Green infrastructure strategies are often cited as best practice for urban water management; however, limited research has been undertaken to compare intervention effectiveness during moderate to extreme intensity rainfall events which are typically responsible for surface water flooding. This research responds to this through applying a cellular automata-based rapid scenario screening framework to predict the flood management performance of green infrastructure strategies across an urban catchment in Melbourne City Centre (Australia). Key findings indicate an intensive application of green infrastructure could substantially reduce flood depth and velocity in the catchment but that residual risk remains, particularly during extreme flood events. The best performing intervention strategy in the study area was found to be catchment-wide decentralised rainwater capture. The research also evidences the utility of rapid scenario screening tools to complement existing flood modelling approaches through screening management strategies, exploring scenarios and engaging a wide range of multidisciplinary stakeholders.

Surface-water flooding in urban areas has become a pressing issue due to changing precipitation patterns, expanding urban areas and ageing drainage infrastructure. Selection of flood-management options for widespread implementation using quantitative performance measures is both technically and computationally demanding, which limits the evidence available for decision support. This study presents a new framework for surface-water flood-intervention assessment at high resolution. The framework improves computational efficiency through utilisation of accessible data, simplified representations of interventions and a resource efficient cellular automata flood model. The advantages of this framework are demonstrated through an example case study where the performance of 12 high-level intervention strategies has been evaluated. Results from the case study demonstrate that the framework is able to provide quantitative performance values for a range of interventions. The speed of analysis supports the application of the framework as a decision-making tool for urban water planning.

This paper outlines the work carried out within the RESCCUE (RESilience to cope with Climate Change in Urban ArEas) project that is, in part, examining the impacts of climate-driven hazards on critical services and infrastructures within cities. In this paper, we examined the methods employed to assess the impacts of pluvial flooding events for varying return periods for present-day (Baseline) and future Climate Change with no adaptation measures applied (Business as Usual) conditions on traffic flows within cities. Two cities were selected, Barcelona and Bristol, with the former using a meso-scale and the latter a micro-scale traffic model. The results show how as the severity of flooding increases the disruption/impacts on traffic flows increase and how the effects of climate change will increase these impacts accordingly.Previous works have investigated both the risks and impacts of flooding poses to the transport sector such as the combined interactions of flood depths and flow velocities on vehicular stability [10,11], the relationship between vehicular speed and standing water depths on road surfaces [2,[12][13][14] and the significance of which roads within a network are flooded [15].This paper investigates the impacts on traffic of pluvial flood events in two European cities (Barcelona and Bristol) via linking flood model outputs with traffic models and examine how the magnitude of these impacts could change in the future with respect to climate change model predictions. Materials and MethodsPrevious work by Pyatkova et al. [12,13] demonstrated the use of loosely coupling flood model outputs with micro-simulation traffic model inputs as a means simulating and assessing the impacts of flood events upon traffic flows.The approach proposed here utilizes maximum flood-depth data derived from flood mapping as the criteria for determining the properties of individual road sections at various timings during the traffic model period to simulate effects of flooding to a transportation network. Figure 1 shows conceptually how the flood model outputs are utilized as a means for preparing the traffic model inputs to simulate the effects of flooding.Sustainability 2020, 12, x FOR PEER REVIEW 2 of 17

Keywords cellular automata flood model; decision support; 2D flood modelling; flood risk management; surface water management plan; urban flooding. Correspondence AbstractThis research evaluates performance of a rapid assessment framework for screening surface water flood risk in urban catchments. Recent advances in modelling have developed fast and computationally efficient cellular automata frameworks which demonstrate promising utility for increasing available evidence to support surface water management, however, questions remain regarding trade-offs between accuracy and speed for practical application. This study evaluates performance of a rapid assessment framework by comparing results with outputs from an industry standard hydrodynamic model using a case study of St Neots in Cambridgeshire, UK. Results from the case study show that the rapid assessment framework is able to identify and prioritise areas of flood risk and outputs flood depths which correlate above 97% with the industry standard approach. In theory, this finding supports a simplified representation of catchments using cellular automata, and in practice presents an opportunity to apply the framework to develop evidence to support detailed modelling.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.