The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the waterbodies is scarce due to the challenges with collecting such information. In this work we have trained a machine learning (Random Forest Regressor) model to predict depth from multispectral Landsat data in waterbodies across the North Slope of Alaska. The greatest challenge is the scarcity of in situ data, which is expensive and difficult to obtain, to train the model. We overcame this challenge by using modeled depth predictions from a prior study as synthetic training data to provide a more diverse training data pool for the Random Forest. The final Random Forest model was more robust than models trained directly on the in situ data and when applied to 208 Landsat 8 scenes from 2016 to 2018 yielded a map with an overall r^2 value of 0.76 on validation. The final map has been made available through the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC). This map represents a first of its kind regional assessment of waterbody depth with per pixel estimates of depth for the entire North Slope of Alaska.

Surface melting impacts 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 (moulin input) and GPS-derived ice surface motion for Rio Behar, a moulin-terminating supraglacial river catchment on the southwest Greenland Ice Sheet. Short-term accelerations in ice speed correlate strongly with lag-corrected measures of supraglacial river discharge ( r = 0.9, τ = 0.7, p < 0.01). Though our 7 days 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 basal in absolute drive accelerations. present of in situ in a large supraglacial river draining the ice sheet surface, just of it plummets into a GPS of ice surface motion record brief accelerations in ice sliding speed that follow daily cycles in meltwater entering the moulin. By comparing these measurements with proglacial river discharges leaving the ice sheet, we identify daily fluctuations in subglacial water storage that track short-term accelerations in ice motion. These findings affirm the importance of supraglacial rivers to subglacial water pressure and ice dynamics, even in relatively thick ice >40 km inland from the ice terminus.

The Pleistocene sand sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by an ancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, which cover approximately 20 % of the Pleistocene sand sea, are relatively deep (up to 25 m). In addition to the natural importance of ACP sand sea lakes for water storage, energy balance, and ecological habitat, the need for winter water for industrial development and exploration activities makes lakes in this region a valuable resource. However, ACP sand sea lakes have received little prior study. Here, we collect in situ bathymetric data to test 12 model variants for predicting sand sea lake depth based on analysis of Landsat-8 Operational Land Imager (OLI) images. Lake depth gradients were measured at 17 lakes in midsummer 2017 using a Humminbird 798ci HD SI Combo automatic sonar system. The field-measured data points were compared to red–green–blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and map bathymetry. Exponential functions using a simple band ratio (with bands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For each lake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetries were integrated with remotely sensed lake surface area to quantify lake water storage volumes, which ranged from 1.056×10-3 to 57.416×10-3 km3. Due to variations in depth maxima, substrate, and turbidity between lakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ data with which to develop a regional model solution. Estimating lake water volumes using remote sensing will facilitate better management of expanding development activities and serve as a baseline by which to evaluate future responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lake bathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) and https://doi.org/10.18739/A2HT2GC6G (Simpson, 2019), respectively.

Abstract. The Pleistocene Sand Sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by an ancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, which cover approximately 20 % of the Pleistocene Sand Sea, are relatively deep (up to 25 m). In addition to the natural importance of ACP Sand Sea lakes for water storage, energy balance, and ecological habitat, the need for winter water for industrial development and exploration activities makes lakes in this region a valuable resource. However, ACP Sand Sea lakes have received little prior study. Here, we use in situ bathymetric data to test 12 model variants for predicting Sand Sea lake depth based on analysis of Landast-8 Operational Land Imager (OLI) images. Lake depth gradients were measured at 17 lakes in mid-summer 2017 using a HumminBird 798ci HD SI Combo automatic sonar system (Simpson and Arp, 2018). The field measured data points were compared to Red-Green-Blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and estimate bathymetry (Simpson, 2019). Exponential functions using a simple band ratio (with bands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For each lake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetries were integrated with remotely sensed lake surface area to quantify lake water storage volumes, which ranged from 1.056 × 10−3 km3 to 57.416 × 10−3 km3. Due to variation in depth maxima, substrate, and turbidity between lakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ data with which to develop a regional model solution. Estimating lake water volumes using remote sensing will facilitate better management of expanding development activities and serve as a baseline by which to evaluate future responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lake bathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) and https://doi.org/10.18739/A2TQ5RD83 (Simpson, 2019), respectively.