Search Results (591)

Landslides pose a significant threat to the safety and stability of settlements in karst regions worldwide. The long-standing tight balance state of settlement funding and infrastructure makes it difficult to allocate disaster prevention resources effectively against landslide impacts. There is an urgent need to fully leverage the landscape resources of karst settlements and develop landslide risk prevention strategies that balance economic viability with local landscape adaptability. However, limited research has explored the differential resilience characteristics and patterns of landslide disaster risk and settlement landscapes from a spatial coupling perspective. This study, based on landslide disaster and disaster-adaptive landscape data from a typical karst province in China, employs the frequency ratio-random forest model and weighted variance method to construct landslide disaster risk (LDR) and disaster-adaptive landscape (DAL) base maps. The spatial characteristics of urban, urban–rural transition zones, and rural settlements were analyzed, and the resilience differentiation and driving factors of the LDR–DAL coupling relationship were assessed using bivariate spatial autocorrelation and geographical detector models. The key findings are as follows: (1) Urban and peri-urban settlements exhibit a high degree of spatial congruence in the differentiation of LDR and DAL, whereas rural settlements exhibit distinct divergence; (2) the Moran’s I index for LDR and DAL is 0.0818, indicating that urban and peri-urban settlements predominantly cluster in H-L and L-L types, whereas rural settlements primarily exhibit H-H and L-H patterns; (3) slope, soil organic matter, and profile curvature are key determinants of LDR–DAL coupling, with respective influence strengths of 0.568, 0.555, and 0.384; (4) in karst settlement development, augmenting local vegetation in residual mountain areas and parks can help maintain forest ecosystem stability, effectively mitigating landslide risks and enhancing disaster-adaptive capacity by 6.77%. This study helps alleviate the contradiction between high LDR and weak disaster-adaptive resources in the karst region of Southwest China, providing strategic references for global karst settlements to enhance localized landscape adaptation to landslide disasters. Full article

Under intensifying climate change impacts, accurate quantification of population exposure to urban flooding has become an imperative component of risk mitigation strategies, particularly when considering the dynamic nature of human mobility patterns. Previous assessments relying on neighborhood block-scale population estimates derived from conventional census data have been constrained by significant spatial aggregation errors. This study presents methodological advancements through the integration of social sensing data analytics, enabling unprecedented spatial resolution at the building scale while capturing real-time population dynamics. We developed an agent-based simulation framework that incorporates (1) building-based urban environment, (2) hydrodynamic flood modeling outputs, and (3) empirically grounded human mobility patterns derived from multi-source geospatial big data. The implemented model systematically evaluates transient population exposure through spatiotemporal superposition analysis of flood characteristics and human occupancy patterns across different urban functional zones in Lishui City, China. Firstly, multi-source points of interest (POIs) data are aggregated to acquire activated time of buildings, and an urban environment system at the building scale is constructed. Then, with population, buildings, and roads as the agents, and population behavior rules, activity time of buildings, and road accessibility as constraints, an agent-based model in an urban flood scenario is designed to dynamically simulate the distribution of population. Finally, the population dynamics of urban flood exposure under a flood scenario with a 50-year return is simulated. We found that the traditional exposure assessment method at the block scale significantly overestimated the exposure, which is four times of our results based on building scale. The proposed method enables a clearer portrayal of the disaster occurrence process at the urban local level. This work, for the first time, incorporates multi-source social sensing data and the triadic relationship between human activities, time, and space in the disaster process into flood exposure assessment. The outcomes of this study can contribute to estimate the susceptibility to urban flooding and formulate emergency response plans. Full article

This article draws on over 40 years of the author’s experience with hydroinformatics tools for water and sustainability challenges, including flooding. It aims to spark discussion on urban flood risk and resilience rather than provide a literature review or definitive answers. Assessing urban flood risk and resilience is complex due to the spatio-temporal nature of rainfall, urban landscape features (e.g., buildings, roads, bridges and underpasses) and the interaction between aboveground and underground drainage systems. Flood simulation methods have evolved to analyse flood mitigation schemes, damage evaluation, flood risk mapping and green infrastructure impacts. Advances in terrain mapping technologies have improved flood analyses. Despite investments in flood management infrastructure, a residual flood risk remains, necessitating an understanding of the recovery and return to normality post-flood. Both risk and resilience approaches are essential for urban flood planning and management. Future challenges and opportunities include both technological and governance solutions, with artificial intelligence advancements offering the potential for digital twins to better protect urban communities and enhance the recovery from flood disasters. Full article

With the global increase in disaster risks, enhancing urban resilience has become a critical strategy for risk mitigation and sustainable development. This study develops a two-dimensional indicator framework based on urban systems and resilience capacity from the perspective of the disaster management cycle and applies an improved CRITIC-TOPSIS method to evaluate the resilience levels of the Chengdu–Chongqing urban agglomeration, China. The spatiotemporal evolution of urban resilience from 2010 to 2022 is systematically examined. Furthermore, the dynamics of urban resilience transitions are investigated using a spatial Markov chain model, and the driving factors behind the spatial distribution of resilience are explored through the Geo-detector method. The results indicate the following: (1) Comprehensive resilience demonstrated a steady upward trend during the study period, with Chengdu and Chongqing, as core cities, driving regional resilience improvement and reducing disparities within the urban agglomeration. (2) Significant spatial heterogeneity was observed in the distribution of the comprehensive resilience index and the indices of individual resilience dimensions. (3) The Markov chain analysis revealed a distinct “club convergence” pattern in the dynamic transitions of resilience levels, with development trends closely tied to spatial factors. (4) The Geo-detector model analysis highlighted that infrastructure development and technological innovation exert long-term and substantial impacts on resilience improvement. These findings provide valuable insights for enhancing resilience and promoting sustainable development in the Chengdu–Chongqing region and other similar urban systems. Full article

As the core carrier of historical and cultural identity, cultural heritage is facing multiple threats such as natural disasters, human activities and its own vulnerability. There is an increasing number of studies on cultural heritage risk assessment around the world, but the risk assessment of cultural heritage in China has not been fully explored. In this paper, the LightGBM model was used to quantitatively analyze the main influencing factors of cultural heritage risk along the Ancient Tea Horse Road in Sichuan, and spatial analysis was carried out by combining geographic information system (GIS) technology. In order to improve the interpretability of the assessment results, the SHAP method was introduced to systematically evaluate the contribution of each influencing factor to the risk of cultural heritage. This study identified seven major risk factors, including landslides, collapses, debris flows, earthquakes, soil erosion, urban road networks, and cultural heritage vulnerability, and constructed a risk assessment framework that comprehensively considers the vulnerability to natural and synthetic factors and the heritage itself. The results of the assessment divided the risk of cultural heritage sites into five levels: very low, low, medium, high and very high, and the results showed that 52.36% of the cultural heritage was classified as at medium and high risk and above, revealing the severe security situation faced by cultural heritage in the region and indicating the urgent need to take effective protective and management measures to deal with multiple risks and challenges. Full article

Urban agglomeration ecosystems are impacted by human activities and natural disasters, so analyzing the spatial and temporal evolution of landscape ecological resilience from the perspective of adaptive cycling is crucial. Using the Changsha–Zhuzhou–Xiangtan urban agglomeration in China as a case study, this research constructs a “Risk-Potential-Connectivity” framework to evaluate ecological resilience. This framework applies exploratory spatial data analysis methods to examine the spatiotemporal evolution and associated patterns of resilience and the Geodetector model to measure the driving factors of spatial variation. This study constructs an adaptive cycle model based on ecological resilience analysis, integrating potential and connectivity indices to classify the development stages of urban agglomeration regions dynamically. The results showed that the overall spatial distribution pattern of ecological risk decreased from the center outward, whereas ecological potential and connectivity increased. The average resilience index from 2000 to 2020 was 0.31, with a declining trend and shifting center of gravity from northwest to southeast. The spatial and temporal distribution of toughness exhibited high and low aggregation, with an overall Moran index greater than 0.75. Land-use intensity had the strongest explanatory power (q = 0.3662) for the spatial differentiation of landscape ecological resilience drivers and the joint effects of factor interaction had a higher explanatory power than single factors. Adaptive cycle analysis revealed that Furong District is in the protection stage, Xiangtan County in the development stage, and Liling City in the reorganization stage, with no region yet in the release stage. The findings offer a better understanding of the interactive adaptation characteristics and evolutionary patterns of social-ecological systems over extended periods, providing scientific support for the formulation of protection strategies to respond to dynamic changes in urban agglomeration ecosystems. Full article

This study examines the factors influencing home elevation decisions among participants in Houston’s Homeowner Assistance Program (HoAP) and the Texas General Land Office’s Homeowner Assistance Program (HAP) in the aftermath of Hurricane Harvey and other flood events. Using a mixed-methods approach, we conducted surveys and semi-structured interviews with 50 homeowners, supplemented by secondary data analyses of program records and GIS-based flood risk assessments. Additionally, 25 undergraduate students engaged in a structured field trip, conducting site observations, interacting with residents, and discussing home elevation with experts. The findings reveal disparities in home elevation outcomes, with lower completion rates in socially vulnerable neighborhoods despite program eligibility. The study also identifies key factors influencing elevation decisions, including mobility concerns, financial constraints, neighborhood esthetics, and perceptions of long-term flood risk. Homeowners aged 60–79 were more likely to elevate their homes, while individuals with disabilities faced additional barriers. This research highlights the need for targeted policy interventions to improve program equity and ensure that vulnerable populations receive adequate support. Beyond its case study implications, this research contributes to broader discussions on disaster recovery, climate adaptation, and urban resilience. It also serves as a model for integrating student learning into community-based participatory research. While this study is limited in scope, it offers insights into the intersection of social vulnerability and housing adaptation, informing future policy efforts to enhance flood resilience in historically marginalized communities. Full article

Extreme rainfall events are significant manifestations of climate change, causing substantial impacts on urban infrastructure and public life. This study takes the extreme rainfall event in Beijing in 2023 as the background and utilizes data from Sina Weibo. Based on large language models and prompt engineering, disaster information is extracted, and a multi-factor coupled disaster multi-sentiment classification model, Bert-BiLSTM, is designed. A disaster analysis framework focusing on three dimensions of theme, location and sentiment is constructed. The results indicate that during the pre-disaster stage, themes are concentrated on warnings and prevention, shifting to specific events and rescue actions during the disaster, and post-disaster, they express gratitude to rescue personnel and highlight social cohesion. In terms of spatial location, the disaster shows significant clustering, predominantly occurring in Mentougou and Fangshan. There is a clear difference in emotional expression between official media and the public; official media primarily focuses on neutral reporting and fact dissemination, while public sentiment is even richer. At the same time, there are also variations in sentiment expressions across different affected regions. This study provides new perspectives and methods for analyzing extreme rainfall events on social media by revealing the evolution of disaster themes, the spatial distribution of disasters, and the temporal and spatial changes in sentiment. These insights can support risk assessment, resource allocation, and public opinion guidance in disaster emergency management, thereby enhancing the precision and effectiveness of disaster response strategies. Full article

This study focuses on the ecological security of the Guangxi–Vietnam karst border region, introducing an innovative framework that integrates traditional ecological knowledge (TEK) with modern GIS-based ecological modeling to promote sustainable development. Using remote sensing, geographic information systems (GIS), and ecological sensitivity assessments, this research identifies key ecological sources, corridors, pinch points, and barriers. Unlike conventional approaches that rely solely on biophysical indicators, this study incorporates TEK-derived ecological practices into ecological network optimization, ensuring that conservation strategies align with local knowledge and cultural sustainability. Ecological sensitivity was evaluated through indicators such as soil erosion, rocky desertification, and geological disaster risks to guide the optimization of ecological networks. TEK practices, including afforestation, rotational farming, and biodiversity conservation, were systematically integrated into the construction of an innovative “three axes, two belts, and six zones” ecological security pattern. The results revealed 55 ecological corridors, 80 ecological pinch points, and 14 ecological barriers, primarily located in areas with high human activity intensity. This study advances ecological security planning by demonstrating a replicable model for TEK-based conservation in transboundary karst landscapes. By integrating traditional knowledge with modern ecological methodologies, it enhances biodiversity conservation, ecosystem connectivity, and resilience. The proposed framework provides actionable insights for sustainable urban–rural coordination and ecological restoration in karst landscapes, contributing to the long-term sustainability of ecologically vulnerable and culturally diverse regions. Full article

Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP). In this study, ten flood causative factors are employed to generate flood-prone zones. AHP, a form of weighted multi-criteria decision analysis, is applied to assess the relative impact weights of these flood causative factors. Subsequently, these factors are integrated into ArcGIS Pro (Version 3.3) to create a flood susceptibility map for the study area using a weighted overlay approach. The resulting flood susceptibility map classified the county into five susceptibility zones: very low (17.48%), low (41.89%), moderate (37.53%), high (2.93%), and very high (0.17%). The FEMA flood hazard map of Davidson County is used to validate the flood susceptibility map created from this approach. Ultimately, this comparison reinforced the accuracy and reliability of the flood susceptibility assessment for the study area using integrated GIS and AHP approach. Full article

The confluence of global warming, the urban heat island effect, and alterations in the nature of underlying surfaces has led to a continuous escalation in the frequency, scale, and intensity of fires within urban green spaces. Mitigating or eliminating the adverse effects of such fires on the service functions of urban ecosystems, while enhancing the resilience of urban greening systems in disaster prevention and risk reduction, has become a pivotal challenge in modern urban development and management. Academic focus has progressively broadened from isolated urban and forest domains to encompass the more intricate environments of the Wildland–Urban Interface (WUI) and urban–suburban forests, with a particular emphasis on the distinctive characteristics of urban greening and in-depth research. This study employs a combination of CiteSpace bibliometric analysis and a narrative literature review to comprehensively examine three critical aspects of urban fire safety as follows: (1) the evaluation of the fire-resistant performance of landscape plants in urban green spaces; (2) the mechanisms of fire behavior in urban greening systems; and (3) the assessment and prediction of urban fire risks. Our findings indicate that landscape plants play a crucial role in controlling the spread of fires in urban green spaces by providing physical barriers and inhibiting combustion processes, thereby mitigating fire propagation. However, the diversity and non-native characteristics of urban greenery species present challenges. The existing research lacks standardized experimental indicators and often focuses on single-dimensional analyses, leading to conclusions that are limited, inconsistent, or even contradictory. Furthermore, most current fire spread models are designed primarily for forests and wildland–urban interface (WUI) regions. Empirical and semi-empirical models dominate this field, yet future advancements will likely involve coupled models that integrate climate and environmental factors. Fire risk assessment and prediction represent a global research hotspot, with machine learning- and deep learning-based approaches increasingly gaining prominence. These advanced methods have demonstrated superior accuracy compared to traditional techniques in predicting urban fire risks. This synthesis aims to elucidate the current state, trends, and deficiencies within the existing research. Future research should explore methods for screening highly resistant landscape plants, with the goal of bolstering the ecological resilience of urban greening systems and providing theoretical underpinnings for the realization of sustainable urban environmental security. Full article

Urban flooding is typically caused by multiple factors, with extreme rainfall and rising water levels in receiving bodies both contributing to increased flood risks. This study focuses on assessing urban flood risks in Jinhua City, Zhejiang Province, China, considering the combined effects of extreme rainfall and high river water levels. Using historical data from Jinhua station (2005–2022), the study constructed a joint probability distribution of rainfall and water levels via a copula function. The findings show that the risk probability of combined rainfall and high water levels is significantly higher than considering each factor separately, indicating that ignoring their interaction could greatly underestimate flood risks. Scenario simulations using the Infoworks ICM model demonstrate that flood areas range from 0.67% to 5.39% under the baseline scenario but increase to 8.98–12.80% when combined with a 50a return period water level. High river water levels play a critical role in increasing both the extent and depth of flooding, especially when low rainfall coincides with high water levels. These findings highlight the importance of considering compound disaster-causing factors in flood risk assessment and can serve as a reference for urban drainage and flood control planning and risk management. Full article

This study investigated the effectiveness of nature-based solutions (NBSs) as a resilient strategy for mitigating urban flood risks in a developing hot arid country. The research method included the following steps: (a) performing a flood hazard risk assessment for the Fifth Settlement district in New Cairo, Egypt, (b) selecting best-fit NBSs, and (c) performance assessment. The process started with flood hazard analysis using hydrological data, topographical maps, urban planning, and land use maps, in addition to the history of storm events. This step defined the urban areas located in flood depth zones and categorized their flood hazard level. Exposure assessment considered the number and characteristics of population and buildings exposed to flood hazards. Vulnerability assessment determined the vulnerable characteristics of exposed populations and buildings to flood risk. The result of this assessment step indicated that there were 2000 buildings distributed in almost twenty neighborhood areas facing high flood risk. One of these urban areas with 72 building units, including residential, public, and services buildings, was selected to test the potential of integrating NBSs for flood-resilient land use planning and disaster preparedness. The selection of best-fit NBSs was based on a weighted-average sum matrix considering their climatic and contextual suitability and applicability. As a final step, numerical simulation models helped assess the efficiency of the selected NBSs for stormwater runoff reduction and the percentage of the volume capture goal. Five simulation models tested the efficiency of each NBS individually. Rain gardens achieved the highest stormwater capture percentage, while green roofs performed the least effectively, with capture rates of 43.6% and 9.9%, respectively. Two more simulation models were developed to evaluate the efficiency of NBSs when implemented in combination compared to the base case of using no NBSs. Permeable paving demonstrated the highest effectiveness in volume capture. The result indicated that applying combined measures of NBSs over 54.1% of the total site area was able to capture 8% more than the required volume capture goal. Consequently, this study underscores the necessity of adopting tailored solutions and integrated approaches using NBSs for flood risk mitigation. This necessitates testing their performance under site-specific conditions and future climate projections. Full article

With the intensification of global climate change, the frequency and intensity of urban flood disasters have been increasing significantly, highlighting the necessity for a scientific assessment of urban flood risks. However, most existing studies focus primarily on the spatial distribution of urban flood data and their socio-economic impacts, with limited attention on the urban flood control engineering system (UFCES) itself and the analysis of urban flood risks from the perspective of the degree of system failure. To address this gap, we proposed a rapid prediction method for key information of the UFCES based on a machine learning model. With the aim of improving the accuracy and timeliness of information prediction, we employed a coupled modeling approach that integrates physical mechanisms with data-driven methods to simulate and predict the information. Taking the Wusha River Basin in Nanchang City as a case study, we generated the training, validation, and testing datasets for machine learning using the urban flood mechanism model. Subsequently, we compared the prediction performance of four machine learning models: random forest (RF), XGBoost (XGB), support vector regression (SVR), and the backpropagation neural network (BP). The results indicate that the XGB model provides more stable and accurate simulation outcomes for key information, with Nash coefficient (R²) values above 0.87 and relative error (RE) values below 0.06. Additionally, the XGB model exhibited significant advantages in terms of simulation speed and model generalization performance. Furthermore, we explored methods for selecting key information indicators and generating samples required for the coupled model. These findings are crucial for the rapid prediction of key information in the UFCES. These achievements improve the technical level of urban flood simulation and provide richer information for urban flood risk management. Full article

In recent years, the increasing frequency of extreme rainfall has exacerbated urban waterlogging, which has seriously constrained the sustainable development of cities. Given the problem that the impact of social information on waterlogging risk is easy to ignore in the urban risk waterlogging assessment process, it is of great significance to carry out a comprehensive waterlogging risk assessment and identify the waterlogging risk for urban waterlogging prevention and control. Based on the hazard–vulnerability assessment framework, this study comprehensively considers the flood disaster hazard and socio-economic vulnerability to carry out a multi-scenario urban waterlogging risk assessment in the central urban area of Zhoukou. The results show that, in comprehensive risk assessment, the area proportions are expressed as medium risk > low risk > higher risk > high risk. For a single waterlogging hazard assessment, the area proportions are shown as low risk > medium risk > higher risk > high risk. The difference ranges in area proportions of low, medium, higher, and high risk are (−61.00%, −54.00%), (49.00%, 56.00%), (1.30%, 2.70%), and (1.80%, 4.00%), respectively. It can be seen that compared with the single waterlogging hazard assessment, in the comprehensive waterlogging risk assessment with the introduction of the vulnerability factor, the waterlogging risk in the area with highly waterlogging vulnerability increases correspondingly, while the waterlogging risk in the area with low waterlogging vulnerability decreases relatively, and the waterlogging risk assessment results are more in line with the actual situation. Full article