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

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 stakeho...

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Bibliographic Details
Main Author: Greve, Adrienne I
Format: Report
Language:English
Subjects:
Administration and Management
DATA BASES
ENVIRONMENTAL MANAGEMENT
Hydrology, Limnology and Potamology
QUALITY ASSURANCE
QUALITY CONTROL
STAKEHOLDERS
WATERSHEDS
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Summary: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 a monitoring program in the Big Thompson River watershed of northern Colorado. The original document contains color images.