Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4279-2022
© Author(s) 2022. 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-26-4279-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2022
Research article |
| 22 Aug 2022
| 22 Aug 2022
An algorithm for deriving the topology of belowground urban stormwater networks
Taher Chegini
Department of Civil and Environmental Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston, TX 77204, USA
Department of Civil and Environmental Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston, TX 77204, USA
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Cited
18 citations as recorded by crossref.
- Time-guided convolutional neural networks for spatiotemporal urban flood modelling Z. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.132250
- Integrating urban water fluxes and moving beyond impervious surface cover: A review C. Oswald et al. https://doi.org/10.1016/j.jhydrol.2023.129188
- Enhancing 2D hydrodynamic flood models through machine learning and urban drainage integration H. Taysi et al. https://doi.org/10.1016/j.jhydrol.2025.133258
- Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization Q. Zhong et al. https://doi.org/10.1016/j.jhydrol.2024.131766
- Urban blocks enable data-reduced, hydraulically sound planning for combined sewer overflow mitigation D. Despot et al. https://doi.org/10.1016/j.wroa.2025.100466
- SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere B. Dobson et al. https://doi.org/10.1016/j.envsoft.2025.106358
- Enhancing urban pluvial flood modeling through graph reconstruction of incomplete sewer networks R. Li et al. https://doi.org/10.5194/hess-29-5677-2025
- Evaluation of drainage efficiency via street inlets under the influence of terrain slope in the course of pluvial urban flood event Y. Xing et al. https://doi.org/10.1016/j.ejrh.2025.102243
- A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment K. Devi et al. https://doi.org/10.1038/s41598-025-13025-z
- Filling data gaps in urban drainage networks: An automated graph theory framework for data collection and reconstruction M. Hajibabaei et al. https://doi.org/10.1016/j.watres.2025.124272
- Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability Z. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.131903
- City-scale high-resolution flood nowcasting based on high-performance hydrodynamic modelling B. Dong et al. https://doi.org/10.1016/j.ijdrr.2025.105584
- A new algorithm for generation of urban underground stormwater networks and its application for enhanced urban flood simulation J. Yang et al. https://doi.org/10.1016/j.jhydrol.2025.133571
- Reconstructing Sewer Network Topology Using Graph Theory B. Haydar et al. https://doi.org/10.3390/w18020222
- SWMManywhere: Synthesise Urban Drainage Network Models Anywhere in the World B. Dobson et al. https://doi.org/10.21105/joss.07729
- Increasing the fidelity of hyperlocal simulations of urban pluvial flooding through street flooding observations S. Annis et al. https://doi.org/10.1016/j.advwatres.2026.105223
- Virtual testbed for multi-risk assessment: defining RETURNVILLEs to support the analysis and testing of DRM and CCA solutions in realistic urban contexts M. Polese et al. https://doi.org/10.1016/j.ijdrr.2026.106230
- The influence of road network topology on street flooding in New York City—A social media data approach C. ZUO et al. https://doi.org/10.1016/j.jhydrol.2024.131471
18 citations as recorded by crossref.
- Time-guided convolutional neural networks for spatiotemporal urban flood modelling Z. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.132250
- Integrating urban water fluxes and moving beyond impervious surface cover: A review C. Oswald et al. https://doi.org/10.1016/j.jhydrol.2023.129188
- Enhancing 2D hydrodynamic flood models through machine learning and urban drainage integration H. Taysi et al. https://doi.org/10.1016/j.jhydrol.2025.133258
- Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization Q. Zhong et al. https://doi.org/10.1016/j.jhydrol.2024.131766
- Urban blocks enable data-reduced, hydraulically sound planning for combined sewer overflow mitigation D. Despot et al. https://doi.org/10.1016/j.wroa.2025.100466
- SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere B. Dobson et al. https://doi.org/10.1016/j.envsoft.2025.106358
- Enhancing urban pluvial flood modeling through graph reconstruction of incomplete sewer networks R. Li et al. https://doi.org/10.5194/hess-29-5677-2025
- Evaluation of drainage efficiency via street inlets under the influence of terrain slope in the course of pluvial urban flood event Y. Xing et al. https://doi.org/10.1016/j.ejrh.2025.102243
- A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment K. Devi et al. https://doi.org/10.1038/s41598-025-13025-z
- Filling data gaps in urban drainage networks: An automated graph theory framework for data collection and reconstruction M. Hajibabaei et al. https://doi.org/10.1016/j.watres.2025.124272
- Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability Z. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.131903
- City-scale high-resolution flood nowcasting based on high-performance hydrodynamic modelling B. Dong et al. https://doi.org/10.1016/j.ijdrr.2025.105584
- A new algorithm for generation of urban underground stormwater networks and its application for enhanced urban flood simulation J. Yang et al. https://doi.org/10.1016/j.jhydrol.2025.133571
- Reconstructing Sewer Network Topology Using Graph Theory B. Haydar et al. https://doi.org/10.3390/w18020222
- SWMManywhere: Synthesise Urban Drainage Network Models Anywhere in the World B. Dobson et al. https://doi.org/10.21105/joss.07729
- Increasing the fidelity of hyperlocal simulations of urban pluvial flooding through street flooding observations S. Annis et al. https://doi.org/10.1016/j.advwatres.2026.105223
- Virtual testbed for multi-risk assessment: defining RETURNVILLEs to support the analysis and testing of DRM and CCA solutions in realistic urban contexts M. Polese et al. https://doi.org/10.1016/j.ijdrr.2026.106230
- The influence of road network topology on street flooding in New York City—A social media data approach C. ZUO et al. https://doi.org/10.1016/j.jhydrol.2024.131471
Saved (final revised paper)
Latest update: 03 Jul 2026
Short summary
Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. However, they are often not available for urban flood modeling at regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available aboveground data at large scales based on graph theory. The algorithm has been validated in different urban areas; thus, it is well transferable.
Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in...
