Measuring edge-of-field water quality: Where we have been and the path forward

Authors: Harmel, Daren; King, Kevin; BUSCH, DENNIS - University Of Wisconsin; Smith, Douglas; BIRGAND, FRANCOIS - North Carolina State University; HAGGARD, BRIAN - University Of Arkansas

Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/13/2017
Publication Date: 1/26/2018
Citation: Harmel, R.D., King, K.W., Busch, D., Smith, D.R., Birgand, F., Haggard, B. 2018. Measuring edge-of-field water quality: Where we have been and the path forward. Journal of Soil and Water Conservation. 73(1):86-96.

Interpretive Summary: Heightened pressure to demonstrate the natural resource benefits of agricultural conservation practices and continued high-profile water quality impairments and concerns are increasing the need to quantify edge-of-field water quality. With this in mind, this manuscript summarizes previous developments in edge-of-field water quality sampling and presents current research and glimpse into the future. As part of the Special edge-of-field Issue, this manuscript focuses on constituent sampling at the field-scale or at the “edge of field”; however, many of the findings are also applicable for small stream or small watershed sampling. With development of automated samplers and initiation of numerous automated sampling projects, it became readily apparent that neither equipment manufacturers nor researchers could provide guidance on design components (e.g., sample initiation, timing/intervals, type). This was problematic, however, as available monitoring resources are too limited and data needs too great for such projects to be designed solely based on field experience and without a scientific basis or with complete disregard for potential data quality implications. Thus practical, science-based guidance for edge-of-field sampling was developed and fundamental understanding of the inherent uncertainty was established to assist researchers, municipalities, consulting firms, and regulatory agencies improve data quality and monitoring resource efficiency. Looking to the future, however, further improvements are needed related to lower cost systems, practical improvements, and enhanced in-stream sampling, along with enhanced understanding and consideration of data uncertainty in modeling and decision-making.

Technical Abstract: Heightened pressure to demonstrate the resource benefits of conservation practices and continued high-profile water quality impairments and concerns are increasing the need to quantify edge-of-field water quality. With this in mind, this manuscript summarizes previous developments in edge-of-field water quality sampling and presents current research and glimpse into the future. As part of the Special edge-of-field Issue, this manuscript focuses on constituent sampling at the field-scale or at the "edge of field"; however, many of the findings are also applicable for small stream or small watershed sampling. With development of programmable automated samplers and initiation of numerous automated sampling projects, it became readily apparent that neither equipment manufacturers nor researchers could provide guidance on design components (e.g., sample initiation, timing/intervals, type). This was problematic, however, as available monitoring resources are too limited and data needs too great for such projects to be designed solely based on field experience and without a scientific basis or with complete disregard for potential data quality implications. Thus practical, science-based guidance for edge-of-field sampling was developed and fundamental understanding of the inherent uncertainty was established to assist researchers, municipalities, consulting firms, and regulatory agencies improve data quality and monitoring resource efficiency. Looking to the future, however, further improvements are needed related to lower cost systems, practical improvements, and enhanced in-situ sampling, along with enhanced understanding and consideration of data uncertainty in modeling and decision-making.