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.
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
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.
Basic Statistical Considerations
Classification of Variables
Characteristics of Water-Resources Data
Population Structure and 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
Appendix 1: Regression Tools
Appendix 2: Linear Regression Methods
Appendix 3: Nonlinear Regression Methods
Appendix 4: Uncertainty Analysis
Appendix 5: Region of Influence Method.
This report is available online in Portable Document Format (PDF). If you do not have the Adobe Acrobat PDF Reader, it is available for free download from Adobe Systems Incorporated.
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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:
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Northborough, MA 01532
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