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  1. Abstract. The Pleistocene sand sea on the Arctic Coastal Plain (ACP) ofnorthern Alaska is underlain by an ancient sand dune field, a geologicalfeature that affects regional lake characteristics. Many of these lakes,which cover approximately 20 % of the Pleistocene sand sea, are relativelydeep (up to 25 m). In addition to the natural importance of ACP sand sealakes for water storage, energy balance, and ecological habitat, the needfor winter water for industrial development and exploration activities makeslakes in this region a valuable resource. However, ACP sand sea lakes havereceived little prior study. Here, we collect in situ bathymetric data totest 12 model variants for predicting sand sea lake depth based on analysisof Landsat-8 Operational Land Imager (OLI) images. Lake depth gradients weremeasured at 17 lakes in midsummer 2017 using a Humminbird 798ci HD SI Comboautomatic sonar system. The field-measured data points were compared tored–green–blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August2016 to select and calibrate the most accurate spectral-depth model for eachstudy lake and map bathymetry. Exponential functions using a simple bandratio (with bands selected based on lake turbidity and bed substrate)yielded the most successful model variants. For each lake, the most accuratemodel explained 81.8 % of the variation inmore »depth, on average. Modeled lakebathymetries were integrated with remotely sensed lake surface area toquantify lake water storage volumes, which ranged from 1.056×10-3 to 57.416×10-3 km3. Due to variations in depthmaxima, substrate, and turbidity between lakes, a regional model iscurrently infeasible, rendering necessary the acquisition of additional insitu data with which to develop a regional model solution. Estimating lakewater volumes using remote sensing will facilitate better management ofexpanding development activities and serve as a baseline by which toevaluate future responses to ongoing and rapid climate change in the Arctic.All sonar depth data and modeled lake bathymetry rasters can be freelyaccessed at (Simpson and Arp, 2018) and (Simpson, 2019), respectively.« less
  2. Abstract. Light transmission into bare glacial ice affects surfaceenergy balance, biophotochemistry, and light detection and ranging (lidar)laser elevation measurements but has not previously been reported for theGreenland Ice Sheet. We present measurements of spectral transmittance at350–900 nm in bare glacial ice collected at a field site in the westernGreenland ablation zone (67.15∘ N, 50.02∘ W). Empirical irradianceattenuation coefficients at 350–750 nm are ∼ 0.9–8.0 m−1 for ice at 12–124 cm depth. The absorption minimum is at∼ 390–397 nm, in agreement with snow transmissionmeasurements in Antarctica and optical mapping of deep ice at the SouthPole. From 350–530 nm, our empirical attenuation coefficients are nearly1 order of magnitude larger than theoretical values for optically pureice. The estimated absorption coefficient at 400 nm suggests the ice volumecontained a light-absorbing particle concentration equivalent to∼ 1–2 parts per billion (ppb) of black carbon, which is similar topre-industrial values found in remote polar snow. The equivalent mineraldust concentration is ∼ 300–600 ppb, which is similar to values forNorthern Hemisphere warm periods with low aeolian activity inferred from icecores. For a layer of quasi-granular white ice (weathering crust)extending from the surface to ∼ 10 cm depth, attenuationcoefficients are 1.5 to 4 times larger than for deeper bubbly ice. Owing tohigher attenuation in this layer of near-surface granular ice, opticalpenetration depth at 532 nm is 14 cmmore »(20 %) lower than asymptoticattenuation lengths for optically pure bubbly ice. In addition to thetraditional concept of light scattering on air bubbles, our results implythat the granular near-surface ice microstructure of weathering crust isan important control on radiative transfer in bare ice on the Greenland IceSheet ablation zone, and we provide new values of flux attenuation,absorption, and scattering coefficients to support model development andvalidation.« less