A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay case study

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 we use Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constit...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Environmental modelling & software : with environment data news 2019-08, Vol.118, p.1-13
Hauptverfasser: Murphy, Rebecca R., Perry, Elgin, Harcum, Jon, Keisman, Jennifer
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 we use Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, an estuary on the east coast of the United States. 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. •A GAM approach was developed for annual estuarine water quality analysis.•Fresh water flow impacts are incorporated and adjusted for in the model structure.•Method changes are accounted for using an intervention parameter.•Insights generated from this case study are informing management efforts for Chesapeake Bay.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2019.03.027