Landesklinikum Korneuburg

The Arctic permafrost is warming widely, and the circumpolar region is heating up four times faster than the global average. Common features of permafrost landscapes are drained lake basins (DLBs) which play an important role for the geomorphological, hydrological and the ecological development of those landscapes. In addition, rivers transport significant amounts of freshwater, dissolved organic carbon, and other materials into the Arctic Ocean. The impact of close-by streams for DLB drainage and refilling events needs to be quantified.Here, we focus on DLBs and their connections to river floodplains on the Yamal Peninsula in northern Siberia, a region underlain by both discontinuous and continuous permafrost and covered with tundra vegetation, thaw lakes, and wetlands. For this study, we investigated manually selected DLBs using DEM derivatives. The lakes represent a North-South climatic gradient and different drained lake basin development stages. DEM derivatives are also used to represent related small-scale landscape features in climate models. The utility of metrics based on the Copernicus DEM, which were recently implemented in the ICON-Land model, was assessed. Potential floodplain linkage could be identified in many cases, leading also to diverting patterns of landcover evolution after drainage.The results provide a foundation for further analysis of lakes and their connections to streams, which play a role in the wetting and drying processes and subsequent impacts on the carbon cycle across the Arctic.

Arctic permafrost has been identified as a critical element in the global climate system, since it stores a vast amount of carbon that is at high risk of being released under climate change. The feedbacks between permafrost carbon and climate change are moderated by complex interactions between physical, hydrological, biogeochemical, and ecological processes. Many of these are not well understood to date, and therefore also only rudimentarily represented in current Earth System Models (ESMs).The Q-ARCTIC project funded by the European Research Council (ERC) follows a synergetic approach by combining remote sensing and local-scale observations with modeling on scales from a few meters to hundreds of kilometers. The primary objective of Q-ARCTIC is to close the gap between process scales and the much coarser grid resolution of Earth System Models (ESMs), with a particular focus on the net effect of disturbance processes and associated changes in hydrology on the pan-Arctic scale. To close this gap, we developed new ESM modules representing subgrid through stochastic parameterizations, trained and evaluated with high-resolution remote sensing data and site-level observations.We will present novel results from in-situ experiments that quantify carbon fluxes and environmental response functions at patch-level within heterogeneous Arctic ecosystems, resulting in optimized strategies to integrate data streams for upscaling. Satellite remote sensing products investigate fine scale (few meters) patterns in Arctic landscapes that are undergoing modifications linked to climate change, including e.g. InSAR data to constrain ground ice content, and related subsidence patterns. Targets investigated include for example sinking surfaces, wetness gradients in heterogeneous landscapes, and drained lake basins. Assimilation of these new datasets supported the development of new ESM model components that successfully capture the statistics of small-scale features, including e.g. lateral connections between hydrologic landscape elements across scales, or thermokarst lake dynamics. Our results demonstrate that the ability to project the response of the high-latitude water, energy and carbon cycles to rising global temperatures may strongly depend on the ability to adequately represent the soil hydrology and disturbance effects in permafrost affected regions.

Infrastructure in the Arctic is expanding rapidly due to ongoing industrial development, yet it faces increasing challenges as climate warming accelerates permafrost degradation and leads to or increases ground instability. Satellite records support the identification of infrastructure developments. Remote sensing based ground surface deformation monitoring is another key component for managing permafrost-related infrastructure risks and supporting community planning.To systematically map human-impacted Arctic coastal regions, the Sentinel-1/2 derived Arctic Coastal Human Impact (SACHI) dataset was developed. In this study, we updated this dataset for selected Arctic settlements in western Greenland using newly acquired imagery that captures recently constructed man-made features. Additionally, we investigated the potential of fully polarimetric PALSAR-3 L-band SAR data to complement the established Sentinel-1 (C-band)/ Sentinel-2 workflow, aiming to improve the detection and characterization of infrastructure. The scheme is based on fusion of two machine learning techniques, Gradient boosting machines (GBM) and a deep learning approach using convolutional neural networks. Specifically, the added value for building detection as part of the GBM analyses can been shown. Eventually we combined the settlement information with long-term vertical ground deformation for selected settlements.

Geographical and ecological research of Arctic tundra requires a clear definition of the biome boundaries. A commonly used dataset for the Arctic is the tundra biome boundary mapped by the Circumpolar Arctic Vegetation Mapping Project (CAVM, 2003). Given recent advances in satellite-image resolution and data availability, in the age of the rapidly changing Arctic climate this previously established delineation needs to be re-evaluated and, where necessary, updated. An updated boundary can support applications such as Arctic climate modelling and assessments of potential disturbances in permafrost rich regions.A recently developed landcover dataset was investigated for this study. The Circumpolar Landcover Unit (CALU) Database provides highly detailed landcover information with a spatial resolution of 10 meters and consists of 23 thematic units, including 12 units representing tundra but also 3 forest classes. The used retrieval scheme of landcover units employed provides an unprecedented level of detail. The landcover units have been derived by fusion of satellite data using Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral). These units reflect gradients in moisture and vegetation structure. The available spatial detail of CALU has been already shown to provide the means to assess the complexity of lowland permafrost regions.The original CALU database of version 1.0 covered the Arctic within the CAVM extent only. The latest version 2.0 partially extends further south, providing additional detail within the transition zone for many areas.The aim of this study was to assess the southern boundary of the CAVM and to identify regions where further developments of the CALU dataset may aid to establish a new boundary. Spatial statistics were collected within selected buffer areas of the CAVM boundary. In addition, longitudinal zones were generated to test whether forest-related CALU classes systematically peak south of the currently mapped boundary.Based on these statistics, in regions such as Alaska and the European part of Russia, the CAVM boundary generally corresponds well with CALU, with forest-related classes mostly dominating within the buffer area. In parts of Siberia and Canada, however, shrub-tundra classes are more prevalent, while forest-related classes occur farther south. This mismatch may reflect regional differences in vegetation structure and terrain-driven zonation, suggesting that a single latitudinal boundary product may not capture local transitions equally well everywhere.Preliminary results indicate that in several regions the CALU database should be extended further south, because current coverage does not fully include forest-related classes. This limitation affects the use of CALU for the tundra-boreal biome boundary evaluation and for applications that require a consistent representation of tundra–taiga transitions.CALU: Bartsch, A., Khairullin, R., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., Leibman, M., & Gruber, C. (2024). Circumarctic Landcover Units (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1423573

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic methane (CH₄) budgets are uncertain because field measurements often capture only fragments of the wet-to-dry gradient that control tundra CH₄fluxes. Wet hotspots are over-represented, while dry, net-sink sites are under-sampled. We paired over 13,000 chamber flux measurements during peak growing season in July (2019&ndash;2024) from Trail Valley Creek in the western Canadian Arctic with co-registered remotely sensed predictor variables to test how spatial resolution (1 m vs. 10 m) and choice of machine-learning algorithm shape upscaled CH₄flux maps over our 3.1 km² study domain. Four algorithms for CH₄ flux scaling (Random Forest (RF), Gradient Boosting Machine (GBM), Generalised Additive Model (GAM), and Support Vector Regression (SVR)) were tuned using the same stack of multispectral indices, terrain derivatives and a six-class landscape classification. Tree-based models such as RF and GBM offered the best balance of 10-fold cross-validated R&sup2; (&le;0.75) and errors, so RF and GBM were used in a subsequent step for upscaling to the study area. With 1 m resolution, GBM captured the full range of microtopographic extremes and predicted a mean July flux of 99 mg CH₄ m^-2 month^-1. In contrast, RF, which smoothed local extremes, yielded an average flux of 519 mg CH₄ m^-2 month^-1. The disagreement between flux estimates using GBM and RF correlated mainly with the Normalized Difference Water Index (NDWI), a moisture proxy, and was most pronounced in waterlogged, low-lying areas. Aggregating predictors to 10 m averaged the sharp metre-scale flux highs in hollows and lows on ridges, narrowing the GBM-RF difference to ~75 mg CH₄ m^-2 month^-1while broadening the overall flux distribution with more intermediate values. At 1 m, microtopography is the main driver. At 10 m, moisture proxies explained about half of the variance. Our results demonstrate that: (i) sub‑metre predictors are indispensable for capturing the wet-dry microtopography and its CH₄ signals, (ii) upscaling algorithm selection strongly controls prediction spread and uncertainty once that microrelief is resolved, and (iii) coarser grids smooth local microtopographic details, resulting in flattened CH₄ flux peaks and wider distribution. All factors combined lead to potentially large differences in scaled CH₄ flux budgets, calling for a careful selection of scaling approaches, spatial predictor layers (e.g., vegetation, moisture, topography), and grid resolution. Future work should couple ultra-high-resolution imagery with temporally dynamic indices to reduce upscaling bias along Arctic wetness gradients.

Permafrost degradation in the Arctic is accelerating and expected to enhance greenhouse gas (GHG) emissions. The Arctic Methane and Permafrost Challenge (AMPAC) was initialized by NASA and ESA as a transatlantic networking action striving to promote scientific research and improve observational capabilities. Earth Observation technology must be harnessed, expanded and synergies exploited to accurately quantify and better understand the state of the permafrost and coincident methane emissions. AMPAC aims at improving the observation capacity over polar regions by evaluating dedicated campaign activities, by analyzing satellite data, and by identifying satellite retrieval improvements. AMPAC provides suggestions for the enhanced exploitation of the increasing Earth observation (EO) capacities of land surface, cryosphere, biosphere and atmosphere missions to enhance the scientific understanding of changes in Arctic permafrost regions and methane emissions and to bridge the gap between top-down (T-D) and bottom-up (B-U) estimates of methane fluxes in the changing Arctic.In particular, monitoring of methane concentrations as well as landcover properties related to wetland and freeze/thaw dynamics is needed. Upcoming synthetic aperture radar missions and constellations of multiple multispectral sensors are expected to advance relevant monitoring capabilities significantly. This will allow better representation of seasonality and advance methane source attribution in general. In addition, continuity of current missions providing greenhouse gas observations, including methane, is crucial. Active optical instruments (lidar) developed for concentration retrieval are expected to substantially enhance detection capabilities across the Arctic.We provide an overview of relevant current and approved atmosphere and land-focused satellite missions of NASA, NOAA, ESA and DLR/CNES with emphasis on advancements and remaining gaps in the context of AMPAC.

Circumpolar permafrost landscapes are undergoing rapid transition and are strongly affected by climate warming. In these high latitude regions, the rising temperatures are disrupting the thermal equilibrium of the ground, influencing the Arctics moisture levels (wetting / drying) which is driving significant changes in hydrological regimes. The Arctic is a water-rich region with abundant freshwater systems. An important feature of large rivers is their discharge of globally significant quantities of freshwater, dissolved organic carbon, and other materials into the Arctic Ocean, while lakes and rivers also contribute to the global emission of carbon dioxide and methane to the atmosphere.For this study, three different Arctic river systems were investigated: the Mackenzie (Canada), the Ob (Russia) and the Lena (Russia). We mapped river systems at different levels of detail and localized floodplain-related areas by combining Digital Elevation Model (DEM) analysis with land cover maps (CALU). The floodplain areas are described by the fraction distribution of different land cover units. For example, the majority of the detected units showed water as the dominant unit. After filtering out the water areas, the remaining floodplain areas primarily consisted of wetland. We also separated the areas according to their bioclimate subzones (CAVM) which showed no significant differences in wet/dry units between the subzones; in all cases, wet areas were the majority.This work contributes to the mapping and characterization of rivers in the Arctic, with a focus on identifying, describing, and analyzing floodplains in relation to river systems. The results will enhance the understanding of Arctic hydrology, providing a foundation for further analysis of the wetting and drying in the Arctic which is the focus of the ERC project Q-Arctic.Datasets:CALU: Bartsch, A., Khairullin, R., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., Leibman, M., &amp;amp; Gruber, C. (2024). Circumarctic Landcover Units (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14235736 CAVM: Walker, D. A., Raynolds, M. K., Dani&amp;#235;ls, F. J., Einarsson, E., Elvebakk, A., Gould, W. A., Katenin, A. E., Kholod, S. S., Markon, C. J., Melnikov, E. S., Moskalenko, N. G., Talbot, S. S., Yurtsev, B. A., and other members of the CAVM Team (2005). The Circumpolar Arctic vegetation map, J. Veg. Sci., 16, 267&amp;#8211;282, https://doi.org/10.1111/j.1654-1103.2005.tb02365.x

Modeling climate-driven changes in permafrost, particularly surface subsidence caused by melting ground ice, remains a significant challenge for Earth System Models (ESMs) due to high spatial and temporal heterogeneity inherent in permafrost dynamics.In this study, we investigate permafrost subsidence using the latest InSAR satellite data on ground displacement. With its meter-scale resolution, InSAR data provides a unique opportunity to examine the highly heterogeneous nature of permafrost subsidence unprecedented sampling density and coverage area. Statistical analyses were conducted on high-resolution data from PALSAR-2, covering five regions: Central North Slope, Inuvik region, Noatak River Basin, Yamal, and Yukon-Kuskokwim Delta.Our findings reveal that permafrost subsidence exhibits consistent statistical properties. The Exponential Weibull distribution (EWD) emerged as the best-fit model across all regions and scales, effectively capturing the skewed and heavy-tailed nature of subsidence distributions. Correlation analyses between subsidence and potential driving factors, including climatic variables derived from ERA5-Land, soil class, and topography, showed low direct correlations. Additional analysis of clustered subsidence distributions in relation to local environmental conditions was performed to explore cross-regional commonalities.Furthermore, we identified key requirements and limitations for improving permafrost subsidence analyses using InSAR data. First, the quality of observation data does not significantly improve beyond a certain threshold of sample size and resolution. While larger datasets produce smoother histograms, the overall shape of the distribution remains unchanged. Second, results from a series of Kolmogorov-Smirnov (K-S) tests show that subsidence data reliability is insensitive to any Gaussian distributed noises.These insights highlight some robustness in the statistical nature of permafrost subsidence while emphasizing the need to focus on other factors, such as temporal and spatial coverage, to advance future analyses on permafrost subsidence under climate impacts. Additionally, the choice of data filters plays a critical role, as effective filtering can preserve large-scale patterns while mitigating atmospheric artifacts.This study provides a statistical perspective on utilizing InSAR data to gain new insights into permafrost subsidence, while identifying current data limitations that urgently need to be addressed.

The identification of spatial soil moisture patterns is of high importance for various applications in high latitude permafrost regions, but challenging with common remote sensing approaches due to high landscape heterogeneity. Seasonal thawing and freezing of near-surface soil lead to subsidence-heave cycles in the presence of ground ice, which can exhibit magnitudes of several centimeters. Our investigations document higher Sentinel-1 InSAR seasonal subsidence rates for locations with higher near-surface soil moisture compared to dryer ones. Based on this, we demonstrate that the relationship of thawing degree days -a measure of seasonal heating -and subsidence signals can be interpreted to assess spatial variations of near-surface soil moisture. A range of challenges, however, need to be addressed. We discuss the implications of using different sources of temperature data for deriving thawing degree days on the results. Atmospheric effects must be considered, as simple spatial filtering can suppress large-scale permafrost-related subsidence signals and lead to the underestimation of displacement values, making GACOS-corrected results preferable for the tested sites. Seasonal subsidence rate retrieval which considers these aspects provides a valuable tool for distinguishing between wet and dry landscape features, which is relevant for permafrost degradation monitoring in Arctic lowland permafrost regions. Spatial resolution constraints, however, remain for smaller typical permafrost features which drive wet versus dry conditions such as high and low centred polygons.

Objective. This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data.Approach. We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario.Main results. Interrater agreement was substantial at a kappa of 0.73 (0.68-0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67-0.76) and a sensitivity and specificity of 0.90 (0.82-0.94) and 0.95 (0.93-0.97), respectively.Significance. The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.