Abstract:
keep_len="250">The food supply chain’s heavy reliance on electricity poses significant vulnerabilities in the event of prolonged and widespread power disruptions. This study introduces a system-dynamics model that integrates five critical infrastructures—electric grid, liquid fossil fuels, Internet, transportation, and human workforce—to evaluate the resilience of food supply chains to major power outages. We validated the model using the 2019 Venezuelan blackouts as a case study, demonstrating its predictive validity. We then explored how more extreme electricity losses would disrupt the supply chain. More specifically, we modeled the impact of a large-scale cyberattack on the US electric grid and a high-altitude electromagnetic pulse (HEMP) event. A cyberattack severely damaging the US electric grid and allowing for recovery within a few weeks or months would lead to substantial drops in food consumption. However, it would likely still be possible to provide adequate calories to everyone, assuming that food is equitably distributed. In contrast, a year-long recovery from a HEMP event affecting most of the continental United States could precipitate a state of famine. Our analysis represents a first attempt at quantifying how food availability progressively worsens as power outages extend over time. Our open-source model is made publicly available, and we encourage its application to other catastrophic scenarios beyond those specifically considered in this work (for example, extreme solar storms, high-lethality pandemics). The food supply chain’s heavy reliance on electricity poses significant vulnerabilities in the event of prolonged and widespread power disruptions. This study introduces a system-dynamics model that integrates five critical infrastructures—electric grid, liquid fossil fuels, Internet, transportation, and human workforce—to evaluate the resilience of food supply chains to major power outages. We validated the model using the 2019 Venezuelan blackouts as a case study, demonstrating its predictive validity. We then explored how more extreme electricity losses would disrupt the supply chain. More specifically, we modeled the impact of a large-scale cyberattack on the US electric grid and a high-altitude electromagnetic pulse (HEMP) event. A cyberattack severely damaging the US electric grid and allowing for recovery within a few weeks or months would lead to substantial drops in food consumption. However, it would likely still be possible to provide adequate calories to everyone, assuming that food is equitably distributed. In contrast, a year-long recovery from a HEMP event affecting most of the continental United States could precipitate a state of famine. Our analysis represents a first attempt at quantifying how food availability progressively worsens as power outages extend over time. Our open-source model is made publicly available, and we encourage its application to other catastrophic scenarios beyond those specifically considered in this work (for example, extreme solar storms, high-lethality pandemics).

Abstract:
keep_len="250">Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity. Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity.

Abstract:
keep_len="250">Developing a regional damage function to quickly estimate direct economic losses (DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function (RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance. The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability. Developing a regional damage function to quickly estimate direct economic losses (DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function (RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance. The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.

Abstract:
keep_len="250">Resettlement and relocation are among the most difficult policies to put into practice, but they may be the best ways to minimize future risks to settlements exposed to natural hazards both before and after disaster events. As climate-related disasters and forced migration become increasingly common worldwide, governments, humanitarian or development actors, and policymakers must now prioritize the implementation of a dignified and effective resettlement program as part of their planning and management responsibilities. Much of this effectiveness depends on the stakeholders and beneficiaries’ understanding and knowledge of the different resettlement phases, culture and customs of affected populations, activities, and the associated implementation challenges, costs, and benefits. Serious games are used in a variety of contexts to increase awareness, train and build capacity in stakeholders and beneficiaries. This article presents a serious game developed to educate practitioners, local agencies, students, and the public to understand the complexities and challenges involved in a successful resettlement. The game is based on a real proposed resettlement project initiated in the Chiradzulu District in southern Malawi after Cyclone Freddy in March 2023, which caused widespread flooding and landslides, forcing some villages to relocate permanently. The progression in the Road to Resettlement Game consists of six primary levels: land and site preparation, housing and livelihood, water, sanitation, and hygiene, health, education, and protection. These levels are meant to be completed in a sequence that adheres to the principles of resettlement. By engaging in the serious table-top board game, players gain an understanding of the resettlement activities, their sequence, and the associated practical (technical and social) and financial challenges. Resettlement and relocation are among the most difficult policies to put into practice, but they may be the best ways to minimize future risks to settlements exposed to natural hazards both before and after disaster events. As climate-related disasters and forced migration become increasingly common worldwide, governments, humanitarian or development actors, and policymakers must now prioritize the implementation of a dignified and effective resettlement program as part of their planning and management responsibilities. Much of this effectiveness depends on the stakeholders and beneficiaries’ understanding and knowledge of the different resettlement phases, culture and customs of affected populations, activities, and the associated implementation challenges, costs, and benefits. Serious games are used in a variety of contexts to increase awareness, train and build capacity in stakeholders and beneficiaries. This article presents a serious game developed to educate practitioners, local agencies, students, and the public to understand the complexities and challenges involved in a successful resettlement. The game is based on a real proposed resettlement project initiated in the Chiradzulu District in southern Malawi after Cyclone Freddy in March 2023, which caused widespread flooding and landslides, forcing some villages to relocate permanently. The progression in the Road to Resettlement Game consists of six primary levels: land and site preparation, housing and livelihood, water, sanitation, and hygiene, health, education, and protection. These levels are meant to be completed in a sequence that adheres to the principles of resettlement. By engaging in the serious table-top board game, players gain an understanding of the resettlement activities, their sequence, and the associated practical (technical and social) and financial challenges.

Abstract:
keep_len="250">This study explored a Bayesian belief networks (BBNs) approach, developing two distinct models for prioritizing the seven indicators related to the “rapid response to and mitigation of the spread of an epidemic” category within the context of both the specific category and the Global Health Security Index (GHS index). Utilizing data from the 2021 GHS index, the methodology involves rigorous preprocessing, the application of the augmented naive Bayes algorithm for structural learning, and k-fold cross-validation. Key findings show unique perspectives in both BBN models. In the mutual value of information analysis, “linking public health and security authorities” emerged as the key predictor for the “rapid response to and mitigation of the spread of an epidemic” category, while “emergency preparedness and response planning” assumed precedence for the GHS index. Sensitivity analysis highlighted the critical role of “emergency preparedness and response planning” and “linking public health and security authorities” in extreme performance states, with “access to communications infrastructure” and “trade and travel restrictions” exhibiting varied significance. The BBN models exhibit high predictive accuracy, achieving 83.3% and 82.3% accuracy for extreme states in “rapid response to and mitigation of the spread of an epidemic” and the GHS index, respectively. This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses. This study explored a Bayesian belief networks (BBNs) approach, developing two distinct models for prioritizing the seven indicators related to the “rapid response to and mitigation of the spread of an epidemic” category within the context of both the specific category and the Global Health Security Index (GHS index). Utilizing data from the 2021 GHS index, the methodology involves rigorous preprocessing, the application of the augmented naive Bayes algorithm for structural learning, and k-fold cross-validation. Key findings show unique perspectives in both BBN models. In the mutual value of information analysis, “linking public health and security authorities” emerged as the key predictor for the “rapid response to and mitigation of the spread of an epidemic” category, while “emergency preparedness and response planning” assumed precedence for the GHS index. Sensitivity analysis highlighted the critical role of “emergency preparedness and response planning” and “linking public health and security authorities” in extreme performance states, with “access to communications infrastructure” and “trade and travel restrictions” exhibiting varied significance. The BBN models exhibit high predictive accuracy, achieving 83.3% and 82.3% accuracy for extreme states in “rapid response to and mitigation of the spread of an epidemic” and the GHS index, respectively. This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

Abstract:
keep_len="250">The adoption of a stakeholder approach to public engagement within the public sector has been extensive. However, there remain critical gaps in the understanding of stakeholder participation arising from hidden disparities that contribute to unequal access to communication channels, information, and hence ultimately knowledge and decision making. The term “epistemic injustice” has been used to describe such inequality of access and consequently, the outcome that ensues. Epistemic injustice is much overlooked in stakeholder theory. This article shows how epistemic injustice can act as a barrier to effective stakeholder engagement and hence to successful public policy formulation and implementation. We use the case of vaccine hesitancy among Scotland’s African, Caribbean, and Black (ACB) communities to illustrate this problem of unequal participation. The study drew on primary data involving 85 participants and secondary data sources from extant literature and explored salient factors shaping barriers to vaccine uptake during the recent pandemic. The findings demonstrate how the failure to grasp epistemic injustice undermines the effectiveness of the stakeholder approach, even with the most well-intentioned efforts. We argue that epistemic injustice is a critical barrier to effective stakeholder approaches. The adoption of a stakeholder approach to public engagement within the public sector has been extensive. However, there remain critical gaps in the understanding of stakeholder participation arising from hidden disparities that contribute to unequal access to communication channels, information, and hence ultimately knowledge and decision making. The term “epistemic injustice” has been used to describe such inequality of access and consequently, the outcome that ensues. Epistemic injustice is much overlooked in stakeholder theory. This article shows how epistemic injustice can act as a barrier to effective stakeholder engagement and hence to successful public policy formulation and implementation. We use the case of vaccine hesitancy among Scotland’s African, Caribbean, and Black (ACB) communities to illustrate this problem of unequal participation. The study drew on primary data involving 85 participants and secondary data sources from extant literature and explored salient factors shaping barriers to vaccine uptake during the recent pandemic. The findings demonstrate how the failure to grasp epistemic injustice undermines the effectiveness of the stakeholder approach, even with the most well-intentioned efforts. We argue that epistemic injustice is a critical barrier to effective stakeholder approaches.

Abstract:
keep_len="250">The primary aim of this study was to develop a model of a socially inclusive climate risk insurance (CRI) mechanism based on the differential risk transfer approach. This study focused on the department of La Guajira, Colombia, as a case study. La Guajira is the department in Colombia that, due to its critical disaster risk conditions, presents the adequate configuration for implementing a climate risk transfer mechanism. The article starts by analyzing risk conditions by using secondary data. Based on fieldwork, this research explored the perspectives of the most vulnerable sectors in La Guajira Department on the socioeconomic impacts and needs they experience regarding climate-related hazards, their adaptive measures for risk reduction, and their willingness to adopt CRI. This represents the fundamental input for the formulation of the CRI model. Consequently, this research proposed an operational structure as input for future implementations of the model. The results indicate that national and local disaster risk management public policies align with the sectors’ needs and priorities. Strengthening sectoral associations can enhance representation in CRI projects. In-kind indemnization is preferred for women entrepreneurs and the indigenous community. The CRI model includes a risk pool through the family compensation fund of La Guajira as a sectoral agglomerator, with contingent credit and traditional/parametric insurance. The methodology developed in this study can be applied in different contexts worldwide as a guidance for informing national and international climate risk finance initiatives. The primary aim of this study was to develop a model of a socially inclusive climate risk insurance (CRI) mechanism based on the differential risk transfer approach. This study focused on the department of La Guajira, Colombia, as a case study. La Guajira is the department in Colombia that, due to its critical disaster risk conditions, presents the adequate configuration for implementing a climate risk transfer mechanism. The article starts by analyzing risk conditions by using secondary data. Based on fieldwork, this research explored the perspectives of the most vulnerable sectors in La Guajira Department on the socioeconomic impacts and needs they experience regarding climate-related hazards, their adaptive measures for risk reduction, and their willingness to adopt CRI. This represents the fundamental input for the formulation of the CRI model. Consequently, this research proposed an operational structure as input for future implementations of the model. The results indicate that national and local disaster risk management public policies align with the sectors’ needs and priorities. Strengthening sectoral associations can enhance representation in CRI projects. In-kind indemnization is preferred for women entrepreneurs and the indigenous community. The CRI model includes a risk pool through the family compensation fund of La Guajira as a sectoral agglomerator, with contingent credit and traditional/parametric insurance. The methodology developed in this study can be applied in different contexts worldwide as a guidance for informing national and international climate risk finance initiatives.

Abstract:
keep_len="250">In tropical regions such as Nicaragua, the population’s vulnerability to hazards has escalated in recent decades. This increase in vulnerability has led to a surge in disasters, particularly those triggered by intense hurricanes. The implications at the national level are still poorly understood. The aim of this article has, therefore, been two-fold. First, to present a historical review of the direct effects of tropical cyclones on society in Nicaragua from 1852 to 2020. Second, to analyze the statistical probabilities of future hurricane-spawned high winds over Nicaragua. Data on cyclones hitting Nicaragua’s coasts were collected, including direct effects, wind speed, pressure, category, direction, and time of landfall. A database was created to classify intensity based on wind speed and frequency. Between 1852 and 2020, Nicaragua experienced 58 tropical cyclones with varying degrees of intensity between September and November. The trajectories of six past hurricanes were considered here regarding the areas that might have been under potential threat. Three zones of influence were delimited along each trajectory according to three wind intensities and the trajectory of these hurricanes. The consequent exposure of each Nicaraguan department and autonomous region was established. The findings are essential to delimitating priority areas for attention regarding the likely impact of tropical cyclones, mainly category 4 and 5 hurricanes. Public officials and the general public can use these data to identify the pressing need for enhanced strategies to mitigate disaster risk and avoid potential disasters. In tropical regions such as Nicaragua, the population’s vulnerability to hazards has escalated in recent decades. This increase in vulnerability has led to a surge in disasters, particularly those triggered by intense hurricanes. The implications at the national level are still poorly understood. The aim of this article has, therefore, been two-fold. First, to present a historical review of the direct effects of tropical cyclones on society in Nicaragua from 1852 to 2020. Second, to analyze the statistical probabilities of future hurricane-spawned high winds over Nicaragua. Data on cyclones hitting Nicaragua’s coasts were collected, including direct effects, wind speed, pressure, category, direction, and time of landfall. A database was created to classify intensity based on wind speed and frequency. Between 1852 and 2020, Nicaragua experienced 58 tropical cyclones with varying degrees of intensity between September and November. The trajectories of six past hurricanes were considered here regarding the areas that might have been under potential threat. Three zones of influence were delimited along each trajectory according to three wind intensities and the trajectory of these hurricanes. The consequent exposure of each Nicaraguan department and autonomous region was established. The findings are essential to delimitating priority areas for attention regarding the likely impact of tropical cyclones, mainly category 4 and 5 hurricanes. Public officials and the general public can use these data to identify the pressing need for enhanced strategies to mitigate disaster risk and avoid potential disasters.

Abstract:
keep_len="250">Global warming and climate change significantly increase the frequency of coastal floods caused by sea level rise (SLR) as a permanent factor and hydrometeorological hazards as tentative factors. The combined risks will affect coastal communities. South Korea is gradually facing SLR risks, mainly in its southern coastal regions; however, disaster risk reduction (DRR) in coastal regions remains fragmented. This study aimed to investigate the status of DRR for coastal communities in South Korea by looking at government practices and testimonies from residents. This study reviewed DRR-related regulations and documents and collected data from interviews with local government officials, field visits, and informal conversations with residents in six coastal communities. The findings show that the coastal communities were less resilient to coastal floods than to other hazards, such as typhoons and heavy rains, and the potential consequences could be expanded due to demographic challenges, fragmented institutional systems, and low risk awareness. Moreover, this study emphasized the necessity of an integrated approach to reducing the impact of coastal floods caused by both SLR and other factors. It also highlighted the importance of coastal community engagement in local DRR activities through increasing risk awareness and adapting to environmental change based on appropriate risk information disclosure by the government. The impacts of coastal floods triggered by SLR and other hazard factors can be reduced by aligning practical regulatory measures with adaptive strategies and enhancing the disaster resilience of coastal communities. Global warming and climate change significantly increase the frequency of coastal floods caused by sea level rise (SLR) as a permanent factor and hydrometeorological hazards as tentative factors. The combined risks will affect coastal communities. South Korea is gradually facing SLR risks, mainly in its southern coastal regions; however, disaster risk reduction (DRR) in coastal regions remains fragmented. This study aimed to investigate the status of DRR for coastal communities in South Korea by looking at government practices and testimonies from residents. This study reviewed DRR-related regulations and documents and collected data from interviews with local government officials, field visits, and informal conversations with residents in six coastal communities. The findings show that the coastal communities were less resilient to coastal floods than to other hazards, such as typhoons and heavy rains, and the potential consequences could be expanded due to demographic challenges, fragmented institutional systems, and low risk awareness. Moreover, this study emphasized the necessity of an integrated approach to reducing the impact of coastal floods caused by both SLR and other factors. It also highlighted the importance of coastal community engagement in local DRR activities through increasing risk awareness and adapting to environmental change based on appropriate risk information disclosure by the government. The impacts of coastal floods triggered by SLR and other hazard factors can be reduced by aligning practical regulatory measures with adaptive strategies and enhancing the disaster resilience of coastal communities.

Abstract:
keep_len="250">The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows. The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows.

Abstract:
keep_len="250">The occurrence of debris flow events in small-scale watersheds with dense vegetation in mountainous areas that result in significant loss of life and missing individuals challenges our understanding and expertise in investigating and preventing these disasters. This has raised concerns about the occurrence of large debris flow disasters from small watersheds. This study focused on a catastrophic debris flow that took place in Longtou Gully (0.45 km²) in Tianquan County, Ya’an City on 25 September 2021, which resulted in 14 deaths and missing individuals. Through comprehensive field investigations, high-precision remote sensing data analyses, and numerical simulations, we analyzed the triggering mechanisms and dynamic processes of this event. Our results indicate that the convergence hollow at the channel head exhibited higher hydraulic conditions during rainfall compared to gentle slopes and convex terrains, leading to the instability of colluvial soil due to the expansion of the saturated zone near the soil–bedrock interface. The entrainment of material eroded from the channel resulted in an approximately 4.7 times increase in volume, and the channel scarp with a height of about 200 m amplified the destructive power of the debris flow. We emphasize the need to take seriously the possibility of catastrophic debris flows in small-scale watersheds, with colluvial deposits in hollows at the channel head under vegetation cover that serve as precursor material sources, and the presence of channel scarps formed by changes in the incision rate of the main river, which is common in the small watershed on both sides. This study provides insights for risk assessment of debris flows in small-scale catchments with dense vegetation cover in mountainous areas, highlighting the importance of vigilance in addressing disasters in small-scale catchments, particularly in regions with increasing human–environment conflicts. The occurrence of debris flow events in small-scale watersheds with dense vegetation in mountainous areas that result in significant loss of life and missing individuals challenges our understanding and expertise in investigating and preventing these disasters. This has raised concerns about the occurrence of large debris flow disasters from small watersheds. This study focused on a catastrophic debris flow that took place in Longtou Gully (0.45 km²) in Tianquan County, Ya’an City on 25 September 2021, which resulted in 14 deaths and missing individuals. Through comprehensive field investigations, high-precision remote sensing data analyses, and numerical simulations, we analyzed the triggering mechanisms and dynamic processes of this event. Our results indicate that the convergence hollow at the channel head exhibited higher hydraulic conditions during rainfall compared to gentle slopes and convex terrains, leading to the instability of colluvial soil due to the expansion of the saturated zone near the soil–bedrock interface. The entrainment of material eroded from the channel resulted in an approximately 4.7 times increase in volume, and the channel scarp with a height of about 200 m amplified the destructive power of the debris flow. We emphasize the need to take seriously the possibility of catastrophic debris flows in small-scale watersheds, with colluvial deposits in hollows at the channel head under vegetation cover that serve as precursor material sources, and the presence of channel scarps formed by changes in the incision rate of the main river, which is common in the small watershed on both sides. This study provides insights for risk assessment of debris flows in small-scale catchments with dense vegetation cover in mountainous areas, highlighting the importance of vigilance in addressing disasters in small-scale catchments, particularly in regions with increasing human–environment conflicts.

Abstract:
keep_len="250">As the global push for sustainable urban development progresses, this study, set against the backdrop of Hangzhou City, one of China’s megacities, addressed the conflict between urban expansion and the occurrence of urban geological hazards. Focusing on the predominant geological hazards troubling Hangzhou—urban road collapse, land subsidence, and karst collapse—we introduced a Categorical Boosting-SHapley Additive exPlanations (CatBoost-SHAP) model. This model not only demonstrates strong performance in predicting the selected typical urban hazards, with area under the curve (AUC) values reaching 0.92, 0.92, and 0.94, respectively, but also, through the incorporation of the explainable model SHAP, visually presents the prediction process, the interrelations between evaluation factors, and the weight of each factor. Additionally, the study undertook a multi-hazard evaluation, producing a susceptibility zoning map for multiple hazards, while performing tailored analysis by integrating economic and population density factors of Hangzhou. This research enables urban decision makers to transcend the “black box” limitations of machine learning, facilitating informed decision making through strategic resource allocation and scheduling based on economic and demographic factors of the study area. This approach holds the potential to offer valuable insights for the sustainable development of cities worldwide. As the global push for sustainable urban development progresses, this study, set against the backdrop of Hangzhou City, one of China’s megacities, addressed the conflict between urban expansion and the occurrence of urban geological hazards. Focusing on the predominant geological hazards troubling Hangzhou—urban road collapse, land subsidence, and karst collapse—we introduced a Categorical Boosting-SHapley Additive exPlanations (CatBoost-SHAP) model. This model not only demonstrates strong performance in predicting the selected typical urban hazards, with area under the curve (AUC) values reaching 0.92, 0.92, and 0.94, respectively, but also, through the incorporation of the explainable model SHAP, visually presents the prediction process, the interrelations between evaluation factors, and the weight of each factor. Additionally, the study undertook a multi-hazard evaluation, producing a susceptibility zoning map for multiple hazards, while performing tailored analysis by integrating economic and population density factors of Hangzhou. This research enables urban decision makers to transcend the “black box” limitations of machine learning, facilitating informed decision making through strategic resource allocation and scheduling based on economic and demographic factors of the study area. This approach holds the potential to offer valuable insights for the sustainable development of cities worldwide.