Modelling and evaluation approach for Sustainable Drainage Systems long-term planning at a catchment scale
File(s)
Author(s)
Muhandes
Type
Thesis
Abstract
Sustainable Drainage Systems (SuDS) can significantly reduce surface water flooding and Combined Sewer Overflows (CSOs) incidents, but modelling their impact is challenging due to the complexity and scale of drainage systems. This thesis investigates three key SuDS-related topics to enhance understanding of their role in managing urban flooding and CSOs at a catchment level.
Firstly, the hydraulic benefits of SuDS have been underestimated due to reliance on synthetic rainfall for assessments. Secondly, while long-term continuous simulations provide more realistic results, they are computationally intensive, a challenge mitigated through spatial aggregation of catchment models. Finally, fully aggregated models overlook crucial temporal CSO patterns and spatial SuDS interventions, necessitating a new emulation approach that preserves the location of compartments at a street or neighbourhood level.
A literature review highlights that continuous rainfall reduces modelling uncertainty compared to design storms by better representing real storm dynamics. To address computational challenges associated with the use of continuous rainfall, a novel modification method is developed to reduce design rainfall hydraulic modelling uncertainty, alongside a clustering algorithm for spatial aggregation that maintains key catchment parameters. The impact of spatial aggregation on hydraulic outcomes is evaluated, recommending its use when time-varying details are less critical.
In addition, a novel emulation algorithm is introduced, capturing both temporal CSO dynamics and the spatial influence of SuDS interventions by recognising catchment hydraulic breakpoints. The findings demonstrate that while the proposed modification method reduces the uncertainty associated with design rainfall, understanding the effectiveness of SuDS requires long-term continuous rainfall data. This necessitates advanced emulation techniques to maintain simulation accuracy without the computational burden of detailed catchment models.
Firstly, the hydraulic benefits of SuDS have been underestimated due to reliance on synthetic rainfall for assessments. Secondly, while long-term continuous simulations provide more realistic results, they are computationally intensive, a challenge mitigated through spatial aggregation of catchment models. Finally, fully aggregated models overlook crucial temporal CSO patterns and spatial SuDS interventions, necessitating a new emulation approach that preserves the location of compartments at a street or neighbourhood level.
A literature review highlights that continuous rainfall reduces modelling uncertainty compared to design storms by better representing real storm dynamics. To address computational challenges associated with the use of continuous rainfall, a novel modification method is developed to reduce design rainfall hydraulic modelling uncertainty, alongside a clustering algorithm for spatial aggregation that maintains key catchment parameters. The impact of spatial aggregation on hydraulic outcomes is evaluated, recommending its use when time-varying details are less critical.
In addition, a novel emulation algorithm is introduced, capturing both temporal CSO dynamics and the spatial influence of SuDS interventions by recognising catchment hydraulic breakpoints. The findings demonstrate that while the proposed modification method reduces the uncertainty associated with design rainfall, understanding the effectiveness of SuDS requires long-term continuous rainfall data. This necessitates advanced emulation techniques to maintain simulation accuracy without the computational burden of detailed catchment models.
Version
Open Access
Date Issued
2024-09-24
Date Awarded
01/08/2025
License URL
Advisor
Mijic, Ana
Publisher Department
Department of Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)