Statistical modeling of agricultural chemical occurrence in midwestern rivers

Authors

William A. Battaglin, U.S. Geological Survey
Donald A. Goolsby, U.S. Geological Survey

Document Type

Article

Date of this Version

1997

Comments

Published in Journal of Hydrology 196 (1997) 1–25

Abstract

Agricultural chemicals in surface water may constitute a human health risk or have adverse effects on aquatic life. Recent research on unregulated rivers in the midwestern USA documents that elevated concentrations of herbicides occur for 1–4 months following application in late spring and early summer. In contrast, nitrate concentrations in unregulated rivers are elevated during fall, winter, and spring months. Natural and anthropogenic variables of river drainage basins, such as soil permeability, amount of agricultural chemicals applied, or percentage of land planted in corn, affect agricultural chemical concentration and mass transport in rivers.

Presented is an analysis of selected data on agricultural chemicals collected for three regional studies conducted by the US Geological Survey. Statistical techniques such as multiple linear and logistic regression were used to identify natural and anthropogenic variables of drainage basins that have strong relations to agricultural chemical concentrations and mass transport measured in rivers. A geographic information system (GIS) was used to manage and analyze spatial data. Statistical models were developed that estimated the concentration, annual transport, and annual mean concentration of selected agricultural chemicals in midwestern rivers.

Multiple linear regression models were not very successful (R2 from 0.162 to 0.517) in explaining the variance in observed agricultural chemical concentrations during post-planting runoff. Logistic regression models were somewhat more successful, correctly matching the observed concentration category in 61–80% of observations. Linear and multiple linear regression models were moderately successful (R2 from 0.522 to 0.995) in explaining the variance in observed annual transport and annual mean concentration of agricultural chemicals. Explanatory variables that were commonly significant in the regression models include estimates of agricultural chemical use, crop acreage, soil characteristics, and basin topography.