Source Apportionment and

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Jul 3, 2007 - Dese Zero. Watershed. 290 km2. Vela. Watershed. 101 km2. Study area. Venice. Lagoon Watershed. 2038 km2. About 35% of the VLW area.
APPLICATION OF SWAT MODEL ON THREE WATERSHEDS WITHIN THE VENICE LAGOON WATERSHED (ITALY): SOURCE APPORTIONMENT AND SCENARIO ANALYSIS R. Salvetti*, A. Azzellino*, D. Gardoni*, R. Vismara*, M. Carpani**, C. Giupponi**, M. Acutis**, M. Vale***, P. Parati***

POLITECNICO DI MILANO

UNIVERSITÀ DEGLI STUDI DI MILANO

* DIIAR - Environmental Engineering Department, Technical University of Milan ** Department of Crop Science (Di ProVe), University of Milan *** ARPAV (Regional Agency for Environmental Prevention and Protection in Veneto), Osservatorio Regionale Acque Interne

ARPA VENETO

4th International SWAT Conference July 3rd-5th 2007

Aims of the study APPLICATION OF SWAT MODEL TO THREE BASINS OF THE VENICE LAGOON WATERSHED TO

• Assess the apportionment of point and non point sources • Quantify the non point sources in terms of rain-driven and not-rain-driven diffuse sources POLITECNICO DI MILANO

•Simulate a scenario analysis to assess the effect of a UNIVERSITÀ DEGLI STUDI DI MILANO

ARPA VENETO

reduction of agricultural loads

Study area

Dese Zero Watershed 290 km2

Vela Watershed 101 km2 Naviglio Brenta Bondante Watershed 307 km2

~ 5.000 tN y-1 POLITECNICO DI MILANO

About 35% of the VLW area

3.000 tN y-1

UNIVERSITÀ DEGLI STUDI DI MILANO

ARPA VENETO

Venice Lagoon Watershed

2038 km2

Watershed characteristics NBB

DZ

VL

Urban Area

24%

26%

13%

Agricultural Area

69%

72%

86%

Dominant crops

Corn, soy, wheat, sugar beet

Corn, soy, wheat, sugar beet

Corn, soy, wheat, sugar beet

Groundwater Recharge Area

POLITECNICO DI MILANO

Contribution from external basins

UNIVERSITÀ DEGLI STUDI DI MILANO

ARPA VENETO

Very complex network of drainage/irrigation channels

Measures available #S

$T

T$$T WWTP discharge Streamflow data

$T $T $T POLITECNICO DI MILANO

UNIVERSITÀ DEGLI STUDI DI MILANO

ARPA VENETO

Industrial discharge Water quality data

T$$T

T$ T$ T$ $T $T # $T $T S #S$T $T $T T$$T

$T

T$#S $T $T

$T T$$T $T T$$T

T$ $T $T

$T

$T

$T

T$$T$T$T$T$T

$T $T$T #S

$T

T$$T

$T

T$ $T T $ T$$T $T $T $T$T $T$T T $ T$ T$ $T

T$$T $T T $ T$ $T

T$ $T $T T$$T$T

T$#S #S

Sources apportionment WET WEATHER LOADS

DRY WEATHER LOADS

Point loads

POLITECNICO DI MILANO

UNIVERSITÀ DEGLI STUDI DI MILANO

ARPA VENETO

Diffuse loads

• WWTPs discharge

• Groundwater recharge

• Industrial discharge

• Channel network

• Direct sewer discharge Direct discharge/instream measurements BOD/nitrogen mass balance

Diffuse loads

• Surface runoff loads

USEPA QUAL2E BASINS-SWAT loads from WWTPs/Industries and from channel network loads from direct sewer discharge and from groundwater recharge

BASINS-SWAT input 20 soil classes

&

18 Subbasins 105 HRU

10 stations - 13 years daily & meteorological data

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UNIVERSITÀ DEGLI STUDI DI MILANO

33 Subbasins 169 HRU

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42 Subbasins

13 landuse212 classes HRU

& years crop rotations 9 - four ## #

SWAT calibration Groundwater flow (m3/s)

8 7 6

GW from external watersheds

5 4 3 Groundwater estimates

2

Groundwater simulated

1 0 jan

feb

mar

apr

may

jun

jul

aug

sep

oct

nov

dec

Additional inlet contribution Parameter calibration

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• BLAI (corn, wheat) • HVSTI (corn, wheat) • CN • OV_N

UNIVERSITÀ DEGLI STUDI DI MILANO

ARPA VENETO

• • • • •

USLE_P ERORGN ERORGP NPERCO FRT_LY1

SWAT model: hydrological calibration DDE station 1999 10 Sutcliffe coefficient of efficiency Nash

E ~ 0.4 6

80 70 4 60

2

50

December

October

12/08/00

August13/07/00

13/06/00

July14/05/00

14/04/00

June15/03/00

14/02/00

May15/01/00

16/12/99

16/11/99

April

17/10/99

March

17/09/99

18/08/99

19/07/99

February

19/06/99

0

Predicted Measured 20/05/99

20/04/99

2

November

4

September

August

July

May

June

-1

21/03/99

19/02/99

20/01/99

0 21/12/98

6

3

20

m sMarch

30

April

8

40 0

January

ARPA VENETO

2000

Predicted Predicted 10 Measured

10 UNIVERSITÀ DEGLI STUDI DI MILANO

E = 0.65

Measured

February

POLITECNICO DI MILANO

Nash Sutcliffe coefficient of efficiency

Daily flowrate - DDE station

January

3 -1

Flowrate (m s )

m3 s-1

8

19/05/96

17/05/96

0 15/05/96

3

Oct(Rain_Event)

15Oct07

14Oct06

13Oct05

12Oct04

11Oct03

8000

13/05/96

-1

10Oct02

June(Rain_Event)

07June06

06June05

May(Rain_Event)

06May05

Predicted Observed

11/05/96

6

09/05/96

07/05/96

05/05/96

ARPA VENETO

15

Flowrate (m s )

UNIVERSITÀ DEGLI STUDI DI MILANO

18

3

POLITECNICO DI MILANO

05May04

10000

04May03

03May02

kg N

SWAT model: RESULTS 12000

kg N during rainstorm events

Error rainstorm event ± 15%

6000

4000

2000

0

21

12

9

Daily event Rainstorm event

SWAT model: RESULTS – mean of 10 years simulation

SWAT output

Total N annual load ~ 2200 t N y-1 Total P annual load ~ 140 t P y-1 Present State - Ntot

WWTP and industrial discharge Tributary channels or irrigation systems Atmospheric deposition runoff Urban runoff 3%

13%

Direct sewer discharge Groundwater recharge Agricultural runoff

6%

8%

20% runoff loads 15% point loads

5%

65% dry weather diffuse loads 31% 34%

Present State - Ptot POLITECNICO DI MILANO

WWTP and industrial discharge Tributary channels or irrigation systems Atmospheric deposition runoff Urban runoff

9%

UNIVERSITÀ DEGLI STUDI DI MILANO

Direct sewer discharge Groundwater recharge Agricultural runoff

13%

24%

19%

ARPA VENETO

35%

Implementation of an agricultural scenario kg N ha-1 y-1

kg P ha-1 y-1

Corn

- 37%

-17%

Corn + manure

-53%

-27%

Agricultural nitrogen runoff load 300 250

Present state Agricultural scenario

tN/y

200 150 100 50 POLITECNICO DI MILANO

0 DZ

NBB

VL

Sum

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∆N = 139 tN y-1 (~ 50% agricultural nitrogen runoff load) ARPA VENETO

∆N = 7 tP y-1 (~ 15% phopshorus runoff load)

Loads at the basin closure

∆N = 139 tN y-1

Nitrogen load at the basin closure

(- 6 %)

2400 2000

Present state Agricultural scenario

tN/y

1600 1200 800 400 0 DZ

NBB

VL

Sum

∆P = 7 tP y-1

Phosphorus load at the basin closure 160 140 120

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Agricultural scenario

100 tP/y

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(- 5 %)

Present state

80 60 40 20

ARPA VENETO

0 DZ

NBB

VL

Sum

Conclusions •

The application of SWAT model allowed to quantify the total annual

nutrient load and to assess the source apportionment

• The dry weather diffuse sources (i.e. groundwater/spring recharge and tributary/irrigation channels coming from bordering watersheds) constitute the most important source (65% N and 35% P);



Runoff loads cover about 20% of the total N load and about 30% of

the total P load. Agricultural runoff constitute about 2/3 of the runoff load;

• POLITECNICO DI MILANO

Better-business agricultural scenario: reduction in agricultural runoff

loads of about 50% for N and of about 15% for P Æ decrease in the total annual load of about 5-6%.

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Most significant model outputs Æ implemented in a decision support

system software (mDSS) ARPA VENETO