Search

Landslides are a frequent natural hazard occurring globally in regions with steep topography. Additionally, landslides play an important role in landscape evolution by transporting sediment downslope. Landslide inventory mapping is a common technique to assess the spatial distribution and extent of landslides in an area of interest. High-resolution digital elevation models (DEMs) have proven to be useful databases to map landslides in large areas across different land covers and topography. So far, Denmark had no national landslide inventory. Here, we create the first comprehensive national landslide inventory for Denmark derived from a 40 cm resolution DEM from 2015 supported by several 12.5 cm resolution orthophotos. The landslide inventory is created based on a manual expert-based mapping approach, and we implemented a quality control mechanism to assess the completeness of the inventory. Overall, we mapped 3202 landslide polygons in Denmark with a level of completeness of 87 %. The complete landslide inventory is freely available for download at 10.6084/m9.figshare.16965439.v2 (Svennevig and Luetzenburg, 2021) or as a web map (https://data.geus.dk/landskred/ , last access: 6 June 2022) for further investigations. [ABSTRACT FROM AUTHOR]

Landslides are a frequent natural hazard occurring globally in regions with steep topography. Additionally, landslides are playing an important role in landscape evolution by transporting sediment downslope. Landslide inventory mapping is a common technique to assess the spatial distribution and extend of landslides in an area of interest. High-resolution digital elevation models (DEMs) have proven to be useful databases to map landslides in large areas across different land covers and topography. So far, Denmark had no national landslide inventory. Here we create the first comprehensive national landslide inventory for Denmark derived from a 40 cm resolution DEM from 2015 supported by several 12.5 cm resolution orthophotos. The landslide inventory is created based on a manual expert-based mapping approach, and we implemented a quality control mechanism to assess the completeness of the inventory. Overall, we mapped 3202 landslide polygons in Denmark with a level of completeness of 87 %. The landslide inventory can act as a starting point for a more comprehensive hazard and risk reduction framework for Denmark. Furthermore, machine-learning algorithms can use the dataset as a training dataset to improve future automated mapping approaches. The complete landslide inventory is made freely available for download at https://doi.org/10.6084/m9.figshare.16965439.v1 (Svennevig and Luetzenburg, 2021) or as web map (https://data.geus.dk/landskred/) for further investigations. [ABSTRACT FROM AUTHOR]

Glacial lake outburst floods (GLOFs) or jökulhlaups from ice-dammed lakes are frequent in Greenland and can influence local ice dynamics and bedrock motion, cause geomorphological changes, and pose flooding hazards. Multidecadal time series of lake drainage dates, volumes, and flood outlets are extremely rare. However, they are essential for determining the scale and frequency of future GLOFs, for identifying drainage mechanisms, and for mitigating downstream flood effects. In this study, we use high-resolution digital elevation models (DEMs) and orthophotos (0.1 × 0.1 m) generated from uncrewed-aerial-vehicle (UAV) field surveys, in combination with optical satellite imagery. This allows us to reconstruct robust lake volume changes associated with 14 GLOFs between 2007 and 2021 at Russell Glacier, West Greenland. As a result, this is one of the most comprehensive and longest records of ice-dammed lake drainages in Greenland to date. Importantly, we find a mean difference of ∼ 10 % between our lake drainage volumes when compared with estimates derived from a gauged hydrograph 27 km downstream. Due to thinning of the local ice dam, the potential maximum drainage volume in 2021 is ∼ 60 % smaller than that estimated to have drained in 2007. Our time series also reveals variations in the drainage dates ranging from late May to mid-September and drainage volumes ranging between 0.9 and 37.7 Mm 3. We attribute these fluctuations between short periods of relatively high and low drainage volumes to a weakening of the ice dam and an incomplete sealing of the englacial tunnel following the large GLOFs. This syphoning drainage mechanism is triggered by a reduction in englacial meltwater, likely driven by late-season drainage and sudden air temperature reductions, as well as annual variations in the glacial drainage system. Furthermore, we provide geomorphological evidence of an additional drainage route first observed following the 2021 GLOF, with a subglacial or englacial flow pathway, as well as supraglacial water flow across the ice margin. It seems probable that the new drainage route will become dominant in the future. This will drive changes in the downstream geomorphology and raise the risk of flooding-related hazards as the existing buffering outlet lakes will be bypassed. [ABSTRACT FROM AUTHOR]

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet. [ABSTRACT FROM AUTHOR]

We compile and analyze all available geothermal heat flow measurements collected in and around Greenland into a new database of 419 sites and generate an accompanying spatial map. This database includes 290 sites previously reported by the International Heat Flow Commission (IHFC), for which we now standardize measurement and metadata quality. This database also includes 129 new sites, which have not been previously reported by the IHFC. These new sites consist of 88 offshore measurements and 41 onshore measurements, of which 24 are subglacial. We employ machine learning to synthesize these in situ measurements into a gridded geothermal heat flow model that is consistent across both continental and marine areas in and around Greenland. This model has a native horizontal resolution of 55 km. In comparison to five existing Greenland geothermal heat flow models, our model has the lowest mean geothermal heat flow for Greenland onshore areas. Our modeled heat flow in central North Greenland is highly sensitive to whether the NGRIP (North GReenland Ice core Project) elevated heat flow anomaly is included in the training dataset. Our model's most distinctive spatial feature is pronounced low geothermal heat flow (< 40 mW m -2) across the North Atlantic Craton of southern Greenland. Crucially, our model does not show an area of elevated heat flow that might be interpreted as remnant from the Icelandic plume track. Finally, we discuss the substantial influence of paleoclimatic and other corrections on geothermal heat flow measurements in Greenland. The in situ measurement database and gridded heat flow model, as well as other supporting materials, are freely available from the GEUS Dataverse (10.22008/FK2/F9P03L; Colgan and Wansing, 2021). [ABSTRACT FROM AUTHOR]

We compile, analyse and map all available geothermal heat flow measurements collected in and around Greenland into a new database of 419 sites and generate an accompanying spatial map. This database includes 290 sites previously reported by the International Heat Flow Commission (IHFC), for which we now standardize measurement and metadata quality. This database also includes 129 new sites, which have not been previously reported by the IHFC. These new sites consist of 88 offshore measurements and 41 onshore measurements, of which 24 are subglacial. We employ machine learning to synthesize these in situ measurements into a gridded geothermal heat flow model that is consistent across both continental and marine areas in and around Greenland. This model has a native horizontal resolution of 55 km. In comparison to five existing Greenland geothermal heat flow models, our model has the lowest mean geothermal heat flow for Greenland onshore areas (44 mW m-2). Our model's most distinctive spatial feature is pronounced low geothermal heat flow (< 40 mW m-2) across the North Atlantic Craton of southern Greenland. Crucially, our model does not show an area of elevated heat flow that might be interpreted as remnant from the Icelandic Plume track. Finally, we discuss the substantial influence of paleoclimatic and other corrections on geothermal heat flow measurements in Greenland. The in-situ measurement database and gridded heat flow model, as well as other supporting materials, are freely available from the GEUS DataVerse (https://doi.org/10.22008/FK2/F9P03L; Colgan and Wansing, 2021). [ABSTRACT FROM AUTHOR]