ALBERT

Description: While increasing Arctic temperatures have been identified to induce widespread thermokarst development in permafrost lowland landscapes over only several decades, disturbances, such as tundra fires can cause similar impacts within a few years. Transition from low-centered to high-centered polygons through the formation of troughs is an immediate result of melting ice wedges 3-4 years after a fire (Jones et al., 2015). Liljedahl et al (2016) have shown that widespread ice-wedge degradation can lead to hydrological connectivity and increased drainage of entire landscapes through newly developing trough networks. Quantifying such dynamics is important for projecting the hydrological outcomes of climate change impacts across vast Arctic landscapes. New VHR remote sensing approaches allow assessing ice wedge polygonal structures and their change in unprecedented detail. Data science methods provide valuable tools for understanding and modeling resulting very large datasets of changing ice wedge networks. Here we quantify thermokarst development representing the network of troughs as a graph, a concept from discrete mathematics used to model complex networks. Our analysis is based on optical VHR aerial imagery of the DLR MACS sensors and DSMs derived from LiDAR. Datasets are available for 2009, 2014 and 2019 of the northern Anaktuvuk River Fire scar in Alaska, which formed due to a large tundra fire in 2007. In particular, the post-fire permafrost degradation is observable in the northern ice-rich region of the fire scar on short timescales, offering an ideal site for the monitoring of degradation processes. We use morphological image analysis to extract a graph from the imagery and further deduce trough parameters, such as soil volume, depth, and water availability. Quantifying these factors for the study area shows that soil erosion and ice melt within individual troughs have progressed, while the overall connectivity of the network has increased, implying strong thermo-erosion since 2009. Using graphs to monitor the ongoing development offers a detailed and computationally efficient method that will allow quantification of ice-wedge degradation over very large spatial and temporal scales and may provide useful metrics for projecting landscape trajectories in thaw-vulnerable permafrost environments.

Description: Thermokarst lakes are one of the most abundant landforms in periglacial landscapes. They develop in regions underlain by permafrost as a consequence of soil subsidence triggered by the melting of excess ground ice. As a result of further permafrost degradation and shoreline erosion, thermokarst lakes increase in size, expanding vertically and laterally. This growth process has strong impacts on local to regional hydrological networks and ecological functions of the surrounding landscape. Previous research on the lateral growth of thermokarst lakes usually focused on decadal time scales which results in averaged expansion rates. These averages mask the temporal and spatial variations of lateral thermokarst expansion that occur over shorter time periods of only a few years. The short-term variability results from complex interactions between local erosion processes and meteorological and permafrost conditions. The aim of our study is to quantify these short-term changes of lake shorelines to provide a better understanding of permafrost landscape processes using multi-temporal high-resolution satellite imagery. The images are in the visible and near-infrared spectrum with a resolution of 0.3 to 0.7 m. They cover the period from 2006 to 2017 with acquisitions every 2 to 4 years. In order to map the lake shoreline changes we developed a fully-automated, open-source workflow for analyzing the changes of waterbodies larger than 1000 m². First, all necessary pre-processing steps are implemented such as pansharpening and smoothing of any speckle over waterbodies. Then, the normalized difference water index (NDWI) is applied to extract waterbodies from the imagery and derive their shoreline geometry. After filtering for potentially misclassified elements that originate from infrastructure, shoreline movement rates are calculated using a nearest point analysis. The workflow is independent of scale, image spatial resolution, and waterbody geometry. Preliminary findings demonstrate that the approach provides reliable shoreline recognition for every time step in the different study areas even under difficult light conditions. Changes can be detected on a sub-meter scale. Finally, we discuss the influence of the waterbody’s size and geometry on the shoreline change processes.

Description: Although ponds make up roughly half of the total area of surface water in permafrost landscapes, their relevance to carbon dioxide emissions on a landscape scale has, to date, remained largely unknown. We have therefore investigated the inflows and outflows of dissolved organic and inorganic carbon from lakes, ponds, and outlets on Samoylov Island, in the Lena Delta of northeastern Siberia in September 2008, together with their carbon dioxide emissions. Outgassing of carbon dioxide (CO2) from these ponds and lakes, which cover 25% of Samoylov Island, was found to account for between 74 and 81% of the calculated net landscape-scale CO2 emissions of 0.2–1.1 g C m�2 d�1 during September 2008, of which 28–43% was from ponds and 27–46% from lakes. The lateral export of dissolved carbon was negligible compared to the gaseous emissions due to the small volumes of runoff. The concentrations of dissolved inorganic carbon in the ponds were found to triple during freezeback, highlighting their importance for temporary carbon storage between the time of carbon production and its emission as CO2. If ponds are ignored the total summer emissions of CO2-C from water bodies of the islands within the entire Lena Delta (0.7–1.3 Tg) are underestimated by between 35 and 62%.

Description: With current remote sensing technologies, it is not possible to directly measure the thermal state of the ground from spaceborne platforms. Here, we demonstrate that such limitations can be overcome by exploiting the combined information content of several remote sensing products in a data fusion approach. For this purpose, time series of remotely sensed land surface temperature, as well as snow cover and snow water equivalent, are employed to force ground thermal models which deliver ground temperatures and thaw depths. First, we present a semi-empirical model approach based on remotely sensed land surface temperatures and reanalysis products from which mean annual ground temperatures (MAGT) can be estimated at a spatial resolution of 1 km at continental scales. The approach is tested for the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2. The results are compared to in-situ temperature measurements in more than 100 boreholes from which the accuracy of the scheme is estimated to approximately 2.5 °C. Furthermore, we explore transient modeling of ground temperatures driven by remotely sensed land surface temperature, snow cover and snow water equivalent. The permafrost model CryoGrid 2 is applied to the Lena River Delta in NE Siberia (~25,000 km2) at 1 km spatial and weekly time resolution for the period 2000-2014. A comparison to in-situ measurements suggests a possible accuracy of around 1 °C for annual average ground temperatures, and around 0.1 m for thaw depths. However, information on subsurface stratigraphies including the distribution of ground ice is required to achieve this accuracy which is currently not available from remote sensing products alone. Finally, we discuss the potential and limitations of such schemes and give a feasibility assessment for both mountain and lowland permafrost regions.

Description: Evapotranspiration (ET) is a key component of the energy and water balances in permafrost tundra, establishing hydrological conditions for the next year and controlling several aspects of the carbon cycle. Both the energy balance and hydrological conditions of the landscape surface are important drivers of how Arctic climate change will impact landscape processes, including the carbon feedback. The accurate measurement of evapotranspiration within an energy balance context therefore provides crucial information on ecosystem functioning and raises our predictive capacity for estimating the impact of climate change. In this study we report field measurements from 13 summers (2002-14) using the eddy covariance method in a lowland ice-wedge polygon landscape within Russia’s Lena River Delta. These time-series are gap-filled and extrapolated with both statistical and process-based models to generate estimates of growing season ET. We find that interannual differences – including two August periods with high ET and two with low ET – are locally driven more by changes in air temperature and vapor pressure deficit (VPD) than in land surface characteristics or radiation. Except for periods of high VPD, aerodynamic resistance was greater than canopy surface resistance. We explore predictive relationships between various land surface indicators (e.g., NDVI, LAI, LST, Growing season length) derived from remote sensing products (MODIS) to quantify local mechanisms necessary for upscaling to the Delta region. Nighttime land surface temperature (MODIS) is found to be a strong predictor of evaporative flux at weekly to monthly time scales. Contrary to expectations resulting from climate change studies, we do not see evidence of a sustained interannual trend in ET or sensible heat flux. We conclude with implications for the local energy balance and responses to changes in sea ice extent and a warming climate.

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: We conducted eddy covariance measurements from April to August 2014 on a Siberian thermokarst lake. The study site is located in the Lena River Delta and characterized as a floating ice lake. Heat fluxes differed in magnitudes, directions and temporal patterns depending on the lake surface conditions (“frozen” ice cover, ice cover melt, and open water). Significant heat release during frozen ice cover conditions highlighted the importance of lakes for the landscape heat budget and water balance. The energy balance was nearly closed during the open water period and highlighted the impact of melting energy on its closure during the ice cover period. Sensible and latent heat dynamics were driven by temperature and water vapor gradients scaled by wind speed, respectively. We calculated bulk aerodynamics transfer coefficients and evaluated the performance of the derived in situ and three independent heat flux parameterization schemes. We found that bulk transfer models perform moderately to poorly for the different lake surface conditions. During the open water period small‐scale temporal variability could not be represented by the models, particularly in case of latent heat flux. The model results were less sensitive to the specific model type than to the accuracy of the surface water temperature measurement, which is dependent on a well‐thought‐out measurement design. Our study stresses considerations that are crucial for similar campaigns in the future, in order to face the measurement challenges encountered on arctic lakes especially during the ice cover period.

Description: We present a summary of validation efforts of MODIS land surface temperature (MOD11A1, MYD11A1) using in-situ observations from the high-arctic sites Ny-Ålesund (79 °N) and Austfonna ice cap (80 °N) on Svalbard, as well as Samoylov Island in NE Siberia (72 °N). For all three sites, multi-year time series of outgoing and incoming long-wave radiation are available from which the skin temperature can be calculated. Our analysis is focused on long-term averages of all-sky temperatures which are required to determine trends of surface temperatures. At all sites, yearly averages computed from all available MODIS LST measurements are cold-biased by up to 3 °C, which is mainly caused by a significant cold-bias during the winter period. A closer analysis using in-situ observations of cloudiness reveals two main error sources. First, winter surface temperatures are systematically warmer for cloudy skies, so that the satellite predominantly samples “cold” clear-sky conditions. Secondly, the cloud detection algorithm fails to exclude a significant number of cloudy scenes, so that colder cloud top temperatures are contained in the surface temperature record. For the Austfonna ice cap, we estimate that the fraction of such cloud top temperatures could exceed 40%, which highlights the importance of this error source. Over the N Atlantic region, the number of MODIS LST retrievals varies by up to a factor of three, with highest numbers on the Greenland ice sheet and lowest numbers on Iceland the coastal regions of Norway. When assessing trends in land surface temperatures through remote sensing, three factors must be considered: a) trends in the “true” fraction of cloudy conditions, b) trends in the surface temperature for cloudy conditions, and c) trends in misidentified cloudy scenes and cloud top temperatures. We demonstrate that a simple gap-filling procedure using downscaled air temperatures from the ERA-interim reanalysis can significantly improve the agreement with in-situ measurements. Such a composite product has the potential to moderate the influence of factors a and b, but cloud top temperatures due to misidentified cloudy scenes are still contained.

Description: In high‐latitude and mountain regions, local processes such as redistribution by wind, snow metamorphism and percolation of water, produce a complex spatial distribution of snow depths and snow densities. With its strong control on the ground thermal regime, this snow distribution has pronounced effects on ground temperatures at small spatial scales which are typically not resolved by land surface models (LSMs). This limits our ability to simulate the local impacts of climate change on for example vegetation and permafrost. Here, we present a tiling approach combining the CryoGrid permafrost model with snow microphysics parametrizations from the CROCUS snow scheme to account for sub‐grid lateral exchange of snow and water in a process‐based way. We demonstrate that a simple setup with three coupled tiles, each representing a different snow accumulation class with a specific topographic setting, can reproduce the observed spread of winter‐time ground surface temperatures (GST) and end‐of‐season snow distribution for a high‐Arctic site on Svalbard. For the three‐year study period, the three‐tile simulations showed substantial improvement compared to traditional single‐tile simulations which naturally cannot account for sub‐grid variability. Amongst others, the representation of the warmest and coldest 5% of the observed GST distribution was improved by 1‐2°C, while still capturing the average of the distribution. The simulations also reveal positive mean annual GSTs at the locations receiving the greatest snow cover. This could be an indication for the onset of localized permafrost degradation which would be obscured in single‐tile simulations.