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.

Water balance models are commonly employed to improve understanding of drivers behind changes in the hydrologic cycle across multiple space and time scales. Generally, these models are physically-based, a feature that can lead to unreconciled biases and uncertainties when a model is not encoded to be faithful to changes in water storage over time. Statistical methods represent one approach to addressing this problem. We find, however, that there are very few historical hydrological modeling studies in which bias correction and uncertainty quantification methods are routinely applied to ensure fidelity to the water balance. Importantly, we know of none (aside from preliminary applications of the model we advance in this study) applied specifically to large lake systems. We fill this gap by developing and applying a Bayesian statistical analysis framework for inferring water balance components specifically in large lake systems. The model behind this framework, which we refer to as the L2SWBM (large lake statistical water balance model), includes a conventional water balance model encoded to iteratively close the water balance over multiple consecutive time periods. Throughout these iterations, the L2SWBM can assimilate multiple preliminary estimates of each water balance component (from either historical model simulations or interpolated in situ monitoring data, for example), and it can accommodate those estimates even if they span different time periods. The L2SWBM can also be executed if data for a particular water balance component are unavailable, a feature that underscores its potential utility in data scarce regions. Here, we demonstrate the utility of our new framework through a customized application to the Laurentian Great Lakes, the largest system of lakes on Earth. Through this application, we find that the L2SWBM is able to infer new water balance component estimates that, to our are knowledge, are the first ever to close the water balance over a multi-decadal historical period for this massive lake system. More specifically, we find that posterior predictive intervals for changes in lake storage are consistent with observed changes in lake storage across this period over simulation time intervals of both 6 and 12 months. In additional to introducing a framework for developing definitive long-term hydrologic records for large lake systems, our study provides important insights into the origins of biases in both legacy and state-of-the-art hydrological models, as well as regional and global hydrological data sets.

Methods for simulating evaporative water loss from Earth's large lakes have lagged behind advances in hydrodynamic modeling. Here we explore use of oceanographic models to simulate lake evaporation from a long‐term water balance perspective. More specifically, we compare long‐term monthly simulations of latent heat flux from two configurations of a current operational hydrodynamic forecasting system (based on the Finite Volume Community Ocean Model, or FVCOM) for the Laurentian Great Lakes. We then compare these simulations to comparable simulations from a legacy conventional lake thermodynamics model, and from a recently developed statistical water balance model. We find that one of the FVCOM configurations that is currently used in operations for short‐term hydrodynamic forecast guidance is also suitable for real‐time simulation of evaporation from very large lakes. The operational versions of FVCOM should therefore be considered a readily available tool for supporting regional water supply management and, pending further research, extended water supply forecasting.

Nonindigenous species pose significant risks to the health and integrity of ecosystems around the world. Tracking and communicating the spread of these species has been of interest to ecologists and environmental managers for many years, particularly in the bi-national Laurentian Great Lakes of North America. In this paper, we introduce the Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) Map Explorer. The Map Explorer provides access to records of documented nonindigenous species and their spatial distributions. Users may view the distributions of well-known nonindigenous species (such as zebra mussels) as well as perform custom queries. Additional map layers allow users to compare the distribution of nonindigenous species to environmental conditions. This tool serves to communicate knowledge to diverse stakeholder groups and to enable further in-depth research on nonindigenous species.

Real-time environmental observations have typically been stored in relatively inaccessible flat text files. In this publication, we present an instance of a PostgreSQL database management system-to ingest real-time observations from four buoys in North America's western Lake Erie. Data are transmitted via a cellular data modem, initially archived as text data, and then ingested into the database for further analysis and retrieval. The database utilizes two tables with parallel structure to archive data in a consistent manner. We assign unique keys to instrumentation configurations as they change within and between monitoring seasons. Daily sets of data are linked to their configurations by key and thus are documented, allowing for efficient browsing of user desired data. In addition, we quality check data and archive the findings in a corresponding matrix. With assistance from server-side processing, we produce a web interface for the database. We hope the design of this database allows for relatively simple deployment in domains other than western Lake Erie.

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.

Growing demand from the general public for centralized points of data access and analytics tools coincides with similar, well-documented needs of regional and international hydrology research and resource management communities. To address this need within the Laurentian Great Lakes region, we introduce the Great Lakes Dashboard (GLD), a dynamic web data visualization platform that brings multiple time series data sets together for visual analysis and download. The platform's adaptable, robust, and expandable Time Series Core Object Model (GLD-TSCOM) separates the growing complexity and size of Great Lakes data sets from the web application interface. Although the GLD-TSCOM is currently applied exclusively to Great Lakes data sets, the concepts and methods discussed here can be applied in other geographical and topical areas of interest.

Between January 2013 and December 2014, water levels on Lake Superior and Lake Michigan-Huron, the two largest lakes on Earth by surface area, rose at the highest rate ever recorded for a 2 year period beginning in January and ending in December of the following year. This historic event coincided with below-average air temperatures and extensive winter ice cover across the Great Lakes. It also brought an end to a 15 year period of persistently below-average water levels on Lakes Superior and Michigan-Huron that included several months of record-low water levels. To differentiate hydrological drivers behind the recent water level rise, we developed a Bayesian Markov chain Monte Carlo (MCMC) routine for inferring historical estimates of the major components of each lake's water budget. Our results indicate that, in 2013, the water level rise on Lake Superior was driven by increased spring runoff and over-lake precipitation. In 2014, reduced over-lake evaporation played a more significant role in Lake Superior's water level rise. The water level rise on Lake Michigan-Huron in 2013 was also due to above-average spring runoff and persistent over-lake precipitation, while in 2014, it was due to a rare combination of below-average evaporation, above-average runoff and precipitation, and very high inflow rates from Lake Superior through the St. Marys River. We expect, in future research, to apply our new framework across the other Laurentian Great Lakes, and to Earth's other large freshwater basins as well.

Since the late 1990s, the Laurentian Great Lakes have experienced persistent low water levels and above average over-lake evaporation rates. During the winter of 2013-2014, the lakes endured the most persistent, lowest temperatures and highest ice cover in recent history, fostering speculation that over-lake evaporation rates might decrease and that water levels might rise. To address this speculation, we examined interseasonal relationships in Lake Michigan's thermal regime. We find pronounced relationships between winter conditions and subsequent fall heat content, modest relationships with fall surface temperature, but essentially no correlation with fall evaporation rates. Our findings suggest that the extreme winter conditions of 2013-2014 may have induced a shift in Lake Michigan's thermal regime and that this shift coincides with a recent (and ongoing) rise in Great Lakes water levels. If the shift persists, it could (assuming precipitation rates remain relatively constant) represent a return to thermal and hydrologic conditions not observed on Lake Michigan in over 15years.

The recent 2-year surge represents one of the most rapid rates of water level change on the Great Lakes in recorded history and marks the end of an unprecedented period of low water levels.

Understanding drivers behind monthly, annual, and decadal water level fluctuations on the North American Great Lakes is a high priority for regional research and water resource management planning. The need for improved understanding of these relationships is underscored by a series of recent unprecedented extreme water level patterns, including (but not limited to) record low water levels on Lakes Michigan and Huron in December 2012 and January 2013. To address this need, we developed the Great Lakes Hydro-Climate Dashboard (GLHCD), a dynamic flash-based web interface that builds upon the previously-released Great Lakes Water Level Dashboard (GLWLD). In addition to including water level data and projections from the GLWLD, the GLHCD presents a range of hydrological and climatological data through an improved graphical user interface specifically designed to manage, and display simultaneously, a variety of data time series from different sources. By serving as a common portal to critical regional hydro-climate and water level data, the GLHCD helps visualize and explain lake level phenomena including water level declines across all of the Great Lakes in the early 1960s and their relationship to changes in regional precipitation, as well as the abrupt water level declines in the late 1990s and their relationship to remarkable changes in over-lake evaporation. By providing insight into these, and other important regional hydro-climate events, the GLHCD helps practitioners, researchers, and the general public improve their understanding of the drivers behind Great Lakes water levels, and to employ that understanding in prudent water resource management planning.

There is a growing need within the international water research and water resources management community, and the general public, for easy access to time-series of projected, measured, and reconstructed marine and freshwater coastal surface water elevations. There is also a need for effectively communicating variability among different surface water elevation data sets, as well as the intrinsic uncertainties in surface water elevation forecasts. Here, we introduce an interactive web-based interface, the Great Lakes Water Level Dashboard (GLWLD), designed to address this need for the North American Laurentian Great Lakes, the largest assemblage of unfrozen fresh surface water bodies on planet Earth, and one with a coastline of over 16,000 km (roughly 10,000 miles). The GLWLD is a Flash-based tool that can simultaneously display time-series of measured monthly and annual water level data and seasonal forecasts for each of the Great Lakes, reconstructed lake levels from paleoclimate research, and decadal lake level projections under alternative climate scenarios. By employing a suite of novel data transfer, processing, and visualization tools, the GLWLD allows users to seamlessly transition not only between alternate displays of Great Lakes water levels over different temporal scales, but between different data sets and forecasts as well. Furthermore, the unique GLWLD interface can help users understand the extent to which decisions regarding the use of the lakes depend on an appreciation of uncertainty and variability within, and between, different sources of Great Lakes water level information.