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Stochastic evaluation of the impact of sewer inlets’ hydraulic capacity on urban pluvial flooding

  • João P. Leitão
  • Nuno E. Simões
  • Rui Daniel Pina
  • Susana Ochoa-Rodriguez
  • Christian Onof
  • Alfeu Sá Marques
Article

Abstract

Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.

Keywords

Sewer inlets Clogging Urban pluvial flooding Flood hazard Stochastic risk analysis GIS 

Notes

Acknowledgments

Rui Pina acknowledges the financial support from the Fundação para a Ciência e Tecnologia—Ministério para a Ciência, Tecnologia e Ensino Superior, Portugal [SFRH/BD/88532/2012]. Susana Ochoa-Rodriguez acknowledges the support of the Interreg IVB NWE RainGain project. Special thanks are due to AC, Águas de Coimbra for providing rainfall and sewer data of the pilot location and to Innovyze for providing research licences of InfoWorks ICM software.

References

  1. Ally M (2011) Modelling road gullies. In: 2011 International Flood and Modelling Conference. http://www.raaltd.co.uk/cms-files/Paper_on_Modelling_Road_Gullies.pdf
  2. Almedeij J, Alsulaili A, Alhomoud J (2006) Assessment of grate sag inlets in a residential area based on return period and clogging factor. J Environ Manag 79(1):38–42. doi: 10.1016/j.jenvman.2005.05.011 CrossRefGoogle Scholar
  3. Artina S, Calenda G, Calomino F, Cao C, La Loggia G, Modica C, Paoletti A, Papiri S, Rasulo G, Veltri P (2001) Sistemi di fognature, manuale di progettazione. Hoepli editore, MilanGoogle Scholar
  4. Barros de MO, Werner CML, Travassos GH (2000) Applying system dynamics to scenario based software project management. In: Proceedings of the 18th International Conference of the System Dynamics Society, Bergen, Norway, pp 35–50Google Scholar
  5. Brown SA, Schall JD, Morris JL, Doherty CL, Stein SM (2009) Urban drainage design manual—hydraulic engineering circular 22, 3rd edn (No. NHI-01-021 HEC-22). US Department of Transportation—Federal Highway AdministrationGoogle Scholar
  6. Butler D, Davies JW (2011) Urban drainage, 3rd edn. Spon Press, London. ISBN 9780415455251Google Scholar
  7. Carroll J, Harms IH (1999) Uncertainty analysis of partition coefficients in a radionuclide transport model. Water Res 33(11):2617–2626CrossRefGoogle Scholar
  8. Clark County Regional Flood Control District (CCRFCD) (1999) Hydrologic criteria and drainage design manual. Clark County Regional Flood Control District, Las VegasGoogle Scholar
  9. Coates G, Rahimifard S (2009) Modelling of post-fragmentation waste stream processing within UK shredder facilities. Waste Manag 29(1):44–53. doi: 10.1016/j.wasman.2008.03.006 CrossRefGoogle Scholar
  10. Colorado Department of Transportation (CDOT) (2000) Hydraulic design criteria for highways. Hydraulic Division Center, DenverGoogle Scholar
  11. Comport BC, Thornton CI (2012) Hydraulic efficiency of grate and curb inlets for urban storm drainage. J Hydraul Eng 138(10):878–884. doi: 10.1061/(ASCE)HY.1943-7900.0000552 CrossRefGoogle Scholar
  12. Dawson RJ, Speight L, Hall JW, Djordjevic S, Savic D, Leandro J (2008) Attribution of flood risk in urban areas. J Hydroinform 10(4):275–288. doi: 10.2166/hydro.2008.054 CrossRefGoogle Scholar
  13. Del Giudice D, Honti M, Scheidegger A, Albert C, Reichert P, Rieckermann J (2013) Improving uncertainty estimation in urban hydrological modeling by statistically describing bias. Hydrol Earth Syst Sci 17(10):4209–4225. doi: 10.5194/hess-17-4209-2013 CrossRefGoogle Scholar
  14. Deletic A, Dotto CBS, McCarthy DT, Kleidorfer M, Freni G, Mannina G, Uhl M, Henrichs M, Fletcher TD, Rauch W, Bertrand-Krajewski JL, Tait S (2012) Assessing uncertainties in urban drainage models. Phys Chem Earth 42–44:3–10. doi: 10.1016/j.pce.2011.04.007 CrossRefGoogle Scholar
  15. Despotovic J, Plavsic J, Stefanovic N, Pavlovic D (2005) Inefficiency of storm water inlets as a source of urban floods. Water Sci Technol 15(2):139–145Google Scholar
  16. DGRN (1991) Manual de Saneamento Básico. Direcção Geral dos Recursos Naturais, LisbonGoogle Scholar
  17. Djordjević S, Prodanović D, Maksimović Č, Ivetić M, Savić DA (2005) SIPSON—simulation of interaction between pipe flow and surface overland flow in networks. Water Sci Technol 52(5):275–283Google Scholar
  18. Dotto CBS, Mannina G, Kleidorfer M, Vezzaro L, Henrichs M, McCarthy DT, Freni G, Rauch W, Deletic A (2012) Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling. Water Res 46(8):2545–2558. doi: 10.1016/j.watres.2012.02.009 CrossRefGoogle Scholar
  19. Gires A, Onof C, Maksimović Č, Schertzer D, Tchiguirinskaia I, Simões N (2012) Quantifying the impact of small scale unmeasured rainfall variability on urban runoff through multifractal downscaling: a case study. J Hydrol 442 and 443:117–128. doi: 10.1016/j.jhydrol.2012.04.005 CrossRefGoogle Scholar
  20. Gómez M, Russo B (2009) Hydraulic efficiency of continuous transverse grates for paved areas. J Irrig Drain Eng 135(2):225–230. doi: 10.1061/(ASCE)0733-9437(2009)135:2(225) CrossRefGoogle Scholar
  21. Gómez M, Rabasseda GH, Russo B (2013) Experimental campaign to determine grated inlet clogging factors in an urban catchment of Barcelona. Urban Water J 10(1):50–61. doi: 10.1080/1573062X.2012.690435 CrossRefGoogle Scholar
  22. Guo JCY (2006) Design of street curb opening inlets using a decay-based clogging factor. J Hydraul Eng 132:1237–1241. doi: 10.1061/(ASCE)0733-9429(2006)132:11(1237) CrossRefGoogle Scholar
  23. Guo JCY, MacKenzie KA, Mommandi A (2009) Design of street sump inlet. J Hydraul Eng 135:1000–1004. doi: 10.1061/(ASCE)HY.1943-7900.0000094 CrossRefGoogle Scholar
  24. Horton RE (1940) An approach towards physical interpretation of infiltration capacity. In: Proceedings of the Soil Science Society of America, vol 5, pp 399–417Google Scholar
  25. Hubbard DW (2009) The failure of risk management: why it’s broken and how to fix it. Wiley, Inc., Hoboken, New Jersey, USA.Google Scholar
  26. Innovyze (2013) Infoworks ICM. www.innovyze.com/products/infoworks_icm
  27. Jing L, Chen B, Zhang B, Li P, Zheng J (2013) Monte Carlo simulation-aided analytic hierarchy process approach: case study of assessing preferred non-point-source pollution control best management practices. J Environ Eng 139(5):618–626. doi: 10.1061/(ASCE)EE.1943-7870.0000673 CrossRefGoogle Scholar
  28. Lake RJ, Cressey PJ, Campbell DM, Oakley E (2010) Risk ranking for foodborne microbial hazards in New Zealand: burden of disease estimates. Risk Anal 30(5):743–752. doi: 10.1111/j.1539-6924.2009.01269.x CrossRefGoogle Scholar
  29. Leitão JP, Djordjević S, Prodanović D, Maksimović Č (2009) Spatially distributed rainfall for surface runoff calculations in urban catchments. In: 8th International Workshop on Precipitation in Urban Areas, St. Moritz, SwitzerlandGoogle Scholar
  30. Leitão JP, Almeida MC, Simões NE, Martins A (2013) Methodology for qualitative urban flood risk assessment. Water Sci Technol 68(4):829–838. doi: 10.2166/wst.2013.310 CrossRefGoogle Scholar
  31. Leitão JP, Moy de Vitry M, Scheidegger A, Rieckermann J (2016) Assessing the quality of digital elevation models obtained from mini-unmanned aerial vehicles for overland flow modelling in urban areas. Hydrol Earth Syst Sci 20:1637–1653. doi: 10.5194/hess-20-1637-2016 CrossRefGoogle Scholar
  32. Löwe R, Mikkelsen PS, Madsen H (2014) Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk. Stoch Env Res Risk Assess 28(3):505–516. doi: 10.1007/s00477-013-0768-0 CrossRefGoogle Scholar
  33. Maksimović Č, Prodanović D, Boonya-aroonnet S, Leitão JP, Djordjević S, Allitt R (2009) Overland flow and pathway analysis for modelling of urban pluvial flooding. J Hydraul Res 47:512–523. doi: 10.1080/00221686.2009.9522027 CrossRefGoogle Scholar
  34. Martins R, Leandro J, de Carvalho RF (2014) Characterization of the hydraulic performance of a gully under drainage conditions. Water Sci Technol 69(12):2423–2430. doi: 10.2166/wst.2014.168 CrossRefGoogle Scholar
  35. Muleta MK, McMillan J, Amenu G, Burian S (2013) Bayesian approach for uncertainty analysis of an urban storm water model and its application to a heavily urbanized watershed. J Hydrol Eng 18(10):1360–1371. doi: 10.1061/(ASCE)HE.1943-5584.0000705 CrossRefGoogle Scholar
  36. National Audit Office (2004) Out of sight—not out of mind: OFWAT and the public sewer network in England and Wales. Report by the Controller and Auditor General, UKGoogle Scholar
  37. Pina RD (2009) Causas das Inundações na Praça 8 de Maio e Propostas de Intervenção. Águas de Coimbra E.E.M, CoimbraGoogle Scholar
  38. Pina RD, Oliveira Sousa J, Santos Temido J, Sá Marques A (2010) O novo paradigma de gestão dos sistemas de drenagem da cidade de coimbra—Causas das inundações na Praça 8 de Maio, em Coimbra, e propostas de intervenção. In: 10th Congresso da Água, Alvor, PortugalGoogle Scholar
  39. Pina RD, Ochoa-Rodriguez S, Simões NE, Mijic A, Marques AS, Maksimović Č (2016) Semi- vs. fully-distributed urban stormwater models: model set up and comparison with two real case studies. Water 8:58. doi: 10.3390/w8020058 CrossRefGoogle Scholar
  40. Rossman LA (2010) Storm water management model user’s manual—version 5.0. United States Environmental Protection Agency, CincinnatiGoogle Scholar
  41. Russo B, Gómez M (2011) Methodology to estimate hydraulic efficiency of drain inlets. Proc ICE: Water Manag 164(2):81–90. doi: 10.1680/wama.900070 Google Scholar
  42. Russo B, Gómez M, Tellez J (2013) Methodology to estimate the hydraulic efficiency of nontested continuous transverse grates. J Irrig Drain Eng 139(10):864–871. doi: 10.1061/(ASCE)IR.1943-4774.0000625 CrossRefGoogle Scholar
  43. Russo B, Sunyer D, Velasco M, Djordjević S (2015) Analysis of extreme flooding events through a calibrated 1D/2D coupled model: the case of Barcelona (Spain). J Hydroinf 17(3):473–491. doi: 10.2166/hydro.2014.063 CrossRefGoogle Scholar
  44. Sá Marques A, Pedroso de Lima J, Sousa J, Simões NE, Pina R (2013) Hidrologia urbana—Sistemas de drenagem de águas pluviais. Curso Técnico 2. [Online]. ERSAR. http://www.ersar.pt
  45. Saegrov S, Schilling W (2002) Computer aided rehabilitation of sewer and storm water networks. In: 9th ICUD: International Conference on Urban Drainage, OregonGoogle Scholar
  46. Salling KB, Leleur S (2012) Modelling of transport project uncertainties: feasibility risk assessment and scenario analysis. Eur J Transp Infrastruct Res 12(1):21–38. doi: 10.1016/j.sbspro.2013.03.047 Google Scholar
  47. Salvadore E, Bronders J, Batelaan O (2015) Hydrological modelling of urbanized catchments: a review and future directions. J Hydrol 529:62–81. doi: 10.1016/j.jhydrol.2015.06.028 CrossRefGoogle Scholar
  48. Saul AJ (2012) Work package reports SWP3. Flood Risk Management Research Consortium—Phase II, UKGoogle Scholar
  49. Segond ML, Neokleous N, Makropoulos C, Onof C, Maksimović Č (2007) Simulation and spatio-temporal disaggregation of multi-site rainfall data for urban drainage applications. Hydrol Sci J 52(5):917–935. doi: 10.1623/hysj.52.5.917 CrossRefGoogle Scholar
  50. Simões NEDC (2012) Urban pluvial flood forecasting. Ph.D. Thesis, Imperial College London, LondonGoogle Scholar
  51. Simões NE, Ochoa-Rodríguez S, Wang L-P, Pina RD, Sá Marques S, Leitão JP (2015) Contribution of spatial-temporal stochastic rainfall events to the generation of stochastic urban pluvial flood hazard maps. Water 7(7):3396–3406. doi: 10.3390/w7073396 CrossRefGoogle Scholar
  52. Straver JM, Janssen AFW, Linnemann AR, van Boekel MAJS, Beumer RR, Zwietering MH (2007) Number of Salmonella on chicken breast filet at retail level and its implications for public health risk. J Food Prot 9:2045–2055CrossRefGoogle Scholar
  53. Tehrany MS, Pradhan B, Jebur MN (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Env Res Risk Assess 29(4):1149–1165. doi: 10.1007/s00477-015-1021-9 CrossRefGoogle Scholar
  54. ten Veldhuis JAE, Clemens FHLR, van Gelder PHAJM (2011) Quantitative fault tree analysis for urban water infrastructure flooding. Struct Infrastruct Eng 7(11):809–821. doi: 10.1080/15732470902985876 CrossRefGoogle Scholar
  55. Thordarson FO, Breinholt A, Moller JK, Mikkelsen PS, Grum M, Madsen H (2012) Evaluation of probabilistic flow predictions in sewer systems using grey box models and a skill score criterion. Stoch Env Res Risk Assess 26(8):1151–1162. doi: 10.1007/s00477-012-0563-3 CrossRefGoogle Scholar
  56. Thorhallsson S, Sveinbjornsson BM (2012) Geothermal drilling cost and drilling effectiveness. In: Short Course on Geothermal Development and Geothermal Wells, UNU-GTP and LaGeo, Santa Tecla, El Salvador, 11–17 MarchGoogle Scholar
  57. Tokarczyk P, Leitão JP, Rieckermann J, Schindler K, Blumensaat F (2015) High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery. Hydrol Earth Syst Sci Dis 12:1205–1245. doi: 10.5194/hessd-12-1205-2015 CrossRefGoogle Scholar
  58. van Bijnen M, Korving H, Clemens F (2012) Impact of sewer condition on urban flooding: an uncertainty analysis based on field observations and Monte Carlo simulations on full hydrodynamic models. Water Sci Technol 65(12):2219–2227. doi: 10.2166/wst.2012.134 CrossRefGoogle Scholar
  59. Vose D (2000) Risk analysis—a quantitative guide. Wiley, Chichester. ISBN 978-0-470-51284-5Google Scholar
  60. Yin J, Ye M, Yin Z, Xu S (2013) A review of advances in urban flood risk analysis over China. Stoch Env Res Risk Assess 29(3):1063–1070. doi: 10.1007/s00477-014-0939-7 CrossRefGoogle Scholar
  61. Yu JJ, Qin XS, Larsen O (2013) Joint Monte Carlo and possibilistic simulation for flood damage assessment. Stoch Env Res Risk Assess 27(3):725–735. doi: 10.1007/s00477-012-0635-4 CrossRefGoogle Scholar
  62. Zhou Q (2014) A review of sustainable urban drainage systems considering the climate change and urbanization impacts. Water 6(4):976–992. doi: 10.3390/w6040976 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Eawag: Swiss Federal Institute of Aquatic Science and TechnologyDübendorfSwitzerland
  2. 2.MARE, Department of Civil EngineeringUniversity of Coimbra, Rua Luís Reis Santos - Pólo IICoimbraPortugal
  3. 3.Department of Civil and Environmental EngineeringImperial College LondonLondonUK

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