H. Xu
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7 records found
1
Land subsidence is a significant issue in many coastal megacities, including Shanghai, where it poses risks to infrastructure and economic stability. Although numerous studies have used SAR datasets to monitor land subsidence in Shanghai, multi-decadal displacement measurements obtained from multi-sensor SAR data remain unavailable. Moreover, the contributions and variations of driving factors behind the evolution of land subsidence remain poorly understood. This study employs multi-sensor SAR fusion method and a Random Forest model, along with Shapley Additive exPlanations (SHAP), to examine subsidence evolution and assess the influence of key drivers over the past 30 years. The results show that severe subsidence has spread from central urban areas to surrounding suburban regions, particularly in the eastern coastal and southern industrial zones in Shanghai. SHAP analysis identified that evapotranspiration, sediment thickness, and groundwater extraction were the dominant factors in the early stage of subsidence, while recent groundwater management and recharge practices have significantly mitigated the subsidence rate. These findings demonstrate the shifting importance of different subsidence factors over time and provide valuable insights for long-term prevention and control measures.
Along the Chinese coastline, particularly in the south, cities like Shanghai and Haikou are prone to flood, especially during the typhoon season. When typhoons hit the coast, high storm surges and heavy rainfall can interact leading to severe impacts. Characterizing compound flooding in coastal regions poses different challenges, including identifying the physical drivers that potentially generate a flood event, selecting an appropriate numerical model to describe the interaction between these drivers in terms of frequency and magnitude, and ensuring the quality and representativity of the available observations.
This thesis aims to tackle these challenges using cities along the Chinese coast as case studies. It seeks to (i) provide a probabilistic characterization of the physical drivers of compound floods, considering the effect of sea level rise, and (ii) integrate this quantification with a hydrodynamic model to assess the extent and depth of compound flood impacts in inundated areas. This approach can lay a solid foundation for developing flood-resilient strategies and mitigating potential impacts.
Chapter 2 introduces a design approach via conditional probability for quantifying compound flood hazards in coastal regions and its implication for infrastructure design considering the seasonal variation in surge peak occurrence. We found that along the southern coast of China, the severity of the expected rainfall events in case of a storm surge peak is larger compared to the expected severity inferred from the probability distribution of annual maxima of precipitation. Consequently, from a design perspective, implementing rainwater storage systems and facilities to mitigate hydrograph peaks is crucial for these regions.
Chapter 3 focuses on Shanghai and investigates how relative sea level rise (RSLR) affects design values for flood protection systems. We employed the D-Flow FM ocean storm surge model to reconstruct 210 historical typhoon storm surge events in Shanghai to overcome the constraint of unavailable water level records. We then applied a copula-based approach to calculate the joint probability and design value of peak water level and accumulated rainfall with the impact of RSLR. This research improves our understanding of how storm surges, rainfall, and RSLR interact, revealing how they collectively contribute to the risk of flood in coastal areas. Thus, it is crucial to monitor and predict the interplay of these factors for developing future design standards for better flood preparedness.
Chapter 4 reveals distinct patterns in the relationship between flooded areas and volume for both single-driven and multi-driven flood scenarios at the coastal city of Haikou by implementing an ocean storm surge generator and urban overland hydrodynamic model. The results highlighted storm tide (a combination of surge and astronomical tide) as the predominant factor contributing to compound flooding in Haikou. Only examining single-driven factors would underestimate flood hazard.
Chapter 5 investigates the sensitivity of inundated areas to the relative timing between the occurrence of the rainfall peak and the storm surge peak in Shanghai and provides a characterization of the consequent inundated areas based on the main flood driver(s). This is achieved by inferring from the probabilistic model the severity of the expected pairs of storm surges and rainfall events. They are then used as forcing of a hydrodynamic model to generate flood extent. We showed that the relative time between the peak of flood drivers affects the extent and depth of the flood and the flood zone classification. This can better suggest potential strategies for dealing with different types of compound flooding for coastal cities. ...
Along the Chinese coastline, particularly in the south, cities like Shanghai and Haikou are prone to flood, especially during the typhoon season. When typhoons hit the coast, high storm surges and heavy rainfall can interact leading to severe impacts. Characterizing compound flooding in coastal regions poses different challenges, including identifying the physical drivers that potentially generate a flood event, selecting an appropriate numerical model to describe the interaction between these drivers in terms of frequency and magnitude, and ensuring the quality and representativity of the available observations.
This thesis aims to tackle these challenges using cities along the Chinese coast as case studies. It seeks to (i) provide a probabilistic characterization of the physical drivers of compound floods, considering the effect of sea level rise, and (ii) integrate this quantification with a hydrodynamic model to assess the extent and depth of compound flood impacts in inundated areas. This approach can lay a solid foundation for developing flood-resilient strategies and mitigating potential impacts.
Chapter 2 introduces a design approach via conditional probability for quantifying compound flood hazards in coastal regions and its implication for infrastructure design considering the seasonal variation in surge peak occurrence. We found that along the southern coast of China, the severity of the expected rainfall events in case of a storm surge peak is larger compared to the expected severity inferred from the probability distribution of annual maxima of precipitation. Consequently, from a design perspective, implementing rainwater storage systems and facilities to mitigate hydrograph peaks is crucial for these regions.
Chapter 3 focuses on Shanghai and investigates how relative sea level rise (RSLR) affects design values for flood protection systems. We employed the D-Flow FM ocean storm surge model to reconstruct 210 historical typhoon storm surge events in Shanghai to overcome the constraint of unavailable water level records. We then applied a copula-based approach to calculate the joint probability and design value of peak water level and accumulated rainfall with the impact of RSLR. This research improves our understanding of how storm surges, rainfall, and RSLR interact, revealing how they collectively contribute to the risk of flood in coastal areas. Thus, it is crucial to monitor and predict the interplay of these factors for developing future design standards for better flood preparedness.
Chapter 4 reveals distinct patterns in the relationship between flooded areas and volume for both single-driven and multi-driven flood scenarios at the coastal city of Haikou by implementing an ocean storm surge generator and urban overland hydrodynamic model. The results highlighted storm tide (a combination of surge and astronomical tide) as the predominant factor contributing to compound flooding in Haikou. Only examining single-driven factors would underestimate flood hazard.
Chapter 5 investigates the sensitivity of inundated areas to the relative timing between the occurrence of the rainfall peak and the storm surge peak in Shanghai and provides a characterization of the consequent inundated areas based on the main flood driver(s). This is achieved by inferring from the probabilistic model the severity of the expected pairs of storm surges and rainfall events. They are then used as forcing of a hydrodynamic model to generate flood extent. We showed that the relative time between the peak of flood drivers affects the extent and depth of the flood and the flood zone classification. This can better suggest potential strategies for dealing with different types of compound flooding for coastal cities.
Low-lying coastal areas are threatened worldwide by compound flooding effects, including sea-level rise (SLR), frequent tropical cyclones (TCs), and accelerated land subsidence (LS). Compound flooding results in a negative effect, creating challenges for decision-making and coastal management for sustainable urban development. To comprehend the precise consequences and the combined effect of LS and SLR on coastal cities and to inform management scientifically, we initially chose historical TCs (TC6311 and TC1415) that resulted in exceptionally high surges and significant losses as reference scenarios for simulating potential compound events. We then utilized a hydrodynamic model, which combines TCs, LS, and SLR, to simulate the combined impact of flood hazards. Our findings indicated that the highest subsidence rate exceeds −20 mm/yr, the cumulative subsidence reaches −119.09 mm and 68.44% of the land is experiencing subsidence from 2015 to 2021. When comparing flood scenarios with and without the influence of SLR, the most severely affected by LS is projected to become submerged and completely inundated by 2100. We found that the combined impact of SLR and LS significantly amplified flood inundation and SLR is the primary amplification factor of flooding, projected to account for 46.2% by 2100. The framework established in this study facilitates the quantitative assessment of the interactions among multi-driver factors of compound flooding, thereby serving as a supplementary and guiding tool for future risk management.
As global warming continues to intensify, the relationship between diurnal temperature range (DTR) and vegetation productivity continues to change over time. However, the impact of DTR changes on vegetation activities remains uncertain. Thus, further study about how DTR changes affect the physiological activities of plants is also urgently needed. In this study, we employed copula function theory to analyze the impact of DTR on Normalized Difference Vegetation Index (NDVI) values during the spring, summer, and autumn seasons from 1982 to 2014 for various land types in the Inner Mongolia Plain (IMP), China. The results showed that the relationship between DTR and NDVI in the IMP was characterized by correlation at the upper tail and asymptotical independence at the lower tail. This demonstrated that the DTR had little effect on NDVI when they reached their minimum value. However, it has a significant impact on NDVI at its maximum values. This study provides valuable insight into the dynamic impact of monthly DTR on different land use types under climate change.
Extreme surges and rainfall represent major driving factors for compound flooding in estuary regions along the Chinese coast. The combined effect of extreme surges and rainfall (that is, compound floods) might lead to greater impacts than if the drivers occurred in isolation. Hence, understanding the frequency and severity of compound flooding is important for improving flood hazard assessment and compound flood resilience in coastal cities. In this study, we examined the dependence between extreme surges and corresponding rainfall events in 26 catchments along the Chinese coastline during typhoon and non-typhoon seasons using copula functions, to identify where the two drivers more often occur together and the implication for flood management in these locations. We found that the interaction between flood drivers is statistically significant in 10 catchments located around Hainan Island (south) and Shanghai, where surge peaks occur mainly during the typhoon season and around the Bohai Sea (north), where surge peaks occur mainly during the non-typhoon season. We further applied the copula-based framework to model the dependence between surge peaks and associated rainfall and estimate their joint and conditional probability in two specific locations—Hainan Island and the Bohai Sea, where the correlation between flood drivers is statistically significant. We observed that in Hainan Island where most of the surge peaks occur during the typhoon season, extreme rainfall events during the typhoon season are generally more intense compared to annual maxima rainfall. In contrast, around the Bohai Sea where surge peaks occur mainly outside the typhoon season, rainfall is less intense than annual maxima rainfall. These results show that the interaction between extreme surges and rainfall can provide valuable insight when designing coastal and urban infrastructure, especially in highly populated urban areas prone to both coastal and pluvial flooding, such as many Chinese coastal 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.