Stormwater hazards are a significant threat across the globe. These are continuing to increase in line with urbanisation and climate change, leading to a recognition that the historic paradigm of passive management using centralised infrastructure is insufficient to manage future hazards to our society, environment, and economy. The cross-sector Internet of Things revolution has inspired a new generation of smart stormwater management systems which offer an effective, cost beneficial and adaptive solution to enhance network capacities and reduce hazards. However, despite growing prominence within research, this technology remains under-utilised, in a large part due to fragmented and inconsistent alignment and terminology, obscuring the strategic co-ordination of research. We respond to this through systematically reviewing the terminology, practice and trajectory for smart stormwater management and developing a framework which can be applied to both coordinate and understand the existing research landscape, as well as identifying key research gaps for future development. We find that literature almost universally agrees that smart technology is, or will be, beneficial to stormwater management and that technology has reached partial maturity in terms of quantity management, although this has not yet transferred to water quality. However, research is dominated by proof-of-concept modelling studies, with limited practical application beyond real time control of large assets, individual pilot studies and monitoring. We recommend that future research explores and evidences the substantial benefits likely through expanding current implementation towards a coordinated, decentralised, and optimised catchment-scale approach.

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 multi-disciplinary stakeholders.

While existing studies on sewer networks have explored topics such as surface water inflow, limited research has delved into groundwater infiltration (GWI). This study aims to fill this void by providing a comprehensive overview of quantitative analyses of GWI in sewer networks plus current status, limitations and future perspectives, considering the most relevant peer-reviewed research, including 83 studies. We propose dividing the existing research into two main groups: (1) phreatic zone, and (2) vadose zone. Most research has focused on the latter, mainly considering Rainfall-Derived Inflow and Infiltration (RDII), including surface water inflow and GWI. The ratio of each is not frequently separated; otherwise, there may be some assumptions, e.g. in dry weather and assuming zero surface water inflow. We also divided the employed approaches in different categories from physically-based numerical models, to simpler ones, e.g. water budget analysis. In fact, a combination of approaches may be applied to find the intricate characteristics of 'urban groundwater' or 'urban karst.' The findings revealed a heightened vulnerability of sewer networks to GWI, due to climate change (CC) and its associated repercussions, e.g. sea level rise (SLR), making the coastal cities the most vulnerable regions. In future research, the criticality of pre-emptive measures and monitoring of networks, especially near the coastline, is emphasised to ensure the resilience and adaptability of sewer networks in the context of GWI amid the potential impacts of CC. However, current monitoring practices lack widespread evidence for spatiotemporal analysis of GWI quantity.

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.

Effective management of stormwater systems is necessary for protection of both the built and natural environments. However, stormwater management is facing multiple, growing challenges, including climate change, ageing infrastructure, population growth, urbanisation, environmental concerns, regulatory and institutional changes and public awareness. While the potential of 'smart', internet-of-things enabled stormwater management systems to address these challenges is increasingly being recognised, with considerable evidence in literature for the benefits of more data-driven approaches, implementation to date remains low. This paper, therefore, provides a comprehensive review of the potential barriers to adoption of smarter stormwater management practices that require addressing, and provides a roadmap for real world application. Barriers related to all elements of stormwater management, from the asset sensing to the data analytics and online optimisation, are identified. Technical challenges discussed include the availability and reliability of technologies, technological and physical limitations, decision making, uncertainty and security. Technical barriers are rapidly reducing and there is increasing evidence in the academic literature of the efficacy of smart technologies. However, socio-economic barriers remain a significant challenge, and issues such as trust and lack of confidence, resistance to change, expense, and lack of knowledge and guidance are reviewed. A 'smart stormwater management wheel' that provides a flexible and iterative approach for implementing smart functionality is also presented. Whilst acting as a roadmap, this aims to facilitate a structured methodology for overcoming barriers and benchmarking progress, and may be used to explore trade-offs and relationships between differing levels of implementation for each of the constituent technologies in a smart stormwater system.

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.