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1 Lumped and distributed modelling of suspended solids in a combined sewer catchment in Santiago de Compostela (Spain) R. Hermida, JOSE ANTA, M. Bermúdez,

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Presentation on theme: "1 Lumped and distributed modelling of suspended solids in a combined sewer catchment in Santiago de Compostela (Spain) R. Hermida, JOSE ANTA, M. Bermúdez,"— Presentation transcript:

1 1 Lumped and distributed modelling of suspended solids in a combined sewer catchment in Santiago de Compostela (Spain) R. Hermida, JOSE ANTA, M. Bermúdez, L. Cea, J. Suárez & J. Puertas GEAMA Research Team Universidade da Coruña

2 22 INTRODUCTION Flow and Pollution Modelling in Urban Systems dust and dirt buildup washoff gully-pot processes sewer erosion - transport

3 33 OBJETIVES Comparison of a lumped and distributed model for TSS in “El Ensanche” combined sewer catchment Model developed with Infoworks CS 9.x –Ackers-White equation –KUL model 10 rain event were used for model calibration. More details presented yesterday: “Mobilized pollution indicators in a combined sewer system during rain events” del Río et al.

4 4 DESCRIPTION OF THE URBAN CATCHMENT

5 55 MODEL DEVELOPMENT Distributed model (del Río, 2011) –316 subcathments: 183 streets, 128 roofs, 5 pervious areas –7 km of pipes (150 – 1200 mm) Lumped model (Hermida, 2012)

6 66 BUILDUP Model parameters : P s, K 1 Model parameters : C 1, C 2, C 3 WHASOFF

7 77 SEWER TRANSPORT MODELS Ackers & White (1996) Model parameter are fixed. Model variables: s, d 50 KUL (Boutelegier and Berlamont, 2002) Too many model parameters (6 parameters). Model variables: s, d 50

8 88 SEWER TRANSPORT MODELS KUL : Shields approach (Shizari and Berlamont, 2010) Shields number has to be re-evaluated in each time – step (not allowed in IF) Ota and Nalluri equation (2003) KUL equation as function of s, d 50

9 99 SENSITIVITY ANALYSIS: POLLUTION MODEL InfoWorks doesn’t allow an easy implementation of formal MC inference Sensitivity analysis of the different Infworks quality subroutines with Matlab. Methodology proposed by Kleidorfer (2009): –Local sensitivity analysis –Global sensitivity analysis Graphical methods Hornberger – Spear – Young

10 1010 SENSITIVITY ANALYSIS RESULTS BUILDUP Buildup factor is more sensitivity than the decay factor Model is sensitivity to both parameters WHASOFF Model is almost insensitivity to C 3 coefficient and can be neglected C 2 is more sensitivity than C 1 Model is sensitivity to both parameters SEDIMENT TRANSPORT MODEL d 50 is more sensitivity than the specific density s Model is sensitivity to both parameters

11 1111 MODEL CALIBRATION Hydraulic model calibration 11 rainy days: NS=0.85 Pollution model calibration Visual calibration: 3 events Model validation: 7 events Distributed model –Ackers – White –KUL (Ota & Nalluri) Lumped model –Ackers – White –KUL (Ota & Nalluri)

12 1212 MODEL CALIBRATION

13 13 Successful application of sensitivity analysis to determine the most relevant parameters for pollution modelling in InfoWorks CS All the sensitivity tests shows similar results Lumped model works better in terms of NS and EMC Distributed model works better in terms of C max KUL – Ota & Nalluri approach avoids the determination of a large number of model parameters Ackers – White is more accurate than KUL approach for lumped model and viceversa. CONCLUSIONS

14 14 THANKS FOR YOUR ATTENTION

15 1515 SENSITIVITY ANALYSIS Hornberger – Spear – Young Method (Kleidorfer, 2009) –MC framework –Comparison of model outputs with a synthetic run with NS –Analysis of the distance of behavioral (NS>0) and non behavioral (NS<0) empirical cumulative pdf Nash- Sutcliffe Synthetic run

16 1616 BUILDUP HSY: P s HSY: K 1


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