Lakes are the most prevalent and predominant water repositories on land surface. A primary objective of the Surface Water and Ocean Topography (SWOT) satellite mission is to monitor the surface water elevation, area, and storage change in Earth’s lakes. To meet this objective, prior information of global lakes, such as locations and benchmark extents, is required to organize SWOT’s KaRIn observations over time for computing lake storage variation. Here, we present the SWOT mission Prior Lake Database (PLD) to fulfill this requirement. This paper emphasizes the development of the “operational PLD”, which consists of (1) a high-resolution mask of ~6 million lakes and reservoirs with a minimum area of 1 ha, and (2) multiple operational auxiliaries to assist the lake mask in generating SWOT’s standard vector lake products. We built the prior lake mask by harmonizing the UCLA Circa-2015 Global Lake Dataset and several state-of-the-art reservoir databases. Operational auxiliaries were produced from multi-theme geospatial data to provide information necessary to embody the PLD function, including lake catchments and influence areas, ice phenology, relationship with SWOT-visible rivers, and spatiotemporal coverage by SWOT overpasses. Globally, over three quarters of the prior lakes are smaller than 10 ha. Nearly 96% of the lakes, constituting over half of the global lake area, are fully observed at least once per orbit cycle. The PLD will be recursively improved during the mission period and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.

The forthcoming Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability, but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. We study the likelihood of misclassification between 15 land cover types and develop the Ka-band Phenomenology Scattering (KaPS) scattering model to simulate changes to radar backscatter as a result of changing surface water fraction and roughness. Using a separability metric, we find that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1-27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. A comparison of KaPS simulated backscatter with AirSWOT observed backscatter shows good overall agreement across the 15 classes (median r2=0.76). KaPS characterization of the sensitivity of near-nadir, Ka-band SAR to small changes in both wet area fraction and surface roughness enables more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent, and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.

Arctic-Boreal lakes emit methane (CH₄), a powerful greenhouse gas. Recent studies suggest ebullition may be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed, but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial datasets of lake area, lake perimeter, permafrost, landcover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R² = 0.211), followed by lake surface area (adj. R² = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes may explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R² = 0.176) and temperature (adj. R² = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R² values are typical and informative for methane attribution studies. A multiple regression model combining LWF, area, and temperature performs best (adj. R² = 0.325). Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-Boreal lake methane emissions to readily available environmental variables.

Areas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under-represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ~1.5 m deep and can be estimated through synthetic aperture radar (SAR) mapping. To assess the importance of lake emergent vegetation (LEV) zones to landscape-scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open-water methane flux. First, we use Uninhabited Aerial Vehicle SAR (UAVSAR) data from the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) to map LEV in 4,572 lakes across four Arctic-boreal study areas and find it comprises ~16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20-25]%, 1.0 [0.8-1.2]%, and 7.0 [5.0-12]% of lake areas in the Peace-Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18-25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane-relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic-boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.

Surface melting can alter ice sheet sliding by supplying water to the bed, but subglacial processes driving ice accelerations are complex. We examine linkages between surface runoff, transient subglacial water storage, and short-term ice motion from 168 consecutive hourly measurements of meltwater discharge (i.e. moulin input) and GPS-derived ice surface motion for Rio Behar, a ~60 km2 moulin-terminating supraglacial river catchment the southwest Greenland ablation zone. Short-term accelerations in ice speed correlate strongly with lag-corrected measures of surface mass loss, specifically supraglacial river discharge (r= 0.9; p<0.001). Though our 7-day record cannot address seasonal-scale forcing, diurnal ice accelerations align with normalized differenced supraglacial and proglacial discharge, a proxy for subglacial storage change, better than GPS-derived ice surface uplift. These observations counter theoretical steady-state basal sliding laws and suggest that moulin- and proglacially induced fluctuations in subglacial water storage, rather than absolute subglacial water storage, drive short-term ice accelerations.

Wetlands are the largest environmental sources of methane, and interannual changes in wetland methane fluxes explain most of the variability in the global flux. Despite their importance, global wetland maps, a key component of methane models, are inaccurate for at least three reasons: (1) Their temporal variability is poorly suited for static maps; (2) Optical remote sensing cannot penetrate foliage, making water hard to identify; and (3) satellites cannot resolve their fine-scale features. Furthermore, small, unmapped water bodies may emit methane disproportionately to their size due their shallow depths inhibiting bacterial oxidation from the water column and their large perimeter: volume ratios, which introduce the potential for organic matter input and plant-mediated fluxes from shorelines. However, in boreal regions, there is conflicting evidence on the effects of water body size on methane and carbon dioxide fluxes. Here, we measure methane emissions in lakes and wetlands in an Arctic-Boreal delta and compare to open water and vegetated area with the goal of improving methane emission estimates in this region. We expect small, shallow, and vegetated wetlands to produce more methane than those bordering deeper lakes. To test this hypothesis, we map wetlands in the Peace-Athabasca Delta, a 5,000 km2 inland delta in northern Alberta, Canada containing abundant open and vegetated wetlands. We use airborne remote sensing from three sources: (1) High-resolution (<5 cm pixel) unmanned aerial vehicle (UAV) imagery, (2) Coincident L-band synthetic aperture radar (SAR) from NASA’s UAVSAR airborne imaging system, and (3) 2017 AirSWOT Ka-band interferometric SAR with color-infrared imagery. With a wavelength of 23.8 cm, UAVSAR L-band returns are ideal for mapping vegetated wetlands due to double-bounce backscatter between vegetation and the water surface. Combining two field campaigns of flux chamber gas sampling from over twenty lakes, walked shoreline surveys, and over 70 thousand UAV photos, we present a collection of wetland maps and a methodology for efficiently mapping them from UAV. We then upscale methane and carbon dioxide emissions to the scale of the delta and compare to existing estimates. These results will help improve greenhouse gas emission estimates for boreal zone wetlands.

Mass loss from the Greenland Ice Sheet (GrIS) is a primary contributor to sea level rise, but substantial uncertainty exists in estimates of future ice sheet losses. Surface mass balance (SMB) models, the current leading approach to sea level rise projection, anticipate continued dominance of runoff as a mass loss pathway. Despite their preeminence, SMB models in vulnerable northern environments lack adequate field validation, particularly for error-sensitive runoff estimates. We have installed a cluster of high quality field instruments at the Minturn Elv, a proglacial river site in Inglefield Land, NW Greenland to provide discharge and weather datasets for the validation and refinement of climate/SMB runoff models. The instrument cluster has meteorological, hydrological, and time lapse camera instrumentation, including a vented water level stage recorder, single shot and scanning lidars, time lapse cameras, and in situ ADCP discharge and terrestrial scanning lidar measurements. The instrument suite provides novel flow and weather datasets with the opportunity to evaluate experimental approaches to stage measurement in adverse, high-latitude areas. Inglefield is a uniquely advantaged location because proglacial runoff is dominated by SMB processes operating on the ice surface without interference from subglacial hydrology. Overall, our hydrometeorological instrument cluster at Inglefield Land will provide one of the few validation datasets for regional climate models outside of Southwest Greenland.