U.S. Department of the Interior U.S. Geological Survey Welcome to the USGS Webinar: New Science and Online Management Tools to Help Guide Action on Nutrients in Rivers in the Upper Midwest Region of the U.S., Especially in the Great Lakes Basin National Water-Quality Assessment Program Phone Line: 712-432-0900 901183# Thanks for joining the webinar, we will be starting soon.
Welcome to the USGS Webinar: New Science and Online Management Tools to Help Guide Action on Nutrients in Rivers in the Upper Midwest Region of the U.S., Especially in the Great Lakes Basin. Phone Line: 712-432-0900 901183# Thanks for joining the webinar, we will be starting soon. - PowerPoint PPT Presentation
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U.S. Department of the InteriorU.S. Geological Survey
Welcome to the USGS Webinar: New Science and Online Management Tools to Help Guide
Action on Nutrients in Rivers in the Upper Midwest Region of the U.S., Especially in the Great Lakes Basin
National Water-Quality Assessment Program
Phone Line: 712-432-0900 901183#
Thanks for joining the webinar, we will be starting soon.
All phone lines will be centrally muted during the presentation.
Please submit your questions throughout the presentation using the GoToWebinar box.
Following the presentation, we willunmute the phone lines and gothrough the submitted questions.
USGS Webinar
U.S. Geological SurveyNational Water Quality Assessment
New Science and Online Management Tools to Help Guide Action on Nutrients in Rivers in
Upper Midwest Region of the U.S., Especially in the Great Lakes Basin
Northeast
Southeast
Upper Midwest
Lower Midwest
Missouri RiverPacificNorthwest
PacificNorthwest
Northeast
Southeast
Upper Midwest
Lower Midwest
Missouri River
National Water Quality Assessment ProgramSurface Water Status and Trends Regions
Southwest
California
Use of SPARROW Models to Determine the Spatial Distribution and Sources of Nutrients
Approach - SPARROW Water-Quality Model – SPAtially Referenced Regression on Watershed Attributes
http://water.usgs.gov/nawqa/sparrow
Separates land and in-stream processes
Mass Balance Model with spatially variable deliveries. Hybrid statistical/ mechanistic process structure. Data-driven, nonlinear estimation of parameters
Fertilizers
Atmospheric Dep.
Sources
ManurePoint Sources
Monitoring DataAnnual Loads
Y variable
X variables
Predictions of mean-annual flux reflect long-term, net effects of nutrient supply and loss processes in watersheds
Once calibrated, the model has physically interpretable coefficients; model supports hypothesis testing and uncertainty estimation