Urbanization and climate change impacts on future flood risk in the Pearl River Delta under shared socioeconomic pathways

A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta

The interactions and collective impacts of different types of hazards within a compound hazard system, along with the influence of geographical covariates on flooding are presently unclear. Understanding these relationships is crucial for comprehending the formation and dynamic processes of the hazard chain and improving the ability to identify flood warning signals in complex hazard scenarios. In this study, we presented a multivariate spatial extreme value hierarchical (MSEVH) framework to assess the spatial extreme water levels (EWL) at different return levels under the influence of a hazard chain and geographical covariates. The Pearl River Delta (PRD) was selected as a research example to assess the effectiveness of the MSEVH framework. Firstly, we identified a hazard chain (extreme streamflow from the Xijiang River (XR) - extreme streamflow from the Beijiang River (BR) - extreme sea level) and three geographical covariates influencing EWL in the PRD. Then, we compared four hazard scenarios in the MSEVH framework to evaluate the spatial EWL at different return levels under the influence of the hazard chain in the PRD. The final step involves assessing spatial EWL with the effect of the hazard chain and geographical covariates. The results indicate that when extreme streamflow from XR and BR occurs concurrently, the extreme streamflow from BR weakens the influence of extreme streamflow from XR on EWL in the PRD. However, it cannot fully offset the overall impact of extreme streamflow from XR on EWL. In addition, when extreme streamflow from XR, extreme streamflow from BR, and extreme sea level occur simultaneously, the extreme sea level enhances the influence of concurrent extreme streamflow from XR and BR on EWL in the PRD. The proposed MSEVH is not only applicable to the PRD but also shows promising potential for evaluating extreme hydrometeorological variables under the influence of other hazard chains.

A novel flood risk management approach based on future climate and land use change scenarios

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Life goes on: Spatial heterogeneity promotes biodiversity in an urbanized coastal marine ecosystem

Both human populations and marine biodiversity are concentrated along coastlines, with growing conservation interest in how these ecosystems can survive intense anthropogenic impacts. Tropical urban centres provide valuable research opportunities because these megacities are often adjacent to mega-diverse coral reef systems. The Pearl River Delta is a prime exemplar, as it encompasses one of the most densely populated and impacted regions in the world and is located just northwest of the Coral Triangle. However, the spatial and taxonomic complexity of this biodiversity, most of which is small, cryptic in habitat and poorly known, make comparative analyses challenging. We deployed standardized settlement structures at seven sites differing in the intensity of human impacts and used COI metabarcoding to characterize benthic biodiversity, with a focus on metazoans. We found a total of 7184 OTUs, with an average of 665 OTUs per sampling unit; these numbers exceed those observed in many previous studies using comparable methods, despite the location of our study in an urbanized environment. Beta diversity was also high, with 52% of the OTUs found at just one site. As expected, we found that the sites close to point sources of pollution had substantially lower diversity (44% less) relative to sites bathed in less polluted oceanic waters. However, the polluted sites contributed substantially to the total animal diversity of the region, with 25% of all OTUs occurring only within polluted sites. Further analysis of Arthropoda, Annelida and Mollusca showed that phylogenetic clustering within a site was common, suggesting that environmental filtering reduced biodiversity to a subset of lineages present within the region, a pattern that was most pronounced in polluted sites and for the Arthropoda. The water quality gradients surrounding the PRD highlight the unique role of in situ studies for understanding the impacts of complex urbanization pressures on biodiversity.

Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques

Real-time flood forecasting is one of the most pivotal measures for flood management, and real-time error correction is a critical step to guarantee the reliability of forecasting results. However, it is still challenging to develop a robust error correction technique due to the limited cognitions of catchment mechanisms and multi-source errors across hydrological modeling. In this study, we proposed a hydrologic similarity-based correction (HSBC) framework, which hybridizes hydrological modeling and multiple machine learning algorithms to advance the error correction of real-time flood forecasting. This framework can quickly and accurately retrieve similar historical simulation errors for different types of real-time floods by integrating clustering, supervised classification, and similarity retrieval methods. The simulation errors "carried" by similar historical floods are extracted to update the real-time forecasting results. Here, combining the Xin'anjiang model-based forecasting platform with k-means, K-nearest neighbor (KNN), and embedding based subsequences matching (EBSM) method, we constructed the HSBC framework and applied it to China's Dufengkeng Basin. Three schemes, including "non-corrected" (scheme 1), "auto-regressive (AR) corrected" (scheme 2), and "HSBC corrected" (scheme 3), were built for comparison purpose. The results indicated the following: 1) the proposed framework can successfully retrieval similar simulation errors with a considerable retrieval accuracy (2.79) and time consumption (228.18 s). 2) four evaluation metrics indicated that the HSBC-based scheme 3 performed much better than the AR-based scheme 2 in terms of both the whole flood process and the peak discharge; 3) the proposed framework overcame the shortcoming of the AR model that have poor correction for the flood peaks and can provide more significant correction for the floods with bad forecasting performance. Overall, the HSBC framework demonstrates the advancement of benefiting the real-time error correction from hydrologic similarity theory and provides a novel methodological alternative for flood control and water management in wider areas.

Socioeconomic impacts from coastal flooding in the 21st century China's coastal zone: A coupling analysis between coastal flood risk and socioeconomic development

Coastal flooding due to sea level rise significantly affects socioeconomic development. The dynamic nature of coastal flood risk (CFR) and socioeconomic development level (SDL) leads to uncertainties in understanding their future interplay. This ambiguity challenges coastal nations in devising effective flood adaptation and coastal management strategies. This study quantitatively examines the expected GDP affected (EGA) and population affected (EPA) by coastal flooding in China's coastal zone (CCZ) from 2030 to 2100 under various climate scenarios (RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP5). The future SDL in CCZ is assessed using a method combining the analytic hierarchy process with entropy weight. The future CFR-SDL dynamic relationship is analyzed using the coupling coordination degree (CCD) model. The results reveal that in CCZ under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios: by 2100, the EGA and EPA will reach $814.90 billion & 6.17 million people, $828.16 billion & 7.63 million people, and $1568.83 billion & 8.05 million people, respectively, where the coastal cities in Jiangsu and Guangdong provinces will face more obvious risks of socioeconomic losses; The total area in the CCZ at "Very high" and "High" level of socioeconomic development by 2100 is projected to reach 11.33 × 103 km2, 12.86 × 103 km2, and 15.82 × 103 km2, respectively, with the Pearl River Delta, Yangtze River Delta, and Tianjin-Hebei remaining pivotal for CCZ's socioeconomic growth. Cities such as Lianyungang, Jiaxing, Shenzhen, Dongguan, and Foshan show notable CCD characteristics, and addressing the trade-off between SDL and CFR is crucial in achieving sustainable development. This study highlights the potential socioeconomic impacts of coastal flooding and emphasizes the importance of considering the interrelationship between CFR and SDL when developing coastal flood adaptation policies.

Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

A simulation and risk assessment framework for water-energy-environment nexus: A case study in the city cluster along the middle reach of the Yangtze River, China

In the Anthropocene, there is a strong interlinkage among water, energy, and the environment. The water-energy-environment nexus (WEEN) has been vigorously advocated as an emerging development paradigm and a global research agenda. Based on the nexus concept, a framework for the WEEN complex system simulation and risk assessment is developed. The three metropolitan areas of the city cluster along the middle reaches of the Yangtze River (CCMRYR) are taken as the objects. Regional policies are combined with generic shared socio-economic pathways (SSPs) to form a localized SSPs suitable for the research region. The dynamic simulation of the WEEN complex system and the risk analysis are carried out with the combination of system dynamics models and copula functions. Results show that: There are obvious differences in water utilization, energy consumption, air pollutant emissions, and water pollutant emissions among the three metropolitan areas. The issue of high carbon intensity in the Wuhan Metropolitan Coordinating Region needs to be emphasized and solved from the perspective of optimizing the industrial structure. Adhering to current development patterns, there will be successive peaks in water utilization, energy consumption, and carbon emissions in Wuhan, Dongting Lake, and Poyang Lake Metropolitan Coordinating Region by 2030, leading to high synergy risks at the systemic level, with maximum values of 0.84, 0.85, 0.62, respectively. A development path based on conservation priorities indicates that future policymaking needs to prioritize a resource-saving and pollution-control development pattern directed by technological upgrading against the backdrop of scarce natural resource endowments. The localized SSPs are a beneficial extension that enriches the narrative of regional-scale SSPs. The evolutionary trajectories and risk assessments of WEEN complex systems under different localized SSPs provide a sweeping insight into the consequences of policy decisions, thus enabling policymakers to appraise policy rationality and implement appropriate corrective measures.

Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49-0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67-0.95 for climate change.

Does the causality between environmental sustainability, non-renewable energy consumption, geopolitical risks, and trade liberalization matter for Pakistan? Evidence from VECM analysis

Geopolitical threats have increased dramatically globally in recent years, adversely affecting the environment tremendously. On the other hand, there is a growing gap between the use of non-renewable energy, trade liberalization, and environmental sustainability. Due to this, the current work simulates the links between geopolitical threats, non-renewable energy use, trade liberalization, and environmental sustainability using a vector error correction model (VECM) and Granger causality test. The analysis includes data spanning from 1980 to 2021. Research outcomes indicated that geopolitical risks (GPR), Non-renewable energy consumption (NRE), Natural Resource (NR) and industrialization (IND) have a negative and statistically significant influence i.e., 0.234, 0.052, 0.028, and 0.070 units respectively on environmental sustainability (ES) while natural resource (NR) have also negative but insignificant impact on environmental sustainability. Alongside, trade liberalization (TR) and urbanization (UB) posed a positive and statistically significant influence i.e., 0.040 and 0.437 units respectively on ES. Further, causality analysis validates the feedback effect among GPR, NRE, TR, and ES. GPR, NRE, and TR granger cause environmental sustainability. The government can prepare for potential environmental disasters such as floods, droughts, and earthquakes by investing in early warning systems, emergency response teams, and disaster relief supplies. This can help mitigate the impact of geopolitical risks that can result in natural disasters. Pakistan should prioritize investing large resources in diplomatic endeavours to improve regional dynamics and ties with neighboring nations. Pakistan should place a high emphasis on developing methods targeted at reducing its non-renewable energy use to mitigate the negative effects. This might entail offering financial incentives, implementing efficient feed-in tariff schemes, and developing a thorough strategy for boosting the capacity of renewable energy sources.

Occurrence and mass loads of N-nitrosamines discharged from different anthropogenic activities in Desheng River, South China

N-nitrosamines are widespread in various bodies of water, which is of great concern due to their carcinogenic risks and harmful mutagenic effects. Livestock rearing, domestic, agricultural, and industrial wastewaters are the main sources of N-nitrosamines in environmental water. However, information on the amount of N-nitrosamines these different wastewaters contribute to environmental water is scarce. Here, we investigated eight N-nitrosamines and assessed their mass loadings in the Desheng River to quantify the contributions discharged from different anthropogenic activities. N-nitrosodimethylamine (NDMA) (< 1.6-18 ng/L), N-nitrosomethylethylamine (NMEA) (< 2.2 ng/L), N-nitrosodiethylamine (NDEA) (< 1.7-2.4 ng/L), N-nitrosopyrrolidine (NPYR) (< 1.8-18 ng/L), N-nitrosomorpholine (NMOR) (< 2.0-3.5 ng/L), N-nitrosopiperidine (NPIP) (< 2.2-2.5 ng/L), and N-nitrosodi-n-butylamine (NDBA) (< 3.3-16 ng/L) were detected. NDMA and NDBA were the dominant compounds contributing 89% and 92% to the total N-nitrosamine concentrations. The mean cumulative concentrations of N-nitrosamines in the livestock rearing area (26 ± 11 ng/L) and industrial area (24 ± 4.8 ng/L) were higher than those in the residential area (16 ± 6.3 ng/L) and farmland area (15 ± 5.1 ng/L). The mean concentration of N-nitrosamines in the tributaries (22 ng/L) was slightly higher than that in the mainstem (17 ng/L), probably due to the dilution effect of the mainstem. However, the mass loading assessment based on the river's flow and water concentrations suggested the negligible mass emission of N-nitrosamines into the mainstem from tributaries, which could be due to the small water flow of tributaries. The average mass loads of N-nitrosamines discharged into the mainstem were ranked as the livestock rearing area (742.7 g/d), industrial area (558.6 g/d), farmland area (93.9 g/d), and residential areas (83.2 g/d). In the livestock rearing, residential, and industrial area, NDMA (60.9%, 53.6%, and 46.7%) and NDBA (34.6%, 33.3%, and 44.9%) contributed the most mass loads; NDMA (23.4%), NDEA (15.8%), NPYR (10.1%), NPIP (12.8%), and NDBA (37.8%) contributed almost all the mass loads in the farmland area. Photodegradation amounts of NDMA (0.65 ~ 5.25 µg/(m³·day)), NDBA (0.37 ~ 0.91 µg/(m³·day)), and NDEA (0 ~ 0.66 µg/(m³·day)) were also calculated according to the mass loading. Quantifying the contribution of different anthropogenic activities to the river will provide important information for regional river water quality protection. Risk quotient (RQ) values showed the negligible ecological risks for fish, daphnid, and green algae.

Urban Flood Risk Assessment Based on Dynamic Population Distribution and Fuzzy Comprehensive Evaluation

Floods are one of the most common natural disasters that can cause considerable economic damage and loss of life in many regions of the world. Urban flood risk assessment is important for urban flood control, disaster reduction, and risk management. In this study, a novel approach for assessing urban flood risk was proposed based on the dynamic population distribution, improved entropy weight method, fuzzy comprehensive evaluation method, and the principle of maximum membership, and the spatial distribution of flood risk in four different sessions or daily time segments (TS1-TS4) in the northern part of the Shenzhen River Basin (China) was assessed using geographic information system technology. Results indicated that risk levels varied with population movement. The areas of highest risk were largest in TS1 and TS3, accounting for 7.03% and 7.07% of the total area, respectively. The areas of higher risk were largest in TS2 and TS4, accounting for 4.54% and 4.64% of the total area, respectively. The findings of this study could provide a theoretical basis for assessing urban flood risk management measures in Shenzhen (and even throughout China), and a scientific basis for development of disaster prevention and reduction strategies by flood control departments.

Modelling urban flooding integrated with flow and sediment transport in drainage networks

Drainage networks play an essential role in mitigating urban flooding, which, nevertheless, are prone to suffer sediment deposits. To date, however, the effects of sediments in drainage networks on urban flooding remain poorly understood. Here an integrated model is proposed for urban flooding. It is composed of a hydrological module for surface runoff integrated with a one-dimensional hydro-sediment-morphodynamic module for coupled open-channel or pressurized flow and sediment transport in drainage networks. The governing equations are solved synchronously using a well-balanced finite volume method. The model is tested against two laboratory cases involving mixed flow and sediment transport in pipes, and the results agree well with observed data. A new residential area with virtually pervious surface and an established urban area with essentially impervious surfaces are studied using the present model to unravel how sediments in drainage networks affect urban flooding under different extreme rainfall and sediment scenarios. The results reveal that sediments alter the discharge hydrographs in the drainage networks to distinct extents under different storm return periods. As far as the present computational cases are concerned, when a third of the pipe diameter is occupied by sediment deposits, the peak pipeline flow discharge decreases by up to 25 %. Accordingly, the surface inundation depth increases by up to 18 %, and the inundation area expands by up to 12 %, characterizing a considerably higher flooding risk. The present findings provide insight into the influences of sediment transport in drainage networks on urban flooding.

Greencoin as an AI-Based Solution Shaping Climate Awareness

Our research aim was to define possible AI-based solutions to be embedded in the Greencoin project, designed as a supportive tool for smart cities to achieve climate neutrality. We used Kamrowska-Załuska's approach for evaluating AI-based solutions' potential in urban planning. We narrowed down the research to the educational and economic aspects of smart cities. Furthermore, we used a systematic literature review. We propose solutions supporting the implementation process of net zero policies benefiting from single actions of urban dwellers based on the Greencoin project developed by us. By following smart city sectors, the paper introduces AI-based solutions which can enrich Greencoin by addressing the following needs: (1) shaping pro-environmental behaviors, (2) introducing instruments to reinforce the urban management process, (3) supporting bottom-up initiatives allowing to shape urban resilience, (4) enhancing smart mobility, (5) shaping local economies supporting urban circularity, and (6) allowing better communication with residents. Our research fills the gap in the limited group of studies focused on shaping climate awareness, enhancing smart governance, and supporting social participation and inclusion. It proves that AI-based educational tools can be supportive when implementing adaptation policies toward climate neutrality based on our proposed AI-based model shaping climate awareness.

A GIS-Based Approach for Flood Risk Zoning by Combining Social Vulnerability and Flood Susceptibility: A Case Study of Nanjing, China

The identification of vulnerable people and places to flood is crucial for effective disaster risk management. Here, we combine flood hazard and social vulnerability index to capture the potential risk of flood. In this paper, Nanjing was taken as the case study to explore the spatial pattern of social vulnerability towards flood at the community scale by developing an index system. Based on the flood risk results of ArcSWAT, the risk of flood disaster in Nanjing was evaluated. The results show the following. (1) Social vulnerability exhibits a central–peripheral pattern in general, which means that the social vulnerability degree is high in the central city and decreases gradually to the suburbs. (2) The susceptibility to flood disaster has a similar circle-layer pattern that is the highest in the urban centre, lower in the exurban areas, and the lowest in the suburb areas. (3) By using the GIS-based zoning approach, communities are classified into four types by comprehensively considering their flood susceptibility and social vulnerability. The spatial pattern is explained, and policy recommendation for reducing flood risk is provided for each type of community. The research has important reference significance for identifying the spatial pattern of social vulnerability to flood and then formulating targeted adaptation countermeasures.

Flood Risk Assessment under Land Use and Climate Change in Wuhan City of the Yangtze River Basin, China

Frequently occurring flood disasters caused by extreme climate and urbanization processes have become the most common natural hazard and pose a great threat to human society. Therefore, urban flood risk assessment is of great significance for disaster mitigation and prevention. In this paper, the analytic hierarchy process (AHP) was applied to quantify the spatiotemporal variations in flood risk in Wuhan during 2000–2018. A comprehensive flood risk assessment index system was constructed from the hazard, sensitivity, and vulnerability components with seven indices. The results showed that the central urban area, especially the area in the west bank of the Yangtze river, had high risk due to its high flood sensitivity that was determined by land use type and high vulnerability with dense population and per unit GDP. Specifically, the Jianghan, Qiaokou, Jiangan, and Wuchang districts had the highest flood risk, more than 60% of whose area was in medium or above-medium risk regions. During 2000–2018, the flood risk overall showed an increasing trend, with Hongshan district increasing the most, and the year of 2010 was identified as a turning point for rapid risk increase. In addition, the comparison between the risk maps and actual historical inundation point records showed good agreement, indicating that the assessment framework and method proposed in this study can be useful to assist flood mitigation and management, and relevant policy recommendations were proposed based on the assessment results.