Watershed-Based Survey Designs

Abstract

Watershed-based sampling design and assessment tools help serve the multiple goals for water quality monitoring required under the Clean Water Act, including assessment of regional conditions to meet Section 305(b), identification of impaired water bodies or watersheds to meet Section 303(d), and development of empirical relationships between causes or sources of impairment and biological responses. Creation of GIS databases for hydrography, hydrologically corrected digital elevation models, and hydrologic derivatives such as watershed boundaries and upstream-downstream topology of subcatchments would provide a consistent seamless nationwide framework for these designs. The elements of a watershed-based sample framework can be represented either as a continuous infinite set defined by points along a linear stream network, or as a discrete set of watershed polygons. Watershed-based designs can be developed with existing probabilistic survey methods, including the use of unequal probability weighting, stratification, and two-stage frames for sampling. Case studies for monitoring of Atlantic Coastal Plain streams, West Virginia wadeable streams, and coastal Oregon streams illustrate three different approaches for selecting sites for watershed-based survey designs.

Using extirpation to evaluate ionic tolerance of freshwater fish

“…The Environmental Monitoring and Assessment Program (data sets 1 and 2), Regional Applied Research Effort (data set 5), and West Virginia Department of Environmental Protection (data set 6) sites were probability samples selected as part of regional surveys [26][27][28]. The Environmental Monitoring and Assessment Program (data sets 1 and 2), Regional Applied Research Effort (data set 5), and West Virginia Department of Environmental Protection (data set 6) sites were probability samples selected as part of regional surveys [26][27][28].…”

“…Fish survey data along with chemical and physical data were collected from a total of 3465 distinct sites during the sampling years 1990 to 2014. The Environmental Monitoring and Assessment Program (data sets 1 and 2), Regional Applied Research Effort (data set 5), and West Virginia Department of Environmental Protection (data set 6) sites were probability samples selected as part of regional surveys [26][27][28]. Sites sampled by the Kentucky Department of Environmental Protection, Division of Water; Stauffer and Ferreri [29]; the Ohio Environmental Protection Agency, Division of Surface Water; and the Pennsylvania Fish and Boat Commission (data sets 3, 4, 7, and 8, respectively) included targeted-sampling sites that were part of bioassessment studies.…”

Integrating sentinel watershed-systems into the monitoring and assessment of Minnesota’s (USA) waters quality

“…However, the TMDL program must evolve beyond individual reaches or waterbodies of impairment, and address the cumulative effects of natural and anthropogenic processes from a watershed perspective. Though EPA has invested time and energy in a random probabilistic based monitoring design (EMAP) for assessing ambient water quality throughout the USA, there is also recognition by EPA that hydrologically stratified data collection is needed to better understand watershed processes (Detenbeck et al 2005). Magner and Brooks (2005) improved the statistical fit of regional hydraulic geometry curves by stratifying the data according to climate and geologic characteristics.…”

Molecular fingerprinting of lacustrian cyanobacterial communities: regional patterns in summer diversity

“…Advances in geographic information systems (GIS) have permitted the better integration of water bodies with their geomorphologic environment and land usage, which can impact on biodiversity and water quality (Aspinall & Pearson, 2000). As a result, the application of regionalisation frameworks to sampling surveys, combining geographical, geological and hydromorphological data to define contiguous spatial regions that share similar features, has increased in recent years (Detenbeck et al, 2005;Wagner et al, 2007;Cheruveril et al, 2008;Katsiapi et al, 2012).…”