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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 draining 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. 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 , Arctic Data Center)

   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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3. Identifying historical and future potential lake drainage events on the western Arctic coastal plain of Alaska

   https://doi.org/10.1002/ppp.2038  

   Jones, Benjamin M. ; Arp, Christopher D. ; Grosse, Guido ; Nitze, Ingmar ; Lara, Mark J. ; Whitman, Matthew S. ; Farquharson, Louise M. ; Kanevskiy, Mikhail ; Parsekian, Andrew D. ; Breen, Amy L. ; et al ( February 2020 , Permafrost and Periglacial Processes)

   Arctic lakes located in permafrost regions are susceptible to catastrophic drainage. In this study, we reconstructed historical lake drainage events on the western Arctic Coastal Plain of Alaska between 1955 and 2017 using USGS topographic maps, historical aerial photography (1955), and Landsat Imagery (ca. 1975, ca. 2000, and annually since 2000). We identified 98 lakes larger than 10 ha that partially (>25% of area) or completely drained during the 62‐year period. Decadal‐scale lake drainage rates progressively declined from 2.0 lakes/yr (1955–1975), to 1.6 lakes/yr (1975–2000), and to 1.2 lakes/yr (2000–2017) in the ~30,000‐km²study area. Detailed Landsat trend analysis between 2000 and 2017 identified two years, 2004 and 2006, with a cluster (five or more) of lake drainages probably associated with bank overtopping or headward erosion. To identify future potential lake drainages, we combined the historical lake drainage observations with a geospatial dataset describing lake elevation, hydrologic connectivity, and adjacent lake margin topographic gradients developed with a 5‐m‐resolution digital surface model. We identified ~1900 lakes likely to be prone to drainage in the future. Of the 20 lakes that drained in the most recent study period, 85% were identified in this future lake drainage potential dataset. Our assessment of historical lake drainage magnitude, mechanisms and pathways, and identification of potential future lake drainages provides insights into how arctic lowland landscapes may change and evolve in the coming decades to centuries.

    

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4. The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: fast-forward into the future

   https://doi.org/10.5194/tc-14-4279-2020  

   Nitze, Ingmar ; Cooley, Sarah W. ; Duguay, Claude R. ; Jones, Benjamin M. ; Grosse, Guido ( January 2020 , The Cryosphere)

   null (Ed.)

   Abstract. Northwestern Alaska has been highly affected by changing climatic patternswith new temperature and precipitation maxima over the recent years. Inparticular, the Baldwin and northern Seward peninsulas are characterized byan abundance of thermokarst lakes that are highly dynamic and prone to lakedrainage like many other regions at the southern margins of continuouspermafrost. We used Sentinel-1 synthetic aperture radar (SAR) and PlanetCubeSat optical remote sensing data to analyze recently observed widespreadlake drainage. We then used synoptic weather data, climate model outputs andlake ice growth simulations to analyze potential drivers and future pathwaysof lake drainage in this region. Following the warmest and wettest winter onrecord in 2017/2018, 192 lakes were identified as having completely orpartially drained by early summer 2018, which exceeded the average drainagerate by a factor of ∼ 10 and doubled the rates of the previousextreme lake drainage years of 2005 and 2006. The combination of abundantrain- and snowfall and extremely warm mean annual air temperatures (MAATs),close to 0 ∘C, may have led to the destabilization of permafrostaround the lake margins. Rapid snow melt and high amounts of excessmeltwater further promoted rapid lateral breaching at lake shores andconsequently sudden drainage of some of the largest lakes of the studyregion that have likely persisted for millennia. We hypothesize that permafrostdestabilization and lake drainage will accelerate and become the dominantdrivers of landscape change in this region. Recent MAATs are already withinthe range of the predictions by the University of Alaska Fairbanks' Scenarios Network for Alaska and Arctic Planning (UAF SNAP) ensemble climate predictions inscenario RCP6.0 for 2100. With MAAT in 2019 just below 0 ∘C at the nearby Kotzebue, Alaska, climate station, permafrost aggradation in drained lake basins will become less likely after drainage, strongly decreasing the potential for freeze-locking carbon sequestered in lake sediments, signifying a prominent regime shift in ice-rich permafrost lowland regions. 

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5. The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

   https://doi.org/10.5194/essd-13-5127-2021  

   Olefeldt, David ; Hovemyr, Mikael ; Kuhn, McKenzie A. ; Bastviken, David ; Bohn, Theodore J. ; Connolly, John ; Crill, Patrick ; Euskirchen, Eugénie S. ; Finkelstein, Sarah A. ; Genet, Hélène ; et al ( January 2021 , Earth System Science Data)

   Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers areexpected to increase in response to warming and associated permafrost thaw.However, the lack of appropriate land cover datasets for scalingfield-measured methane emissions to circumpolar scales has contributed to alarge uncertainty for our understanding of present-day and future methaneemissions. Here we present the Boreal–Arctic Wetland and Lake Dataset(BAWLD), a land cover dataset based on an expert assessment, extrapolatedusing random forest modelling from available spatial datasets of climate,topography, soils, permafrost conditions, vegetation, wetlands, and surfacewater extents and dynamics. In BAWLD, we estimate the fractional coverage offive wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes(17 % of the global land surface). Land cover classes were defined usingcriteria that ensured distinct methane emissions among classes, as indicatedby a co-developed comprehensive dataset of methane flux observations. InBAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain)with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetlandclasses, covering ∼ 28 % each of the total wetland area,while the highest-methane-emitting marsh and tundra wetland classes occupied5 % and 12 %, respectively. Lakes, defined to include all lentic open-waterecosystems regardless of size, covered 1.4 × 106 km2(6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lakearea, while high-emitting peatland and yedoma lakes covered 18 % and 4 %,respectively. Small (<0.1 km2) glacial, peatland, and yedomalakes combined covered 17 % of the total lake area but contributeddisproportionally to the overall spatial uncertainty in lake area with a95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated withhigh-methane-emitting headwaters that drain organic-rich landscapes.Distinct combinations of spatially co-occurring wetland and lake classeswere identified across the BAWLD domain, allowing for the mapping of“wetscapes” that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales. With BAWLD, we provide adataset which avoids double-accounting of wetland, lake, and river extentsand which includes confidence intervals for each land cover class. As such,BAWLD will be suitable for many hydrological and biogeochemical modellingand upscaling efforts for the northern boreal and arctic region, inparticular those aimed at improving assessments of current and futuremethane emissions. Data are freely available athttps://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021). 

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