H120-01
Best Management Practices for Diffuse Nutrient Pollution: Wicked Problems Across Urban and Agricultural Watersheds
Friday, 11 December 2020: 07:00
Virtual
Anna Lintern, Monash University, Melbourne, VIC, Australia, Lauren E McPhillips, Pennsylvania State University, University Park, PA, United States; Pennsylvania State University, Civil & Environmental Engineering, University Park, PA, United States, Brandon Winfrey, Monash University, Civil Engineering, Melbourne, Australia, Jonathan M Duncan, Pennsylvania State University Main Campus, Ecosystem Science and Management, University Park, PA, United States and Caitlin Grady, Pennsylvania State University Main Campus, Civil and Environmental Engineering, University Park, PA, United States
Abstract:
Diffuse nutrient pollution has degraded water quality in rivers, lakes and bays across the world. This has negatively impacted the ecology of these systems, as well as the tourism and fishing industries. Substantial economic investment and time has been spent to reduce diffuse nutrient pollution. However, we have not seen significant reductions in nutrient levels in many aquatic environments. For example, in 2013, 30% of watersheds in the Great Barrier Reef catchment implemented Best Management Practices (BMPs) for the reduction of nutrient pollution. Despite this, recent reports have stated that the water quality and ecological condition of the Great Barrier Reef remains poor. In this study, we investigated the key factors contributing to this lack of improvement in nutrient levels in waterways and water bodies throughout the world.
We reviewed 94 studies that assessed the watershed-scale effectiveness of BMPs in reducing diffuse nutrient pollution in agricultural and urban watersheds. 60% of these studies identified that water quality improved after BMPs were implemented in urban and agricultural watersheds. However, most of these studies that found improved water quality were modelling studies, rather than field-based investigations. Lack of improvement in water quality was attributed to: (i) lack of knowledge about how BMPs function, (ii) lag times between implementation of the BMP and improvement and water quality, (iii) non-optimal placement and density of BMPs in the watershed, (iv) BMP failure, and (v) socio-political and economic challenges. In our study, we have classified these factors as known unknowns.
We also argue that there are also unknown unknowns that act as obstacles to BMP effectiveness in agricultural and urban watersheds. We suggest machine learning, methods from business management and operations research, and long-term convergent studies will assist us in identifying and resolving the unknown unknowns that hinder water quality improvement.
