Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches

Coastal erosion and flooding risk assessment based on grid scale: A case study of six coastal metropolitan areas

Coastal areas, situated at the critical juncture of sea-land interaction, are confronted with significant challenges from coastal erosion and flooding. It is imperative to evaluate these risks and offer scientific guidance to foster regional sustainable development. This article developed a coastal risk assessment model based on grid scale, integrating both coastal exposure and socio-ecological environment. Fourteen indicators were selected, aiming to offer a systematic approach for estimating and comparing disaster risks in coastal areas. This risk assessment model was applied to Shanghai, New York, Sydney, San Francisco, Randstad, and Tokyo metropolitan areas. The results indicate: (1) Accounting for the protective role of habitat types like mangroves and the distance attenuation effect offered a more precise representation of hazard situation; (2) The integration of the Game Theory weighting method with both subjective Analytic Hierarchy Process and objective CRITIC weighting enhanced the scientific validity and rationality of the results by minimizing deviations between subjective and objective weights; (3) Shanghai exhibited the highest average hazard and vulnerability, San Francisco had the lowest average hazard and Sydney had the lowest average vulnerability; In terms of comprehensive risk, Shanghai possessed the highest average risk, while Sydney presented the lowest. The proposed model framework is designed to swiftly identify high-risk zones, providing detailed information references for local governments to devise efficacious risk management and prevention strategies.

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks.

Evaluating the impacts of land use change on ecosystem service values under multiple scenarios in the Hunshandake region of China

Changes in land use in an agro-pastoral region affect the delivery of ecosystem services. The trajectory of future land use change and its impacts on human society are not yet well understood, which poses a challenge to efforts to balance the socioeconomic development with the supply of ecosystem services. Taking the Hunshandake region for a case study, we developed four land use scenarios, and projected the future land use patterns under those scenarios using the GeoSOS-FLUS model. We then assessed the ecosystem service values (ESV) using a modification of the equivalent-coefficient method that accounts for differences in net primary production, and explored the impacts of land use change on ESV from 2018 to 2030. We found important land use conversions among the forest, grassland, and cropland uses, mainly in the southern part of our study area. The presence of ESV change hotspots and cold spots suggested that the changes were clustered in the southeastern part. The ESV gain and loss matrix showed that the land use changes under a scenario that prioritized ecosystem services and the comprehensive development scenario increased ESV from 2018 to 2030 with the conversion of cropland to forest. Our results provide important knowledge to inform land use decisions and facilitate sustainable development in the Hunshandake region.