With the focus on microplastic in current research, macroplastic is often not further considered. Thus, this review paper is the first to analyse the entry paths, accumulation zones, and sinks of macroplastic in the aquatic, terrestrial, and atmospheric environment by presenting transport paths and concentrations in the environment as well as related risks. This is done by applying the Source–Pathway–Receptor model on macroplastic in the environment. Based on this model, the life cycle of macroplastic is structurally described, and knowledge gaps are identified. Hence, current research aspects on macroplastic as well as a sound delimitation between macro- and microplastic that can be applied to future research are indicated. The results can be used as basic information for further research and show a qualitative assessment of the impact of macroplastic that ends up in the environment and accumulates there. Furthermore, the applied model allows for the first time a quantitative and structured approach to macroplastic in the environment.

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.

Microplastic particles are found in environmental compartments all over the world and receive a great deal of attention, especially in the aquatic environment. Currently, a particularly high input of microplastics via Asian rivers is assumed, but so far, there are hardly any data through field measurements. Three rivers in South India were considered for this purpose to focus on their microplastic load. The emphasis was on the comparison of microplastic concentrations in urban and rural rivers. While two rivers in the megacity Chennai (Tamil Nadu) were found to have an average microplastic concentration of 0.4 microplastic particles/L, a rural river near Munnar (Kerala) had an average concentration of 0.2 microplastic particles/L. Rough estimates of annual microplastic discharge from the Adyar River (Chennai) into the Bay of Bengal are found to be as high as 11.6 trillion microplastic particles. This study should be one of the first baseline studies for microplastic loads in South Indian streams and should be complemented with further environmental sampling before, during and after the monsoon season to get more detailed information on the storage and transportation of fluvial microplastics under different weather conditions.

Despite water shortages and infrequent rainfall in arid and semi-arid areas, their recent floods show that flooding tends to be more severe and life-threatening. However, flooding is a poorly understood natural hazard in arid and semi-arid areas and its management needs to address a set of challenges, some of which are unique to these zones. Hence, it is of great demand to develop a comprehensive understanding of Flood Risk Management (FRM) difficulties and needs that are significant in these regions. This review paper aimed to understand the current state of FRM in arid and semi-arid areas and to explore challenges existing in FRM of these zones, based on the authors’ experiences from practice-based research. The study identified four main classes of challenges: (i) regional features and flood characteristics, (ii) operational challenges, (iii) technical challenges, and (iv) management/organizational challenges. It was then concluded that these FRM challenges are multidimensional and coupled together. After discussing the challenges, the study addressed requirements and provided an outlook on solutions, further improvements, and possibilities for FRM in these regions.

In times of increasing weather extremes and expanding vulnerable cities, a significant risk to civilian security is posed by heavy rainfall induced flash floods. In contrast to river floods, pluvial flash floods can occur anytime, anywhere and vary enormously due to both terrain and climate factors. Current early warning systems (EWS) are based largely on measuring rainfall intensity or monitoring water levels, whereby the real danger due to urban torrential floods is just as insufficiently considered as the vulnerability of the physical infrastructure. For this reason, this article presents a concept for a risk-based EWS as one integral component of a multi-functional pluvial flood information system (MPFIS). Taking both the pluvial flood hazard as well as the damage potential into account, the EWS identifies the urban areas particularly affected by a forecasted heavy rainfall event and issues object-precise warnings in real-time. Further, the MPFIS performs a georeferenced documentation of occurred events as well as a systematic risk analysis, which at the same time forms the foundation of the proposed EWS. Based on a case study in the German city of Aachen and the event of 29 May 2018, the operation principle of the integrated information system is illustrated.