Applying the Large Lake Statistical Water Balance Model (L2SWBM) to Reduce Uncertainty in Great Lakes Water Balance Components
Date:
Poster: American Geophysical Union (AGU) Fall Meeting, Chicago, IL
Luo, Y., Gronewold, A., Paulson, H., Muir, M., and Huber, J. 2022. “Applying the Large Lake Statistical Water Balance Model (L2SWBM) to Reduce Uncertainty in Great Lakes Water Balance Components.” American Geophysical Union (AGU) Fall Meeting. (Poster)
Abstract
The Laurentian Great Lakes and St. Lawrence River basin comprises the largest freshwater system on Earth, containing about one-fifth of the world’s surface fresh water. However, the Great Lakes basin has recently experienced rapid shifts between highs and lows in some water balance components. To resolve the regional water budget in the Great Lakes over an extended historical period, and provide insight moving forward in our changing climate, we applied the Large Lake Statistical Water Balance Model (L2SWBM) to quantify uncertainty and reconcile discrepancies between model- and measurement-based estimates of each water balance component from various datasets. The L2SWBM contextualizes and reduces uncertainty while closing the water balance over consecutive historical periods. The model assimilates multiple datasets for each hydrologic component (i.e., over-lake precipitation, over-lake evaporation, runoff, connecting channel flows, and inter-basin diversions) and runs millions of iterations to reconstruct potential historical water budgets. Using observed and modeled water balance data across the historical record, the L2SWBM iteratively solves for coefficient values and estimates a range of reasonable uncertainties in individual components that remain faithful to the water balance. After applying the L2SWBM, uncertainty in the Great Lakes water balance was significantly reduced by approximately 10–40%, depending on original estimates.
Citation
Luo, Y., Gronewold, A., Paulson, H., Muir, M., and Huber, J. (2022). Applying the Large Lake Statistical Water Balance Model (L2SWBM) to Reduce Uncertainty in Great Lakes Water Balance Components. AGU Fall Meeting Abstracts, 2022, H32P-1128.