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Bayesian SPARROW Model

Bayesian SPARROW Model. Song Qian Ibrahim Alameddine The University of Toledo American University of Beirut. SPARROW. SPARROW : SPA tially R eferenced R egressions O n W atershed attributes SPARROW estimates the origin and fate of contaminants in river networks

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Bayesian SPARROW Model

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  1. Bayesian SPARROW Model Song Qian Ibrahim Alameddine The University of Toledo American University of Beirut

  2. SPARROW • SPARROW: SPAtiallyReferenced Regressions On Watershed attributes • SPARROW estimates the origin and fate of contaminants in river networks • It is a semi-empirical non-linear model • It is spatial in structure andtakes into account the nested configuration of monitoring stations in a basin • Can be used to link changes in the watershed to changes in water quality

  3. SPARROW EQUATION Nutrient loading (L) at a downstream water quality monitoring station i: Contribution fromDifferent sources (S) # of sources Losses/sinks Multiplicativeerror term # of upstream reaches

  4. SPARROW Shortcomings Some of the shortcomings of SPARROW: • Temporal and Spatial average • Coarse spatial resolution  regional specifics often omitted • Spatial autocorrelationin model residuals • Model developed to run under

  5. What Did We Do? • We changed the model’s architecture to make it temporally dynamic • We developed a new regionalizingapproach • Substitute space (# of stations) with time (# of years) • We nestedthe model within a larger scale regional model • We assessed changes in loading over time for the Neuse subwatersheds • We moved the model to an open source platform

  6. Neuse SPARROW: Bayesian, Dynamic, & Regional • Nested the model within the lager scale Nitrogen Southeast model (Hoos & McMahon, 2009) • Updated the model over time (time step = 2 years) • Used 12 years of data Regionalization over time • Data and model parameters change over time (dynamic) • Bayesian updating(posterior of t-1 = prior at t)

  7. How Did the Neuse BSPARROW Model Perform Over Time?

  8. 90-91 92-93 94-95 3 1 2 Neuse SPARROW: Model Fit 96-97 98-99 00-01 4 6 5

  9. How Do We Compare to the SE Model? (Hoos & McMahon, 2009)

  10. Where Are the Areas of Concern?Have They Changed Over Time?

  11. 1990 Neuse Nitrogen Export by Basin 2001

  12. Yield to Neuse Estuary by Basin Raleigh Raleigh Durham Durham CaryMorrisville CaryMorrisville Kinston Kinston 1990 2001

  13. Conclusions • Regionalization of SPARROW to basin level possible:Bayesian temporally dynamic nestedmodeling framework • Loads (and model coefficients) across the basin changeover time and the model is capturing these changes • Urban runoff seems to be a concern for TN loading in the Neuse • Nitrogen loading to the Neuse Estuary have decreased  relative success in environmental management

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