Articles | Volume 13, issue 10
https://doi.org/10.5194/essd-13-4779-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4779-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
20 Oct 2021
Data description paper |
| 20 Oct 2021
| 20 Oct 2021
The Bellinge data set: open data and models for community-wide urban drainage systems research
Agnethe Nedergaard Pedersen
CORRESPONDING AUTHOR
VCS Denmark, Odense, 5000, Denmark
DTU Environment, DTU, Kongens Lyngby, 2800, Denmark
Jonas Wied Pedersen
DTU Environment, DTU, Kongens Lyngby, 2800, Denmark
Antonio Vigueras-Rodriguez
Department of Civil Engineering, Universidad Politécnica de Cartagena, 30203 Cartagena, Spain
Annette Brink-Kjær
VCS Denmark, Odense, 5000, Denmark
Morten Borup
DTU Environment, DTU, Kongens Lyngby, 2800, Denmark
Peter Steen Mikkelsen
DTU Environment, DTU, Kongens Lyngby, 2800, Denmark
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Cited
17 citations as recorded by crossref.
- All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling A. Pedersen et al. 10.5194/hess-26-5879-2022
- SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere B. Dobson et al. 10.1016/j.envsoft.2025.106358
- Fully automated simplification of urban drainage models on a city scale M. Pichler et al. 10.2166/wst.2024.337
- Information-theoretic sensor placement for large sewer networks G. Crowley et al. 10.1016/j.watres.2024.122718
- Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins A. Pedersen et al. 10.2166/wst.2022.059
- Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows T. Chiaburu & F. Bießmann 10.1016/j.procs.2024.05.091
- A simplified approach for the hydrological simulation of urban drainage systems with SWMM A. Farina et al. 10.1016/j.jhydrol.2023.129757
- What Is the Contribution of Urban Trees to Mitigate Pluvial Flooding? K. Medina Camarena et al. 10.3390/hydrology9060108
- Bayesian Inference for the Urban Drainage Models Uncertainty Quantification Based on Heteroscedastic Residual Assumption T. Yu et al. 10.1007/s11269-025-04176-9
- Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research X. Fei 10.32604/iasc.2023.038607
- A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
- Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment R. Lin et al. 10.1007/s11269-022-03325-8
- Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospects Z. He et al. 10.1016/j.watres.2025.123954
- swmm_api: A Python Package for Automation, Customization, and Visualization in SWMM-Based Urban Drainage Modeling M. Pichler 10.3390/w17091373
- Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models Y. Huang et al. 10.1029/2022WR032440
- A robust method to simultaneously place sensors and calibrate parameters for urban drainage pipe system models using Bayesian decision theory Y. Huang et al. 10.1080/19942060.2025.2473992
- Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al. 10.1029/2021WR031808
16 citations as recorded by crossref.
- All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling A. Pedersen et al. 10.5194/hess-26-5879-2022
- SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere B. Dobson et al. 10.1016/j.envsoft.2025.106358
- Fully automated simplification of urban drainage models on a city scale M. Pichler et al. 10.2166/wst.2024.337
- Information-theoretic sensor placement for large sewer networks G. Crowley et al. 10.1016/j.watres.2024.122718
- Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins A. Pedersen et al. 10.2166/wst.2022.059
- Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows T. Chiaburu & F. Bießmann 10.1016/j.procs.2024.05.091
- A simplified approach for the hydrological simulation of urban drainage systems with SWMM A. Farina et al. 10.1016/j.jhydrol.2023.129757
- What Is the Contribution of Urban Trees to Mitigate Pluvial Flooding? K. Medina Camarena et al. 10.3390/hydrology9060108
- Bayesian Inference for the Urban Drainage Models Uncertainty Quantification Based on Heteroscedastic Residual Assumption T. Yu et al. 10.1007/s11269-025-04176-9
- Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research X. Fei 10.32604/iasc.2023.038607
- A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
- Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment R. Lin et al. 10.1007/s11269-022-03325-8
- Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospects Z. He et al. 10.1016/j.watres.2025.123954
- swmm_api: A Python Package for Automation, Customization, and Visualization in SWMM-Based Urban Drainage Modeling M. Pichler 10.3390/w17091373
- Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models Y. Huang et al. 10.1029/2022WR032440
- A robust method to simultaneously place sensors and calibrate parameters for urban drainage pipe system models using Bayesian decision theory Y. Huang et al. 10.1080/19942060.2025.2473992
1 citations as recorded by crossref.
Latest update: 10 Jul 2025
Short summary
A comprehensive data set from a combined sewer system in a 1.7 km2 suburban area is presented. Up to 10 years of observations (2010–2020) from level sensors, a flow meter, position and power sensors, rain gauges, X- and C-band weather radars, and a weather station; distributed hydrodynamic models; and CCTV pipe network data are included. This will enable independent testing and replication of results from future scientific developments within urban hydrology and urban drainage system research.
A comprehensive data set from a combined sewer system in a 1.7 km2 suburban area is presented....
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