A retrospective analysis of climate and land management drivers of nutrient export from the western Lake Erie watershed: 1980-2019

Authors

  • Anna Apostel Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI USA
  • Margaret Kalcic iological Systems Engineering, University of Wisconsin-Madison, Madison, WI USA
  • Rebecca Logsdon- Muenich Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR USA
  • Kevin King USDA-ARS Soil Drainage Research Unit, Columbus, OH USA
  • Jay Martin Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH USA
  • Donald Scavia School for Environment and Sustainability, University of Michigan, Ann Arbor, MI USA

Abstract

The return of harmful algal blooms to western Lake Erie has heightened the focus on managing nutrient loading from its watershed, and particularly the large, agricultural Maumee River Watershed (MRW). Increased dissolved reactive phosphorus (DRP) loads over the last twenty years are suspected to be a primary cause of the recurrence and severity of these blooms. The primary cause of increasing DRP is still unclear, and therefore management efforts to reverse this trend are difficult to develop. We used a refined model of the MRW to investigate changes in climate and land management between 1980 and 2019 to identify key factors driving trends in DRP as well as discharge and other nutrient forms that impact algal biomass and toxicity. We found that the dominant drivers of discharge and nutrients varied: historical climate trends drove discharge and nitrogen concentrations, while historical management changes were more responsible for changing phosphorus concentrations. Among the land management changes examined, the rising adoption of minimal- and no-tillage strategies had the greatest impact on nutrient trends, leading to reductions in total phosphorus (TP), total nitrogen (TN), and nitrate (NO3), yet increases in DRP. We posit that a better understanding of the water quality impacts of past land management enables modelers and managers to more accurately predict the impacts of potential future management changes.

Published

2025-06-16