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Updated: Jun 13, 2026
13:48
Design and Construction of an Urban Runoff Research Facility
Published on: August 8, 2014
Sensitivity analysis of surface runoff generation in urban flood forecasting
N E Simões1, J P Leitão, C Maksimović
1Department of Civil and Environmental Engineering, Imperial College, London, United Kingdom. nsimoes@imperial.ac.uk
Summary
This study compares 1D overland flow networks generated using different sewer network simplification levels. Results show that sewer network skeletonization significantly impacts overland flow network characteristics, crucial for accurate pluvial flood forecasting.
Area of Science:
- Hydraulic Engineering
- Urban Hydrology
- Computational Fluid Dynamics
Background:
- Accurate pluvial flood forecasting is essential for operational responses.
- 1D/1D hydraulic models offer computational advantages over 2D models for speed.
- Sewer network simplification can affect overland flow network generation.
Purpose of the Study:
- To compare overland flow networks generated with varying sewer network skeletonization levels.
- To evaluate the sensitivity of overland flow network characteristics to sewer network simplification.
- To assess the impact of sewer network simplification on pluvial flood modeling.
Main Methods:
- Development of a tool by the Urban Water Research Group (UWRG) to generate 1D overland flow networks.
- Systematic simplification of a 1D sewer network model.
- Generation and comparison of multiple overland flow networks based on different sewer network skeletonization levels.
- Sensitivity analysis conducted in a catchment area in Coimbra, Portugal.
Main Results:
- Overland flow network characteristics, including ponds and flow pathways, are dependent on the number of sewer manholes and inlets.
- Different levels of sewer network skeletonization result in distinct overland flow networks.
- The study quantifies the impact of simplification on network features.
Conclusions:
- Sewer network skeletonization is a critical factor influencing the accuracy of generated overland flow networks.
- The findings are vital for optimizing computational efficiency in pluvial flood forecasting models.
- Further research should explore the implications of these findings for real-time flood prediction systems.
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