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

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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 - PowerPoint PPT Presentation

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Page 1: Bayesian SPARROW Model

Bayesian SPARROW Model

Song QianIbrahim Alameddine

The University of ToledoAmerican University of Beirut

Page 2: Bayesian SPARROW Model

SPARROW• SPARROW: SPAtially Referenced 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 and takes 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

Page 3: Bayesian SPARROW Model

SPARROW EQUATIONNutrient loading (L) at a downstream water

quality monitoring station i:

i

N

n iJj

Rji

Sjijnni HHeSL

)log()log(1 )(

,,,)jαZ(

# of sources

# of upstream reaches

Contribution fromDifferent sources (S)Losses/sinks

Multiplicativeerror term

Page 4: Bayesian SPARROW Model

SPARROW ShortcomingsSome of the shortcomings of SPARROW:• Temporal and Spatial average• Coarse spatial resolution regional specifics

often omitted• Spatial autocorrelation in model residuals• Model developed to run under

Page 5: Bayesian SPARROW Model

What Did We Do?• We changed the model’s architecture to make it

temporally dynamic• We developed a new regionalizing approach

– Substitute space (# of stations) with time (# of years)

• We nested the 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

Page 6: Bayesian SPARROW Model

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)

Page 7: Bayesian SPARROW Model

How Did the Neuse BSPARROW Model Perform

Over Time?

Page 8: Bayesian SPARROW Model

1 2 3

4 5 6

Neu

se S

PARR

OW

: Mod

el F

it

90-91 92-93 94-95

96-97 98-99 00-01

Page 9: Bayesian SPARROW Model

How Do We Compare to the SE Model?

(Hoos & McMahon, 2009)

Page 10: Bayesian SPARROW Model

Where Are the Areas of Concern?

Have They Changed Over Time?

Page 11: Bayesian SPARROW Model

1990

Neuse Nitrogen Export by Basin

2001

Page 12: Bayesian SPARROW Model

Yield to Neuse Estuary by Basin

1990 2001

Durham

CaryMorrisville

Raleigh

Kinston

Durham

CaryMorrisville

Raleigh

Kinston

Page 13: Bayesian SPARROW Model

Conclusions• Regionalization of SPARROW to basin level

possible:Bayesian temporally dynamic nested modeling framework

• Loads (and model coefficients) across the basin change over 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