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R. Hermida , JOSE ANTA , M. Bermúdez, L. Cea, J. Suárez & J. Puertas GEAMA Research Team

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. INTRODUCTION.

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R. Hermida , JOSE ANTA , M. Bermúdez, L. Cea, J. Suárez & J. Puertas GEAMA Research Team

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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, L. Cea, J. Suárez & J. Puertas GEAMA ResearchTeam Universidade da Coruña

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

  3. 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. DESCRIPTION OF THE URBAN CATCHMENT

  5. MODEL DEVELOPMENT • Distributedmodel (del Río, 2011) • 316 subcathments: 183 streets, 128 roofs, 5 perviousareas • 7 km of pipes (150 – 1200 mm) • Lumpedmodel (Hermida, 2012)

  6. BUILDUP Model parameters : Ps, K1 Model parameters : C1, C2, C3 WHASOFF

  7. SEWER TRANSPORT MODELS Ackers & White (1996) Model parameter are fixed. Model variables: s, d50 KUL (Boutelegier and Berlamont, 2002) Too many model parameters (6 parameters). Model variables: s, d50

  8. 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, d50

  9. SENSITIVITY ANALYSIS: POLLUTION MODEL • InfoWorksdoesn’tallowan easyimplementation of formal MC inference • Sensitivityanalysis of thedifferentInfworksqualitysubroutineswithMatlab. • MethodologyproposedbyKleidorfer (2009): • Local sensitivityanalysis • Global sensitivityanalysis • Graphicalmethods • Hornberger – Spear – Young

  10. SENSITIVITY ANALYSIS RESULTS BUILDUP Buildup factor is more sensitivitythanthedecay factor Modelissensitivitytobothparameters WHASOFF Modelisalmostinsensitivityto C3coefficient and can be neglected C2is more sensitivitythan C1 Modelissensitivitytobothparameters SEDIMENT TRANSPORT MODEL d50is more sensitivitythanthespecificdensity s Modelissensitivitytobothparameters

  11. 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. MODEL CALIBRATION

  13. CONCLUSIONS • 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 Cmax • 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.

  14. www.geama.org/hidraulica THANKS FOR YOUR ATTENTION jose.anta@udc.es

  15. 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 Syntheticrun

  16. BUILDUP HSY: Ps HSY: K1

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