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<p>Wetland restoration has emerged in recent years as an essential strategy for improving water quality to mitigate harmful algal blooms and the associated decline in water quality, especially across the Midwest. As a consequence, there is a need for more long-term monitoring datasets to better understand the nutrient and sediment processing potential of these systems over time. In this study, surface water samples were collected and analyzed, and water volumes were tracked weekly from the inlet and outlet of a pump driven, flow through wetland along Prairie Creek in the Grand Lake St. Marys watershed over an 8-year period (2017–2024) in order to estimate yearly and seasonal nutrient load reductions. During this time, the wetland processed 4.44&#xa0;million m<sup>3</sup>, roughly 5.7%, of the annual Prairie Creek flows, wherein it showed promising overall concentration reductions between stream and wetland for TP (56%), SRP (81%), NO<sub>3</sub>–N (60%), and TSS (10%). A Bayesian nonlinear model was used to describe within dataset variation among seasons and years highlighting the kind of ranges these systems can exhibit in water quality improvements. The results from this study show significant nutrient and sediment load reductions can be achieved using restored wetlands as a mitigation tool. Furthermore, these data contribute to our understanding of long-term efficiency, within year seasonal changes, as well as how management strategies can help restored wetlands realize their potential as tools to improve water quality.</p>

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Long term water quality improvements associated with the restored Prairie Creek wetlands in Ohio’s Grand Lake St. Marys Watershed

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Stephen J. Jacquemin,
  • Jason C. Doll,
  • Morgan C. Grunden,
  • Haley N. Hoehn,
  • Theresa A. Dirksen

摘要

Wetland restoration has emerged in recent years as an essential strategy for improving water quality to mitigate harmful algal blooms and the associated decline in water quality, especially across the Midwest. As a consequence, there is a need for more long-term monitoring datasets to better understand the nutrient and sediment processing potential of these systems over time. In this study, surface water samples were collected and analyzed, and water volumes were tracked weekly from the inlet and outlet of a pump driven, flow through wetland along Prairie Creek in the Grand Lake St. Marys watershed over an 8-year period (2017–2024) in order to estimate yearly and seasonal nutrient load reductions. During this time, the wetland processed 4.44 million m3, roughly 5.7%, of the annual Prairie Creek flows, wherein it showed promising overall concentration reductions between stream and wetland for TP (56%), SRP (81%), NO3–N (60%), and TSS (10%). A Bayesian nonlinear model was used to describe within dataset variation among seasons and years highlighting the kind of ranges these systems can exhibit in water quality improvements. The results from this study show significant nutrient and sediment load reductions can be achieved using restored wetlands as a mitigation tool. Furthermore, these data contribute to our understanding of long-term efficiency, within year seasonal changes, as well as how management strategies can help restored wetlands realize their potential as tools to improve water quality.