Improvement of City Rainfall Model Subcatchment Structure Based on Urban Hydrology Process
Publication: Journal of Hydrologic Engineering
Volume 29, Issue 2
Abstract
This study proposes a hierarchical approach to urban subcatchment division that incorporates topography and road network factors, as well as a subcatchment-pipe network connection strategy. By taking into account all relevant hydrological components and the efficiency of drainage networks, the precision of urban flooding simulation is boosted. The suggested approach makes use of each stormwater well in the subcatchment as a confluence outlet. The intensity variations of flood simulations conducted using the traditional Thiessen polygon method and the proposed method are compared. The current study focused on the area of Panyu District in Guangzhou City, China. Using the InfoWorks ICM model, four comparison scenarios were made to simulate flooding conditions during four return periods. The results suggest the following: (1) While maintaining the production and confluence parameters at a constant level, the simulation results of flooding present discrepancies depending on the methodology used for subcatchment division and confluence connection with the pipe network system. (2) The utilization of the Thiessen polygon method for subcatchment division results in a certain degree of overestimation of the flooding condition as compared to the more comprehensive subcatchment classification. (3) When considering all the collection nodes inside the subcatchment, the estimation of the inundation situation is notably lower compared to when only a single stormwater well is chosen as the subcatchment outlet. In the event of 100-year rainfall, the overall overflow can be decreased by 8.44%. A more accurate model is a solid foundation for mimicking reality. This study revises the urban stormwater model, and the proposed enhancement is applicable to simulations of urban waterlogging in any global region and can guide the development of a more scientific approach to urban water management.
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Data Availability Statement
Some or all data, models that support the findings of this study are available from the corresponding author upon reasonable request. These mainly include the pipe network and land-use type data of the study area. The model parameters and subcatchment division files are also available.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos.: 52279015, 52109018), the Guangdong Basic and Applied Basic Research Foundation (Grant No.: 2022A1515010131), and the Science and Technology Program of Guangzhou, China (Grant No.: 202201010271).
References
Abdi, B., O. Bozorg-Haddad, and X. Chu. 2021. “Uncertainty analysis of model inputs in riverine water temperature simulations.” Sci. Rep. 11 (1): 19908. https://doi.org/10.1038/s41598-021-99371-0.
Bosley, E. K., II. 2008. “Hydrologic evaluation of low impact development using a continuous, spatially-distributed model.” Master thesis, Virginia Polytechnic Institute and State Univ.
Chalfen, M., and A. Niemiec. 1986. “Analytical and numerical solution of Saint-Venant equations.” J. Hydrol. 86 (1): 1–13. https://doi.org/10.1016/0022-1694(86)90002-8.
Cho, K., and Y. Kim. 2022. “Improving streamflow prediction in the WRF-Hydro model with LSTM networks.” J. Hydrol. 605 (Apr): 127297. https://doi.org/10.1016/j.jhydrol.2021.127297.
Dai, Y., L. Chen, and Z. Shen. 2020. “A cellular automata (CA)–Based method to improve the SWMM performance with scarce drainage data and its spatial scale effect.” J. Hydrol. 581 (Feb): 124402. https://doi.org/10.1016/j.jhydrol.2019.124402.
David, S. R., B. P. Murphy, J. A. Czuba, M. Ahammad, and P. Belmont. 2023. “USUAL watershed tools: A new geospatial toolkit for hydro-geomorphic delineation.” Environ. Modell. Software 159 (Jan): 105576. https://doi.org/10.1016/j.envsoft.2022.105576.
Dong, Z., D. J. Bain, M. Akcakaya, and C. A. Ng. 2023. “Evaluating the Thiessen polygon approach for efficient parameterization of urban stormwater models.” Environ. Sci. Pollut. Res. 30 (11): 30295–30307. https://doi.org/10.1007/s11356-022-24162-7.
Elliott, A. H., S. A. Trowsdale, and S. Wadhwa. 2009. “Effect of aggregation of on-site storm-water control devices in an urban catchment model.” J. Hydrol. Eng. 14 (9): 975–983. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000064.
Fassoni-Andrade, A. C., F. M. Fan, W. Collischonn, A. C. Fassoni, and R. C. D. de Paiva. 2018. “Comparison of numerical schemes of river flood routing with an inertial approximation of the Saint Venant equations.” RBRH 23 (Mar): e10. https://doi.org/10.1590/2318-0331.0318170069.
Ghosh, I., and F. L. Hellweger. 2011. “Effects of spatial resolution in urban hydrologic simulations.” J. Hydrol. Eng. 17 (1): 129–137. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000405.
Han, D., and M. Bray. 2006. “Automated Thiessen polygon generation.” Water Resour. Res. 42 (11): 28. https://doi.org/10.1029/2005WR004365.
Hu, P., Q. Zhang, P. Shi, B. Chen, and J. Fang. 2018. “Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors.” Sci. Total Environ. 643 (Dec): 171–182. https://doi.org/10.1016/j.scitotenv.2018.06.197.
Jain, G. V., R. Agrawal, R. J. Bhanderi, P. Jayaprasad, J. N. Patel, P. G. Agnihotri, and B. M. Samtani. 2016. “Estimation of sub-catchment area parameters for storm water management model (SWMM) using geo-informatics.” Geocarto Int. 31 (4): 462–476. https://doi.org/10.1080/10106049.2015.1054443.
Kertesz, R., J. Heaney, and J. Sansalone. 2012. Disaggregated modeling for urban hydrologic controls, 1–11. Reston, VA: ASCE. https://doi.org/10.1061/40927(243)61.
Lai, W., and A. A. Khan. 2018. “Numerical solution of the Saint-Venant equations by an efficient hybrid finite-volume/finite-difference method.” J. Hydrodyn. 30 (2): 189–202. https://doi.org/10.1007/s42241-018-0020-y.
Mahaut, V., and H. Andrieu. 2019. “Relative influence of urban-development strategies and water management on mixed (separated and combined) sewer overflows in the context of climate change and population growth: A case study in Nantes.” Sustainable Cities Soc. 44 (Jan): 171–182. https://doi.org/10.1016/j.scs.2018.09.012.
Mark, O., S. Weesakul, C. Apirumanekul, S. B. Aroonnet, and S. Djordjević. 2004. “Potential and limitations of 1D modelling of urban flooding.” J. Hydrol. 299 (3): 284–299. https://doi.org/10.1016/S0022-1694(04)00373-7.
McCuen, R. H. 1973. “The role of sensitivity analysis in hydrologic modeling.” J. Hydrol. 18 (1): 37–53. https://doi.org/10.1016/0022-1694(73)90024-3.
Park, S. Y., K. W. Lee, I. H. Park, and S. R. Ha. 2008. “Effect of the aggregation level of surface runoff fields and sewer network for a SWMM simulation.” Desalination 226 (1–3): 328–337. https://doi.org/10.1016/j.desal.2007.02.115.
Rashid, I., S. A. Dar, and S. U. Bhat. 2022. “Modelling the hydrological response to urban land-use changes in three wetland catchments of the Western Himalayan region.” Wetlands 42 (7): 64. https://doi.org/10.1007/s13157-022-01593-z.
Sheng, J. G., Y. D. Dan, C. S. Liu, and L. M. Ma. 2012. “Study of simulation in storm sewer system of Zhenjiang urban by infoworks ICM model.” Appl. Mech. Mater. 193–194 (Jun): 683–686. https://doi.org/10.4028/www.scientific.net/AMM.193-194.683.
Sidek, L. M., A. S. Jaafar, W. H. A. W. A. Majid, H. Basri, M. Marufuzzaman, M. M. Fared, and W. C. Moon. 2021. “High-resolution hydrological-hydraulic modeling of urban floods using infoworks ICM.” Sustainability 13 (18): 10259. https://doi.org/10.3390/su131810259.
Singh, M., N. Acharya, S. Jamshidi, J. Jiao, Z.-L. Yang, M. Coudert, Z. Baumer, and D. Niyogi. 2022. “Urban precipitation downscaling using deep learning: A smart city application over Austin, Texas, USA.” Preprint, submitted August 15, 2022. http://arxiv.org/abs/2209.06848.
Stephenson, D. 1989. “Selection of stormwater model parameters.” J. Environ. Eng. 115 (1): 210–220. https://doi.org/10.1061/(ASCE)0733-9372(1989)115:1(210).
Sun, A. Y., and G. Tang. 2020. “Downscaling satellite and reanalysis precipitation products using attention–Based deep convolutional neural nets.” Front. Water 2 (Nov): 536743. https://doi.org/10.3389/frwa.2020.536743.
Sun, N., B. Hong, and M. Hall. 2014. “Assessment of the SWMM model uncertainties within the generalized likelihood uncertainty estimation (GLUE) framework for a high-resolution urban sewershed.” Hydrol. Process. 28 (6): 3018–3034. https://doi.org/10.1002/hyp.9869.
Sun, Y., C. Liu, X. Du, F. Yang, Y. Yao, S. Soomro, and C. Hu. 2022. “Urban storm flood simulation using improved SWMM based on K-means clustering of parameter samples.” J. Flood Risk Manage. 15 (4): e12826. https://doi.org/10.1111/jfr3.12826.
Tanaka, T., H. Yoshioka, S. Siev, H. Fujii, L. Sarann, and C. Yoshimura. 2018. “Performance comparison of the three numerical methods to discretize the local inertial equation for stable shallow water computation.” In Proc., Methods and Applications for Modeling and Simulation of Complex Systems: 18th Asia Simulation Conf. AsiaSim 2018, edited by L. Li, K. Hasegawa, and S. Tanaka, 451–465. Singapore: Springer.
Teweldebrhan, A. T., J. F. Burkhart, and T. V. Schuler. 2018. “Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches.” Hydrol. Earth Syst. Sci. 22 (9): 5021–5039. https://doi.org/10.5194/hess-22-5021-2018.
Wang, W., W. Chen, and G. Huang. 2021. “Urban stormwater modeling with local inertial approximation form of shallow water equations: A comparative study.” Int. J. Disaster Risk Sci. 12 (5): 745–763. https://doi.org/10.1007/s13753-021-00368-0.
Yang, C., M. Xu, S. Kang, C. Fu, and D. Hu. 2023. “Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin.” J. Hydrol. 625 (Jun): 129990. https://doi.org/10.1016/j.jhydrol.2023.129990.
Yang, Y., L. Sun, R. Li, J. Yin, and D. Yu. 2020. “Linking a storm water management model to a novel two-dimensional model for urban pluvial flood modeling.” Int. J. Disaster Risk Sci. 11 (4): 508–518. https://doi.org/10.1007/s13753-020-00278-7.
Zaghloul, N. A. 1981. “SWMM model and level of discretization.” J. Hydraul. Div. 107 (11): 1535–1545. https://doi.org/10.1061/JYCEAJ.0005768.
Zhao, D., J. Chen, H. Wang, and Q. Tong. 2012. “Application of a sampling based on the combined objectives of parameter identification and uncertainty analysis of an urban rainfall-runoff model.” J. Irrig. Drain. Eng. 139 (1): 66–74. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000522.
Zhou, Y., Z. Cui, K. Lin, S. Sheng, H. Chen, S. Guo, and C.-Y. Xu. 2022. “Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques.” J. Hydrol. 604 (Jan): 127255. https://doi.org/10.1016/j.jhydrol.2021.127255.
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© 2024 American Society of Civil Engineers.
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Received: May 15, 2023
Accepted: Nov 1, 2023
Published online: Jan 9, 2024
Published in print: Apr 1, 2024
Discussion open until: Jun 9, 2024
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