Water Resources of Colorado

Data-Quality Measures of Stakeholder-Implemented Watershed-Monitoring Programs

by Adrienne I. Greve

Available from the U.S. Geological Survey, Branch of Information Services, Box 25286, Denver Federal Center, Denver, CO 80225, USGS Open-File Report 02–141, 19 p., 5 figs.

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Community-based watershed groups, many of which collect environmental data, have steadily increased in number over the last decade. The data generated by these programs are often underutilized due to uncertainty in the quality of data produced. The incorporation of data-quality measures into stakeholder monitoring programs lends statistical validity to data.

Data-quality measures are divided into three steps: quality assurance, quality control, and quality assessment. The quality-assurance step attempts to control sources of error that cannot be directly quantified. This step is part of the design phase of a monitoring program and includes clearly defined, quantifiable objectives, sampling sites that meet the objectives, standardized protocols for sample collection, and standardized laboratory methods. Quality control (QC) is the collection of samples to assess the magnitude of error in a data set due to sampling, processing, transport, and analysis. In order to design a QC sampling program, a series of issues needs to be considered: (1) potential sources of error, (2) the type of QC samples, (3) inference space, (4) the number of QC samples, and (5) the distribution of the QC samples. Quality assessment is the process of evaluating quality-assurance measures and analyzing the QC data in order to interpret the environmental data. Quality assessment has two parts: one that is conducted on an ongoing basis as the monitoring program is running, and one that is conducted during the analysis of environmental data.

The discussion of the data-quality measures is followed by an example of their application to a monitoring program in the Big Thompson River watershed of northern Colorado.

Table of Contents




Purpose and Scope


Quality Assurance

Possible Quality-Assurance Approaches for Stakeholder Groups

Quality Control

Quality-Control Sample Design

Determining the Potential Sources of Error

Type of Quality-Control Samples Needed

Determining the Inference Space for Quality-Control Samples

Number of Quality-Control Samples Needed

Distribution of Quality-Control Samples within an Inference Space

Quality Assessment

Ongoing Quality-Assessment Measures

Using Quality-Assessment Measures during Environmental Data Analysis

Estimating Variability by Using Field-Replicate Quality-Control Samples

Interpreting Environmental Data by Using Field-Replicate Quality-Control Samples

Estimating Bias by Using Field-Blank Quality-Control Samples

Interpreting Environmental Data by Using an Estimate of Bias

Estimating Matrix Interaction and Sample Degradation with Field-Spike Data

Interpreting Environmental Data by Using Spike-Recovery Data

A Data-Quality Program in the Big Thompson River Watershed

Quality Assurance for the Big Thompson Watershed Forum

Quality Control for the Big Thompson Watershed Forum

Types of Basic Quality-Control Samples to be Collected

Types of Topical Quality-Control Samples to be Collected

Inference Space

The Number of Quality-Control Samples to be Collected

Distribution of Quality-Control Samples

Summary and Conclusions


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Water Resources of Colorado

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