Abstract
Catchment discretization plays a key role in constructing stormwater models. Traditional methods usually require aerial or topographic data to manually partition the catchment, but this approach is challenging in areas with poor data access. Here, we propose an alternative approach, by drawing Thiessen polygons around sewer nodes to construct a sewershed model. The utility of this approach is evaluated using the EPA’s Storm Water Management Model (SWMM) to simulate pipe flow in a sewershed in the City of Pittsburgh. Parameter sensitivities and model uncertainties were explored via Monte Carlo simulations and a simple algorithm applied to calibrate the model. The calibrated model could reliably simulate pipe flow, with a Nash–Sutcliffe efficiency (NSE) of 0.82 when compared to measured flow. The potential influence of sewer data availability on model performance was tested as a function of the number of nodes used to build the model. No statistical differences were observed in model performance when randomly reducing the number of nodes used to build the model (up to 40%). Based on our analyses, the Thiessen polygon approach can be used to construct urban stormwater models and generate good pipe flow simulations even for sewer data limited scenarios.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The simplified sewer network and model parameterization is available from the authors on request.
References
3 Rivers Wet Weather (3RWW) (2008) Rain gauge and calibrated radar rainfall data. https://www.3riverswetweather.org/municipalities/calibrated-radar-rainfall-data. Accessed 2 Oct 2021
Aad A, Maya P, Suidan MT, Shuster WD (2010) Modeling techniques of best management practices: rain barrels and rain gardens using EPA SWMM-5. J Hydrol Eng 15(6):434–443. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000136
Ahiablame LM, Engel BA, Chaubey I (2012) Effectiveness of low impact development practices: literature review and suggestions for future research. Water Air Soil Pollut 223(7):4253–4273. https://doi.org/10.1007/s11270-012-1189-2
Allegheny County Division of Computer Services Geographic Information Systems Group (2016). Allegheny county boundary. https://www.pasda.psu.edu/uci/FullMetadataDisplay.aspx?file=AlleghenyCounty_Boundary2016.xml. Accessed 2 Oct 2021
Allegheny County Division of Computer Services Geographic Information Systems Group (2017) Allegheny county lidar and terrain products. https://www.pasda.psu.edu/uci/FullMetadataDisplay.aspx?file=AlleghenyCountyLidar2017.xml. Accessed 2 Oct 2021
Allegheny County Sanitary Authority (ALCOSAN) (2022) Conveyance and treatment system with regional collection system outfalls. https://www.alcosan.org/docs/default-source/system-mapping/sanitary-combined-sewer-outfalls_october2022_i.pdf. Accessed 17 Nov 2022
Alves A, Sanchez A, Vojinovic Z, Seyoum S, Babel M, Brdjanovic D (2016) Evolutionary and holistic assessment of green-grey infrastructure for CSO reduction. Water 8(9):402. https://doi.org/10.3390/w8090402
American Society of Civil Engineers and Water Environment Federation (2018) Design and construction of urban stormwater management systems. American Society of Civil Engineers, Reston, VA. https://doi.org/10.1061/9780872628557
Arnold CL, Gibbons CJ (1996) Impervious surface coverage: the emergence of a key environmental indicator. J Am Plann Assoc 62(2):243–258. https://doi.org/10.1080/01944369608975688
Barco J, Wong KM, Stenstrom MK (2008) Automatic calibration of the U.S. EPA SWMM model for a large urban catchment. J Hydraul Eng 134(4):466–474. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:4(466)
Beven K (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv Water Resour 16(1):41–51. https://doi.org/10.1016/0309-1708(93)90028-E
Bizier P (ed) (2007) Gravity sanitary sewer design and construction. In: ASCE Manuals and Reports on Engineering Practice, 2nd edn, no. 60. American Society of Civil Engineers, Reston, VA; Alexandria, VA
Brassel KE, Reif D (2010) A procedure to generate Thiessen polygons. Geogr Anal 11(3):289–303. https://doi.org/10.1111/j.1538-4632.1979.tb00695.x
Coffman LS, Robert G and Rod F (1999). “Low-impact development: an innovative alternative approach to stormwater management.” In Low-Impact Development: An Innovative Alternative Approach to Stormwater Management, 1–10.https://doi.org/10.1061/40430(1999)118
Eckart K, McPhee Z, Bolisetti T (2017) Performance and implementation of low impact development – a review. Sci Total Environ 607–608(December):413–432. https://doi.org/10.1016/j.scitotenv.2017.06.254
Esri Inc (2016) ArcMap (Version 10.5.1) (version 10.5.1). Redlands, CA: Esri Inc
Hopkins KG, Bain DJ, Copeland EM (2014) Reconstruction of a century of landscape modification and hydrologic change in a small urban watershed in Pittsburgh, PA. Landscape Ecol 29(3):413–424. https://doi.org/10.1007/s10980-013-9972-z
Horton Robert E (1941) An approach toward a physical interpretation of infiltration-capacity. Soil Sci Soc Am J 5(C):399–417. https://doi.org/10.2136/sssaj1941.036159950005000C0075x
Huang M, Jin S (2019) A methodology for simple 2-D inundation analysis in urban area using SWMM and GIS. Nat Hazards 97(1):15–43. https://doi.org/10.1007/s11069-019-03623-2
Jang S, Cho M, Yoon J, Yoon Y, Kim S, Kim G, Kim L, Aksoy H (2007) Using SWMM as a tool for hydrologic impact assessment. Desalination 212(1–3):344–356. https://doi.org/10.1016/j.desal.2007.05.005
Krebs G, Kokkonen T, Valtanen M, Koivusalo H, Setälä H (2013) A high resolution application of a stormwater management model (SWMM) using genetic parameter optimization. Urban Water J 10(6):394–410. https://doi.org/10.1080/1573062X.2012.739631
Krebs G, Kokkonen T, Valtanen M, Setälä H, Koivusalo H (2014) Spatial resolution considerations for urban hydrological modelling. J Hydrol 512(May):482–497. https://doi.org/10.1016/j.jhydrol.2014.03.013
Lee JG, Selvakumar A, Alvi K, Riverson J, Zhen JX, Shoemaker L, Lai F-H (2012) A watershed-scale design optimization model for stormwater best management practices. Environ Model Softw 37(November):6–18. https://doi.org/10.1016/j.envsoft.2012.04.011
Li C, Wang W, Xiong J, Chen P (2014) Sensitivity analysis for urban drainage modeling using mutual information. Entropy 16(11):5738–5752. https://doi.org/10.3390/e16115738
Li H, Sharkey LJ, Hunt WF, Davis AP (2009) Mitigation of impervious surface hydrology using bioretention in North Carolina and Maryland. J Hydrol Eng 14(4):407–415. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(407)
Mantovan P, Todini E (2006) Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology. J Hydrol 330(1–2):368–381. https://doi.org/10.1016/j.jhydrol.2006.04.046
McCall A-K, Palmitessa R, Blumensaat F, Morgenroth E, Ort C (2017) Modeling in-sewer transformations at catchment scale – implications on drug consumption estimates in wastewater-based epidemiology. Water Res 122(October):655–668. https://doi.org/10.1016/j.watres.2017.05.034
McCuen RH, Knight Z, Gillian Cutter A (2006) Evaluation of the Nash-Sutcliffe Efficiency Index. J Hydrol Eng 11(6):597–602. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597)
McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42(1):55–61. https://doi.org/10.1080/00401706.2000.10485979
Muleta MK, McMillan J, Amenu GG, Burian SJ (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. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000705
National Research Council (2009) Urban stormwater management in the United States. The National Academies Press: National Research Council, Washington, DC. https://doi.org/10.17226/12465
Okabe A, Boots B, Sugihara K, Chiu SN (2000) Spatial Tessellations: concepts and applications of Voronoi Diagrams, 2nd edn. John Wiley & Sons, Chichester
Palla A, Gnecco I (2015) Hydrologic modeling of low impact development systems at the urban catchment scale. J Hydrol 528(September):361–368. https://doi.org/10.1016/j.jhydrol.2015.06.050
Peterson EW, Wicks CM (2006) Assessing the importance of conduit geometry and physical parameters in Karst systems using the storm water management model (SWMM). J Hydrol 329(1–2):294–305. https://doi.org/10.1016/j.jhydrol.2006.02.017
Rossman LA (2017) Storm water management model reference manual volume II – hydraulics. EPA/600/R-17/111: U.S. Environmental Protection Agency, Washington, DC. https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NRMRL&dirEntryId=337162. Accessed 2 Oct 2021
Rossman LA, Huber WC (2016) Storm water management model reference manual volume I – hydrology. EPA/600/R-15/162A: US EPA Office of Research and Development, Washington, DC. https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NRMRL&dirEntryId=309346. Accessed 2 Oct 2021
Saltelli A, Tarantola S, Campolongo F (2000) Sensitivity analysis as an ingredient of modeling. Stat Sci 15:377–395
Schueler TR, Fraley-McNeal L, Cappiella K (2009) Is impervious cover still important? Review of recent research. J Hydrol Eng 14(4):309–315. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(309)
Spearman C (1904) ‘General intelligence’, objectively determined and measured. Am J Psychol 15:201–292
Stedinger JR, Vogel RM, Lee SU, and Batchelder R (2008) “Appraisal of the generalized likelihood uncertainty estimation (GLUE) method: APPRAISAL OF THE GLUE METHOD.” Water Resources Res 44 (12). https://doi.org/10.1029/2008WR006822
Sun N, Hong B, Hall M (2013) Assessment of the SWMM model uncertainties within the generalized likelihood uncertainty estimation (GLUE) framework for a high-resolution urban sewershed. Hydrol Process 28(May):3018–3034. https://doi.org/10.1002/hyp.9869
Tsai L-Y, Chen C-F, Fan C-H, Lin J-Y (2017) Using the HSPF and SWMM models in a high pervious watershed and estimating their parameter sensitivity. Water 9(10):780. https://doi.org/10.3390/w9100780
U.S. Environmental Protection Agency (2014) Estimating Change in Impervious Area (IA) and Directly Connected Impervious Areas (DCIA) for Massachusetts Small MS4 Permit. Boston, MA. https://www3.epa.gov/region1/npdes/stormwater/ma/MADCIA.pdf. Accessed 2 Oct 2021
U.S. Geological Survey (2014) NLCD 2001 Land Cover (2011 Edition, Amended 2014) - National Geospatial Data Asset (NGDA) Land Use Land Cover. Sioux Falls, SD. https://www.mrlc.gov/downloads/sciweb1/shared/mrlc/metadata/landcover.html. Accessed 2 October 2021
Acknowledgements
We thank the Allegheny County Sanitary Authority and Pittsburgh Water & Sewer Authority for graciously providing sewer flow data and field data.
Funding
This work was supported by the National Science Foundation, USA (NSF grant number 1854827).
Author information
Authors and Affiliations
Contributions
Zhaokai Dong contributed to the investigation, methodology, validation, visualization, software, and wrote the original draft. Dr. Daniel Bain contributed to the methodology, resources, review, and editing. Dr. Murat Akcakaya contributed to the funding acquisition, methodology, review, and editing. Dr. Carla Ng contributed to the funding acquisition, methodology, project administration, resources, supervision, review, and editing.
Corresponding author
Ethics declarations
Ethics approval
This study did not involve human subjects or animal research.
Consent to participate
Not applicable.
Consent for publication
All authors agree with the content to submit and publish.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Marcus Schulz
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dong, Z., Bain, D.J., Akcakaya, M. et al. Evaluating the Thiessen polygon approach for efficient parameterization of urban stormwater models. Environ Sci Pollut Res 30, 30295–30307 (2023). https://doi.org/10.1007/s11356-022-24162-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-022-24162-7