Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate that the most important factor affecting river pollution is the dry period followed by average rainfall intensity, maximum rainfall in 10 min, total amount of rainfall, and initial runoff intensity. In addition, an artificial neural network model was developed to predict the event mean concentration (EMC) of COD in the river based on the correlations between rainfall characteristics and EMC. Compared to under light rain (< 10 mm/day), the predicted EMC was five times lower under heavy rain (25–49.9 mm/day) and two times lower under moderate rain (10–24.9 mm/day). By converting the EMC to chemical oxygen demand in the river, the pollution load under non-point-source runoff was estimated to be 497.6 t/year (with an accuracy of 95.98%) in Shiyan River under typical rainfall characteristics. The results of this study can be used to guide urban rainwater utilization and engineering design in Shenzhen. The findings also provide insights for predicting the risk of rainfall-runoff pollution and developing related policies in other cities.@en

Compound flooding is generated when two or more flood drivers occur simultaneously or in close succession. Multiple drivers can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers would facilitate a more accurate assessment of compound flood risk in coastal regions. This study employed the D-Flow Flexible Mesh model to simulate the historical peak coastal water level, consisting of the storm surge, astronomical tide, and relative sea level rise (RSLR), in Shanghai over the period 1961-2018. It then applies a copula-based methodology to calculate the joint probability of peak water level and rainfall during historical tropical cyclones (TCs) and to calculate the marginal contribution of each driver. The results indicate that the astronomical tide is the leading driver of peak water level, followed by the contribution of the storm surge. In the longer term, the RSLR has significantly amplified the peak water level. This study investigates the dependency of compound flood events in Shanghai on multiple drivers, which helps us to better understand compound floods and provides scientific references for flood risk management and for further studies. The framework developed in this study could be applied to other coastal cities that face the same constraint of unavailable water level records.@en

Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning.@en

Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management.@en

It has been shown that the proportion of intense tropical cyclones (TCs) has been increasing together with a poleward migration of TC track. However, their relative importance to TC surge at landfall remains unknown. Here we examine the sensitivity of TC surge in Shanghai to landfall location and intensity with a new dynamical modelling framework. We find a surge sensitivity of 0.8 m (°N)−1 to landfall location, and 0.1 m (m s−1)−1 to wind speed in Shanghai during landfall. The landfall location and intensity are comparably important to surge variation. However, based on a plausible range of reported trends of TC poleward migration and intensity, the potential surge hazard due to poleward migration is estimated to be about three times larger than that by intensity change. The long-term surge risk in Shanghai is therefore substantially more sensitive to changes of TC track and landfall location than intensity. This may also be true elsewhere and in the future.@en

Urban pluvial flooding now occurs more frequently than it has in past decades, mainly due to an increasing number of extreme precipitation events occurring in the context of a changing climate. To limit the evolving risks of urban pluvial flooding in a more environmentally friendly manner, the research community has recently paid increasing attention to Nature‐Based Solutions (NBS), which are based on new green technologies. To meet the urgent demand for a comprehensive review of the most recent literature, this review conducts a systematic survey of the literature to characterize various NBS adopted in different regions of the world and to elaborate on the benefits and limitations of such NBS. The review highlights the role of NBS in urban flood risk management under ongoing climate change and rapid urbanization. It shows that NBS could effectively mitigate urban flooding caused by high‐frequency precipitation events, with additional economic, ecological, and social benefits. However, NBS are less effective at helping cope with pluvial flooding caused by extreme precipitation events over a short period of time, while gray infrastructures also have limitations as a mitigation measure against extreme pluvial flooding. We thus recommend identifying flood risk management strategies by evaluating the performance of alternative combinations of NBS with gray infrastructures in preventing pluvial flooding in the cities. Finally, recent advances made in the applications of NBS are presented with suggestions (e.g., long‐term monitoring) to improve urban flood adaptive management.@en