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1. Recent warming reverses forty-year decline in catastrophic lake drainage and hastens gradual lake drainage across northern Alaska

   https://doi.org/10.1088/1748-9326/ac3602  

   Lara, Mark J. ; Chen, Yaping ; Jones, Benjamin M. ( November 2021 , Environmental Research Letters)

   Lakes represent as much as ∼25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e. 1975–2019) across 32 690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning 388 968 km². Drivers of lake drainage were determined with boosted regression tree models, using both static (e.g. lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g. temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10% and 16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ∼37% decade⁻¹from 1975–1979 (102–273 lakes draining year⁻¹) to 2010–2014 (3–8 lakes drainingmore »year⁻¹). However this 40 year negative trend was reversed during the most recent time-period (2015–2019), with observations of catastrophic drainage among the highest on record (i.e. 100–250 lakes draining year⁻¹), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R²_adj= 0.42, CV = 0.35,p< 0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R²_adj= 0.75, CV = 0.67,p< 0.0001). Models forecast a continued decline in lake area across northern Alaska by 15%–21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt ‘catastrophic’ drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization.

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2. Lake basin water level and ground temperature data in Arctic Alaska, 2019-2021

   https://doi.org/10.18739/A20R9M51B  

   Arp, Christopher ( January 2021 )

   Abstract

   Lakes are abundant features on coastal plains of the Arctic and most are termed &#34;thermokarst&#34; because they form in ice-rich permafrost and gradually expand over time. The dynamic nature of thermokarst lakes also makes them prone to catastrophic drainage and abrupt conversion to wetlands, called drained thermokarst lake basins (DTLBs). Together, thermokarst lakes and DTLBs cover up to 80% of arctic lowland regions, making understanding their response to ongoing climate change essential for coastal plain environmental assessment. Datasets presented here document water level and temperature (surface and ground) regimes for a large (38 sites) array of lake with high drainage potential and lake basin (DTLBS), which have already drained, located on differing terrain units of Alaska&#39;s Arctic Coastal Plain. Lake data was measured along deep protected shorelines using pressure transducers to record hourly water level and bed temperature. Wetland (DTLB) data was also measured with pressure transducers and ground thermistors at 25 and 100 centimeters (cm) depth. Of special interest at some DTLB sites was the potential occurrence of snow-dam outburst events during the early summer snowmelt periods. In these cases, pressure transducers were set to log at 10 minute intervals for this period. All data archived here are summarized at daily average values.
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3. Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model

   https://doi.org/10.5194/gmd-15-7421-2022  

   Clark, Jason A. ; Jafarov, Elchin E. ; Tape, Ken D. ; Jones, Benjamin M. ; Stepanenko, Victor ( January 2022 , Geoscientific Model Development)

   Abstract. Lakes in the Arctic are important reservoirs of heat withmuch lower albedo in summer and greater absorption of solar radiation thansurrounding tundra vegetation. In the winter, lakes that do not freeze totheir bed have a mean annual bed temperature >0 ∘C inan otherwise frozen landscape. Under climate warming scenarios, we expectArctic lakes to accelerate thawing of underlying permafrost due to warmingwater temperatures in the summer and winter. Previous studies of Arcticlakes have focused on ice cover and thickness, the ice decay process,catchment hydrology, lake water balance, and eddy covariance measurements,but little work has been done in the Arctic to model lake heat balance. Weapplied the LAKE 2.0 model to simulate water temperatures in three Arcticlakes in northern Alaska over several years and tested the sensitivity ofthe model to several perturbations of input meteorological variables(precipitation, shortwave radiation, and air temperature) and several modelparameters (water vertical resolution, sediment vertical resolution, depthof soil column, and temporal resolution). The LAKE 2.0 model is aone-dimensional model that explicitly solves vertical profiles of waterstate variables on a grid. We used a combination of meteorological data fromlocal and remote weather stations, as well as data derived from remotesensing, to drive the model. We validated modeled water temperatures withdatamore »of observed lake water temperatures at several depths over severalyears for each lake. Our validation of the LAKE 2.0 model is a necessarystep toward modeling changes in Arctic lake ice regimes, lake heat balance,and thermal interactions with permafrost. The sensitivity analysis shows usthat lake water temperature is not highly sensitive to small changes in airtemperature or precipitation, while changes in shortwave radiation and largechanges in precipitation produced larger effects. Snow depth and lake icestrongly affect water temperatures during the frozen season, which dominatesthe annual thermal regime of Arctic lakes. These findings suggest thatreductions in lake ice thickness and duration could lead to more heatstorage by lakes and enhanced permafrost degradation.« less
4. First pan-Arctic assessment of dissolved organic carbon in lakes of the permafrost region

   https://doi.org/10.5194/bg-18-3917-2021  

   Stolpmann, Lydia ; Coch, Caroline ; Morgenstern, Anne ; Boike, Julia ; Fritz, Michael ; Herzschuh, Ulrike ; Stoof-Leichsenring, Kathleen ; Dvornikov, Yury ; Heim, Birgit ; Lenz, Josefine ; et al ( January 2021 , Biogeosciences)

   Abstract. Lakes in permafrost regions are dynamic landscapecomponents and play an important role for climate change feedbacks. Lakeprocesses such as mineralization and flocculation of dissolved organiccarbon (DOC), one of the main carbon fractions in lakes, contribute to thegreenhouse effect and are part of the global carbon cycle. These processesare in the focus of climate research, but studies so far are limited to specificstudy regions. In our synthesis, we analyzed 2167 water samples from 1833lakes across the Arctic in permafrost regions of Alaska, Canada, Greenland,and Siberia to provide first pan-Arctic insights for linkages between DOCconcentrations and the environment. Using published data and unpublisheddatasets from the author team, we report regional DOC differences linked tolatitude, permafrost zones, ecoregions, geology, near-surface soil organiccarbon contents, and ground ice classification of each lake region. The lakeDOC concentrations in our dataset range from 0 to1130 mg L−1 (10.8 mg L−1 median DOC concentration). Regarding thepermafrost regions of our synthesis, we found median lake DOC concentrationsof 12.4 mg L−1 (Siberia), 12.3 mg L−1 (Alaska),10.3 mg L−1 (Greenland), and 4.5 mg L−1 (Canada). Our synthesisshows a significant relationship between lake DOC concentration and lakeecoregion. We found higher lake DOC concentrations at boreal permafrostsites compared to tundra sites. We found significantly higher DOCconcentrations in lakes in regions with ice-rich syngenetic permafrostdepositsmore »(yedoma) compared to non-yedoma lakes and a weak but significantrelationship between soil organic carbon content and lake DOC concentrationas well as between ground ice content and lake DOC. Our pan-Arctic datasetshows that the DOC concentration of a lake depends on its environmentalproperties, especially on permafrost extent and ecoregion, as well asvegetation, which is the most important driver of lake DOC in this study.This new dataset will be fundamental to quantify a pan-Arctic lake DOC poolfor estimations of the impact of lake DOC on the global carbon cycle andclimate change.« less
5. Potential Future Lake Drainage for the Western Arctic Coastal Plain in northern Alaska from an Interferometric Synthetic Aperture Radar (IFSAR)-Derived Digital Surface Model, 2002-2003

   https://doi.org/10.18739/A2JH3D29N  

   Jones, Benjamin ; Arp, Christopher ; Grosse, Guido ; Nitze, Ingmar ; Lara, Mark ; Whitman, Matthew ; Farquharson, Louise ; Kanevskiy, Mikhail ; Parsekian, Andrew ; Breen, Amy ; et al ( January 2019 )

   Abstract

   Assessment of lakes for their future potential to drain relied on the 2002/03 airborne Interferometric Synthetic Aperture Radar (IFSAR) Digital Surface Model (DSM) data for the western Arctic Coastal Plain in northern Alaska. Lakes were extracted from the IfSAR DSM using a slope derivative and manual correction (Jones et al., 2017). The vertical uncertainty for correctly detecting lake-based drainage gradients with the IfSAR DSM was defined by comparing surface elevation differences of several overlapping DSM tile edges. This comparison showed standard deviations of elevation between overlapping IfSAR tiles ranging from 0.0 to 0.6 meters (m). Thus, we chose a minimum height difference of 0.6 m to represent a detectable elevation gradient adjacent to a lake as being most likely to contribute to a rapid drainage event. This value is also in agreement with field verified estimates of the relative vertical accuracy (~0.5 m) of the DSM dataset around Utqiaġvik (formerly Barrow) (Manley et al., 2005) and the stated vertical RMSE (~1.0 m) of the DSM data (Intermap, 2010). Development of the potential lake drainage dataset involved several processing steps. First, lakes were classified as potential future drainage candidates if the difference between the elevation of the lake surface and the lowest elevation within a 100 m buffer of the lake shoreline exceeded our chosen threshold of 0.6 m. Next, we selected lakes with a minimum size of 10 ha to match the historic lake drainage dataset. We further filtered the dataset by selecting lakes estimated to have low hydrological connectivity based on relations between lake contributing area as determined for specific surficial geology types and presented in Jones et al. (2017). This was added to the future projection workflow to isolate the lake population that likely responds to changes in surface area driven largely by geomorphic change as opposed to differences in surface hydrology. Lakes within a basin with low to no hydrologic connectivity that had an elevation change gradient between the lake surface and surrounding landscape are considered likely locations to assess for future drainage potential. Further, the greater the elevation difference, the greater the drainage potential. This dataset provided a first-order estimate of lakes classified as being prone to future drainage. We further refined our assessment of potential drainage lakes by identifying the location of the point with the lowest elevation within the 100 m buffer of the lake shoreline and manually interpreted lakes to have a high drainage potential based on the location of the likely drainage point to known lake drainage pathways using circa 2002 orthophotography or more recent high resolution satellite imagery available for the Western Coastal Arctic Plain (WACP). Lakes classified as having a high drainage potential typically had the likely drainage location associated with one or more of the following: (1) an adjacent lake, (2) the cutbank of a river, (3) the ocean, (4) were located in an area with dense ice-wedge networks, (5) appeared to coincide with a potentially headward eroding stream, or (6) were associated with thermokarst lake shoreline processes in the moderate to high ground ice content terrain. We also added information on potential lake drainage pathways to the high potential drainage dataset by manually interpreting the landform associated with the likely drainage site to draw comparisons with the historic lake drainage dataset.
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