Applying the Large Lake Statistical Water Balance Model to Reduce Uncertainty in Great Lakes Water Balance Components
Date:
Luo, Y. Applying the Large Lake Statistical Water Balance Model to Reduce Uncertainty in Great Lakes Water Balance Components. American Geophysical Union Fall Meeting. December 2022. (Poster)
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 some rapid shifts between high and low in some water balance components. In order 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 incorporated the Large Lake Statistical Water Balance Model (L2SWBM) to adequately quantify uncertainty and reconcile the discrepancies between model- and measurement-based estimates of each water balance component from various datasets. 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 data of water balance components through the historical record, the L2SWBM can be used to iteratively solve the coefficient values and estimate a range of reasonable uncertainties in individual components that are faithful to the water balance. After applying the L2SWBM, uncertainty was significantly reduced in the Great Lakes water balance by approximately 10-40% depending on original estimates.