Development and analysis of a Bayesian water balance model for large lake systems
This work addresses the need for more accurate water balance models for large lakes, which is important for hydrologists and environmental scientists, though it is incremental as it builds on an existing model.
The researchers tackled the problem of water balance models for large lakes lacking statistical methods to handle input uncertainty, by developing an analytical framework that improved a previous model, identifying 26 alternatives that adequately closed the water balance for Lakes Superior and Michigan-Huron with satisfactory computation times.
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.