In the United Kingdom, decision-makers use hydraulic model outputs to inform funding, connection consent, adoption of new drainage networks, and planning applications.Current practice requires application of design storms to calculate sewer catchment performance metrics, such as flood volume, discharge rate, and flood count. With flooding incidents occurring more frequently than their designs specify (1 in 30-years), hydraulic modelling outputs required by practice are questionable. In this paper, the main focus is on the peakedness factor (ratio of maximum to average rainfall intensity) of design storms, adjudging that it is a key contributor to model bias. Hydraulic models of two UK sewer catchments are simulated under historical storms, design storms, and design storms with modified peakedness to test bias in modelling outputs and the effectiveness of peakedness modification in reducing the bias. Sustainable Drainage Systems (SuDS) has been implemented at catchment scale and the betterment achieved in the modelling outputs is tested. The proposed design storm modification reduces the bias that occurs when driving hydraulic models using design storms in comparison to historical storms. It is concluded that SuDS benefits are underestimated when using design rainfall because the synthetic rainfall shape prevents infiltration. Thus, SuDS interventions cannot accurately be evaluated by design storms, modified or otherwise.
Hydraulically based models are used to simulate how sewer networks of urban catchments will respond to precipitation events (Salvadore et al., 2015). These models enable planners to design interventions that might resolve current network issues, and to plan for changes in the urban catchment. However, the models also require the network and catchment to be represented at a high temporal resolution with a fixed spatial representation, resulting in accurate simulations but lengthy simulation times and a lack of flexibility in modeling options. As regulations, such as the UK's newly introduced "Drainage and Wastewater Management Planning," require more computationally expensive applications of sewer network models, such as optimization, real-time control, or scenario analysis to explore the impacts of, for example, changes in climate and land cover (Water, 2019), it is increasingly clear that alternative and more flexible approaches are needed to complement traditional high-fidelity sewer network modeling.
Highlights:-Automatic graph partitioning can flexibly reduce the complexity of sewer networks to enable surrogate modelling -CityWat-SemiDistributed can model these reduced networks without needing parameter derivation from high-fidelity simulations -The combined approach provides computationally cheap simulations and performs accurately even when no high-fidelity model is available
The role of Sustainable Drainage Systems (SuDS) in reducing combined sewer overflows (CSOs) and flood volumes can be accurately assessed using the available high-fidelity sewer network modelling software packages in the market. However, these tools are too slow for a range of modern applications such as optimisation or uncertainty analysis where long-term climate projection simulations are required. In this study, we create a novel representation of combined sewer systems to enhance an existing spatially aggregated model (CityWat) with additional functionalities to assess flood volumes, discharge to rivers and CSOs. We validate the developed model (CityWatStorm) by comparing the simulation results with a high-fidelity InfoWorks ICM model. Finally, we implement SuDS at a city scale and assess the betterment achieved in the context of flood volumes and CSOs. We conclude that CityWatStorm is able to capture the SuDS betterment within 95% accuracy, and the total flood volume and CSOs with an accuracy ranging from 78 to 83%. This makes the aggregated model suitable for a wide range of applications such as sensitivity analysis of catchment interventions for long-term planning under future uncertainties.
<p>Graph partitioning algorithms separate nodes of a graph into clusters, resulting in a smaller graph that maintains the connectivity of the original. In this study we use graph partitioning to produce reduced complexity sewer networks that can be simulated by a novel urban hydrology model. We compare a variety of algorithms, including spatial clustering, spectral clustering, heuristic methods and we propose two novel methods. We show that the reduced network that is produced can provide accurate simulations in a fraction of the time (100-1000x speed up) of typical urban hydrology models. We address some likely use cases for this approach. The first is enabling a user to pre-specify the desired size of the resultant network, and thus the fidelity and speed of simulation. The second is enabling a user to preserve desired locations that must remain in their own cluster, for example, locations with complex hydraulic structures or where monitoring data exists. The third is a case where detailed sewer network data is not available and instead the network must be simulated hundreds of times in a random sampling of network parameters, something that is only possible with the speed gains that our method allows. We envisage that this reduced complexity approach to urban hydrology will transform how we operate and manage sewer systems, enabling a far wider range of model applications than are currently possible, including optimisation and scenario analysis.</p>
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