Holistic water balance modeling: a large lake perspective on combining Bayesian inference, in situ data, model simulations and remote sensing
Date:
Presentation: American Geophysical Union (AGU) Fall Meeting, San Francisco, CA
Gronewold, A., Fetrow, A., Akey, L., Gossard, A., Katz, S. A., Levin, N. E., Luo, Y., Passey, B., Langston, J., VanDeWeghe, A., and Calzada, C. 2023. “Holistic water balance modeling: a large lake perspective on combining Bayesian inference, in situ data, model simulations and remote sensing.” American Geophysical Union (AGU) Fall Meeting. (Presentation)
Abstract
We present a synthesis of, and insights gained from, recently-completed and ongoing projects that focus on understanding and closing the water balance for some of Earth’s largest lakes. Each project is anchored and connected through novel application of probabilistic modeling and Bayesian inference, but differentiated by the unique physical properties that characterize each lake (such as dam-controlled lake outflows, or high latitude evaporation cycles) and for which unique data sets and model simulations are needed. Each project relies on a suite of data selected for its accessibility, and the extent to which it represents these physical processes. The geographic domain of our work spans high salinity terminal lakes in the North American Great Basin (many of which are rapidly disappearing and are in urgent need of state-of-the-art water balance data and projections), to the massive African and Laurentian Great Lakes (including Lake Superior and Lake Victoria, the two largest lakes on Earth by surface area).
Citation
Gronewold, A., Fetrow, A., Akey, L., Gossard, A., Katz, S. A., Levin, N. E., Luo, Y., Passey, B., Langston, J., VanDeWeghe, A., and Calzada, C. (2023). Holistic water balance modeling: a large lake perspective on combining Bayesian inference, in situ data, model simulations and remote sensing. AGU Fall Meeting Abstracts, 2023, H42B-01.