012.html

Description: A Permafrost Information System (PerSys) has been setup as part of the GlobPermafrost ESA DUE GlobPermafrost project (2016-2019, www.globpermafrost.info). This includes a data catalogue as well as a WebGIS, both linked to the Pangaea repository for easy data access. The thematic products available include InSAR-based land surface deformation maps, rock glacier velocity fields, spatially distributed permafrost model outputs, land surface properties and changes, and ground-fast lake ice. Extended permafrost modelling (time series) is implemented in the new ESA CCI+ Permafrost project (2018-2021), which will provide the key for our understanding of the changes of surface features over time. Special emphasis in CCI+ Permafrost will be on the evaluation and development of land surface models to gain better understanding of the impact of climate change on permafrost and land-atmosphere exchange. Additional focus will be on documentation of kinematics from rock glaciers in several mountain regions across the world. We will present an overview on technical developments made within GlobPermafrost and demonstrate its utility and challenges for an area prone to change of permafrost features. We will focus on the central Yamal Peninsula and the unusually warm years of 2012 and 2016. Conditions of 2012 triggered widespread retrogressive thaw slumps and the development of a gas emission crater. Thaw slumps have been reactivated in 2016, the first year with extensive coverage of Sentinel-1 as well as Sentinel-2 data. We present the documentation of these developments based on InSAR subsidence, Landsat trend analyses, ground fast lake ice, Sentinel-2 landcover information as well as a time series of the first version of ground temperatures from the ESA CCI+ Permafrost project. While landcover documents the occurrence of disturbances, InSAR provides insight into soil properties and impacts of unusually warm conditions during the unfrozen period. These space-based observations have been evaluated by in situ measurements at the long-term monitoring site Vaskiny Datchi. Ground fast lake ice and ground temperature modelling results provide additional insight into interannual variability.