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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The Three Gorges Project (TGP) has greatly enhanced the heterogeneity of the underlying surface in the Three Gorges Reservoir Area (TGRA), thereby affecting the hydrologic processes and water quality. However, the influence of the differences of underlying surfaces on the hydrologic processes and water quality in the TGRA has not been studied thoroughly. In this research, the influence of the heterogeneity of landscape pattern and geographical characteristics on the spatial distribution difference of hydrologic processes and water quality in the different tributary basins of the TGRA was identified. The TGRA was divided into 23 tributary basins with 1840 sub-basins. The spatial differentiation of the hydrologic processes and water quality of the 23 tributary basins was examined by the Soil and Water Assessment Tool (SWAT). The observed data between 1 January 2010 and 31 December 2013 were used to calibrate and validate the model, after which the SWAT model was applied to further predict the runoffand water quality in the TGRA. There are 25 main model parameters, including CN2, CH_K2 and SOL_AWC, which were calibrated and validated with SWAT-Calibration and Uncertainty Procedures (SWAT-CUP). The landscape patterns and geomorphologic characteristics in 23 tributary basins were investigated and spatially visualized to correlate with surface runoffand nutrient losses. Due to geographical difference, the average total runoffdepth (2010-2013) in the left bank area (538.6 mm) was 1.4 times higher than that in the right bank area (384.5 mm), total nitrogen (TN) loads in the left bank area (6.23 kg/ha) were 1.9 times higher than in the right bank area (3.27 kg/ha), and total phosphorus (TP) loads in the left bank area (1.27 kg/ha) were 2.2 times higher than in the right bank area (0.58 kg/ha). The total runoffdepth decreased from the head region (553.3 mm) to the tail region (383.2 mm), while the loads of TN and TP were the highest in the middle region (5.51 kg/ha for TN, 1.15 kg/ha for TP), followed by the tail region (5.15 kg/ha for TN, 1.12 kg/ha for TP) and head region (3.92 kg/ha for TN, 0.56 kg/ha for TP). Owing to the different spatial distributions of land use, soil and geographical features in the TGRA, correlations between elevation, slope gradient, slope length and total runoffdepth, TN and TP, were not clear and no consistency was observed in each tributary basin. Therefore, the management and control schemes of the water security of the TGRA should be adapted to local conditions.