Publications—Open-File Report 00–491
By Gary D. Tasker and Gregory E. Granato
U.S. Geological Survey Open-File Report 00–491
Prepared in cooperation with the Federal Highway Administration. A Contribution to the National Highway Runoff Data and Methodology Synthesis.
ONLINE ONLY
This report is available in Portable Document Format (PDF):
OFR 00–491 (555 KB) – 67
pages
Decision makers need viable methods for the
interpretation of local, regional, and national-highway
runoff and urban-stormwater data including flows, concentrations
and loads of chemical constituents and sediment,
potential effects on receiving waters, and the
potential effectiveness of various best management
practices (BMPs). Valid (useful for intended purposes),
current, and technically defensible stormwater-runoff
models are needed to interpret data collected in field
studies, to support existing highway and urban-runoffplanning
processes, to meet National Pollutant Discharge
Elimination System (NPDES) requirements,
and to provide methods for computation of Total
Maximum Daily Loads (TMDLs) systematically and
economically.
Historically, conceptual, simulation, empirical,
and statistical models of varying levels of detail, complexity,
and uncertainty have been used to meet various
data-quality objectives in the decision-making processes
necessary for the planning, design, construction,
and maintenance of highways and for other land-use
applications. Water-quality simulation models attempt
a detailed representation of the physical processes and
mechanisms at a given site. Empirical and statistical
regional water-quality assessment models provide a
more general picture of water quality or changes in
water quality over a region. All these modeling techniques
share one common aspect—their predictive
ability is poor without suitable site-specific data for
calibration.
To properly apply the correct model, one must
understand the classification of variables, the unique
characteristics of water-resources data, and the concept
of population structure and analysis. Classifying variables
being used to analyze data may determine which
statistical methods are appropriate for data analysis. An
understanding of the characteristics of water-resources
data is necessary to evaluate the applicability of different
statistical methods, to interpret the results of these
techniques, and to use tools and techniques that
account for the unique nature of water-resources data
sets. Populations of data on stormwater-runoff quantity
and quality are often best modeled as logarithmic transformations.
Therefore, these factors need to be considered
to form valid, current, and technically defensible
stormwater-runoff models.
Regression analysis is an accepted method for
interpretation of water-resources data and for prediction
of current or future conditions at sites that fit the
input data model. Regression analysis is designed to
provide an estimate of the average response of a system
as it relates to variation in one or more known variables.
To produce valid models, however, regression
analysis should include visual analysis of scatterplots,
an examination of the regression equation, evaluation
of the method design assumptions, and regression
diagnostics. A number of statistical techniques are
described in the text and in the appendixes to provide
information necessary to interpret data by use of
appropriate methods.
Uncertainty is an important part of any decisionmaking
process. In order to deal with uncertainty problems,
the analyst needs to know the severity of the statistical
uncertainty of the methods used to predict water
quality. Statistical models need to be based on information
that is meaningful, representative, complete, precise,
accurate, and comparable to be deemed valid, up
to date, and technically supportable. To assess uncertainty
in the analytical tools, the modeling methods,
and the underlying data set, all of these components
need be documented and communicated in an accessible
format within project publications.
Abstract
Introduction
Background
Basic Statistical Considerations
Classification of Variables
Characteristics of Water-Resources Data
Population Structure and Analysis
Transformations
Regression Analysis
The Analytical Process
Linear Regression Methods
Nonlinear Regression Methods
Uncertainty, Quality Assurance, and Quality Control
Benchmarking of Analytical Tools
Uncertainty in Modeling Efforts
Uncertainty in Input Data
Summary
References
Appendix 1: Regression Tools
Appendix 2: Linear Regression Methods
Appendix 3: Nonlinear Regression Methods
Appendix 4: Uncertainty Analysis
Appendix 5: Region of Influence Method.
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Suggested Citation:
Tasker, G.D., Granato, G.E., 2000, Statistical Approaches to Interpretation
of Local, Regional, and National Highway-Runoff and Urban-Stormwater Data:
U.S. Geological Survey Open-File Report 00–491, 59 p.
For additional information write to:
Director,
USGS Massachusetts–Rhode Island Water Science Center
10 Bearfoot Road
Northborough, MA 01532or visit our Web site at:
http://ma.water.usgs.gov
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