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 (
- Award ID(s):
- 1934721
- PAR ID:
- 10346169
- Date Published:
- Journal Name:
- Frontiers in Water
- Volume:
- 4
- ISSN:
- 2624-9375
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Z max;n = 16,831) and mean (Z mean;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 (Z max; root mean square error [RMSE] = 8.0 m andZ mean; 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 whetherZ maxorZ meanwas 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. -
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
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Abstract Absence of dissolved oxygen (anoxia) in the hypolimnion of lakes can eliminate habitat for sensitive species and may induce the release of sediment‐bound phosphorus. Lake anoxia generally results from decomposition of organic matter, which is exacerbated by high nutrient loads. Total phosphorus (TP) in lakes is regulated by static aspects of the lake’s watershed, but lake TP can be readily increased by human activities. In some low‐nutrient lakes, basin morphometry may induce naturally occurring anoxia. The occurrence of natural anoxia is especially important to consider in lake water quality assessments that compare observed conditions to expected reference conditions. To investigate the occurrence of natural vs. anthropogenically influenced anoxia, we constructed a logistic regression model to calculate the probability of low‐nutrient lakes (TP < 15 µg/L) developing aerial anoxic extent ≥10% by testing the predictive potential of variables related to basin morphometry, depths of lake thermal strata, epilimnetic TP, and dissolved organic carbon (DOC). Maximum lake depth and the proportion of lake area under the top of the metalimnion were the most important variables to predict the likelihood of hypolimnetic anoxia, which correctly predicted anoxic condition in 84% of lakes (Model 1). Adding TP as a third variable to Model 1 produced a significantly improved model (Model 2) but the prediction success rate was comparable (86%). We also present a model for lakes with limited bathymetric data, which predicts anoxia with 81% accuracy based on maximum lake depth and mean thermocline depth at peak stratification. DOC was relatively low (4.3 ± 1.5 mg/L [mean ± SD]) in the study lakes and its inclusion did not improve model performance. In Model 1, lakes with an anoxic extent ≥10% of lake area had significantly higher epilimnetic TP than lakes with oxic hypolimnia, regardless of prediction category or success. Our results indicate that including TP as a variable helps refine models based on morphometry alone, but lake morphometry and stratification dynamics are the most important factors in the development of anoxic extent in low‐nutrient temperate lakes. Our approach informs studies concerned with identifying key factors that influence regime shifts in a variety of ecosystems.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frwa.2022.886964
