An official website of the United States government
Abstract Depth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Zmax;n = 16,831) and mean (Zmean;n = 5,881) depth observations to create new depth models, focusing on lakes < 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (Zmax; root mean square error [RMSE] = 8.0 m andZmean; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. In addition, despite orders of magnitude variation in surface area, most size classes had a modal maximum depth of ~ 5 m. Concave (bowl‐shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel‐shaped) as surface area increased. Across the conterminous United States, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous United States is 1,057–1,294 km3, depending on whetherZmaxorZmeanwas modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern United States.
more »
« less
Prediction of lake depth across a 17-state region in the United States
Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.
more »
« less
- Award ID(s):
- 1065786
- PAR ID:
- 10025551
- Date Published:
- Journal Name:
- Inland waters
- Volume:
- 6
- ISSN:
- 2044-2041
- Page Range / eLocation ID:
- 314-324
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Snowdrifts formed by wind transported snow deposition represent a vital component of the earth surface processes on Arctic tundra. Snow accumulation on steep slopes particularly at the margins of rivers, coasts, lakes, and drained lake basins (DLBs) comprise a significant water storage component for the ecosystem during spring and summer snowmelt. The tundra landscape is in constant change as lakes drain, substantially altering the surface morphology that partially controls how snow drifts and accumulates throughout the cold seasons. Here, we combine field measurements, remote sensing observations, and snow modeling to investigate how lake drainage affects snow redistribution at Inigok on the Arctic Coastal Plain of Alaska, where the snow movement is controlled by wind. Field observations included measurements of snow depth using ground penetrating radar and probe. We mapped mid‐July snow cover and modeled snow redistribution before and after drainage simulation for 33 lakes (∼30 km2) in our study area (∼140 km2). Our results show the advantage of using a wide range of snow depth measurements on frozen lakes, DLBs, and upland to validate the snow modeling in order to capture the variability inherent in the landscape. The lake drainage simulation suggests an increase in snow storage of up to ∼24% at DLBs compared to extant lakes, ∼35% considering only snowdrifts (assumed as ≥1 m depth), and ∼4% considering the whole study area. This increase in snow accumulation could significantly impact the landscape when it melts, including wildlife, vegetation, biogeochemical processes, and potential natural hazards like snow‐dam outburst floods.more » « less
-
null (Ed.)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 in 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 https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) andhttps://doi.org/10.18739/A2HT2GC6G (Simpson, 2019), respectively.more » « less
-
Antarctic subglacial lakes can play an important role in ice sheet dynamics, biology, geology, and oceanography, but it is difficult to definitively constrain their character and locations. Subglacial lake locations are related to factors including heat flux, ice surface slope, ice thickness, and bed topography, though these relationships are not fully quantified. Bed topography is particularly important for determining where water flows and accumulates, but digital elevation models of the ice sheet bed rely on interpolation and are unrealistically smooth, biasing estimates of subglacial lake location and surface area. To address this issue, we use geostatistical methods to simulate realistically rough bed topography. We use our simulated topography to predict subglacial lake distribution across the continent using a binomial logistic regression, which uses physical parameters and known lake locations to calculate the probabilities of lake occurrences. Our results suggest that topography models interpolated without appropriate geostatistics overestimate subglacial lake surface area and that total lake surface area is lower than previously predicted. We find that radar‐detected lakes are more likely to occur in the interior of East Antarctica, while altimetry‐detected (active) lakes are expected to be found in West Antarctica and near the grounding line. We observe that radar‐detected lakes have a high correlation with heat flux and ice thickness, while active lakes are associated with higher ice velocity.more » « less
-
null (Ed.)The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations ( n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5268/IW-6.3.957
