Preprints
https://doi.org/10.5194/essd-2019-226
https://doi.org/10.5194/essd-2019-226

  10 Feb 2020

10 Feb 2020

Review status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Remote sensing of lake water volumes on the Arctic Coastal Plain of Northern Alaska

Claire E. Simpson1, Christopher D. Arp2, Yongwei Sheng1, Mark L. Carroll3, Benjamin M. Jones2, and Laurence C. Smith1,4,5 Claire E. Simpson et al.
  • 1Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, California, 90095, USA
  • 2Water and Environmental Research Center, University of Alaska, Fairbanks, 306 Tanana Loop Rd., Fairbanks, Alaska, 99775, USA
  • 3Computational and Information Science and Technology Office, NASA-GSFC, Greenbelt, Maryland, 20771, USA
  • 4Department of Earth, Environmental and Planetary Sciences, Brown University, 324 Brook St, Providence, Rhode Island, 02912, USA
  • 5Institute at Brown for the Environment and Society, Brown University, 85 Waterman St, Providence, Rhode Island, USA, 02912

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.

Claire E. Simpson et al.

 
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Claire E. Simpson et al.

Data sets

Sonar Depth Measurements at Lakes on the Inner Arctic Coastal Plain of Alaska C. Simpson and C. Arp https://doi.org/10.18739/A2SN01440

Modeled Bathymetry Maps of 17 Lakes on the Arctic Coastal Plain of Alaska C. Simpson https://doi.org/10.18739/A2TQ5RD83

Claire E. Simpson et al.

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Short summary
Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are used to train and validate models to map lake bathymetry. These models predict depth from remotely sensed lake color and are able to explain 58.5–97.6 % of depth variability. To calculate water volumes, we integrate this modeled bathymetry with lake surface area. Knowledge of Alaskan lake bathymetries and volumes is crucial to better understanding water storage, energy balance and ecological habitat.