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Publication Date

2005

Keywords

GIS, Non-point source pollution, nutrient export, spatial distribution, regression modeling

Abstract

This paper proposes a method to improve landscape-pollution interaction regression models through the inclusion of a variable that describes the spatial distribution of a land type with respect to the pattern of runoff within a drainage catchment. The proposed index is used as an independent variable to enhance the strength, as quantified by R2 values, of regression relationships between empirical observations of in-stream pollutant concentrations and land type by considering the spatial distribution of key land-type categories within the sample point’s drainage area. We present an index that adds a new dimension of explanatory power when used in conjunction with a variable describing the proportion of the land type.

We demonstrate the usefulness of this index by exploring the relationship between nitrate (NO-3 ) and land type within 40 drainage sub-catchments in the Ipswich River watershed, Massachusetts. Nutrient loads associated with non-point source pollution paths are related to land type within the up-stream drainage catchments of sample sites. Past studies have focused on the quantity of particular land type within a sample point’s drainage catchment. Quantifying the spatial distribution of key land-type categories in terms of location on a runoff surface can improve our understanding of the relationship between sampled NO-3 concentrations and land type.

Regressions that employ the proportion of residential and agricultural land type within catchments provide a fair fit (R2 = 0.67). However, we find that a regression adding a variable that indicates the spatial distribution of residential land improves the overall relationship between in- stream NO-3 measurements and associated land types (R2 = 0.712). We test the sensitivity of the results with respect to variations in the surface definition in order to determine the conditions under which the spatial index variable is useful.

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