Historical datasets (1950-2022) of monthly water balance components for the Laurentian Great Lakes.

This study develops a 73-year dataset of water balance components from 1950 to 2022 for the Laurentian Great Lakes Basins. This is carried out using the Large Lakes Statistical Water Balance Model (L2SWBM), which provides a Bayesian statistical framework that assimilates binational input datasets sourced from the United States and Canada. The L2SWBM infers feasible water balance component estimates through this Bayesian framework by constraining the output with a standard water balance equation.

Can continental transboundary compacts hold water?

The growing number of threats facing continental-scale transboundary water treaties warrants contemporary evaluation of not only the political and climatological conditions under which they were constructed, but also of how different management strategies for accommodating changes in those conditions can lead to treaty success or failure. We assess these threats by highlighting key attributes and vulnerabilities of water treaties across North America that frame a diverse set of future water management priorities. While these threats are ubiquitous globally, they are particularly pronounced in North America where water-abundant basins along the border between the United States (US) and Canada contrast with arid basins along the border between the US and Mexico.

A database of in situ water temperatures for large inland lakes across the coterminous United States.

Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing and in situ data for validation. In situ monitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data.

Seasonal overturn and stratification changes drive deep-water warming in one of Earth's largest lakes.

Most of Earth's fresh surface water is consolidated in just a few of its largest lakes, and because of their unique response to environmental conditions, lakes have been identified as climate change sentinels. While the response of lake surface water temperatures to climate change is well documented from satellite and summer in situ measurements, our understanding of how water temperatures in large lakes are responding at depth is limited, as few large lakes have detailed long-term subsurface observations. We present an analysis of three decades of high frequency (3-hourly and hourly) subsurface water temperature data from Lake Michigan.

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

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.

The compartment bag test (CBT) for enumerating fecal indicator bacteria: Basis for design and interpretation of results.

For the past several years, the compartment bag test (CBT) has been employed in water quality monitoring and public health protection around the world. To date, however, the statistical basis for the design and recommended procedures for enumerating fecal indicator bacteria (FIB) concentrations from CBT results have not been formally documented. Here, we provide that documentation following protocols for communicating the evolution of similar water quality testing procedures.

Integrating quantitative PCR and Bayesian statistics in quantifying human adenoviruses in small volumes of source water.

Rapid quantification of viral pathogens in drinking and recreational water can help reduce waterborne disease risks. For this purpose, samples in small volume (e.g.

Differentiating Enterococcus concentration spatial, temporal, and analytical variability in recreational waters.

Monitoring recreational waters for fecal contamination is an important responsibility of water resource management agencies throughout the world, yet fecal indicator bacteria (FIB)-based recreational water quality assessments rarely distinguish between analytical, spatial, and temporal variability. To address this gap in water resources research and management protocol, we compare two methods for quantifying FIB concentration variability at a frequently-used beach on Lake Huron (Michigan, USA). The first method calculates differences between most probable number (MPN) and colony-forming unit (CFU) values derived from conventional analysis procedures.

Improving water quality assessments through a hierarchical Bayesian analysis of variability.

Water quality measurement error and variability, while well-documented in laboratory-scale studies, is rarely acknowledged or explicitly resolved in most model-based water body assessments, including those conducted in compliance with the United States Environmental Protection Agency (USEPA) Total Maximum Daily Load (TMDL) program. Consequently, proposed pollutant loading reductions in TMDLs and similar water quality management programs may be biased, resulting in either slower-than-expected rates of water quality restoration and designated use reinstatement or, in some cases, overly conservative management decisions. To address this problem, we present a hierarchical Bayesian approach for relating actual in situ or model-predicted pollutant concentrations to multiple sampling and analysis procedures, each with distinct sources of variability.

Addressing uncertainty in fecal indicator bacteria dark inactivation rates.

Assessing the potential threat of fecal contamination in surface water often depends on model forecasts which assume that fecal indicator bacteria (FIB, a proxy for the concentration of pathogens found in fecal contamination from warm-blooded animals) are lost or removed from the water column at a certain rate (often referred to as an "inactivation" rate). In efforts to reduce human health risks in these water bodies, regulators enforce limits on easily-measured FIB concentrations, commonly reported as most probable number (MPN) and colony forming unit (CFU) values. Accurate assessment of the potential threat of fecal contamination, therefore, depends on propagating uncertainty surrounding "true" FIB concentrations into MPN and CFU values, inactivation rates, model forecasts, and management decisions.

Calibrating and validating bacterial water quality models: a Bayesian approach.

Water resource management decisions often depend on mechanistic or empirical models to predict water quality conditions under future pollutant loading scenarios. These decisions, such as whether or not to restrict public access to a water resource area, may therefore vary depending on how models reflect process, observation, and analytical uncertainty and variability. Nonetheless, few probabilistic modeling tools have been developed which explicitly propagate fecal indicator bacteria (FIB) analysis uncertainty into predictive bacterial water quality model parameters and response variables.

An assessment of fecal indicator bacteria-based water quality standards.

Fecal indicator bacteria (FIB) are commonly used to assess the threat of pathogen contamination in coastal and inland waters. Unlike most measures of pollutant levels however, FIB concentration metrics, such as most probable number (MPN) and colony-forming units (CFU), are not direct measures of the true in situ concentration distribution. Therefore, there is the potential for inconsistencies among model and sample-based water quality assessments, such as those used in the Total Maximum Daily Load (TMDL) program.

Modeling the relationship between most probable number (MPN) and colony-forming unit (CFU) estimates of fecal coliform concentration.

Most probable number (MPN) and colony-forming-unit (CFU) estimates of fecal coliform bacteria concentration are common measures of water quality in coastal shellfish harvesting and recreational waters. Estimating procedures for MPN and CFU have intrinsic variability and are subject to additional uncertainty arising from minor variations in experimental protocol. It has been observed empirically that the standard multiple-tube fermentation (MTF) decimal dilution analysis MPN procedure is more variable than the membrane filtration CFU procedure, and that MTF-derived MPN estimates are somewhat higher on average than CFU estimates, on split samples from the same water bodies.