Development and analysis of a Bayesian water balance model for large lake systems

Abstract

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.

Monitoring the Water Mass Balance Variability of Small Shallow Lakes by an ERA5-Land Reanalysis and Water Level Measurement-Based Model. An Application to the Trasimeno Lake, Italy

“…Different approaches can be followed for evaluating quantities in Equation (1). Precipitation over the lake is usually measured at the ground base [17] even if recently satellite observations are used as gridded data instead of local scattered measurements [19,[21][22][23]. Unlike precipitation, evaporation and runoff are not usually measured at the ground base, but parametric formulas are often utilised instead.…”

“…For the evaporation, the most popular relationships are the Penman-Monteith [17,[24][25][26] and Thornthwaite [27] ones. Possible alternatives for evaluating the evaporation are the use of the output of the numerical weather prediction (NWP) models, run over the lake basin area [19,28], as well as the remotely sensed observations [22,23].…”