Change Point Analysis of Phosphorus Trends in the Illinois River (Oklahoma) Demonstrates the Effects of Watershed Management
- J. Thad Scott *a,
- Brian E. Haggardb,
- Andrew N. Sharpleya and
- J. Joshua Romeisc
- a Crop, Soil, and Environmental Sciences Dep., Division of Agriculture, Univ. of Arkansas, Fayetteville, AR 72701
b Arkansas Water Resources Center, Biological and Agricultural Engineering Dep., Division of Agriculture, Univ. of Arkansas, Fayetteville, AR 72701
c formerly Crop, Soil, and Environmental Sciences Dep., Division of Agriculture, Univ. of Arkansas, Fayetteville, AR 72701. Assigned to Associate Editor Peter Vadas
Detecting water quality improvements following watershed management changes is complicated by flow-dependent concentrations and nonlinear or threshold responses that are difficult to detect with traditional statistical techniques. In this study, we evaluated the long-term trends (1997–2009) in total P (TP) concentrations in the Illinois River of Oklahoma, and some of its major tributaries, using flow-adjusted TP concentrations and regression tree analysis to identify specific calendar dates in which change points in P trends may have occurred. Phosphorus concentrations at all locations were strongly correlated with stream flow. Flow-adjusted TP concentrations increased at all study locations in the late 1990s, but this trend was related to a change in monitoring practices where storm flow samples were specifically targeted after 1998. Flow-adjusted TP concentrations decreased in the two Illinois River sites after 2003. This change coincided with a significant decrease in effluent TP concentrations originating with one of the largest municipal wastewater treatment facilities in the basin. Conversely, flow-adjusted TP concentrations in one tributary increased, but this stream received treated effluent from a wastewater facility where effluent TP did not decrease significantly over the study period. Results of this study demonstrate how long-term trends in stream TP concentrations are difficult to quantify without consistent long-term monitoring strategies and how flow adjustment is likely mandatory for examining these trends. Furthermore, the study demonstrates how detecting changes in long-term water quality data sets requires statistical methods capable of identifying change point and nonlinear responses.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
Copyright © 2011. . Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.