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.
Highlights
Background & SummaryAmong the most severe impacts of climate change is the intensification of the hydrologic cycle[1,2]
The Clausius-Clapyeron relation[3], which defines specific humidity of the atmosphere as a function of temperature, suggests that the rising trend of global mean surface air temperature will lead to an increase in evaporation and precipitation[4], and potentially exacerbate observed changes in river flows[5], hydrological extremes[6,7] and water availability[8,9]. These changes are pronounced over Earth’s large lakes[10], where rapid increases in lake temperature[11] have led to unprecedented water level dynamics on many of those lakes[12,13]
To inform the Bayesian framework encoded within the L2SWBM, we selected eight independent data sources, including:
Summary
Among the most severe impacts of climate change is the intensification of the hydrologic cycle[1,2]. To provide a framework for incorporating independent data sets and informing water management decisions for large lakes, a statistical framework (the Large Lakes Statistical Water Balance Model, hereafter referred to as the L2SWBM) has been recently developed[48,49]. This article presents a seventy-year record of Great Lakes water balance estimates using the L2SWBM This dataset can be used to explore the mechanisms underlying long-term changes as well as the most recent surge of Great Lakes water levels, and provide new insight into how climate change has influenced, and might continue to influence large lakes. The inputs and source code of the L2SWBM are made available, and can be customized to incorporate new measurements, estimates or simulations when they become available in the future
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