Representing a large region with few sites: The Quality Index approach for field studies

Citation

Rosamond, M.S., Wellen, C., Yousif, M.A., Kaltenecker, G., Thomas, J.L., Joosse, P.J., Feisthauer, N.C., Taylor, W.D., Mohamed, M.N. (2018). Representing a large region with few sites: The Quality Index approach for field studies. Science of the Total Environment, [online] 633 600-607. http://dx.doi.org/10.1016/j.scitotenv.2018.03.113

Plain language summary

The Ministry of the Environment and Climate Change in the province of Ontario established a Multi-Watershed Nutrient Study (MWNS) to develop an understanding of the relationship between agricultural land use, landscape variables, and phosphorus export across the agricultural region of southern Ontario. However, as with many environmental studies, only a small number of study watersheds could be intensively monitored. Therefore, selecting study watersheds that represent the larger region is important to develop robust and scientifically defensible results. We developed a novel method of selecting small watersheds representing a larger region that reduces subjectivity, uses commonly-available geographic datasets, does not depend on pre-existing water quality data, and considers practical constraints to establish a site. We illustrate the method using the MWNS as a case study. From an initial set of 108 potential study watersheds, we selected 11 watersheds that covered the existing range in variables. A geographic area of 110,000 km2 was represented with good coverage of the four key variables representing agricultural P inputs and loss mechanisms. We developed a Quality Index to assess regional coverage, taking the range and distribution of sites relative to the larger area into account. We also adapted an optimization algorithm that allows for objectively choosing sites for the highest possible Quality Index score. This site selection approach can easily be adapted to different landscapes and study goals, as we include an algorithm and computer code to reproduce our approach elsewhere.

Abstract

Many environmental studies require the characterization of a large geographical region using a range of representative sites amenable to intensive study. A systematic approach to selecting study areas can help ensure that an adequate range of the variables of interest is captured. We present a novel method of selecting study sites representing a larger region, in which the region is divided into subregions, which are characterized with relevant independent variables, and displayed in mathematical variable space. Potential study sites are also displayed this way, and selected to cover the range in variables present in the region. The coverage of sites is assessed with the Quality Index, which compares the range and standard deviation of variables among the sites to that of the larger region, and prioritizes sites that are well-distributed (i.e. not clumped) in variable space. We illustrate the method with a case study examining relationships between agricultural land use, physiography and stream phosphorus (P) export, in which we selected several variables representing agricultural P inputs and landscape susceptibility to P loss. A geographic area of 110,000 km2 was represented with 11 study sites with good coverage of four variables representing agricultural P inputs and transport mechanisms taken from commonly-available geospatial datasets. We use a genetic algorithm to select 11 sites with the highest possible QI and compare these, post-hoc, to our sites. This approach reduces subjectivity in site selection, considers practical constraints and easily allows for site reselection if necessary. This site selection approach can easily be adapted to different landscapes and study goals, as we provide an algorithm and computer code to reproduce our approach elsewhere.

Publication date

2018-08-15

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