A Generalized Additive Model Approach to Evaluating Water Quality
This study used Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, providing a tool for developing insights to a range of management- and research-focused questions.
Description
Nutrient reduction efforts have been undertaken in recent decades to mitigate the impacts of eutrophication in coastal and estuarine systems worldwide. To track progress in response to one of these efforts, this study, published in Environmental Modelling & Software, used Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay. Model development included selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporating hydrologic variability via either river flow or salinity, and using interventions to model method or laboratory changes suspected to impact data. This approach, transferable to other systems, allows for evaluation of water quality data in a statistically rigorous way, while being suitable for application to many sites and variables. This enables consistent generation of annual updates, while providing a tool for developing insights to a range of management- and research-focused questions.
Citation
Rebecca R. Murphy, Elgin Perry, Jon Harcum, Jennifer Keisman, 2019. "A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay case study",
Environmental Modelling & Software, Volume 118, 2019, Pages 1-13, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2019.03.027.
Category: Report