  Water balance models (WBMs) are often employed to understand regional
hydrologic cycles over various time scales. Most WBMs, however, are
physically-based, and few employ state-of-the-art statistical methods to
reconcile independent input measurement uncertainty and bias. Further, few WBMs
exist for large lakes, and most large lake WBMs perform additive accounting,
with minimal consideration towards input data uncertainty. Here, we introduce a
framework for improving a previously developed large lake statistical water
balance model (L2SWBM). Focusing on the water balances of Lakes Superior and
Michigan-Huron, we demonstrate our new analytical framework, identifying
L2SWBMs from 26 alternatives that adequately close the water balance of the
lakes with satisfactory computation times compared with the prototype model. We
expect our new framework will be used to develop water balance models for other
lakes around the world.
