Seventy-year long record of monthly water balance estimates for Earth’s largest lake system

Abstract

We develop new estimates of monthly water balance components from 1950 to 2019 for the Laurentian Great Lakes, the largest surface freshwater system on Earth. For each of the Great Lakes, lake storage changes and water balance components were estimated using the Large Lakes Statistical Water Balance Model (L2SWBM). Multiple independent data sources, contributed by a binational community of research scientists and practitioners, were assimilated into the L2SWBM to infer feasible values of water balance components through a Bayesian framework. A conventional water balance model was used to constrain the new estimates, ensuring that the water balance can be reconciled over multiple time periods. The new estimates are useful for investigating changes in water availability, or benchmarking new hydrological models and data products developed for the Laurentian Great Lakes Region. The source code and inputs of the L2SWBM model are also made available, and can be adapted to include new data sources for the Great Lakes, or to address water balance problems on other large lake systems.

Intra‐Annual and Interannual Dynamics of Evaporation Over Western Lake Erie

“…We showed that peak monthly E occurred mostly from August to October at Lake Erie and the winter months (October–March) accounted for near 30% of annual E . Such seasonality of E is distinct from other Great Lakes, which showed peak monthly E in December–January and that the majority of annual E was contributed during the winter months (Cleave et al, 2014; Do et al, 2020; Lofgren & Zhu, 2000; Moukomla & Blanken, 2017). The unique seasonality of E over Lake Erie likely is due to its bathymetry, latitude, and climate (Table 6).…”

“…Our study provided the first direct and multiyear measurements of E over western Lake Erie, with annual E of 635 ± 42 mm and 604 ± 32 mm at the nearshore (CB) and offshore (LI) sites, respectively. Previous studies based on modeling and indirect approaches (e.g., energy budget, water balance, and mass transfer) reported a wide range of annual E over Lake Erie, on the order of 500–1,000 mm (Derecki, 1976; Do et al, 2020; Gronewold et al, 2019; Lofgren & Zhu, 2000; Moukomla & Blanken, 2017; Neff & Nicholas, 2005). We advocate that the E estimates need to be better constrained, especially those based on indirect approaches that often substantially overestimate E over Lake Erie (e.g., 898–903 mm) (Derecki, 1976; Neff & Nicholas, 2005; Schertzer, 1987).…”

“…We advocate that the E estimates need to be better constrained, especially those based on indirect approaches that often substantially overestimate E over Lake Erie (e.g., 898–903 mm) (Derecki, 1976; Neff & Nicholas, 2005; Schertzer, 1987). It is worth mentioning that even the most recent estimates using the state‐of‐the‐art model continue to overestimate annual E by around 200–350 mm over Lake Erie (e.g., Do et al, 2020; Gronewold et al, 2019), which again highlight the need for further assessing, validating, and refining the models using a network of eddy‐covariance towers to improve E predictions over the Great Lakes (Charusombat et al, 2018).…”

A Tug‐of‐War Within the Hydrologic Cycle of a Continental Freshwater Basin

“…Data sets and model simulations for this project derived from the GLM-HMD (Hunter et al, 2015), L2S-WBM (Do et al, 2020;Gronewold et al, 2020), WCPS (Deacu et al, 2012;Durnford et al, 2018), AHPS (Apps et al, 2020;Gronewold et al, 2011), WATFLOOD (Kouwen, 1988), CaPA (Lespinas et al, 2015;Mahfouf et al, 2007), MPE (Seo, 1998;Seo & Breidenbach, 2002), and the "Merged" overlake precipitation data set (Gronewold et al, 2018) have been compiled and sotred on the University of Michigan Deep Blue archive at https://deepblue.lib.umich.edu/data/concern/data_sets/sb3978457.…”

“…We then aggregated these estimates, using the surface area of each lake, into a single value of total overlake precipitation, total overlake evaporation, and total lake inflow through tributary runoff. Details of our parameterization of the L2SWBM for this study, as well as the L2SWBM simulations and corresponding code, are available via the University of Michigan's Deep Blue archive (Do et al., 2020).…”