Benjamin Jones University of Alaska Fairbanks Research Assistant Professor

PO Box 755860 FAIRBANKS AK 99775 United States

9074746794 bmjones3@alaska.edu http://ine.uaf.edu/werc/people/faculty/benjamin-jones/ https://orcid.org/0000-0002-1517-4711 Christopher Arp University of Alaska Fairbanks Research Profressor https://orcid.org/0000-0002-6485-6225 Guido Grosse Alfred Wegener Institute Professor https://orcid.org/0000-0001-5895-2141 Ingmar Nitze Alfred Wegener Institute Post-Doctoral Researcher https://orcid.org/0000-0002-1165-6852 Mark Lara University of Illinois Research Professor https://orcid.org/0000-0002-4670-7031 Matthew Whitman Bureau of Land Management Ecologist Louise Farquharson University of Alaska Fairbanks Post-Doctoral Researcher https://orcid.org/0000-0001-8884-511X Mikhail Kanevskiy University of Alaska Fairbanks Research Professor Andrew Parsekian University of Wyoming Professor https://orcid.org/0000-0001-5072-9818 Amy Breen University of Alaska Fairbanks Research Professor Nori Ohara University of Wyoming Professor Rodrigo Rangel University of Wyoming PhD Student Kenneth Hinkel Michigan Tech University Professor Emeritus 2019 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. Arctic Lake Lake Drainage Drained Lake Basin Thermokarst Lake None This work is dedicated to the public domain under the Creative Commons Universal 1.0 Public Domain Dedication. To view a copy of this dedication, visit https://creativecommons.org/publicdomain/zero/1.0/. The 30,400 km2 study area encompasses the majority of the lake-rich western Arctic Coastal Plain of northern Alaska . It represents the intersection of the lake drainage region analyzed in Hinkel et al. (2007) with the acquisition area of an airborne IfSAR-derived DSM acquired in 2002 and 2003. -158.766874 -150.948560 71.494934 69.506487 2002 2003 Benjamin Jones UAF Research Assistant Professor

PO Box 755860 FAIRBANKS AK 99775 United States

9074746794 bmjones3@alaska.edu http://ine.uaf.edu/werc/people/faculty/benjamin-jones/ https://orcid.org/0000-0002-1517-4711 Lakes were extracted from an IfSAR DSM using a slope derivative and manual correction (Jones et al., 2017). 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. Lakes with a minimum size of 10 ha were selected for further analysis. 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) were extracted. 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 ca. 2002 orthophotography or more recent high resolution satellite imagery available for the WACP. The 30,400 km2 study area encompasses the majority of the lake-rich western Arctic Coastal Plain of northern Alaska. It represents the intersection of the lake drainage region analyzed in Hinkel et al. (2007) with the acquisition area of an airborne IfSAR-derived DSM acquired in 2002 and 2003. Potential future lake drainage candidates were based on the 2002-2003 IfSAR DSM dataset. Assessment of lakes for their future potential to drain relied on the 2002/03 airborne IfSAR 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 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 ca. 2002 orthophotography or more recent high resolution satellite imagery available for the 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. Benjamin Jones bmjones3@alaska.edu Principal Investigator Christopher Arp Former Principal Investigator Christopher Larsen Co-Principal Investigator Amy Breen Co-Principal Investigator Mikhail Kanevskiy Co-Principal Investigator Christopher Arp Co-Principal Investigator NSF 1806213 Jones_et_al_2019_wacp_future_potential_lake_drainage_sites.zip zipped vector shapefile of potential lake drainage sites Jones_et_al_2019_wacp_future_potential_lake_drainage_sites.zip 1919039 1e447a7c27effbf744ca84b0ae825cc3 application/zip https://cn.dataone.org/cn/v2/resolve/urn:uuid:0f530b2d-f4ab-47cb-9bc8-8971d8cc5011 Area_sq_km Lake area in square kilometers squareKilometer real Lake_hgt_m Lake elevation in meters meter real X100m_hgt_m Lowest elevation within 100 meter buffer of lake in meters meter real X100m_diff Difference between lake elevation and lowest elevation within 100 meter buffer meter real High_Poten Classification of potential future drainage candidates 0 low potential 1 high potential Drain_loca Drainage location any text Polygon NAD_1983_UTM_Zone_5N unit of area