Collecting Edge-of-Field Runoff and Water Quality Data: Data Quality and Resource Considerations.

In 2000, scientists with the UDSA-ARS in Temple, Texas, initiated a targeted research program to develop improved techniques and enhanced understanding of runoff and water quality data collection at the edge-of-field and small watershed scale.  Prior to this research, little practical guidance was available to assist researchers, cities, consulting firms, and regulatory agencies in conducting such data collection efforts.  Similarly, the uncertainty associated with measured runoff and water quality data at the edge-of-field and small watershed scale has been traditionally overlooked probably due to a lack of scientific understanding.  Thus, the goals of this research program were: 1) to establish practical, science-based guidance for data collection to improve data quality and efficiency of monitoring expenditures, and 2) to establish fundamental understanding of the uncertainty in measured runoff and water quality data.

The resulting body of knowledge (more than 10 journal articles and 3 book chapters available at http://www.ars.usda.gov/spa/hydro-collection) was designed to assist users in increasing the likelihood of project success, specifically accurate characterization of runoff and water quality within project resource constraints.  This science-based guidance, which will be briefly summarized in the presentation, has since been requested and utilized by governmental agencies, universities, cities, and consulting firms in the US, Canada, and Europe.

Resources are too limited and data needs too great for data collection projects to be designed solely based on field experience or with complete disregard for potential data quality implications. Similarly, the environmental and socio-economic ramifications of decisions based on runoff and water quality data are too great for the inherent uncertainty to continue to be ignored.  It is hoped that the results of this targeted research program are increasingly used to improve edge-of-field and small watershed data collection in terms of data quality and resource efficiency.