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.

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Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of a driver's individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the same frequency amplification (SFA) method to transform the design event values in hourly time series so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver, and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, that the peak rainfall occurs 2 h before the peak storm surge would cause the deepest average cumulative inundation depth. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagates upstream in the Huangpu River, resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.@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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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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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