Living with Urban Flooding: A Continuous Learning Process for Local Municipalities and Lessons Learnt from the 2021 Events in Germany

In 2021, heavy precipitation events in Germany have confirmed once again that pluvial flooding can cause catastrophic damage in large, medium, and small cities. However, despite several hazard-oriented strategies already in place, to date there is still a lack of integrated approaches to actually preventing negative consequences induced by heavy rainfall events. Furthermore, municipalities across the world are still learning from recent episodes and there is a general need to explore new techniques and guidelines that could help to reduce vulnerability, and enhance the resilience, adaptive capacity, and sustainability of urban environments, considering the already predicted future challenges associated with climate variability. To address this gap, this paper presents the outcomes of the research project “Heavy Rainfall Checklist for Sewer Operation” which was conducted by IKT Institute for Underground Infrastructure, to involve all the stakeholders affected by pluvial flooding within cities, and implement a series of documents that can be adopted by municipalities across the world to support organizations and their operational staff in preventing problems caused by heavy rainfall incidents. More in detail, three different rainfall scenarios have been deeply analysed, and for each of them a list of specific tasks and suggestions has been provided for aiding decision-making.

Efficient Hazard Assessment for Pluvial Floods in Urban Environments: A Benchmarking Case Study for the City of Berlin, Germany

The presence of impermeable surfaces in urban areas hinders natural drainage and directs the surface runoff to storm drainage systems with finite capacity, which makes these areas prone to pluvial flooding. The occurrence of pluvial flooding depends on the existence of minimal areas for surface runoff generation and concentration. Detailed hydrologic and hydrodynamic simulations are computationally expensive and require intensive resources. This study compared and evaluated the performance of two simplified methods to identify urban pluvial flood-prone areas, namely the fill–spill–merge (FSM) method and the topographic wetness index (TWI) method and used the TELEMAC-2D hydrodynamic numerical model for benchmarking and validation. The FSM method uses common GIS operations to identify flood-prone depressions from a high-resolution digital elevation model (DEM). The TWI method employs the maximum likelihood method (MLE) to probabilistically calibrate a TWI threshold (τ) based on the inundation maps from a 2D hydrodynamic model for a given spatial window (W) within the urban area. We found that the FSM method clearly outperforms the TWI method both conceptually and effectively in terms of model performance.

floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time

Using machine learning for pluvial flood prediction tasks has gained growing attention in the past years. In particular, data-driven models using artificial neuronal networks show promising results, shortening the computation times of physically based simulations. However, recent approaches have used mainly conventional fully connected neural networks which were (a) restricted to spatially uniform precipitation events and (b) limited to a small amount of input data. In this work, a deep convolutional generative adversarial network has been developed to predict pluvial flooding caused by nonlinear spatial heterogeny rainfall events. The model developed, floodGAN, is based on an image-to-image translation approach whereby the model learns to generate 2D inundation predictions conditioned by heterogenous rainfall distributions—through the minimax game of two adversarial networks. The training data for the floodGAN model was generated using a physically based hydrodynamic model. To evaluate the performance and accuracy of the floodGAN, model multiple tests were conducted using both synthetic events and a historic rainfall event. The results demonstrate that the proposed floodGAN model is up to 106 times faster than the hydrodynamic model and promising in terms of accuracy and generalizability. Therefore, it bridges the gap between detailed flood modelling and real-time applications such as end-to-end early warning systems.

Comprehensive Understanding the Disaster-Causing Mechanism, Governance Dilemma and Targeted Countermeasures of Urban Pluvial Flooding in China

Urban pluvial flooding in China has become one of the major challenges for sustainable development. This paper analyzes the impact of climate change, urbanization, and integrated disaster drivers on urban pluvial flooding hazards, starting from the disaster-causing mechanisms of urban pluvial flooding in China. This paper then analyzes the main features and progress of urban pluvial flooding governance in China. In particular, this paper describes the progress of sponge cities in China. On the basis of the above contents, this paper describes three manifestations of the fragmentation dilemma at the level of governance, namely, fragmentation in value integration due to conflicting management orders and service values, fragmentation in resource and power allocation due to the lack of vertical top-level design and blurred horizontal departmental management boundaries, and fragmentation in policy formulation and implementation due to outdated urban flood control standards and interdepartmental information compartmentalization. In response to the fragmentation dilemma in urban pluvial flooding management in China, this paper introduces the concept of holistic governance and clarifies the path of urban waterlogging management, i.e., forming a collaborative and diversified governance subjects, deeply optimizing the organizational structure of urban waterlogging management, creating a mature information-based governance platform, and improving the legal and rule of law construction model. This paper is informative for understanding the governance of urban pluvial flooding in China from a government-led management level.

Reliability Assessment in Wireless Apparatus Using LoRa and Sigfox in Catch Basin

The importance of preventing damage from pluvial flooding has been increasing under global climate change. The discovery of premonitory symptoms of pluvial flooding enables effective evacuation and inundation prevention activities. However, apparatuses that automatically detect this in real time are not widespread. There are difficulties in the cost of installing them and the agreements made by the parties concerned, especially in cities. To solve this problem, we devised an apparatus to be installed inside a catch basin that detects its water level. The water level in the catch basin may indicate a sign of pluvial flooding, and the number of people involved in operating the catch basin is smaller than that of facilities on the ground. In order to reduce the cost of installation and operation, we adopted Low Power Wide Area (LPWA), which is a communication method that enables wireless transmission of detected information over long distances for a long time using batteries. So far, for catch basins, a wireless transmission experiment was conducted using LoRa, which is part of LPWA. However, Sigfox, which uses the same frequency as LoRa but has a different wireless system, has not been verified. In this study, the reliability of wireless communication was assessed by apparatuses using LoRa and Sigfox side by side in each catch basin in two places in a densely populated city. The number of experiment days and transmissions differed depending on the apparatus, with the number of days ranging from 97–151 and the number of transmissions from 2328–3748. The reliability in the experiment ranged from 99.97–99.53%. The experimental results showed that wireless transmission was possible with high reliability using either the LoRa or Sigfox system from inside these catch basins. This study expands the options for communication infrastructure that can be used for apparatuses that detect premonitory symptoms of pluvial flooding. This will enable a reduction in installation costs and will expand the range of areas of potential installation.

Special Issue on Actions Toward Futuristic Urban Flood Risk Research and Management

As rainfalls exceeding the designed level have increased, so has damage associated with pluvial flooding. Typhoon Hagibis, which swept Japan in 2019, left 140 municipalities in 15 prefectures scarred from flooding. The sewage networks damaged by the typhoon affected civic life by paralyzing urban functions, raising concerns in urban flood risk and management. Increases in heavy rainfall events associated with global climate change are expected to increase damage from pluvial flooding, thereby necessitating reviews of current urban flood risk management for the purpose of making further improvements against future threats. As we enter an era of frequent urban flooding, it is vitally important that we prepare for urban flood risk management by sharing scientific and technological knowledge among academics, private companies, and administrators. In this context, the current issue is a compilation of contemporary research studies in academia, technological advances in private companies, and practical applications in public administrations in Japan. The works include: the application of urban flood modeling in safe evacuation strategies, the assessment of economic loss, and the impact of climate change; state of the art technologies for urban flood management with the Internet of Things (IoT) and Internet Communication Technology (ICT), Unmanned Aerial Vehicles (UAV), and the next generation of weather radars; and best practices for flood countermeasures, based on knowledge and experience from historical flooding and applied in prefectural governments and local municipalities. We are grateful to all the authors and reviewers who contributed to this special issue, and we hope that it may internationally enhance knowledge-exchange in preparation for growing urban flood risks.