The management of urban stormwater has become increasingly complex over recent decades. Consequently, terminology describing the principles and practices of urban drainage has become increasingly diverse, increasing the potential for confusion and miscommunication. This paper documents the history, scope, application and underlying principles of terms used in urban drainage and provides recommendations for clear communication of these principles. Terminology evolves locally and thus has an important role in establishing awareness and credibility of new approaches and contains nuanced understandings of the principles that are applied locally to address specific problems. Despite the understandable desire to have a ‘uniform set of terminology’, such a concept is flawed, ignoring the fact that terms reflect locally shared understanding. The local development of terminology thus has an important role in advancing the profession, but authors should facilitate communication between disciplines and between regions of the world, by being explicit and accurate in their application.

While the practice of rainwater harvesting (RWH) can be traced back millennia, the degree of its modern implementation varies greatly across the world, often with systems that do not maximize potential benefits. With a global focus, the pertinent practical, theoretical and social aspects of RWH are reviewed in order to ascertain the state of the art. Avenues for future research are also identified. A major finding is that the degree of RWH systems implementation and the technology selection are strongly influenced by economic constraints and local regulations. Moreover, despite design protocols having been set up in many countries, recommendations are still often organized only with the objective of conserving water without considering other potential benefits associated with the multiple-purpose nature of RWH. It is suggested that future work on RWH addresses three priority challenges. Firstly, more empirical data on system operation is needed to allow improved modelling by taking into account multiple objectives of RWH systems. Secondly, maintenance aspects and how they may impact the quality of collected rainwater should be explored in the future as a way to increase confidence on rainwater use. Finally, research should be devoted to the understanding of how institutional and socio-political support can be best targeted to improve system efficacy and community acceptance.

Flooding can cause major disruptions in cities, and lead to significant impacts on people, the economy and on the environment. These impacts may be exacerbated by climate and socio-economic changes. Resilience thinking has become an important way for city planners and decision makers to manage flood risks. Despite different definitions of resilience, a consistent theme is that flood resilient cities are impacted less by extreme flood events. Therefore, flood risk professionals and planners need to understand flood impacts to build flood resilient cities. This paper presents a state-of-the-art literature review on flood impact assessment in urban areas, detailing their application, and their limitations. It describes both techniques for dealing with individual categories of impacts, as well as methodologies for integrating them. The paper will also identify future avenues for progress in improving the techniques.

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Building resilience in urban drainage systems requires consideration of a wide range of threats that contribute to urban flooding. Existing hydraulic reliability based approaches have focused on quantifying functional failure caused by extreme rainfall or increase in dry weather flows that lead to hydraulic overloading of the system. Such approaches however, do not fully explore the full system failure scenario space due to exclusion of crucial threats such as equipment malfunction, pipe collapse and blockage that can also lead to urban flooding. In this research, a new analytical approach based on global resilience analysis is investigated and applied to systematically evaluate the performance of an urban drainage system when subjected to a wide range of structural failure scenarios resulting from random cumulative link failure. Link failure envelopes, which represent the resulting loss of system functionality (impacts) are determined by computing the upper and lower limits of the simulation results for total flood volume (failure magnitude) and average flood duration (failure duration) at each link failure level. A new resilience index that combines the failure magnitude and duration into a single metric is applied to quantify system residual functionality at each considered link failure level. With this approach, resilience has been tested and characterised for an existing urban drainage system in Kampala city, Uganda. In addition, the effectiveness of potential adaptation strategies in enhancing its resilience to cumulative link failure has been tested.