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.

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In a world influenced by climate change and consequently sea-level rise, extreme floods are expected to become more frequent in the future, representing a serious threat for riverine and coastal settlements. Therefore, flood protection is a large component of climate adaptation and should be closely related to other measures of climate adaptation and societal needs. In this context, SARCC (Sustainable And Resilient Coastal Cities) supports the use of integrated Nature Based Solutions into coastal management, urban planning and design, integrating them into existing infrastructure and flood defenses. This paper will focus on the strategy developed for Southend-On-Sea (UK), presenting the different approaches that were used to manage coastal flooding and make it part of a long-term large scale urban development strategy. In particular, this study estimated overtopping discharges during extreme storm conditions and analyzed their inland propagation using Delft3D FM numerical simulations. Based on these results, mitigation, and adaptation measures as a part of the spatial strategy were developed through a joint collaboration of hydraulic engineers, urban designers, maritime archaeologists and local authorities, pointing out the strength of interdisciplinary approaches for reliable and well-integrated flood protection strategies. Important highlight of the study is how flood risk management is integrated in spatial planning and how hydraulic engineering modeling is directly use as indicators to make spatial design decisions.@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.

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Mean sea level rise (SLR) could increase up to 2m by 2100, which would see damage caused by coastal flooding in Europe increase from €1.25bn per annum currently to €961bn in just over 80 years. Urban areas situated along the North Sea coastline are particularly vulnerable to extreme sea level rise (a combination of SLR, tide and storm surges). The main goal of this study is to assess the paradigm shift in flood risk management from reducing probability of the flood event to reducing its consequences in the city of Vlissingen, in the Netherlands. Two spatial adaptation strategies are modeled and compared by using spatial, climatic, and socioeconomic projections for the year 2100: the “Vlissings Model” and the “Spuikom Model”. The Vlissings Model is about increased coastal protection through the heightening of existing grey infrastructure by 3 m, which includes the dike and buildings constructed on top of it. The Spuikom Model is accepting and rerouting overtopping water towards an existing former backshore water basin. The study brings forth an interdisciplinary "Design & Assess" framework that brings together design strategies with flood damage models and cost/benefit analyses to compare the effectiveness of two paradigms in dealing with extreme SLR. Results show that raising the dike would ensure full protection from extreme events against an initial investment and maintenance cost of €215 mil. Accepting and rerouting overtopping water would, on the contrary, reduce the impact of the flood to €8,6 million damage and less than a hundred affected inhabitants, without requiring the construction of major infrastructure but of a flood retention basin integrated to the new urban development. On the other hand, the comparison between the two strategies remains complex in quantitative terms given the different cost-benefit assessment models for such interventions.@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.

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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.

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Climate change leads to sea level rise worldwide, as well as increases in the intensity and frequency of tropical cyclones (TCs). Storm surge induced by TC’s, together with spring tides, threatens to cause failure of flood defenses, resulting in massive flooding in low-lying coastal areas. However, limited research has been done on the combined effects of the increasing intensity of TCs and sea level rise on the characteristics of coastal flooding due to the failure of sea dikes. This paper investigates the spatial variation of coastal flooding due to the failure of sea dikes subject to past and future TC climatology and sea level rise, via a case study of a low-lying deltaic city- Shanghai, China. Using a hydrodynamic model and a spectral wave model, storm tide and wave parameters were calculated as input for an empirical model of overtopping discharge rate. The results show that the change of storm climatology together with relative sea level rise (RSLR) largely exacerbates the coastal hazard for Shanghai in the future, in which RSLR is likely to have a larger effect than the TC climatology change on future coastal flooding in Shanghai. In addition, the coastal flood hazard will increase to a large extent in terms of the flood water volume for each corresponding given return period. The approach developed in this paper can also be utilized to investigate future flood risk for other low-lying coastal regions.

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Flood risk is expected to increase in coastal cities, particularly in Asian megacities such as Shanghai. This paper presents an integrated modeling framework to simulate changes in the flood risk in Shanghai and provide a cost-benefit analysis of multiple adaptation strategies used to reduce risk. The results show that the potential flood risk will increase dramatically as a result of sea level rise, land subsidence, and socioeconomic development. By 2100, the expected annual damage could reach 0.8% (uncertainty range: 0.4%–1.4%) of local GDP under an optimistic emission scenario (RCP4.5), compared to the current value of 0.03%. All of the adaptation strategies can effectively reduce the flood risk under the current conditions and those in 2050. In contrast to the ‘hard’ flood protection strategies (i.e., storm-surge barriers and floodwalls), the ‘soft’ strategies (i.e., building codes and nature-based measures) cannot substantially reduce the flood risk in 2100. However, the soft strategies can play a critical role in reducing the residual risk resulting from the hard strategies. A ‘hybrid’ strategy combining a storm-surge barrier, wet-proofing, and coastal wetland development outperforms both hard and soft strategies in terms of low residual risk and high benefit/cost ratio. Additionally, the hybrid strategy can also enable a larger reduction in casualties. These findings imply that managing flood risk is more than the use of single adaptation measures. The methodology developed in this paper can enlighten Shanghai and other coastal cities on an economically and socially feasible adaptation strategy in an uncertain future.

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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

Sea level rise (SLR) and subsidence are expected to increase the risk of flooding and reliance on flood defenses for cities built on deltas. Here, we combine reliability analysis with hydrodynamic modeling to quantify the effect of projected relative SLR on dike failures and flood hazards for Shanghai, one of the most exposed delta cities. We find that flood inundation is likely to occur in low-lying and poorly protected periurban/rural areas of the city even under the present-day sea level. However, without adaptation measures, the risk increases by a factor of 3–160 across the densely populated floodplain under projected SLR to 2100. Impacts of frequent flood events are predicted to be more affected by SLR than those with longer return periods. Our results imply that including reliability-based dike failures in flood simulations enables more credible flood risk assessment for global delta cities where conventional methods have assumed either overtopping only or complete failure.

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China has implemented main functional areas planning (MFAP) since 2010, which is essential for improving the efficiency of land space utilization and achieving sustainable urban development. Quantitative assessments of the urban development levels (UDLs) at the county level across China after the implementation of MFAP have not been well-documented. In this study, a unified indicator system was developed, and the UDLs of 2850 counties in China after MFAP implementation were evaluated. The results showed that MFAP played a positive role in urban development in China. The UDLs in China generally increased but showed obvious spatial differences. The higher UDLs were mostly found in the counties in the five urban belts, which reflects the overall urban layout of China. The UDLs were generally low in the western counties in comparison with those in the eastern part of China. The differences in the UDLs from east to west were greater than those from north to south. Moreover, the differences in the UDLs presented a spatial agglomeration effect. This study could offer insight into the refinement of MFAP in China and sustainable urban development in developing countries.

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Flash floods in mountainous areas have become more severe and frequent as a result of climate change and are a threat to public safety and social development. This study explores the application of distributed hydrological models in flash floods risk management in a small watershed in Sichuan Province, China, and aims to increase early warning lead time in mountainous areas. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) model was used to simulate the flash flood process and analyze the variation in flood hydrographs. First, the HEC-HMS model was established based on geospatial data and the river network shape, and eight heavy rainfall events from 2010 to 2015 were used for model calibration and validation, showing that the HEC-HMS model was effective for the simulation of mountain floods in the study area. Second, with the assumption that rainfall and flood events have the same frequency, the flood hydrographs with different frequencies (p = 1%, 2%, 5%, and 10%) were calculated by the HEC-HMS model. The rising limbs of the flood hydrographs were significantly different and can be divided into three parts (0-5 h, 6-10 h, and 11-15 h). The rising rate of the flood stage for each part of the flood hydrograph increases in multiples. According to the analysis of the flood hydrographs, two critical early warning indicators with an invention patent were determined in the study: the flood stage for immediate evacuation and the rising rate. The application of the indicators in the study shows that it is feasible to advance the time of issuing an early warning signal, and it is expected that the indicators can offer a reference for flash flood early warning in the study area and other small watersheds in mountainous areas.

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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.

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The co-occurrence of storm surge, high tides and heavy precipitation increases flood probability and potential consequences compared to each hazard separately in Delta cities. The Huangpu River (HP) is a tributary of the Yangtze River in China, which drains Tai Lake to the west of Shanghai city, and meanders through downtown Shanghai. During typhoon events, storm surge reverses the river flow and pushes the water level up as far as 100km upstream. If storm surge (1-2 m above msl) co-occurs with high tide (2-3 m above msl), it poses great threats to the HP floodwall (crest level from ~3-5.5m above msl). At the same time, typhoons cause heavy precipitation (up to 70-80mm/hour) in the city, which increases urban drainage discharge in the pipeline system. In order to prevent elevated water levels in the river, stormwater drainage is ceased to effectively work in this situation. Pluvial and fluvial flooding occur simultaneously. The objective of this paper is to develop a hydrodynamic model to simulate simultaneous pluvial and fluvial flooding and to produce inundation maps due to failure of floodwalls and the urban drainage system. We apply the Delft3D FM numerical model to compound flood events in Shanghai. Results raise risk awareness for decision-makers during compound flood events and demonstrate the importance of compound flood modelling at a city scale.@en

Ecological function reflects the ability of the land to provide ecological goods to support high-quality human production and life. The evaluation of the county-level ecological function across the whole nation is crucial for sustainable land management, which has not been implemented effectively in China. In this study, we proposed a unified system for the assessment of ecological function that would be comparable at the county level, and we applied the system to evaluate the ecological function of 2850 counties across China based on multi-source data. We also examined the spatiotemporal changes in ecological function from 2009 to 2015 using standard deviational ellipses and coefficients of variation. The results showed that the number of counties with high levels of ecological function in China decreased by 8.13% from 2009 to 2012 and then increased by 0.6% from 2012 to 2015. The spatial pattern of the degradation of ecological function shifted from the east-west direction to the southwest-northeast direction and became centralized in the Central Plains area of China. The regional imbalance in ecological function followed the order from high to low of the central, western, northeastern, and eastern areas of China. Targeted policies were proposed to control the degradation of the ecological function in the four regions in China.

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Estuary deltas and coastal cities are facing with the rising risk of the compound flooding due to the coincidence of storm surge, extreme precipitation, high tide and high river discharge. A new integrated numerical modeling system, which coupled atmosphere, ocean and coast processes, was developed to simulate the extreme compound coastal flooding (including four threats of flooding) in Shanghai. The new developed model was calibrated to provide a valid numerical solution for the integrated compound flooding simulation. The results showed that flooding area (inundation depth > 0.2 m) was reduced by approximately 62% after the enhancement of seawall and levee in 1998. Therefore, it is concluded that seawall and levee construction plays a key role in coastal flooding prevention in Shanghai. The accurate simulation of extreme compound coastal flooding provides rational information for the future planning of property insurance and city development.@en

Extreme flooding usually causes huge losses of residential buildings and household properties, which is critical to flood risk analysis and flood resilience building in Shanghai. We developed a scenario-based multidisciplinary approach to analyze the exposure, losses and risks of residential buildings and household properties, and their spatial patterns at the neighborhood committee level in Shanghai, based on extreme storm flood scenarios of 1/200, 1/500, 1/1000 and 1/5000-year. Our findings show that the inundation area of the residential buildings caused by a 1/200-year storm flood reaches 24.9 km², and the total loss of residential buildings and household properties is 29.7 billion CNY (Chinese Yuan) (or 4.4 billion USD), while the inundation area of residential buildings and the total loss increases up to 162.4 km² and 366.0 billion CNY (or 54.2 billion USD), respectively for a 1/5000-year storm flood. The estimated average annual loss (AAL) of residential buildings and household properties for Shanghai is 590 million CNY/year (or 87.4 million USD/year), with several hot spots distributed around the main urban area and on the bank of the Hangzhou Bay. Among sixteen districts, Pudong has the highest exposure and annual expected loss, while the inner city is also subject to extreme flooding with an AAL up to near half of the total. An analysis of flood risk in each of 209 subdistricts/towns finds that those most vulnerable to storm flooding are concentrated in Pudong, Jiading, Baoshan Districts and the inner city. Our work can provide meaningful information for risk-sensitive urban planning and resilience building in Shanghai. The methodology can also be used for risk analysis in other coastal cities facing the threat of storm flooding.

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The coupled Delft3D Flexible Mesh (D3D FM) - SWAN model with an unstructured grid has been developed recently to simulate storm surge and waves; and another open source model, Telemac-Tomawac, has been widely used for storm surge and wave propagation simulation in coastal areas for more than 20 years. However, the choice of a hydrodynamic model for a specific area in terms of cost, efficiency and accuracy is often a dilemma at the beginning of a modeling project. The objective of this research is to examine the effects of two software packages in terms of accuracy and performance with a case study in the Shanghai area of China. Model performance has been assessed based on model configuration, model calibration, grid generation and computational efficiency. Comparing measured water levels with model results, both approaches were able to accurately predict hydrodynamic conditions in a complex estuarine environment. Both models showed that it can efficiently simulate hydrodynamics in the coastal area under various scenarios for further climate adaptation research. Additionally, both models were used to simulate inundation propagation due to hypothetical failures of flood defenses in the coastal area. A comparison of inundation extent and maximum inundation depth showed that they were equally well-suited for overtopping and inundation simulation. @en

Green infrastructure (GI) plays a fundamental role in achieving urban pluvial flood management, mitigating urban heat island effect, and improving living suitability. Residents' participation is the main driving force of GI implementation. Based on semi-structured interviews, GIS spatial analysis, and multiple regression, we investigated residents' willingness to participate in the implementation of GI in public and private space and identified the influence factors in Shanghai, China. The results show that, compared with private space, residents prefer to implement GI in public space, where they have different preferences of GI measures. On urban scale, residents' willingness to participate in the implementation of GI in private space is characterized as "high in the inner city, low in the suburban areas", while the spatial difference is insignificant for public space. In addition, the factors affecting residents' willingness to participate in the implementation of GI are different in private and public space. The deterministic factors of GI participation are gender, education level, and floor for private space, while only include building age for public space, in addition to the common factors of free time, cognition of GI, perception of pluvial flood risk, supportive factors, and environment-improving factors that can influence both private and public space GI participation. Our analysis therefore provides valuable information for policymakers concerning nature-based solutions to climate change adaptation and urban sustainability.@en