LAND‐USE AND WATER‐QUALITY RELATIONSHIPS IN THE GEORGES CREEK BASIN, MARYLAND¹

Abstract

Models developed in Ohio to predict water quality conditions resulting from various land uses associated with the surface mining of coal are employed to ascertain their transferability to Maryland conditions. Discriminant analysis is employed to assess patterns of association between water quality and land use variables, and predictive models were then constructed with which to quantify changes in stream quality to be expected from the changing mosaic of upstream land uses in the Georges Greek basin of western Maryland. Data collected under procedures specified by the regulatory authority in Maryland may have accounted for the lack of statistically significant results from these models. Suggested changes in the collection of data are made for the coal region of Maryland. (KEY TERMS: Maryland; surface mining; water quality monitoring; land use modeling.)

Detailed study of selenium in soil, representative plants, water, bottom sediment, and biota in the Kendrick Reclamation Project area, Wyoming, 1988-90

“…Several other studies have linked land use factors to water quality. Duckson (1989) and Hren and others (1984) used statistical approaches to evaluate large data sets and to correlate the effects of land-use activities on water quality in coal-mining areas. Gilliland and Baxter-Potter (1987) used a GIS to predict water-quality problems associated with nonpoint-source agricultural pollution.…”

MULTIVARIATE ANALYSIS OF WATER QUALITY AND PHYSICAL CHARACTERISTICS OF SELECTED WATERSHEDS IN PUERTO RICO1

“…For example, Pope and Bevans (1987) used multiple regression techniques to estimate storm flow metal and ion concentrations based on land cover, watershed physical characteristics, and storm characteristics but found no significant watershed physical parameters. Duckson (1989) used (DA) to predict land covers from WQ constituents as well as multiple regression analysis to estimate WQ. However, no statistical significance was found in any of the multivariate analyses, and the author discusses the need for more accurate watershed characteristic data.…”

GIS‐ASSISTED REGRESSION ANALYSIS TO IDENTIFY SOURCES OF SELENIUM IN STREAMS1

“…Several other studies have linked land use to water quality. Hren et al (1984) and Duckson (1989) used statistical approaches to evaluate large data sets and correlate the effects of land-use activities on water quality in coal-mining areas. Gilliland and Baxter-Potter (1987) used a GIS to predict water-quality problems associated with nonpoint-source agricultural pollution.…”