High-tech Limnology

Can we use new technologies and knowledge-guided machine learning to better monitor, model, and manage lakes?

We are investing in the development and application of environmental sensors to understand and manage lakes and rivers. We build and deploy sensors on lakes (buoys) to provide continuous real-time monitoring of lake water quality. We’ve also developed the Fast Limnological Automated Measurements (FLAMe) platform which measures spatial patterns of water quality. With this vast amounts of data comes the need to manage and analyze the data. The CFL leads the Environmental Data Initiative (EDI) – a leading ecological data repository comprised of tens of thousands of datasets. In addition, CFL researchers are collaborating with computer scientists and pioneering the application of knowledge-guided machine learning (KGML) in aquatic ecology. High-tech limnology is revolutionizing how we study and manage lakes. See also LAGOS open-access research platform and Global Lake Ecological Observatory Network (GLEON). Read CFL Fall 2023 Newsletter article Eye in the Sky: Cutting Edge Technology May Change What We Mean When We Say Lake View

Recent publications:

Buelo, C., M.E. Pace, S.R. Carpenter, E.H. Stanley, D. Ortiz, and D. Ha. 2022. Evaluating the performance of temporal and spatial early warning statistics of algal blooms. Ecological Applications 32:e2616. https://doi.org/10.1002/eap.2616

Gries C., P.C. Hanson, M. O’Brien, M. Servilla, K. Vanderbilt, R. Waide. 2023. The Environmental Data Initiative: Connecting the past to the future through data reuse. Ecology and Evolution. Vol 3, Issue 1. https://doi.org/10.1002/ece3.9592

Hanson, P., A.B. Stillman, X. Jia, A. Karpatne, H. Dugan, C.C. Carey, J. Stachelek, N.K. Ward, Y. Zhang, J.S. Read, V. Kumar. 2020. Predicting lake surface water phosphorus dynamics using process-guided machine learning. Ecological Modelling. Volume 430, 15. https://doi.org/10.1016/j.ecolmodel.2020.109136

Read J.S., X. Jia, J. Willard, A.P. Appling, J.A. Zwart, S.K. Oliver, A. Karpatne, G. J. A. Hansen, P.C. Hanson, W. Watkins, M. Steinbach, V.  Kumar. 2019. Process-Guided Deep Learning Predictions of Lake Water Temperature. Advancing Earth and Space Sciences. https://doi.org/10.1029/2019WR024922