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At least 595 records · Page 33Linked to original sources
USGS2021

Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

Environmental Science and Technology↗
USGS2002

Variations in suspended sediment and associated trace element concentrations in selected riverine cross sections

Detailed sampling and subsequent analyses of riverine suspended sediment obtained from six rivers in the United States indicate substantial differences in suspended sediment concentrations and possibly in some associated trace elements, depending on whether depth- and width-integrated, point, or pumping samples are used. In addition, the data from time-series, depth-integrated sampling indicate that there can be substantial short-term (on the order of 20-30 min) spatial and/or temporal variations in suspended-sediment concentrations. Despite this, major element concentrations are remarkably stable both spatially and temporally. Trace element concentrations are generally stable; however, some spatial and temporal variations may occur.

Environmental Science and Technology↗
USGS2014

Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches

Quantitative assessment of bacterial pathogens, their geographic variability, and distribution in various matrices at Great Lakes beaches are limited. Quantitative PCR (qPCR) was used to test for genes from E. coli O157:H7 ( eae O157 ), shiga-toxin producing E. coli ( stx2 ), Campylobacter jejuni ( mapA ), Shigella spp. ( ipaH ), and a Salmonella enterica -specific ( SE ) DNA sequence at seven Great Lakes beaches, in algae, water, and sediment. Overall, detection frequencies were mapA > stx2 > ipaH > SE > eae O157 . Results were highly variable among beaches and matrices; some correlations with environmental conditions were observed for mapA , stx2 , and ipaH detections. Beach seasonal mean mapA abundance in water was correlated with beach seasonal mean log 10 E. coli concentration. At one beach, stx2 gene abundance was positively correlated with concurrent daily E. coli concentrations. Concentration distributions for stx2 , ipaH , and mapA within algae, sediment, and water were statistically different (Non-Detect and Data Analysis in R). Assuming 10, 50, or 100% of gene copies represented viable and presumably infective cells, a quantitative microbial risk assessment tool developed by Michigan State University indicated a moderate probability of illness for Campylobacter jejuni at the study beaches, especially where recreational water quality criteria were exceeded. Pathogen gene quantification may be useful for beach water quality management.

Environmental Science & Technology↗
USGS2009

Mercury cycling in stream ecosystems. 3. Trophic dynamics and methylmercury bioaccumulation

Trophic dynamics (community composition and feeding relationships) have been identified as important drivers of methylmercury (MeHg) bioaccumulation in lakes, reservoirs, and marine ecosystems. The relative importance of trophic dynamics and geochemical controls on MeHg bioaccumulation in streams, however, remains poorly characterized. MeHg bioaccumulation was evaluated in eight stream ecosystems across the United States (Oregon, Wisconsin, and Florida) spanning large ranges in climate, landscape characteristics, atmospheric Hg deposition, and stream chemistry. Across all geographic regions and all streams, concentrations of total Hg (THg) in top predator fish and forage fish, and MeHg in invertebrates, were strongly positively correlated to concentrations of filtered THg (FTHg), filtered MeHg (FMeHg), and dissolved organic carbon (DOC); to DOC complexity (as measured by specific ultraviolet absorbance); and to percent wetland in the stream basins. Correlations were strongest for nonurban streams. Although regressions of log[Hg] versus δ 15 N indicate that Hg in biota increased significantly with increasing trophic position within seven of eight individual streams, Hg concentrations in top predator fish (including cutthroat, rainbow, and brown trout; green sunfish; and largemouth bass) were not strongly influenced by differences in relative trophic position. Slopes of log[Hg] versus δ 15 N, an indicator of the efficiency of trophic enrichment, ranged from 0.14 to 0.27 for all streams. These data suggest that, across the large ranges in FTHg (0.14−14.2 ng L −1 ), FMeHg (0.023−1.03 ng L −1 ), and DOC (0.50−61.0 mg L −1 ) found in this study, Hg contamination in top predator fish in streams likely is dominated by the amount of MeHg available for uptake at the base of the food web rather than by differences in the trophic position of top predator fish.

Environmental Science & Technology↗
USGS2011

BioData: A national aquatic bioassessment database

BioData is a U.S. Geological Survey (USGS) web-enabled database that for the first time provides for the capture, curation, integration, and delivery of bioassessment data collected by local, regional, and national USGS projects. BioData offers field biologists advanced capabilities for entering, editing, and reviewing the macroinvertebrate, algae, fish, and supporting habitat data from rivers and streams. It offers data archival and curation capabilities that protect and maintain data for the long term. BioData provides the Federal, State, and local governments, as well as the scientific community, resource managers, the private sector, and the public with easy access to tens of thousands of samples collected nationwide from thousands of stream and river sites. BioData also provides the USGS with centralized data storage for delivering data to other systems and applications through automated web services. BioData allows users to combine data sets of known quality from different projects in various locations over time. It provides a nationally aggregated database for users to leverage data from many independent projects that, until now, was not feasible at this scale. For example, from 1991 to 2011, the USGS Idaho Water Science Center collected more than 816 bioassessment samples from 63 sites for the National Water Quality Assessment (NAWQA) Program and more than 477 samples from 39 sites for a cooperative USGS and State of Idaho Statewide Water Quality Network (fig. 1). Using BioData, 20 years of samples collected for both of these projects can be combined for analysis. BioData delivers all of the data using current taxonomic nomenclature, thus relieving users of the difficult and time-consuming task of harmonizing taxonomy among samples collected during different time periods. Fish data are reported using the Integrated Taxonomic Information Service (ITIS) Taxonomic Serial Numbers (TSN's). A simple web-data input interface and self-guided, public data-retrieval web site provides access to bioassessment data. BioData currently accepts data collected using two national protocols: (1) NAWQA and (2) U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). Additional collection protocols are planned for future versions.

Fact Sheet↗
USGS2002

Measuring streamflow in Virginia (2002 revision)

The U.S. Geological Survey (USGS), U.S. Department of the Interior, is the Nation's largest Earth-science information agency. Among its many responsibilities, such as map making and providing information on earthquakes and other natural hazards, the USGS provides information on the Nation's water resources. The USGS has collected and analyzed hydrologic (water-related) information for more than 100 years. In 1889, the first streamflow-gaging station (a site where regular observations of streamflow data are collected) operated in the United States by the USGS was established on the Rio Grande near Embudo, New Mexico. As the need for streamflow data increased, the USGS's streamflow-gaging program has grown to include more than 7,000 continuous-record streamflow-gaging stations. More than 90 percent of these stations are operated with at least partial support from State, local, or other Federal agencies In Virginia, the Department of Environmental Quality (DEQ) is a major cooperator in the streamflow-gaging program, which consists of 161 continuous-record streamflow-gaging stations located throughout the State (fig. 1). The USGS and DEQ cooperate to publish the annual USGS State data report, 'Water Resources Data-Virginia'; this two-volume publication includes streamflow data collected at the 161 streamflow-gaging stations, chemical data collected at 19 streamflow-gaging stations, and ground-water data collected from more than 250 wells located in Virginia.

Virginia↗
USGS1999

Measuring streamflow in Virginia (1999 revision)

The U.S. Geological Survey (USGS), U.S. Department of the Interior, is the Nation's largest Earth-science information agency. Among its many responsibilities, such as map making and providing information on earthquakes and other natural hazards, the USGS provides information on the Nation's water resources. The USGS has collected and analyzed hydrologic (water-related) information for more than 100 years. In 1889, the first streamflow-gaging station (a site where regular observations of streamflow data are collected) operated in the United States by the USGS was established on the Rio Grande near Embudo, New Mexico. As the need for streamflow data increased, the USGS's streamflow-gaging program has grown to include more than 7,000 continuous-record streamflow-gaging stations. More than 90 percent of these stations are operated with at least partial support from State, local, and other Federal agencies. In Virginia, the Department of Environmental Quality (DEQ) is a major cooperator in the streamflow-gaging program, which consists of 152 continuous-record streamflow-gaging stations located throughout the State (fig. 1). The USGS and DEQ cooperate to publish the annual USGS State data report, "Water Resources Data-Virginia;" this two-volume publication includes streamflow data collected at the 152 streamflow-gaging stations, chemical data collected at more than 24 streamflow-gaging stations, and ground-water data collected from more than 338 wells.

Virginia↗
USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗
USGS2008

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance- based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods. ?? 2008 Springer Science+Business Media, LLC.

Environmental and Ecological Statistics↗
USGS2003

Multichannel analysis of surface wave method with the autojuggie

The shear (S)-wave velocity of near-surface materials and its effect on seismic-wave propagation are of fundamental interest in many engineering, environmental, and groundwater studies. The multichannel analysis of surface wave (MASW) method provides a robust, efficient, and accurate tool to observe near-surface S-wave velocity. A recently developed device used to place large numbers of closely spaced geophones simultaneously and automatically (the 'autojuggie') is shown here to be applicable to the collection of MASW data. In order to demonstrate the use of the autojuggie in the MASW method, we compared high-frequency surface-wave data acquired from conventionally planted geophones (control line) to data collected in parallel with the automatically planted geophones attached to steel bars (test line). The results demonstrate that the autojuggie can be applied in the MASW method. Implementation of the autojuggie in very shallow MASW surveys could drastically reduce the time required and costs incurred in such surveys. ?? 2003 Elsevier Science Ltd. All rights reserved.

Soil Dynamics and Earthquake Engineering↗
USGS2023

The bee lab

Introduction The U.S. Geological Survey (USGS) Bee Lab is a collaborative interagency joint venture and international leader for bee (Hymenoptera: Apoidea) identification, survey design, quantification of bee and plant interrelations, and development and maintenance of occurrence databases. Each of these objectives supports native bee conservation by providing critical data and tools for the United States and other countries. The Bee Lab is part of the USGS Eastern Ecological Science Center (EESC) and located in Laurel, Maryland, at the U.S. Fish and Wildlife Service (USFWS) Patuxent Research Refuge. The laboratory houses scientists from the EESC, USGS’s Cooperative Fish and Wildlife Research Units, and the USFWS to develop identification tools and survey design support for State, Federal, Tribal, and nongovernment organization partners. In addition to the development of identification tools, important objectives include developing keys for native and nonnative bee species and making those tools accessible to partners and the public. Among the most visible and reused products produced during the development of the tools are the detailed photographs of the bees themselves. Accurate bee identification allows for better monitoring of bee species and examination of environmental factors that may influence their populations.

Fact Sheet↗
USGS2003

USGS Alaska Tissue Archival Projects: An update on FY02 activities

The banking of environmental specimens under cryogenic conditions for future retrospective analysis has been recognized for many years as an important part of environmental monitoring programs. Since 1987, the Alaska Marine Mammal Tissue Archival Project (AMMTAP) has been collecting tissue samples from marine mammals for archival in the National Biomonitoring Specimen Bank (NBSB) at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland, USA. The USGS, Alaska Biological Science Center (ABSC), the NOAA Fisheries, Office of Protected Resources (NMFS), and the NIST conduct this partnership project, which began under the Mineral Management Service (MMS) Outer Continental Shelf Environmental Assessment Program. MMS remains the primary client agency for the AMMTAP providing programmatic guidance and review. The purpose of the project is to collect tissue samples from Alaska marine mammals and to store these specimens under the best conditions so that they can be analyzed for environmental contaminants and other constituents. A substantial part of the sample collection is from Arctic species and, since most of the animals sampled are from Alaska Native subsistence harvests, the project relies on cooperation and collaboration with several Alaska Native organizations and local governmental agencies. Although a substantial amount of recent research has been conducted on contaminants in Alaskan marine mammals, few data exists on colonial seabirds nesting in Alaska. Like marine mammals, seabirds are an important group of upper trophic level marine organisms with a potential for accumulating lipophilic contaminants and are identified by MMS as species of interest for monitoring activities. More than 95% of the seabirds breeding in the continental United States nest at colonies in the Bering and Chukchi seas and Gulf of Alaska (see USFWS 1992). Realizing the value of colonial seabirds in environmental monitoring and the lack of recent data from Alaskan seabird colonies, the U.S. Fish and Wildlife Service Alaska Maritime National Wildlife Refuge (USFWS-AMNWR), the U.S. Geological Survey Biological Resources Division (USGS-BRD), and the National Institute of Standards and Technology (NIST) initiated the Seabird Tissue Archival and Monitoring Project (STAMP) in 1998. The project was designed as a 100-year-long program to monitor long-term trends in environmental quality by collecting eggs at nesting colonies using standardized protocols, banking the egg contents under conditions that ensure chemical stability during long-term (decadal) storage, and analyzing subsamples of the stored material to establish baseline levels for persistent bioaccumulative contaminants (e.g., chlorinated pesticides, PCBs, mercury).

Conference Paper↗
USGS2021

A new remote sensing-based Carbon Sequestration Potential Index (CSPI): A tool to support land carbon management

Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.

Hawaii↗
USGS2017

The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center collected 303 surface sediment samples from Dauphin Island, Alabama, and the surrounding water bodies in August 2015. These sediments were processed to determine physical characteristics such as organic content, bulk density, and grain-size. The environments where the sediments were collected include high and low salt marshes, washover deposits, dunes, beaches, sheltered bays, and open water. Sampling by the USGS was part of a larger study to assess the feasibility and sustainability of proposed restoration efforts for Dauphin Island, Alabama, and assess the island’s resilience to rising sea level and storm events. The data presented in this publication can be used by modelers to attempt validation of hindcast models and create predictive forecast models for both baseline conditions and storms. This study was funded by the National Fish and Wildlife Foundation, via the Gulf Environmental Benefit Fund. This report serves as an archive for sedimentological data derived from surface sediments. Downloadable data are available as Excel spreadsheets, JPEG files, and formal Federal Geographic Data Committee metadata.

Alabama↗
USGS2020

Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment

The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.

Endangered Species Research↗
USGS2015

Applications of optical sensors for high-frequency water-quality monitoring and research

The recent commercial availability of in-situ optical sensors, together with new techniques for data collection and analysis, provides the opportunity to monitor a wide range of water-quality constituents over time scales during which environmental conditions actually change. Traditional approaches for data collection (daily to monthly discrete samples) are often limited by high sample collection, processing, and analytical costs, difficult site access, and logistical challenges, particularly for long-term sampling at a large number of sites. Optical sensors that continuously measure constituents in the environment by absorbance or fluorescence properties (Figure 1) have had a long history of use in oceanography for measuring highly resolved concentrations and fluxes of organic matter, nutrients, and algal material. However, much of the work using commercially-available optical sensors in rivers and streams has taken place in only the last few years. Figure 1. [NOT SHOWN] Optical sensor technology is now sufficiently developed to warrant broader application for research and monitoring in coastal and freshwater systems, and the United States Geological Survey (a U.S. science agency) is now using these sensors in a variety of research and monitoring programs to better understand water quality in-situ and in real-time. Examples are numerous and range from the applications of nitrate sensors for calculating loads to estuaries susceptible to hypoxia (Pellerin et al., 2014) to the use of fluorometers to estimate methymercury fluxes (Bergamaschi et al., 2011) and disinfection byproduct formation (Carpenter et al., 2013). Transmitting these data in real-time provides information that can be used for early trend detection, help identify monitoring gaps critical for water management, and provide science-based decision support across a range of issues related to water quality, freshwater ecosystems, and human health. Despite the value of these sensors, collecting data that meet high-quality standards requires investment in and adherence to tested and established methods and protocols for sensor operation and data management (Pellerin et al., 2013). For example, optical sensor measurements can be strongly influenced by a variety of matrix effects, including water temperature, inner filtering from highly colored water, and scattering of light by suspended particles (Downing et al., 2012). Characterizing and correcting sensors for these effects – as well as the continued development of common methodologies and protocols for sensor use – will be critical to ensuring comparable measurements across sites and over time. In addition, collaborative efforts such as the Nutrient Sensor Challenge (www.nutrients-challenge.org) will continue to accelerate the development, production and use of affordable, reliable and accurate sensors for a range of environments. REFERENCES Bergamaschi .B.A., Fleck J.A., Downing B.D., Boss E., Pellerin B.A., Ganju N.K., Schoellhamer D.H., Byington A.A., Heim W.A., Stephenson M., Fujii R. (2011), Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4): 1355-1371. Carpenter K.D., Kraus T.E.C., Goldman J.H., Saraceno J., Downing B.D., Bergamaschi B.A., McGhee G., Triplett T. (2013), Sources and Characteristics of Organic Matter in the Clackamas River, Oregon, Related to the Formation of Disinfection By-products in Treated Drinking Water: U.S. Geological Survey Scientific Investigations Report 2013–5001, 78 p. Downing .B.D., Pellerin B.A., Bergamaschi B.A., Saraceno J., Kraus T.E.K. (2012), Seeing the light: The effects of particles, temperature and inner filtering on in situ CDOM fluorescence in rivers and streams. Limnology and Oceanography: Methods, 10: 767-775. Pellerin B.A., Bergamaschi B.A., Downing B.D., Saraceno J., Garrett J.D., Olsen L.D. (2013), Optical Techniques for the Determination of Nitrate in En

Conference Paper↗
USGS2025

Missing data in ecology: Syntheses, clarifications, and considerations

In ecology and related sciences, missing data are common and occur in a variety of different contexts. When missing data are not handled properly, subsequent statistical estimates tend to be biased, inefficient, and lack proper confidence interval coverage. Missing data are often grouped into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). We review each category and compare their benefits and drawbacks. We review several approaches to handling missing data including complete case analysis, imputation, inverse probability weighting, and data augmentation. We clarify what types of variables should accompany imputation methods and how those variables are influenced by the analysis methods. Additionally, we discuss missing data that lack a formal basis for measurement and hence are fundamentally different from MCAR, MAR, and MNAR missing data. Throughout, we introduce concepts and numeric examples using both simulated data and data from the United States Environmental Protection Agency's 2016 National Wetland Condition Assessment. We conclude by providing five considerations for ecologists and other scientists handling missing data.

Ecological Monographs↗
USGS2016

Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

Lake Michigan, Ogden Dunes↗