Articles | Volume 27, issue 18
https://doi.org/10.5194/hess-27-3329-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3329-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Sep 2023
Research article |
| 20 Sep 2023
| 20 Sep 2023
An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume
Francesco Fatone
Department of Science and Engineering of Matter, Environment and Urban Planning (SIMAU), Polytechnic University of Marche Ancona, 60121 Ancona, Italy
Bartosz Szeląg
CORRESPONDING AUTHOR
Institute of Environmental Engineering, Warsaw University of Life Sciences (SGGW), 02-797 Warsaw, Poland
Przemysław Kowal
Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Arthur McGarity
Department of Engineering, Swarthmore College, Swarthmore, PA 19081, USA
Adam Kiczko
Institute of Environmental Engineering, Warsaw University of Life Sciences (SGGW), 02-797 Warsaw, Poland
Grzegorz Wałek
Institute of Geography and Environmental Sciences, Jan Kochanowski University of Kielce, 25–406 Kielce, Poland
Ewa Wojciechowska
Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Michał Stachura
Faculty of Law and Social Sciences, Jan Kochanowski University of Kielce, 25–406 Kielce, Poland
Nicolas Caradot
Kompetenzzentrum Wasser Berlin, 10709 Berlin, Germany
Related authors
Bartosz Szeląg, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Francesco Fatone
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-109, https://doi.org/10.5194/hess-2022-109, 2022
Manuscript not accepted for further review
Short summary
Short summary
A methodology for the development of a sewer network performance simulator and risk assesssment is given. The influence of catchment characteristics, sewer network and SWMM parameters on specific flood volume was taken into account in comparison with developed methods. The influence of spatial variability of catchment and sewer network characteristics on the relation between SWMM parameters and sewage flooding was determined, which can be used for spatial planning and urban catchment management.
Francesco Fatone, Bartosz Szeląg, Adam Kiczko, Dariusz Majerek, Monika Majewska, Jakub Drewnowski, and Grzegorz Łagód
Hydrol. Earth Syst. Sci., 25, 5493–5516, https://doi.org/10.5194/hess-25-5493-2021, https://doi.org/10.5194/hess-25-5493-2021, 2021
Short summary
Short summary
A sensitivity analysis based on a simulator of hydrograph parameters (volume, maximum flow) is shown. The method allows us to analyze the impact of calibrated hydrodynamic model parameters, including rainfall distribution and intensity, on the hydrograph. A sensitivity coefficient and the effect of the simulator uncertainty on calculation results are presented. This approach can be used to select hydrographs for calibration and validation of models, which has not been taken into account so far.
Adam P. Kozioł, Adam Kiczko, Marcin Krukowski, Elżbieta Kubrak, Janusz Kubrak, Grzegorz Majewski, and Andrzej Brandyk
Hydrol. Earth Syst. Sci., 29, 535–545, https://doi.org/10.5194/hess-29-535-2025, https://doi.org/10.5194/hess-29-535-2025, 2025
Short summary
Short summary
Floodplain trees play a crucial role in increasing flow resistance. Their impact extends beyond floodplains to affect the main channel. The experiments reveal the influence of floodplain trees on the discharge capacity of channels with varying roughness. We determine resistance coefficients for different roughness levels of the main channel bottom. The research contributes to a deeper understanding of open-channel flow dynamics and has practical implications for river engineering.
Monika Barbara Kalinowska, Kaisa Västilä, Michael Nones, Adam Kiczko, Emilia Karamuz, Andrzej Brandyk, Adam Kozioł, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 27, 953–968, https://doi.org/10.5194/hess-27-953-2023, https://doi.org/10.5194/hess-27-953-2023, 2023
Short summary
Short summary
Vegetation is commonly found in rivers and channels. Using field investigations, we evaluated the influence of different vegetation coverages on the flow and mixing in the small naturally vegetated channel. The obtained results are expected to be helpful for practitioners, enlarge our still limited knowledge, and show the further required scientific directions for a better understanding of the influence of vegetation on the flow and mixing of dissolved substances in real natural conditions.
Bartosz Szeląg, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Francesco Fatone
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-109, https://doi.org/10.5194/hess-2022-109, 2022
Manuscript not accepted for further review
Short summary
Short summary
A methodology for the development of a sewer network performance simulator and risk assesssment is given. The influence of catchment characteristics, sewer network and SWMM parameters on specific flood volume was taken into account in comparison with developed methods. The influence of spatial variability of catchment and sewer network characteristics on the relation between SWMM parameters and sewage flooding was determined, which can be used for spatial planning and urban catchment management.
Francesco Fatone, Bartosz Szeląg, Adam Kiczko, Dariusz Majerek, Monika Majewska, Jakub Drewnowski, and Grzegorz Łagód
Hydrol. Earth Syst. Sci., 25, 5493–5516, https://doi.org/10.5194/hess-25-5493-2021, https://doi.org/10.5194/hess-25-5493-2021, 2021
Short summary
Short summary
A sensitivity analysis based on a simulator of hydrograph parameters (volume, maximum flow) is shown. The method allows us to analyze the impact of calibrated hydrodynamic model parameters, including rainfall distribution and intensity, on the hydrograph. A sensitivity coefficient and the effect of the simulator uncertainty on calculation results are presented. This approach can be used to select hydrographs for calibration and validation of models, which has not been taken into account so far.
Cited articles
Babovic, F., Mijic, A., and Madani, K.: Decision making under deep uncertainty for adapting urban drainage systems to change, Urban Water J., 15, 552–560, https://doi.org/10.1080/1573062X.2018.1529803, 2018.
Ball, J. E.: An Assessment of Continuous Modeling for Robust Design Flood Estimation in Urban Environments, Front. Earth Sci., 8, 1–10, https://doi.org/10.3389/feart.2020.00124, 2020.
Bui, D. T., Hoang, N. D., Martínez-Álvarez, F., Ngo, P. T. T., Hoa, P. V., Pham, T. D., Samui, P., and Costache, R.: A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area, Sci. Total Environ., 701, 134413, https://doi.org/10.1016/j.scitotenv.2019.134413, 2018.
Cea, L. and Costabile, P.: Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review, Hydrology, 9, 50, https://doi.org/10.3390/hydrology9030050, 2022.
Chang, H., Pallathadka, A., Sauer, J., Grimm, N. B., Zimmerman, R., Cheng, C., Iwaniec, D. M., Kim, Y., Lloyd, R., McPhearson, T., Rosenzweig, B., Troxler, T., Welty, C., Brenner, R., and Herreros-Cantis, P.: Assessment of urban flood vulnerability using the social-ecological-technological systems framework in six US cities, Sustain. Cities Soc., 68, 102786, https://doi.org/10.1016/j.scs.2021.102786, 2020.
Chen, L., Li, S., Zhong, Y., and Shen, Z.: Improvement of model evaluation by incorporating prediction and measurement uncertainty, Hydrol. Earth Syst. Sci., 22, 4145–4154, https://doi.org/10.5194/hess-22-4145-2018, 2018.
Chen, S., Garambois, P.-A., Finaud-Guyot, P., Dellinger, G., Mosé, R., Terfous, A., and Ghenaim, A.: Variance based sensitivity analysis of 1D and 2D hydraulic models: An experimental urban flood case, Environ. Model. Softw., 109, 167181, https://doi.org/10.1016/j.envsoft.2018.08.008, 2018.
Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Zhang, S., Pradhan, B., and Bin Ahmad, B.: Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods, Sci. Total Environ., 701, 134979, https://doi.org/10.1016/j.scitotenv.2019.134979, 2019.
Cristiano, E., ten Veldhuis, M. C., Wright, D. B., Smith, J. A., and van de Giesen, N.: The Influence of Rainfall and Catchment Critical Scales on Urban Hydrological Response Sensitivity, Water Resour. Res., 55, 3375–3390, https://doi.org/10.1029/2018WR024143, 2019.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and Rieckermann, J.: Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013.
Dotto, C. B. S., Kleidorfer, M., Deletic, A., Rauch, W., and McCarthy, D. T.: Impacts of measured data uncertainty on urban stormwater models, J. Hydrol., 508, 28–42, https://doi.org/10.1016/j.jhydrol.2013.10.025, 2014.
Duncan, A. P., Chen, A. S., Keedwell, E. C., Djordjević, S., and Savić, D. A.: Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks, Weather Radar and Hydrology: IAHS Red Book Proceedings, 18–21 April 2011, University of Exeter, UK, 2012.
DWA-A118E: Hydraulic Dimensioning and Verification of Drain and Sewer Systems, Ger. Assoc. Water Wastewater Waste, 2006.
Fatone, F., Szeląg, B., Kiczko, A., Majerek, D., Majewska, M., Drewnowski, J., and Łagód, G.: Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments, Hydrol. Earth Syst. Sci., 25, 5493–5516, https://doi.org/10.5194/hess-25-5493-2021, 2021.
Fraga, I., Cea, L., Puertas, J., Suárez, J., Jiménez, V., and Jácome, A.: Global sensitivity and GLUE-based uncertainty analysis of a 2D-1D dual urban drainage model, J Hydrol Eng., 21, 04016004, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001335, 2016.
Freni, G. and Oliveri, O.: Mitigation of urban flooding: a simplified approach for distributed stormwater management practices selection and planning, Urban Water J., 2, 215226, https://doi.org/10.1080/15730620500386461, 2005.
Fu, G. and Butler, D.: Copula-based frequency analysis of overflow and flooding in urban drainage systems, J. Hydrol., 510, 49–58, https://doi.org/10.1016/j.jhydrol.2013.12.006, 2014.
Fu, G., Butler, D., Khu, S.-T., and Sun, S.: Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory, Water Resour. Res., 47, 1–13, https://doi.org/10.1029/2009WR008944, 2011.
Guo, K., Guan, M., and Yu, D.: Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, 2021.
Harrell, F. E.: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, Springer Series in Statistics, New York, ISBN 9781475734621, 2001.
Hettiarachchi, S., Wasko, C., and Sharma, A.: Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns, Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, 2018.
Huang, H., Chen, X., Zhu, Z., Xie, Y., Liu, L., Wang, X., Wang, X., and Liu, K.: The changing pattern of urban flooding in Guangzhou, China, Sci. Total Environ., 622623, 394401, https://doi.org/10.1016/j.scitotenv.2017.11.358, 2018.
Hung, W. and Hobbs, F. B.: How can learning-by-doing improve decisions in stormwater management? A Bayesian-based optimization model for planning urban green infrastructure investments, Environ. Modell. Softw., 113, 59–72, https://doi.org/10.1016/j.envsoft.2018.12.005, 2019.
Jato-Espino, D., Sillanpää, N., Andrés-Doménech, I., and Rodriguez-Hernandez, J.: Flood Risk Assessment in Urban Catchments Using Multiple Regression Analysis, J. Water Resour. Plan. Manag., 144, 04017085, https://doi.org/10.1061/(asce)wr.1943-5452.0000874, 2018.
Jato-Espino, D., Sillanpää, N., and Pathak, S.: Flood modelling in sewer networks using dependence measures and learning classifier systems, J. Hydrol., 578, 124013, https://doi.org/10.1016/j.jhydrol.2019.124013, 2019.
Jiang, Y., Zevenbergen, C., and Mab, Y.: Urban pluvial flooding and stormwater management: A contemporary review of China's challenges and “sponge cities” strategy, Environ. Sci. Policy, 80, 132–143, https://doi.org/10.1016/j.envsci.2017.11.016, 2018.
Karamouz, M. and Nazif, S.: Reliability-based flood management in urban watersheds considering climate change impacts, J. Water Resour. Plann. Manage., 139, 520–533, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000345, 2013.
Ke, Q., Bricker, J., Tian, Z., Guan, G., Cai, H., Huang, X., Yang, H., and Liu, J.: Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzen city, China, Adv. Water Resour., 145, 103719, https://doi.org/10.1016/j.advwatres.2020.103719, 2020.
Kelleher, C., McGlynn, B., and Wagener, T.: Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding, Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, 2017.
Khan, M. P., Hubacek, K., Brubaker, K. L., Sun, L., and Moglen, G. E.: Stormwater Management Adaptation Pathways under Climate Change and Urbanization, J. Sustain. Water Built Environ., 8, 04022009, https://doi.org/10.1061/JSWBAY.0000992, 2022.
Kiczko, A., Szeląg, B., Kozioł, A. P., Krukowski, M., Kubrak, E., Kubrak, J., and Romanowicz, R. J.: Optimal capacity of a stormwater reservoir for flood peak reduction, J. Hydrol. Eng., 23, 04018008, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001636, 2018.
Kim, Y., Eisenberg, D. A., Bondank, E. N., Chester, M. V., Mascaro, G., and Underwood, S.: Fail-safe and safe-to-fail adaptation: decision-making for urban flooding under climate change, Clim. Change, 145, 397–412, https://doi.org/10.1007/s10584-017-2090-1, 2015.
Kirshen, P., Caputo, L., Vogel, R. M., Mathisen, P., Rosner, A., and Renaud, T.: Adapting urban infrastructure to climate change: a drainage case study, J. Water Resour. Plan. Manag., 141, 04014064, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000443, 2015.
Knighton, J., Lennon, E., Bastidas, L., and White, E.: Stormwater detention system parameter sensitivity and uncertainty analysis using SWMM, J. Hydrol. Eng., 21, 05016014, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001382, 2016.
Kobarfard, M., Fazloula, R., Zarghami M., and Akbarpour: Evaluating the uncertainty of urban flood model using glue approach, Urban Water J., 19, 600–615, https://doi.org/10.1080/1573062X.2022.2053865, 2022.
Kotowski, A., Kaźmierczak, B., and Nowakowska, M. A.: Analysis of the drainage system load in case of the predicted increase in frequency and intensity of rain due to climate change, Ochrona Środowiska, 35, 25–32, 2013.
Lama, G. F. C., Crimaldi, M., De Vivo, A., Chirico, G. B., and Sarghini, F.: Eco-hydrodynamic characterization of vegetated flows derived by UAV-based imagery, 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 273–278, https://doi.org/10.1109/MetroAgriFor52389.2021.9628749, 2021a.
Lama, G. F. C., Rillo Migliorini Giovannini, M., Errico, A., Mirzaei, S., Chirico, G. B., and Preti, F.: The impacts of Nature Based Solutions (NBS) on vegetated flows' dynamics in urban areas, 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 3–5 November 2021, Trento-Bolzano, Italy, 58–63, https://doi.org/10.1109/MetroAgriFor52389.2021.9628438), 2021b.
Lei, X., Chen, W., Panahi, M., Falah, F., Rahmati, O., Uuemaa, E., Kalantari, Z., Ferreira, C. S. S., Rezaie, F., Tiefenbacher, J. P., Lee, S., and Bian, H.: Urban flood modeling using deep-learning approaches in Seoul, South Korea, J. Hydrol., 601, 126684, https://doi.org/10.1016/j.jhydrol.2021.126684, 2021.
Lense, G. H. E., Lämmle, L., Ayer, J. E. B., Lama, G. F. C., Rubira, F. G., and Mincato, R. L.: Modeling of Soil Loss by Water Erosion and Its Impacts on the Cantareira System, Brazil. Water, 15, 1490, https://doi.org/10.3390/w15081490, 2023.
Li, X. and Willems, P.: A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction, Water Resour. Res., 56, e2019WR025128, https://doi.org/10.1029/2019WR025128, 2020.
Ma, B., Wu, Z., Hu, C., Wang, H., Xu, H., Yan, D., and Soomro, S.: Process-oriented SWMM real-time correction and urban flood dynamic simulation, J. Hydrol., 605, 127269, https://doi.org/10.1016/j.jhydrol.2021.127269, 2022.
Martins, R., Leandro, J., and Djordjević, S.: Influence of sewer network models on urban flood damage assessment based on coupled 1D/2D models, J. Flood Risk Manag., 11, 717–728, https://doi.org/10.1111/jfr3.12244, 2018.
Mignot, E., Li, X., and Dewals, B.: Experimental modelling of urban flooding: A review, J. Hydrol., 568, 334–342, https://doi.org/10.1016/j.jhydrol.2018.11.001, 2019.
Miller, J., Kim, H., Kjeldsen, T. R., Packman, J., Grebby, S., and Dearden, R.: Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover, J. Hydrol., 515, 59–70, https://doi.org/10.1016/j.jhydrol.2014.04.011, 2014.
Mohammad, L., Bandyopadhyay, L., Sk, R., Mondal, I., Nguyen, T. T., Lama, G. F. C., and Ahn, D. T.: Estimation of agricultural burned affected area using NDVI and dNBR satellite-based empirical models, J. Environ. Manage., 343, 118226, https://doi.org/10.1016/j.jenvman.2023.118226, 2023.
Morio, J.: Global and local sensitivity analysis methods for a physical system, Eur. J. Phys., 32, 1577–1583, https://doi.org/10.1088/0143-0807/32/6/011, 2011.
Petersen, B., Gernaey, K., Henze, M., and Vanrolleghem, P. A.: Evaluation of an ASM1 model calibration procedure on a municipal industrial wastewater treatment plant, J. Hydroinf., 4, 1538, https://doi.org/10.2166/hydro.2002.0003, 2002.
Prodanovic, V., Jamali, B., Kuller, M., Wang, Y., Bach, P. M., Coleman, R. A., Metzeling, L., McCarthy, D. T., Shi, B., Deletic, A.: Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS), Water Sci. Technol., 85, 961969, https://doi.org/10.2166/wst.2022.046, 2022.
Ray, R., Das, A., Hasan, M. S. U., Aldrees, A., Islam, S., Khan, M. A., and Lama, G. F. C.: Quantitative Analysis of Land Use and Land Cover Dynamics using Geoinformatics Techniques: A Case Study on Kolkata Metropolitan Development Authority (KMDA) in West Bengal, India, Remote Sens., 15, 959, https://doi.org/10.3390/rs15040959, 2023.
Razavi, S. and Gupta, H. V.: A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models, Environ. Model. Softw., 114, 1–11, https://doi.org/10.1016/j.envsoft.2018.12.002, 2019.
Reyes-Silva, J. D., Bangura, E., Helm, B., Benisch, J., and Krebs, P.: The Role of Sewer Network Structure on the Occurrence and Magnitude of Combined Sewer Overflows (CSOs), Water, 12, 2675, https://doi.org/10.3390/w12102675, 2020.
Rosenzweig, B. R., Cantis, H., Kim, Y., Cohn, A., Grove, K., Brock, J., Yesuf, J., Mistry, P., Welty, C., McPhearson, T., Sauer, J., and Chang, H.: The value of urban flood modeling, Earth's Future, 9, e2020EF001739, https://doi.org/10.1029/2020EF001739, 2021.
Salman, B. and Salem, O.: Modeling Failure of Wastewater Collection Lines Using Various Section-Level Regression Models, J. Infrastruct. Syst., 18, 146–154, https://doi.org/10.1061/(ASCE)IS.1943-555X.0000075, 2012.
Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., and Shirzadi, A.: Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping, J. Environ. Manage., 217, 1–11, https://doi.org/10.1016/j.jenvman.2018.03.089, 2018.
Shrestha, A., Mascaro, G., and Garcia, M.: Effects of stormwater infrastructure data completeness and model resolution on urban flood modeling, J. Hydrol., 607, 127498, https://doi.org/10.1016/j.jhydrol.2022.127498, 2022.
Siekmann, M. and Pinnekamp, J.: Indicator based strategy to adapt urban drainage systems in regard to the consequences caused by climate change. 12th International Conference on Urban Drainage, 11–16 September 2011, Porto Alegre/Brazil, 2011.
Siekmann, M., Vomberg, N., Mirgartz, M., Pinnekamp, J., and Mühle, S.: Multifunctional Land Use in Urban Spaces to Adapt Urban Infrastructure, in: Climate Change and the Sustainable Use of Water Resources. Climate Change Management, edited by: Leal Filho, W., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-22266-5_37, 2011.
Sonavane N., Rangari, V. A., Waikar, M. L., and Patil, M.: Urban storm-water modeling using EPA SWMM – a case study of Pune city, 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), 8–10 October 2020, Vijiyapur, India, https://doi.org/10.1109/B-HTC50970.2020.9297900, 2020.
Sun, Y., Liu, Ch., Du, X., Yang, F., Yao, Y., Soomro, S., and Hu, C.: Urban storm flood simulation using improved SWMM based on K-means clustering of parameter samples, J. Flood Risk Manag., 15, e12826, https://doi.org/10.1111/jfr3.12826, 2022.
Szeląg, B.: Influence of the Hydrogramme Shape on the Capacity and Selection of Drains of a Small Retention Reservoir, PhD thesis, University of Technology, Kielce, 2013.
Szeląg, B., Kiczko, A., and Dąbek, L.: Sensitivity and uncertainty analysis of hydrodynamic model (SWMM) for storm water runoff forecasting in an urban basin – a case study, Ochr. Sr., 38, 15–22, 2016.
Szeląg, B., Suligowski, R., Studziński, J., and De Paola, F.: Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach, Hydrol. Earth Syst. Sci., 24, 595–614, https://doi.org/10.5194/hess-24-595-2020, 2020.
Szeląg, B., Kiczko, A., Łagód, G., and De Paola, F.: Relationship between rainfall duration and sewer system performance measures within the context of uncertainty, Water Res Manage., 35, 5073–5087, https://doi.org/10.1007/s11269-021-02998-x, 2021a.
Szeląg, B., Suligowski, R., Drewnowski, J., De Paola, F., Fernandez-Morales, F. J., and Bąk, Ł.: Simulation of the number of storm overflows considering changes in precipitation dynamics and the urbanisation of the catchment area: A probabilistic approach, J. Hydrol., 598, 126275, https://doi.org/10.1016/j.jhydrol.2021.126275, 2021b.
Szeląg, B., Majerek, D., Kiczko, A., Łagód, G., Fatone, F., and McGarity, A.: Analysis of sewer network performance in context of modernization: modeling, sensitivity, uncertainty analysis, 12, 148, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001610, 2022a.
Szeląg, B., Suligowski, R., De Paola, F., Siwicki, P., Majerek, D., and Łagód, G.: Influence of urban catchment characteristics and rainfall origins on the phenomenon of stormwater flooding: Case study, Environ. Model. Softw., 150, 105335, https://doi.org/10.1016/j.envsoft.2022.105335, 2022b.
Taromideh, F., Fazloula, R., Choubin, B., Emadi, A., and Berndtsson, R.: Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning, Sustainability, 14, 4483, https://doi.org/10.3390/su14084483, 2022.
Thorndahl, S.: Stochastic long term modelling of a drainage system with estimation of return period uncertainty, Water Sci. Technol., 59, 2331–2339, https://doi.org/10.2166/wst.2009.305, 2009.
Thorndahl, S., Schaarup-Jensen, K., and Jensen, J. B.: Probabilistic modelling of combined sewer overflow using the First Order Reliability Method, Water Sci. Technol., 57, 1337–1344, https://doi.org/10.2166/wst.2008.301, 2008.
Ursino, N.: Reliability analysis of sustainable storm water drainage systems, WIT Transactions on The Built Environment, 139, 149–157, https://doi.org/10.2495/UW140131, 2014.
Venvik, G., Bang-Kittilsen, A., and Boogaard, F. C.: Risk assessment for areas prone to flooding and subsidence: a case study from Bergen, Western Norway, Hydrol. Res., 51, 322-338, https://doi.org/10.2166/nh.2019.030, 2021.
Vorobevskii, I., Al Janabi, F., Schneebeck, F., Bellera, J., and Krebs, P.: Urban Floods: Linking the Overloading of a Storm Water Sewer System to Precipitation Parameters, Hydrology, 7, 35, https://doi.org/10.3390/hydrology7020035, 2020.
Wałek, G.: Wpływ dróg na kształtowanie spływu powierzchniowego w obszarze zurbanizowanym na przykładzie zlewni rzeki Silnicy w Kielcach, Jan Kochanowski University Press, Kielce, 2019 (in Polish).
Wu, J. Y., Thompson, J. R., Kolka, R. K., Franz, K. J., and Stewart, T. W.: Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change, Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, 2013.
Xing, Y., Shao, D., Yang, Y., Ma, X., and Zhang, S.: Influence and interactions of input factors in urban flood inundation modeling: An examination with variance-based global sensitivity analysis, J. Hydrol., 600, 126524, https://doi.org/10.1016/j.jhydrol.2021.126524, 2021.
Yang, Q., Ma, Z., and Zhang, S.: Urban Pluvial Flood Modeling by Coupling Raster-Based Two-Dimensional Hydrodynamic Model and SWMM, Water, 14, 1760, https://doi.org/10.3390/w14111760, 2022.
Yang, Y. and Chui, T. F. M.: Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods, Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, 2021.
Yao, Y., Hu, C., Liu, C., Yang, F., Ma, B. Wu, O. Li, X., and Soomro, S.: Comprehensive performance evaluation of stormwater management measures for sponge city construction: A case study in Gui'an New District, China, J. Flood Risk Manage., 15, e12834, https://doi.org/10.1111/jfr3.12834, 2022.
Zhou, Y., Shen, D., Huang, N., Guo, Y., Zhang, T., and Zhang, Y.: Urban flood risk assessment using storm characteristic parameters sensitive to catchment-specific drainage system, Sci. Total Environ., 659, 1362–1369, https://doi.org/10.1016/j.scitotenv.2019.01.004, 2019.
Short summary
A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
A novel methodology for the development of a stormwater network performance simulator including...
