Coastal cities increasingly face compound flooding risks due to sea-level rise and intensifying storms. This study systematically evaluates the synergistic regulation of coastal hydrodynamics by mangrove vegetation and intertidal topography as a nature-based solution (NbS) for coastal defense. Based on the Delft3D Flexible Mesh (FM) system, we simulate tidal and storm surge scenarios in two contrasting shorelines in Shenzhen, China, the naturally evolved Xiwan Mangrove Park and the engineered Bao'an Airport coastline. Results show that intertidal topography plays a dominant role in attenuating flow velocity, while mangrove vegetation becomes the primary factor in reducing peak water levels during extreme events. A functional shift in mitigation zones occurs, from mid and low tidal flats under tidal conditions to high flats during storm surges, driven by increased inundation and canopy engagement. Additionally, a clear design threshold of 600 m planting width is identified, beyond which additional vegetation provides diminishing returns due to the complete submergence of mangrove vegetation. These findings underscore the complementary roles of topography and vegetation and offer actionable guidance for optimizing NbS strategies in site-specific, climate-adaptive coastal management.

As the world grapples with the profound impacts of climate change, water scarcity has become a pressing issue. However, there is a shortage of in-depth research on the trade-offs between water resource dependence and the economic, ecological, and social needs of arid and semi-arid regions like Lanzhou, China. Flower cultivation in Lanzhou relies heavily on the Yellow River, often overlooking the potential of natural rainfall. Here we first calibrated a water balance model through artificial precipitation experiments in a Soil and Water Conservation Demonstration Park in Lanzhou. We then developed a multi-objective optimization model to balance the cost-benefit considerations of various plausible measures across economic, ecological, and social dimensions in the searching for solutions that are more adaptable to climate change and local development needs. Model simulations show that the solutions we designed can effectively manage water-shortage days, significantly reduce Yellow River water extraction, and improve cost-effectiveness, meeting 66%–80% of water needs for flower cultivation in the studied park. The findings highlight the potential of rainwater collection and utilization solutions to mitigate water scarcity in arid and semi-arid cities, thereby enriching water resource management.

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.

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.

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.

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.

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.