Prediction of lake depth across a 17-state region in the United States

Samantha K. Oliver, Patricia A. Soranno, C. Emi Fergus, Tyler Wagner, Luke A. Winslow, Caren E. Scott, Katherine E. Webster, John A. Downing, Emily H. Stanley

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)314-324
Number of pages11
JournalInland Waters
Issue number3
StatePublished - 2016

Bibliographical note

Funding Information:
We thank all data providers who contributed to the LAGOS database and the other members of the CSI Limnology team who contributed to the data integration effort to build LAGOS. We are grateful to 2 anonymous reviewers for their constructive input. This work was funded by the National Science Foundation (1065786, 1065818, 1065649). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Publisher Copyright:
© International Society of Limnology 2016.


  • Lake depth
  • Mixed model
  • Morphology
  • Regional scale
  • Topography


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