Assessing and visualising hazard impacts to enhance the resilience of Critical Infrastructures to urban flooding.

The design, construction and maintenance of Critical Infrastructures (CI) is commonly based on standards that are rigorous, so as to withstand any climate or weather-linked pressures. However, due to climate change, climate characteristics may shift, resulting in increased frequency/magnitude of potential failures, or exposure to new unknown risks. As vital components for the normal functioning of modern societies, the resilience of CIs under climate stressors encompasses their structural integrity, their operational elements, and their capacity to maximize business output. In this work, we propose an integrated and participatory methodological approach to enhance the resilience of interconnected CIs to urban flooding under climate change, by assessing the risk and introducing adaptation measures. The main objectives of the proposed methodology and approach are: (i) to provide scientific evidence for better understanding of how future climate regimes might affect normal operation of interconnected CI in urban areas during their lifespan; (ii) to assess the cost-effectiveness of different adaptation measures; (iii) to involve local stakeholders and operators in the co-design of the approach, as well as the assessment and the evaluation of adaptation measures; (iv) to combine computational modelling with advanced 3D visualisation techniques for effectively engaging stakeholders in decision making; (v) to include risk assessment and damage functions co-designed by end-users and local stakeholders; (vi) to integrate all of the aforementioned components in a specifically designed cloud platform as a Decision Support System for end-users, (vii) to validate the DSS by the end users and local stakeholders. The paper presents the computational background and tools. Additionally, it describes a Case Study in Torbay, UK, where the full methodology and the proposed participatory approach have been applied, with all the specifics, i.e., the scenarios of extreme flooding, the numerical and visualisation results, the response of the stakeholders and the evaluation of selected adaptation measures.

Calibration of a 1D/1D urban flood model using 1D/2D model results in the absence of field data.

Recently increased flood events have been prompting researchers to improve existing coupled flood-models such as one-dimensional (1D)/1D and 1D/two-dimensional (2D) models. While 1D/1D models simulate sewer and surface networks using a one-dimensional approach, 1D/2D models represent the surface network by a two-dimensional surface grid. However their application raises two issues to urban flood modellers: (1) stormwater systems planning/emergency or risk analysis demands for fast models, and the 1D/2D computational time is prohibitive, (2) and the recognized lack of field data (e.g. Hunter et al. (2008)) causes difficulties for the calibration/validation of 1D/1D models. In this paper we propose to overcome these issues by calibrating a 1D/1D model with the results of a 1D/2D model. The flood-inundation results show that: (1) 1D/2D results can be used to calibrate faster 1D/1D models, (2) the 1D/1D model is able to map the 1D/2D flood maximum extent well, and the flooding limits satisfactorily in each time-step, (3) the 1D/1D model major differences are the instantaneous flow propagation and overestimation of the flood-depths within surface-ponds, (4) the agreement in the volume surcharged by both models is a necessary condition for the 1D surface-network validation and (5) the agreement of the manholes discharge shapes measures the fitness of the calibrated 1D surface-network.

An analysis of the combined consequences of pluvial and fluvial flooding.

Intense rainfall in urban areas often generates both pluvial flooding due to the limited capacity of drainage systems, as well as fluvial flooding caused by deluges from river channels. The concurrence of pluvial and fluvial flooding can aggravate their (individual) potential damages. To analyse the impact caused by individual and composite type of flooding, the SIPSON/UIM model, an integrated 1D sewer and 2D overland flow was applied to numerical modelling. An event matrix of possible pluvial scenarios was combined with hypothetic overtopping and breaching situations to estimate the surface flooding consequences in the Stockbridge area, Keighley (Bradford, UK). The modelling results identified different flooding drivers in different parts of the study area and showed that the worst scenarios resulted from synthesised events.

An effective multi-objective approach to prioritization of sewer pipe inspection.

The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimization problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK.

Risk- and robustness-based solutions to a multi-objective water distribution system rehabilitation problem under uncertainty .

The water distribution system (WDS) rehabilitation problem is defined here as a multi-objective optimisation problem under uncertainty. Two alternative problem formulations are considered. The first objective in both approaches is to minimise the total rehabilitation cost. The second objective is to either maximise the overall WDS robustness or to minimise the total WDS risk. The WDS robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. Total risk is defined as the sum of nodal risks, where nodal risk is defined as the product of the probability of pressure failure at that node and consequence of such failure. Decision variables are the alternative rehabilitation options for each pipe in the network. The only source of uncertainty is the future water consumption. Uncertain demands are modelled using any probability density functions (PDFs) assigned in the problem formulation phase. The corresponding PDFs of the analysed nodal heads are calculated using the Latin Hypercube sampling technique. The optimal rehabilitation problem is solved using the newly developed rNSGAII method which is a modification of the well-known NSGAII optimisation algorithm. In rNSGAII a small number of demand samples are used for each fitness evaluation leading to significant computational savings when compared to the full sampling approach. The two alternative approaches are tested, verified and their performance compared on the New York tunnels case study. The results obtained demonstrate that both new methodologies are capable of identifying the robust (near) Pareto optimal fronts while making significant computational savings.

Scheduling of water distribution system rehabilitation using structured messy genetic algorithms.

A methodology is presented for the optimal design and scheduling of investment for the rehabilitation of water distribution networks. Based on the evolutionary programming technique known as Structured Messy Genetic Algorithms, the methodology utilizes a multi-objective formulation which improves the evolutionary process and provides nondominated optimal solutions over a range of costs and benefits. The model is applied to an example-a small artificial network of fifteen pipes. The effects on the optimal solutions of varying parameters such as interest rate and inflation rate are also investigated.