U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2013–3068

October 2013 Printed on recycled paper

Tools for Beach Health Data Management, Data Processing, 

and Predictive Model Implementation

Background

Poor recreational water quality can 

adversely affect human health through 

exposure to pathogens (disease-causing 

organisms). To address this risk to public 

health, the U.S. Environmental Protection 

Agency (EPA) Beach Act was founded 

with specific aims to establish waterquality monitoring programs, improve 

understanding of beach health, clean up 

U.S. coastal recreational waters, and put 

forth a criterion for posting advisories 

or closing beaches during periods of 

poor water quality (U.S. Environmental 

Protection Agency, 2000). Beach managers face several challenges with respect to 

issuing these advisories and closures:

• Traditional water-quality testing is 

slow. Most beach managers collect 

water samples and analyze them for 

fecal indicator bacteria (FIB), such as 

Escherichia coli (E. coli) or Enterococcus, which is a measure of the water 

quality at the beach. This analysis can 

take up to 24 hours for accurate results, 

and therefore may not provide adequate results for timely decisions. This 

may result in beach advisories that are 

issued too late to protect visitor health, 

or unnecessarily issued the next day, 

after the water quality has improved. 

• Monitoring is expensive. Resources of 

local agencies that are responsible for 

monitoring are limited, making it difficult at times to implement adequate 

monitoring programs for assessing 

beach water quality. 

• It is difficult to efficiently access data.

Environmental data can come from a 

wide variety of sources and in a variety 

of formats. Finding and compiling the 

available data necessary for studying 

and predicting beach water quality can 

be a very time-consuming task.

USGS scientists have developed 

empirical models in order to predict 

E. coli concentrations (a surrogate for 

water quality) in nearshore waters based 

on environmental data (rainfall, water 

current, turbidity or water clarity, and 

temperature). These predictive models 

of beach water quality can address some 

of the challenges faced by beach managers with respect to issuing advisories and 

closures. To perform predictive modeling efficiently, data must be collected 

from multiple sources, aggregated, and 

processed before being used in a model. 

Real-time or near real-time monitoring 

data is required for use in these models, 

so that beach managers can assess the 

water quality of their beach as close as 

possible to the time they need to make 

a decision about a closure or advisory. 

Many modelers are moving toward model 

implementations that predict beach water 

quality based on environmental data from 

external sources. 

This fact sheet describes USGS tools 

created for efficient data management 

(data entry, access, aggregation, and processing) and predictive modeling to assist 

beach managers in making decisions to 

protect public health. The Environmental Data Discovery and Transformation 

(EnDDaT) Web service accesses, 

identifies, compiles, and sorts environmental data from a variety of sources. 

The Great Lakes Beach Health Database 

(GLBH-DB), which is comprised of both 

a relational database and a Web application, is a tool designed for flexible input, 

export, and storage of beach water-quality and sanitary-survey monitoring data. 

A real-time predictive modeling strategy 

has been implemented that incorporates 

both tools. USGS scientists also have 

developed a concept for a web-accessible 

geographic information system (GIS)-

based tool for beach data visualization 

and summary.

Environmental Data Discovery and 

Transformation

Efficient data discovery, aggregation, and processing can be a barrier to 

sophisticated environmental modeling 

efforts. As environmental aquatic models 

have become more intricate and comprehensive, the amount of data necessary to 

build and run the models has increased 

significantly. An increase in available 

environmental data could lead to more 

reliable models, but only if scientists 

are aware of the data and can gather and 

make efficient use of the data. EnDDaT 

(http://cida.usgs.gov/enddat/) is a Web 

tool used to identify, access, process, and 

compile data from our natural environment. It is a multi-disciplinary data integration framework currently implemented 

for projects and models that require realtime or historical climate, hydrology, 

The overall mission of this work is to provide science-based information and methods that will allow beach managers to more 

accurately make beach closure and advisory decisions, understand the sources and physical processes affecting beach contaminants, 

and understand how science-based information can be used to mitigate and restore beaches and protect the public. 

The U.S. Geological Survey (USGS), in collaboration with many Federal, State, and local agencies and universities, has conducted research on beach health issues in the Great Lakes Region for more than a decade. The work consists of four science elements 

that align with the USGS Beach Health Initiative Mission: real-time assessments of water quality; coastal processes; pathogens and 

source tracking; and data analysis, interpretation, and communication. The ongoing or completed research for the data analysis, 

interpretation, and communication topic is described in this fact sheet.

Photograph by Tracey Reinke, U.S. Geological Survey. 
and coastal hydrodynamics data. The end 

result is that environmental data from 

multiple sources are sorted into a single 

table (see “EnDDaT Options” section). 

EnDDaT was developed with the capabilities of retrieving publicly available data 

resources through standard Web services, 

aggregating the disparate data sources, 

and processing the data through a single 

Web-accessible user interface. In addition, the tool provides a variety of output 

formats and data visualization tools.

The initial EnDDaT application 

was built to assist Great Lakes beach 

managers in retrieving historical data for 

development of statistical models, as well 

as executing predictive models of beach 

water quality using real-time data. It was 

used successfully during summer 2012 

in running 17 operational models, which 

assisted beach managers in posting swimming advisories at Great Lakes beaches 

owing to exceedances of EPA recreational 

water-quality criteria. 

Figure 1. Screenshot of the data discovery page in the Environmental Data Discovery and 

Transformation (EnDDaT) Web service. The user may choose data to be retrieved from tabs 

on the right.

Figure 2. Screenshot of the data visualization options in the Environmental Data Discovery 

and Transformation (EnDDaT) Web service. (cfs, cubic feet per second)

User Interface

The initial EnDDaT data discovery page has an interactive map, which 

allows users to create a ‘Project Location.’ From that choice in location, the 

user can specify a bounding box (in 

miles) to search for data, and identify 

which data sources are available. Once 

those parameters are set, locations where 

data are available are displayed on the 

map, and available parameters and 

temporal extents are listed on the sidebar 

(fig. 1). 

On subsequent pages of EnDDaT, 

users can refine their requests, specify 

the dates and times for which they want 

data, and request post-processing (see

“EnDDaT Processing” section). If a user 

only wants data with the same sample 

dates and times as a field monitoring 

sample, or as close as possible, they also 

may upload these dates and times to get 

datasets with matching time stamps. 

There are several options for using 

EnDDaT data, including export of data 

in comma or tab delimited data tables, 

visualization in interactive graphics, 

and a Web service accessed through a 

Web address or uniform resource locator 

(URL), which may be called or executed 

manually or automatically by custom 

programming scripts. 

The USGS has worked with the 

EPA to facilitate use of EnDDaT data 

in Virtual Beach, which is a software 

package for building and evaluating 

beach water-quality predictive models 

(U.S. Environmental Protection Agency, 

2012; http://www.epa.gov/ceampubl/

swater/vb2/). This includes importing an 

EnDDaT-formatted file into Virtual Beach 

version 2.0 and earlier or direct import 

from EnDDaT into Virtual Beach version 

3.0 (see “Predictive Modeling” section). 

Data visualization (fig. 2) can be 

useful for data exploration and simple 

analysis. There are options on the 

EnDDaT visualization page to refine the 

plotted data, zoom and pan, and create 

linear or logarithmic y-axis scaling. The 

customized image can be saved (using the 

‘Create Image’ button), or the data can be 

downloaded directly into a text file. 
EnDDaT Data Resources

The EnDDaT framework is designed to access multiple and diverse datasets and 

to compile them into one data table. EnDDaT connects to various data sources, merges 

them together, and provides instantaneous post-processing on chosen data. 

The following data currently are available in EnDDaT; additional data sources can 

be added in the future:

• National Water Information System (NWIS): USGS time-series data.

• Water Quality Portal data (cooperation between USGS and EPA): Discrete waterquality sample data.

• Great Lakes Coastal Forecasting System (GLCFS): National Oceanic and Atmospheric Administration (NOAA) model output data for wind, waves, currents, temperatures, and ice.

• National River Forecasting Centers Precipitation Data: Radar indicated rainfall.

Figure 3. Screenshot of processing options in the Environmental Data Discovery and 

Transformation (EnDDaT) Web service showing how users can have the data delivered as 

reported, or processed with rolling means, minimums, maximums, etc., over a user-specified 

time period.

EnDDaT Options

The following processing tools currently are provided (fig. 3):

• Time-Windowed Statistics: Mean, minimum, maximum, summation, difference, 

maximum difference, and standard deviation over a user-specified time range on a 

data source can be provided as output. This not only provides a method to combine 

data from sources that have very different time resolutions (for example, 5-minute 

data combined with hourly or daily data), but also allows the user to choose the most 

relevant time period for each individual parameter requested. 

• Beach Orientation Calculator: EnDDaT offers an interactive tool to calculate the 

angle of beach orientation for computation of parallel and perpendicular components 

of directional vectors. The user can adjust markers on a map to automatically calculate the offset of the beach alignment from north. 

• Vector Processing: Using the orientation calculated by EnDDaT, or a user-specified 

angle, data that are provided as directional vectors such as currents and wind can be 

converted to components that are parallel and perpendicular to the beach. 

For further information regarding EnDDaT, contact Laura De Cicco 

(ldecicco@usgs.gov).

The Great Lakes Beach Health 

Database 

The GLBH-DB was developed to 

meet the need for a common and flexible 

system for input, export, and storage of 

beach water-quality and sanitary-survey 

monitoring data. The sanitary survey 

established by the EPA is a method for 

investigating contamination at a beach 

and the surrounding watershed, which 

contains a standard set of data collected. 

Data commonly collected for the sanitary 

survey include physical water-quality 

parameters such as turbidity and water 

temperature, as well as other parameters 

that may have an influence on water 

quality such as the number of birds at the 

beach, and amount of algae on the beach. 

Many beach managers have conducted 

sanitary surveys at their beaches to assess 

the baseline water quality and potential 

contamination sources.

The adaptable nature of the system 

allows it to be used across States and 

agencies, regardless of which parameters are collected or which monitoring methods are used. Users are able to 

create customized input forms, if desired, 

for each of their beaches each season 

with multiple monitoring sites and (or) 

tributaries. The system currently stores 

more than 5,600 sanitary-survey records 

from beaches in Wisconsin, New York, 

Michigan, and Ohio, and is currently 

available for use by any USGS scientist 

or collaborator monitoring Great Lakes 

beaches. Users also are able to view and 

edit records, and perform custom exports 

from the system by filtering based on 

date, monitoring location (beach, monitoring point, or tributary), export format, 

and file type. Lastly, the system has 

been integrated with EnDDaT, in order 

to allow beach water-quality prediction 

models to be run in real-time, based 

on collected sanitary-survey data and 

environmental data from external sources 

gathered by EnDDaT.
User Interface

Beach managers are able to create a 

new customized data entry form for each 

of their beaches before the season starts 

(fig. 4). The system allows managers to 

add, remove, reorder, and organize fields 

into subsections. This often is useful as 

many beach managers like to organize 

their digital data-entry form to mimic 

their field forms. This flexibility allows 

different groups to monitor completely 

different sets of data and use a form that 

has only the fields they need. The system 

has the flexibility to establish a set of 

monitoring points and tributaries for a 

beach. It provides a form that allows for 

entry of monitoring data for the entire 

beach, for each monitoring point, and for 

each tributary as necessary. Furthermore, 

the data are stored in a way that relates 

the beach data to its respective monitoring point and tributary-monitoring data. 

Figure 4. Screenshot of the U.S. Geological Survey Great Lakes Beach Health Database 

(GLBH-DB) edit-form page.

Great Lakes Beach Health 

Database Export and Reporting

Detailed data reporting as well as 

summary reports are available from the 

GLBH-DB. 

Summary reports give beach managers the ability to get an overview of the 

data records completed for their beaches. 

Available summary information includes 

the following:

• Counts of completed records per beach

• Date and time the first and last record 

was entered or edited for that season

• Data-export options were designed to 

provide the necessary flexibility for 

management, quality assurance, and 

scientific purposes, including the following: 

– Data Format: Users can choose to 

download data organized per survey 

or per parameter 

– Date Range: Users can optionally 

choose to filter data by date

– Datasets: Users may choose overall 

beach data, data from individual 

monitoring points, or associated 

tributary data

– Parameter Group: The parameters 

in the form were classified thematically into groups, and users are able 

to limit their download to data from 

one or more of these parameter 

groups (e.g., Algae, FIB, Physical 

conditions)

– File Format: Microsoft Excel or 

text files formatted as eXtensible 

Markup Language (XML), Comma 

Separated Value (CSV), or Tab 

Separated Value (TSV) output 

For further information regarding the 

GLBH-DB, contact Jessica Lucido 

(jlucido@usgs.gov).

Real-Time Model Implementation

The GLBH-DB was integrated with 

EnDDaT to run predictive models by 

automated means. Predictive models, 

such as those developed using Virtual 

Beach or other statistical software, draw 

upon numerous sources of data to predict 

water quality and assist beach managers 

with beach-advisory decisions. These 

statistical models can be incorporated in 

to the GLBH-DB (see “Predictive Modeling” section). Data used to run these 

models include data collected daily from 

the beach, such as turbidity and wave 

height, in combination with hydrological 

data, climatic data, and hydrodynamic 

data that are retrieved online using the 

EnDDaT system. After data are entered 

into the database using the Great Lakes 

Beach Web Application, an “execute 

model” button is available to generate 

model predictions. Upon execution of 

the predictive model, model input and 

resulting predictions are archived in the 

database and used to evaluate model 

effectiveness.

Understanding Beach Health 

Using Geographic Information 

Systems

The 2005 Great Lakes Beach Health 

Needs Workshop (Great Lakes Beach 

Association, 2006) identified improved 

Great-Lakes-wide data analysis, interpretation, and communication as critical 

research needs. Specifically recommended was a means to reach beyond 

political boundaries to map beaches 

based on their physical and biological 

characteristics. USGS scientists developed a prototype for a Web-accessible 

GIS-based tool for beach data visualization and summary. The prototype tool 

was shared with numerous partners and 

collaborators, and provided concepts 

and information to aid in the development of new and more advanced beach 

data Web-access portals, such as the EPA 

Beach Advisory and Closing On-line 

Notification system (http://watersgeo.

epa.gov/beacon2/) and EnDDaT. Webaccessible GIS maps remain a goal for 

future development and could encompass 

multiple scales of analysis—watershed, 
shoreline, and beach. Furthermore, these 

systems could incorporate driving factors 

for beach pollution, such as tributaries, 

stormwater outfalls, or land-use analysis, 

into spatial models of beach-bacteria 

dynamics. For example, GIS has been 

used in previous research to relate waterassociated illness to spatial patterns and 

demographics (Turbow and others, 2008) 

and, in conjunction with antibiotic-resistance patterns, to determine sources of 

fecal pollution in an estuary (Kelsey and 

others, 2003). Research by USGS scientists has shown that temporal patterns of 

FIB may be similar among beaches along 

adjacent stretches of coastline (Whitman 

and Nevers, 2008). Recently, USGS scientists reported that this also may be the 

case for some bacterial pathogens (Haack 

and others, 2012).

Predictive Modeling 

There currently are three methods 

available to execute predictive models 

from the tools described in this paper.

Great Lakes Beach Health Database

Beaches that have been configured for data entry and storage within 

the GLBH-DB can be set up for model 

execution through a link between the 

GLBH-DB and EnDDaT (fig. 5). 

• Predictive models from Virtual Beach 

or other linear model-development 

methods are entered into this system,

• Daily sanitary survey data are collected 

and entered into the GLBH-DB,

• The “Execute model” option is chosen,

• The GLBH-DB contacts EnDDaT 

and requests the most current model 

parameters,

• EnDDaT runs the model, returns 

predictions to a specified user list via 

email, and logs results in the database.

For further information regarding linking 

these tools to predictive modeling, contact Steve Corsi (srcorsi@usgs.gov).

Step 1

Step 2

Beach

initialization in

database GLBH-DB

GLBH-DB

Step 4

Step 3 Model

implementation

Prediction EnDDaT

GLCFS

NWIS

NDBC

WQP

Figure 5. Schematic of data flow for predictive model implementation through the Great 

Lakes Beach Health Database (GLBH-DB) and the Environmental Data Discovery and 

Transformation (EnDDaT) Web service. (GLCFS, Great Lakes Coastal Forecasting System; 

NWIS, National Water Information System; WQP, water-quality portal; NDBC, National Data 

Buoy Center)

Figure 6. Screenshot of the Virtual Beach 3.0 software tool for direct import of 

Environmental Data Discovery and Transformation (EnDDaT) Web service data.

Virtual Beach

Virtual Beach 3.0 offers an option 

for direct import of EnDDaT data (fig. 6). 

Using the URL generated by EnDDaT, 

data can be imported to obtain a prediction result (U.S. Environmental Protection Agency, 2012). Virtual Beach 3.0 

should be publically available in early 

summer 2013.

Web Service to External Models

Using a custom URL to retrieve 

model-specific parameters, a table can be 

downloaded from EnDDaT and imported 

to predictive beach models (including 

older versions of Virtual Beach) and any 

other model that can make use of data 

that are accessible from EnDDaT. 
Future Work

Plans for future work include continuation of the use of EnDDaT and the 

GLBH-DB for predictive modeling and 

water-quality research at Great Lakes 

beaches. Three methods for incorporation of these tools in predictive modeling 

will be used: custom web services to 

retrieve the latest model-parameter values 

through EnDDaT to be uploaded to 

models; direct import of model parameters into Virtual Beach from EnDDaT; 

and execution of predictive models that 

have been uploaded directly into the 

EnDDaT system. Enhancements of the 

EnDDaT system will be made to include 

additional sources of data for predictivemodeling purposes at beaches and for 

other environmental research as demand 

evolves. Other research conducted as part 

of the same USGS initiative indicates 

that the geographic setting of Great Lakes 

beaches has a significant influence on 

FIB, bacterial-pathogen abundance, and 

temporal patterns. Functionality to define 

geographic settings of beaches, such as 

that of the prototype GIS-based tool, will 

be important for future studies.

References

Great Lakes Beach Association, 2006, 

Great Lakes beach health research 

needs—Workshop summary: National 

Oceanic and Atmospheric Administration Technical Memorandum GLERL138, accessed March 28, 2013, at 

http://www.glerl.noaa.gov/ftp/publications/tech_reports/glerl-138/tm-138.

pdf.

Haack, S.K., Francy, D.S., Fogarty, L.R., 

Brennan, A.K., Stelzer, Erin, IsaacsCosgrove, N., Kephart, C.M., Johnson, 

H.E., Spencer, C., Fuller, L.M., and 

Duris, J.W., 2012, Factors influencing bacterial pathogen genes at Great 

Lakes beaches, in 12th Annual Great 

Lakes Beach Association Conference, 

Oct. 16–18, 2012, Mackinac Island, 

Mich., accessed June 6, 2013, at www.

glin.net/glba/pdf/2012conf/HaackUSGSMich.pdf. 

Kelsey, R.H., Scott, G.I., Porter, D.E., 

Thompson, Brian, and Webster, Laura, 

2003, Using multiple antibiotic resistance and land use characteristics to 

determine sources of fecal coliform 

bacterial pollution: Environmental 

Monitoring and Assessment, v. 81, 

nos. 1–3, p. 337–348. 

Turbow, D.J., Kent, E.E., and Jiang, S.C., 

2008, Web-based investigation of 

water associated illness in marine bathers: Environmental Research, v. 106, 

no. 1, p. 101–109.

U.S. Environmental Protection Agency, 

2000, Beaches Environmental Assessment and Coastal Health Act of 2000, 

accessed May 15, 2013, at http://water.

epa.gov/lawsregs/lawsguidance/beachrules/act.cfm.

U.S. Environmental Protection Agency, 

2012, Exposure Assessment Models—

Virtual Beach, accessed March 15, 

2013, at http://www.epa.gov/ceampubl/

swater/vb2/. 

Whitman, R.L., and Nevers, M.B., 2008, 

Summer E. coli patterns and responses 

along 23 Chicago beaches: Environmental Science & Technology, v. 42, 

no. 24, p. 9217–9224.

Program Information

Funding for USGS beach projects 

and research in the Great Lakes comes 

from a variety of sources including the 

Ocean Research Priority Plan, the USGS, 

the Great Lakes Restoration Initiative, the 

EPA, the NOAA Center for Great Lakes 

and Human Health, and many State and 

local partner agencies and organizations 

throughout the region. 

Contact

James R. Morris, Director

USGS Michigan and Ohio Water Science Centers

614-430-7700

http://greatlakesbeaches.usgs.gov/

Photograph by Tracey Reinke, U.S. Geological Survey.