CALL FOR PROPOSALS:

ORGANIZERS

  • Harvey Thorleifson, Chair
    Minnesota Geological Survey
  • Carrie Jennings, Vice Chair
    Minnesota Geological Survey
  • David Bush, Technical Program Chair
    University of West Georgia
  • Jim Miller, Field Trip Chair
    University of Minnesota Duluth
  • Curtis M. Hudak, Sponsorship Chair
    Foth Infrastructure & Environment, LLC

 

Paper No. 10
Presentation Time: 10:25 AM

A WEB-BASED DECISION SUPPORT SYSTEM FOR ASSESSING REGIONAL WATER-QUALITY CONDITIONS AND MANAGEMENT ACTIONS


BOOTH, Nathaniel1, EVERMAN, Eric1, KUO, I-Lin1, SPRAGUE, Lori2 and MURPHY, Lorraine1, (1)Wisconsin Water Science Center, U.S. Geological Survey, 8505 Research Way, Middleton, WI 53562, (2)U.S. Geological Survey, Colorado Water Science Center, Denver Federal Center, PO Box 25046, MS 415, Denver, CO 80225, nlbooth@usgs.gov

A new web-based decision support system has been developed as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Program’s (NAWQA) effort to provide ready access to Spatially Referenced Regressions On Watershed attributes (SPARROW) results of stream water-quality conditions and to offer sophisticated scenario testing capabilities for research and water-quality planning via an intuitive graphical user interface with a map-based display. The SPARROW Decision Support System (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water-quality conditions, distribution of nutrient sources, nutrient delivery to downstream waterbodies, and simulations of altered nutrient inputs. The DSS offers other features for analysis including various background map layers, model output exports, and the ability to save and share prediction scenarios. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000-scale River Reach File (RF1) and 1:100,000-scale National Hydrography Dataset (medium-resolution, NHDPlus) stream networks. The underlying software framework and server infrastructure illustrate innovations in the information technology field for delivering SPARROW model predictions over the web by performing intensive model computations and rendering map images of the predicted conditions within the stream network.
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