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The first-order second-moment checking point method was introduced to judge the instability probability and evaluate the stability of the TCURM. The frequency–response stability calculation and the reliability results were compared, and a frequency–response stability and reliability analysis method was proposed. Taking the Zengziyan W12# unstable rock mass in Nanchuan, Chongqing, China, as an example, the calculation shows that the dynamic indexes and geological indexes decrease as the stiffness of the deterioration area decreases. According to the statistical data of the laboratory test and the field investigation, reliability theory is used to evaluate the stability of the TCURM, and the failure probabilities are 80.3% and 96.27% under natural and saturated conditions, which correspond to states of poor stability and instability, respectively. The reliability evaluation results are consistent with the conclusion of the frequency–response stability analysis. The new method can provide a theoretical basis for developing the dynamic monitoring and early warning indicators of the TCURM and disaster prevention and mitigation in mountainous areas. [ABSTRACT FROM AUTHOR]

Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China's Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals' cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual's cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual's knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual's familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers' following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings. [ABSTRACT FROM AUTHOR]

Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model's prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment. [ABSTRACT FROM AUTHOR]

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation. [ABSTRACT FROM AUTHOR]

To address the problem of ignoring unpaved roads when planning off-road emergency rescue paths, an improved A* algorithm that incorporates road factors is developed to create an off-road emergency rescue path planning model in this study. To reduce the number of search nodes and improve the efficiency of path searches, the current node is classified according to the angle between the line connecting the node and the target point and the due east direction. Additionally, the search direction is determined in real time through an optimization method to improve the path search efficiency. To identify the path with the shortest travel time suitable for emergency rescue in wilderness scenarios, a heuristic function based on the fusion of road factors and a path planning model for off-road emergency rescue is developed, and the characteristics of existing roads are weighted in the process of path searching to bias the selection process toward unpaved roads with high accessibility. The experiments show that the improved A* algorithm significantly reduces the travel time of off-road vehicles and that path selection is enhanced compared to that with the traditional A* algorithm; moreover, the improved A* algorithm reduces the number of nodes by 16.784% and improves the search efficiency by 27.18% compared with the traditional 16-direction search method. The simulation results indicate that the improved algorithm reduces the travel time of off-road vehicles by 21.298% and improves the search efficiency by 93.901% compared to the traditional A* algorithm, thus greatly enhancing off-road path planning. [ABSTRACT FROM AUTHOR]

Multi-object tracking in satellite videos (SV-MOT) is an important task with many applications, such as traffic monitoring and disaster response. However, the widely studied multi-object tracking (MOT) approaches for general images can rarely be directly introduced into remote sensing scenarios. The main reasons for this can be attributed to the following: (1) the existing MOT approaches would cause a significant rate of missed detection of the small targets in satellite videos; (2) it is difficult for the general MOT approaches to generate complete trajectories in complex satellite scenarios. To address these problems, a novel SV-MOT approach enhanced by high-resolution feature fusion and a two-step association method is proposed. In the high-resolution detection network, a high-resolution feature fusion module is designed to assist detection by maintaining small object features in forward propagation. By utilizing features of different resolutions, the performance of the detection of small targets in satellite videos is improved. Through high-quality detection and the use of an adaptive Kalman filter, the densely packed weak objects can be effectively tracked by associating almost every detection box instead of only the high-score ones. The comprehensive experimental results using the representative satellite video datasets (VISO) demonstrate that the proposed HRTracker with the state-of-the-art (SOTA) methods can achieve competitive performance in terms of the tracking accuracy and the frequency of ID conversion, obtaining a tracking accuracy score of 74.6% and an ID F1 score of 78.9%. [ABSTRACT FROM AUTHOR]

Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris flow events, as well as to explore their long-term development and impact on internal and external channels. Using multi-source remote sensing images from four perspectives, hillslope, channel, accumulation fan, and their relationship with the mainstream, we reconstructed a debris flow event dataset from 2008 to 2020, explored a method for identifying these events, and analyzed their impacts on channels and accumulation fans in Mozi Gully affected by the Wenchuan earthquake. Loose matter was predominantly found in areas proximate to the channel and at lower elevations during debris flow events. Alterations in channel width, accumulation fans downstream, and their potential to obstruct rivers proved to be vital for identifying the large scale of debris flow event. Finally, we encapsulated the evolution patterns and constraints of post-earthquake debris flows. Determination in frequency and scale could offer valuable supplementary data for scenario hypothesis parameters in post-earthquake disaster engineering prevention and control. [ABSTRACT FROM AUTHOR]

Sudden mountain landslides can pose substantial threats to human lives and property. On 4 June 2023, a landslide occurred in Jinkouhe District, Leshan City, Sichuan Province, resulting in 19 deaths and 5 injuries. This study, drawing on field investigations, geological data, and historical imagery, elucidates the characteristics and causes of the landslide and conducts a reverse analysis of the landslide movement process using Massflow V2.8 numerical simulation software. The results indicate that rainfall and human engineering activities are key factors that triggered this landslide. Numerical simulation shows that the landslide stopped after 60 s of sliding, with a movement distance of approximately 286 m, a maximum sliding speed of 17 m/s, and a maximum accumulation thickness of 7 m, eventually forming a loose landslide debris accumulation of approximately 5.25 × 103 m3. The findings of this study provide significant reference value for research on landslide movement characteristics and disaster prevention and mitigation in mountainous areas. [ABSTRACT FROM AUTHOR]

Transportation resilience, as a component of city sustainability, plays a crucial role in the daily management and emergency response of urban road systems. With coastal cities becoming increasingly vulnerable to typhoons, rainstorms, and other disasters, it is essential to assess the resilience of urban road transportation in a refined and differentiated approach. Existing resilience assessment methods often overlook significant biases, neglecting the dynamic response of road traffic and non-stationary characteristics of traffic systems. To address these limitations, we develop a quantitative resilience assessment method for urban road transportation during rainfall that is based on the improved Resilience Triangle. The method is applied to DiDi urban traffic speed and meteorological data of Shenzhen, China, from April to September 2018, with a focus on Typhoon Mangkhut as an extreme weather case. By analyzing transportation resilience variations across road densities, road hierarchies, and rainfall scenarios, we found that road densities and rainfall intensities explain resilience variations better than road hierarchies. Specifically, as accumulative precipitation exceeds 100 mm, a substantial surge in loss of performance is observed. Typhoon rainfalls result in a greater loss in urban road traffic compared to general rainfalls. The results offer valuable insights for urban road planning, traffic emergency management, and transportation resilience construction in the face of increasingly severe weather challenges. [ABSTRACT FROM AUTHOR]

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness. [ABSTRACT FROM AUTHOR]

Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya'an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models' resilience in an open-source landslide database. The results show that in the Ya'an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent. [ABSTRACT FROM AUTHOR]

Dam landslides occurred frequently during recent years, and the secondary disasters caused by it can cause great harm to local resident in an extensive area. Therefore, it is important to understand the mechanisms of dam failure. Experiments were designed to study the mechanisms of dam failure, analyze the effects of multi-factors, and establish a method for the critical condition of dam breach. The results showed that before the collapse, the existence of the notch surface accelerates infiltration and causes advancement of the speed of the wet front. The dam failure process can be divided into three stages: erosion initiation, erosion acceleration, and erosion weakening. During the breaching, three important erosion phenomena were observed including slope instability, steep slope, and collapse. Based on statistical analyses of the changes in the longitudinal slope profile and erosion rates observed during the dam failure, characteristics of erosion rate along the longitudinal profile have been summarized well. The slope of the bottom groove, size of the dam body, upstream inflow discharge, and median particle size of the dam body significantly affected the characteristics of the dam body collapse. Then, a critical water head that leads to an overtopping outburst was established based on the experimental data and theory of incipient motion of sediment, providing critical information to predict dam failure. The results of this research would be helpful to predict the disaster chain and enhance the risk management of this kind of disaster chain. [ABSTRACT FROM AUTHOR]

With the development of economic globalization and increasing international trade, the maritime transportation system (MTS) is becoming more and more complex. A failure of any supply line in the MTS can seriously affect the operation of the system. Resilience describes the ability of a system to withstand or recover from a disaster and is therefore an important method of disaster management in MTS. This paper analyzes the impact of disasters on MTS, using the data of Suez Canal "Century of Congestion" as an example. In practice, the severity of a disaster is dynamic. This paper categorizes disasters into different levels, which are then modelled by the Markov chain. The concept of a repair line set is proposed and is determined with the aim to minimize the total loss and maximize the resilience increment of the line to the system. The resilience measure of MTS is defined to determine the repair line sequence in the repair line set. Finally, a maritime transportation system network from the Far East to the Mediterranean Sea is used to validate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

• Improving Coastline Edge Detail: EDS module enhances edge fitting. • Bridging Semantic Gap: CSDS and AFM collaboration enhances semantic information. • Outperforms with mIoU: CSAFNet achieves remarkable 96.72% mIoU value. The extraction of coastlines from remote sensing images is vital for promoting sustainable development in coastal areas, conserving marine environments, strengthening disaster response capabilities, and supporting scientific research. However, current coastline detection approaches using remote sensing face challenges related to resolution, terrain, boundary, and data, requiring accurate solutions for reliability. Here, we introduce the Collaborative Supervision and Attention Fusion (CSAFNet) model for pixel-level sea-land segmentation, with a primary goal of improving the accuracy of coastline extraction. The model integrates the Edge Deep Supervision (EDS) module to enhance coastline edge detail fitting. Additionally, the Collaborative Semantic Deep Supervision (CSDS) module and Attention Fusion Module (AFM) collaborate to bridge the semantic gap between different hierarchical features, resulting in a more precise and detailed delineation of coastlines. Experimental validation on the publicly available SLSD 1 1 The dataset was initially labeled as "sea-land segmentation data" by the authors. For ease of discussion, we will adopt the practice of using the initial letters of each word as an abbreviation. dataset has demonstrated superiority over various advanced methods, with an impressive mIoU value of 96.72%. Through simple optimization, detailed and rich coastlines can be extracted, validating the feasibility of coastline extraction. [ABSTRACT FROM AUTHOR]

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Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

States of India like Bihar, Uttar Pradesh, and West Bengal along the Ganga River, endure natural disasters periodically, resulting in repeated trends of economic loss and damages. Especially, most of the districts of Bihar, India, are prone to floods. Based on this background, this study aims to assess the flood vulnerabilities across districts of Bihar, India, employing data from the Central Water Commission from 1953 to 2020. Further, we explore trends and patterns of loss and damage due to flood risks in Bihar. Using the flood vulnerability integrated method and the principal component analysis, the index is constructed by incorporating the three major indicators: exposure, sensitivity, and adaptive capacity. This study is unique, and advances from previous studies in using a greater number of variables in exposure indicator. The proxy variable for each indicator is identified through both inductive and deductive approaches, and the composite index is constructed using all three indicators. Also, we identify the districts with high level of education and per capita income are less likely to expose flood vulnerability. The comparison of the districts reflects wide range of variation in terms of flood vulnerability as per their adaptive capacity and sensitivity. Specifically, these findings align with Target Sustainable Development Goal 11.5. This study addresses the policy for disaster prevention, risk reduction, and mitigation measures, as well as the enhancement of the capability of adaptation to floods by the affected community. [ABSTRACT FROM AUTHOR]

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks. [ABSTRACT FROM AUTHOR]

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards. [ABSTRACT FROM AUTHOR]

While urban populations are rapidly increasing around the world, floods have been frequently and seriously occurring due to the climate crisis. As existing disaster prevention facilities have specific limitations in completely protecting against flood damages, the concept of resilience, which emphasizes the ability to recover after becoming injured and harmed by a flood, is necessary to mitigate such damages. However, there is still a scarcity of studies that quantitatively show the relationship between the resilience and the socioeconomic costs, even though a variety of evaluation methods exist in the literature. This study aims to quantitively analyze the socioeconomic impact of flooding on the urban environment based on the concept of resilience. A method of evaluating four properties of resilience (redundancy, rapidity, resourcefulness, and robustness) through damage function and network analysis was used to measure changes in resilience against flood damages. In addition, to determine the socioeconomic impact of flooding, the costs incurred due to transportation delays and the lack of labor participation were evaluated. Differences in structural and social systems have led to variations in resilience and socioeconomic costs. As a future study, if the circumstances after flood events based on risk-based recovery can be evaluated, more effective urban flooding defense decisions would be expected. [ABSTRACT FROM AUTHOR]

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and property of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient, accurate, and rapid assessment of high-risk landslide zones carries important implications for decision making in disaster response and for mitigating potential secondary disasters. The high-intensity zones VII to IX of the Luding Ms6.8 earthquake on 5 September, 2022, were used as a case study here. Based on the simple Newmark model, the difference method and the cumulative displacement method were employed to assess earthquake-triggered landslides. The assessment results from both methods demonstrated that the areas posing an extremely high risk of earthquake-triggered landslides were predominantly situated on the western side of the Xianshuihe Fault. Verification using actual landslide data showed that both methods had high predictive accuracy, with the difference method slightly outperforming the cumulative displacement method. Moreover, this study recommends determining threshold values for each landslide risk interval having physical meanings using previous data on strong earthquakes when utilizing the difference method to assess the risk of earthquake-triggered landslides. [ABSTRACT FROM AUTHOR]

Background: In May 2023, Romagna, Italy, faced a devastating flood resulting in 16 fatalities, forced displacement of 26,000 citizens, and significant economic losses. Due to potential water contamination, implementing public health strategies became imperative for the Local Health Authority to mitigate the health consequences, analyze the flood's impact on the local population's health, and detect early anomalies requiring timely public health interventions. Methods: Between June and July 2023, general practitioners who were part of the RespiVirNet surveillance network completed weekly structured forms. These forms collected data on individuals exposed or not to floodwaters and clinical syndromes. Rates per 1000 resident population aged > 14 were stratified by district, week of observation, and symptomatology. Missing data were addressed by imputation using second-order autoregressive modeling. Results: An incidence of 3.52 syndromes potentially related to flood water exposure per 1000 individuals (95% CI 2.82–4.35) was estimated. Ravenna, the city most affected by the flood, recorded the highest rate (6.05 per 1000, 95% CI 4.59–7.82). Incidence decreased in the weeks post-event. Anxiety, or trauma and stress symptoms, exhibited higher rates among the exposed, diminishing over weeks. The incidence for the non-exposed (12.76 per 1000, 95% CI 10.55–15.29) showed no significant territorial differences compared to the exposed ones. Conclusions: Syndromic surveillance provided timely information on the flood's health impact, revealing a higher incidence of individual syndromes among the non-exposed. This study contributes to guiding the implementation of future public health preparedness and response strategies for populations facing similar natural disasters. [ABSTRACT FROM AUTHOR]

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management. [ABSTRACT FROM AUTHOR]

This study evaluates the storm surge inundation risk in three anthropogenically infilled estuaries—Xichong, Renshan, and Kaozhouyang—located in the Guangdong–Macao–Hong Kong Great Bay Area, China. By integrating GIS spatial analysis with storm surge modeling, we conducted 204 numerical experiments to simulate storm surge inundation under varying typhoon intensities and astronomical tide conditions. Results revealed that coastal terrain plays a crucial role in influencing storm surge levels and inundation extents. Specifically, the pocket-shaped terrain in the Renshan and Kaozhouyang estuaries amplified storm surges, resulting in higher inundation levels compared to the relatively open terrain of Xichong. Furthermore, anthropogenically reclaimed land in these estuaries appear to be particularly vulnerable to storm-induced inundation. Overall, this study underscores the importance of considering coastline morphology and the anthropogenic modifications of coastal terrain in storm surge risk assessments, offering valuable insights for disaster prevention and mitigation strategies. The use of ArcGIS spatial analysis coupled with storm surge modeling, facilitated by high-resolution DEMs, provides a statistical risk assessment of inundation. However, more complex flooding dynamics models need to be developed, particularly when terrestrial bottom friction information, which is heavily modified by human activities, can be accurately incorporated. [ABSTRACT FROM AUTHOR]

Water security, as a crucial component of national security, plays a significant role in maintaining regional stability and ensuring the healthy and rapid development of the economy and society. The Yellow River water-receiving area of Henan Province (YRWAR-HN) is selected as the research area in this study. Firstly, a comprehensive evaluation index system is constructed based on the actual water security problems of the research area, and the single index quantification–multiple indices syntheses–poly-criteria integration method (SMI-P) is introduced to quantify the water security degree of 14 cities in the YRWAR-HN from 2010 to 2021. Then, the obstacle degree model is used to identify the key obstacle indexes that restrict the improvement of water security. Finally, the harmonious behavior set optimization method is adopted to carry out the regulation of water security, and the improvement path of water security in the YRWAR-HN is formulated. The results indicate the following: (1) the water security degree of the YRWAR-HN shows a fluctuating upward trend, increasing from 0.4348 (2010) to 0.6766 (2021), a significant rise of 55.61%. The water security level improves from the relatively unsafe level to the relatively safe level. Hebi City exhibits the fastest rate of water security improvement, while Xinxiang City shows the slowest rate. (2) The density of the river network (X1) and the proportion of investment in water conservancy and environmental protection in the total investment (X15) are the two indexes with the highest obstacle degree, with the average obstacle degrees being 15.09% and 10.79%, respectively. (3) The combination of the composite regulation scenario and improvement Path 2 is the optimal regulation strategy for water security in the YRWAR-HN. From the implementation process, Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu may prioritize improving their flood and drought disaster defense capabilities and emergency response capabilities, continuously enhancing the flood prevention and disaster reduction system. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou may prioritize resolving the regional water supply and demand conflicts, balancing development and conservation, actively seeking transboundary and external water transfers, and strengthening the capacity for water conservation and intensive utilization. Puyang City may prioritize enhancing its comprehensive water environment management capabilities, increasing investment in water conservancy and the environment, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution. [ABSTRACT FROM AUTHOR]

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region's reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region's flood preparedness, response strategies, and overall disaster management. [ABSTRACT FROM AUTHOR]

Accurate and timely information on building age is essential for mitigating the impacts of natural disasters such as earthquakes and floods. Traditional methods for collecting these data are often inefficient and costly. This study leverages remote sensing and machine learning to classify building age and integrate this information into a comprehensive flood hazard map for Al Ain City. By combining building age with elevation, topography, land use, population density, and other factors, we generated a flood hazard map that effectively identifies high-risk areas. Our results show that a significant portion of the city is has low potential for flooding (61%), while others face moderate (32%), high, or very high flood hazards. This study provides crucial information for informed decision-making regarding future urban planning, disaster management, and development strategies in Al Ain City. By identifying vulnerable buildings in advance, it supports efforts to reduce disaster risks, protect lives, and optimize resource allocation. [ABSTRACT FROM AUTHOR]

Many cities across the United States (U.S.) face threats from natural hazards, and as climate change continues to worsen, cities will face increased risks. Emergency management helps cities to mitigate these risks and adapt to shifting threats. Based on the Rockefeller City Resilience Framework and City Resilience Index Driver 3, Indicator 3.4, the scope of this research was to develop an emergency management maturity model and explore emergency management capacity for future disasters based on an assessment of five cities across the U.S. With the resulting data, a city's score would serve as an evaluation of the emergency response indicator (Indicator 3.4) in order to eventually enter the City Resilience Index. The cities selected were Buffalo, New York; Honolulu, Hawaii; Memphis, Tennessee; New Orleans, Louisiana; and Saint Paul, Minnesota, based on their hazard/risk factors. It should be noted that these scores were intended to compare resilience in a city over time and identify unique areas in which the cities could improve emergency management and were not meant to rank the cities against each other. An interesting find was the overlap between the counties and cities regarding policy data, creating a new avenue for research in local level emergency management. [ABSTRACT FROM AUTHOR]

When assessing road network resilience, emergency management behavior should be considered, as this represents the road network's capacity to adapt to and recover from traffic accidents. Given the timeliness and variability of emergency management behavior, deterministic approaches seem inadequate to represent real road network performance. Thus, this paper innovatively designs an emergency management perspective-based dynamic evaluation method of road network resilience to traffic accidents. Firstly, based on four stages of emergency management, a road network resilience evaluation index system encompassing resilience capabilities, resilience attributes and traffic accident emergency management ability indicators is constructed. Afterwards, the gray relational technique for order preference by similarity to the ideal solution (GRA-TOPSIS) evaluation method based on combination weighting, which integrates factor analysis with hesitant intuitionistic fuzzy expert scoring, is designed to quantify resilience. Finally, the obstacle degree model is utilized for identifying resilience constraints as the input of a long short-term memory (LSTM) model to predict the resilience variation trend. The fast road network of Shanghai in China is adopted as a case study, and the results indicate that road network resilience embodies significant spatial distribution characteristics. Road length, number of tractors, perception and response and disposal time of traffic accidents cast notable effects on resilience. Additionally, some roads are forecast to show descending resilience. The proposed method is valuable for helping policymakers identify current and potential vulnerable roads and to formulate proposals to effectively improve the resilience of urban agglomerations and promote sustainable cities. [ABSTRACT FROM AUTHOR]

In a context of climate change, flash‐floods are expected to increase in frequency. Considering their devastating impacts, it is primordial to safeguard the exposed population and infrastructure. This is the responsibility of crisis managers but they face difficulties due to the rapidity of these events. The focus of this study was to characterize the extent of the link between hydrologists and crisis managers. It also aimed to determine the limiting and the fostering factors to an effective integration of forecasting in crisis management during flash‐floods. This was achieved through an extensive and methodological study of available literature in selected platforms. The models encountered were characterized on multiple levels including the physical, geographical and crisis management level. The results revealed a limited link between the two involved parties with limiting factors such as the complexity of the modeling approach, the insufficient projection in the implications of operationality of the models proposed and the financial aspect. On the other hand, acknowledging the threat of flash‐floods and conducting cost–benefit‐analysis were pinpointed as fostering factors. This study showed to reconsider the forecasting methods employed, particularly, the integration of machine learning, and the needs of end‐user in these applications in a crisis management context. [ABSTRACT FROM AUTHOR]

Pig slurry (PS) management is a challenge that needs appropriate strategies. Anaerobic digestion (AD) has proved to be an interesting option to follow when it comes to livestock effluent management. Although this technology is well established, it is crucial to investigate different scenarios that demonstrate its suitability to motivate farmers to adopt new strategies for PS management. Previous research, based on a daily feeding regime and a 2-day starvation-induced period, investigated the impact of feast/ famine cycles on the AD process. There was evidence of a positive link between AD performance and the starvation regime. From this assumption, new scenarios were designed combining different feeding frequencies: a) one daily feeding (F1) and six feedings per day (F6), b) two days of starvation with one feeding (S1) and with six feedings (S6). The operational parameters were settled in advance: organic loading rate (1.5±0.2 g VS/Lreactor. d), hydraulic retention time (15 days), and mesophilic conditions (37±1 °C). The results obtained in this work indicate a significant improvement (P < 0.05) of 92% in specific methane production when comparing the trial F1 with F6. VS reduction remained constant in F1 and F6, but the starvation period (S1) led to an increase in VS reduction compared to both F1 (27%) and F6 (33%). The results obtained are in agreement with the previous work conducted by the authors. This study highlights how feeding frequency and starvation affect biogas production, assessing their effectiveness on biogas yield. This tool will give farmers a key decision factor to make an evidence-based decision and can work as a contingency planning strategy, aligning their PS management needs with AD versatility. [ABSTRACT FROM AUTHOR]

This project is based on the Beidou satellite navigation and positioning technology developed by China. The integrated analysis method is used to obtain the monitoring data of high-precision power cables in high-altitude areas with high-precision millimeters to realize the whole process of monitoring various disasters. An early warning system for power line swing and tower bottom geological hazards is established. The track data of cable swing is obtained by using single-frequency dynamic data post-processing (PPK) based on synchronous power grid monitoring. The applicability and trajectory accuracy of the scheme are evaluated by combining theory with practical engineering. This provides essential technical support for the mechanism and prevention of cable sloshing. The experimental simulation shows that the monitoring efficiency of the system is high. [ABSTRACT FROM AUTHOR]

Thunderstorms are known to have the potential to cause damage to the population. In Mexico, these weather events can bring threats such as lightning, hail, damaging winds, and even tornadoes. Various parameters and indices have been developed for mid‐latitude regions to improve forecasting and reduce the potential damage from thunderstorms; however, they do not consider the specific conditions of tropical and subtropical environments. This research used a comprehensive set of thunderstorm reports and the proximity‐sounding approach using ERA5 reanalysis data to examine five important instability indices commonly used for thunderstorm forecasting regarding their spatial and temporal characteristics. The results reveal significant variations in the values of the instability indices during thunderstorm events associated with different atmospheric systems in both the high‐activity and low‐activity seasons. The above suggests that no single index can reliably predict thunderstorms throughout Mexico and during all seasons. Analyses provide forecasters with a more comprehensive understanding of the various atmospheric conditions under which thunderstorms develop in the country, which can lead to more accurate forecasting and better disaster risk management for these natural phenomena. [ABSTRACT FROM AUTHOR]

Background: Vaccination remains one of the most successful public health interventions in preventing severe disease and death. The roll-out of Covid-19 vaccination programmes has helped protect billions of people around the world against Covid-19. Most of these programmes have been unprecedented in terms of scale and resources, and have been implemented at times of significant humanitarian crisis. This study aims to outline the lessons learnt from the implementation of a regional Covid-19 vaccination programme. These will help inform emergency preparedness and future crisis management. Methods: This qualitative study sought to explore the key drivers to the successful implementation of the Covid-19 vaccination programme in a region in the Southwest of England, applying the Normalisation Process Theory lens (NPT) to examine multi-stakeholder perspectives. Data collection involved semi-structured interviews with 75 participants. Document analysis was also used to corroborate the findings emerging from the interviews. Inductive thematic analysis of the data was used to identify the key drivers for the successful implementation of the programme. The NPT lens was then applied to map the themes identified to the domains and constructs of the framework. Results: Ten key drivers to the successful implementation of the Covid-19 vaccination programme locally were identified, including: the clarity and consistency of the programme's goal; the diverse representation of stakeholders within the programme leadership team and the mechanisms created by this team to ensure psychological safety, autonomy, operational flexibility and staff empowerment; Communication and data specialists' input, and collaboration with local communities to maximise the reach of the programme; and allocating funding to tackle health inequalities. Conclusions: This study highlights the lessons learnt from the implementation of the Covid-19 vaccination programme at a local level, and the mechanisms that can be used in future crises to respond efficiently to the needs of individuals, communities and governments. [ABSTRACT FROM AUTHOR]

Like many Mediterranean areas, the Italian island of Sicily faces multiple environmental pressures such as soil loss, fire hazards, and extreme meteorological events, all of which negatively impact local food systems. In response to these threats, a re-thinking of local agriculture and natural resource management is increasingly needed. Agroecology is recognized as a robust proposal for building more resilient food systems grounded in farmers' knowledge and practices. However, agroecological farming experiences struggle to operate and survive in Sicily due to unfavorable political-cultural, environmental, and socio-economic conditions. Learning from small-scale farmers about the ways they perceive, understand, and overcome structural limits and environmental constraints is key for a transition to agroecology in the study area. Understanding its potentials and limits is essential for planning and identifying transformative actions. We approached the problem by adopting a participatory action research methodology involving selected groups of farmers in Western Sicily. We applied a co-creative approach and developed a systemic analysis of the socio-ecological narratives to identify possible leverage points for a transition to agroecology in the study area. We identified a local potential for shifting the current system of water and fire hazards management to new systems of participatory land stewardship. To be effective, these systems should support agroecological farmers' income by altering social practices related to food and reducing the influence of dominant agribusiness actors. Our findings indicate that implementing solutions based on the circulation of local ecological knowledge within systems of participatory guarantees can favor the development of solidarity economies and mutualistic relations between farmers, scientists, and communities. Our work suggests that scientists' facilitation and knowledge cocreation might be of key importance in structuring local, more sustainable food systems. [ABSTRACT FROM AUTHOR]

Background: To investigate the impact of children's inborn error of metabolism (IEMs) on the children's and their parents' lives from the parents' perspective. We focused on disease-related restrictions in various issues of daily life, experienced discrimination, parental family planning, and management of metabolic emergencies. Methods: We conducted a questionnaire-based survey with 108 parents of 119 children with IEM who attended a metabolic outpatient clinic. The children were categorized into 4 cohorts, based on increasing disease severity (cohort 1: IEMs with lowest severity, cohort 4: IEMs with highest severity), and compared by using Tobit regressions. Results: The severity of the child's IEM was associated with an increase in the intensity of perceived restrictions from the parents' perspective for themselves and their children in all aspects of life: in general, in contact with friends, in the pursuit of hobbies, in childcare/school/occupation, and due to emotional stress. The highest intensity of restrictions in all cohorts was found for the parents themselves in contact with friends (compared to cohort 1: cohort 2: c. 3.556, p = 0.002; cohort 3: c. 4.159, p = 0.003; cohort 4: c. 7.224, p < 0.001). Parents of 8% of children reported that their children were discriminated against because of IEM, with the highest proportion of affected children (43%) in cohort 4. Parental family planning decisions were influenced in 34% of parents, with fear of recurrence being a predominant aspect. Of the parents of children diagnosed with IEMs associated with metabolic emergencies, 68% stated that they felt well or very well prepared for the occurrence of a metabolic emergency, and 100% of parents were able to name the necessary action steps from memory. Nevertheless, 58% stated that they experienced an occurring emergency as rather or very stressful. Conclusions: From the parents' perspective, the intensity of restrictions increased with the severity of the child's IEM. The study shows the high impact of IEM on parents of children with IEM and the daily challenges they face. These findings emphasize the importance of comprehensive support for parents of children with IEM. [ABSTRACT FROM AUTHOR]

Introduction: Hospitals as the main providers of healthcare services play an essential role in the management of disasters and emergencies. Nurses are one of the important and influential elements in increasing the surge capacity of hospitals. Accordingly, the present study aimed to assess the effect of surge capacity enhancement training for nursing managers on hospital disaster preparedness and response. Methods: All nursing managers employed at Motahari Hospital in Tehran took part in this interventional pre- and post-test action research study. Ultimately, a total of 20 nursing managers were chosen through a census method and underwent training in hospital capacity fluctuations. The Iranian version of the "Hospital Emergency Response Checklist" was used to measure hospital disaster preparedness and response before and after the intervention. Results: The overall hospital disaster preparedness and response score was 184 (medium level) before the intervention and 216 (high level) after the intervention. The intervention was effective in improving the dimensions of hospital disaster preparedness, including "command and control", "triage", "human resources", "communication", "surge capacity", "logistics and supply", "safety and security", and "recovery", but had not much impact on the "continuity of essential services" component. Conclusion: The research demonstrated that enhancing the disaster preparedness of hospitals can be achieved by training nursing managers using an action research approach. Encouraging their active participation in identifying deficiencies, problems, and weaknesses related to surge capacity, and promoting the adoption and implementation of suitable strategies, can enhance overall hospital disaster preparedness. [ABSTRACT FROM AUTHOR]

Background: As first responders, paramedics are an extremely important part of the care chain. COVID-19 significantly impacted their working circumstances. We examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic. Methods: This qualitative study utilized reflective essay material written by experienced, advanced-level Finnish paramedics (n = 30). The essays used in this study were written during the fall of 2020 and reflected the period when Finland had declared a state of emergency (on 17.3.2020) and the Emergency Powers Act was implemented. The data was analyzed using an inductive thematic analysis. Results: The emotions experienced by the EMS personnel in their new working circumstances formed three themes: (1) New concerns arose that were constantly present; (2) Surviving without proper guidance; and (3) Rapidly approaching breaking point. Three themes were formed from work-related factors that were identified as resources for the well-being of the EMS personnel. These were: (1) A high level of organizational efficiency was achieved; (2) Adaptable EMS operations; and (3) Encouraging atmosphere. Conclusions: Crisis management practices should be more attentive to personnel needs, ensuring that managerial and psychological support is readily available in crisis situations. Preparedness that ensures effective organizational adaptation also supports personnel well-being during sudden changes in working circumstances. [ABSTRACT FROM AUTHOR]

Background: Identifying factors associated with post-disaster youth substance use is a crucial element of developing evidence-based prevention and intervention efforts. Hurricane María struck Puerto Rico in September of 2017 and the wide-spread impact from this disaster, including exposure to trauma, displacement, and disrupted social supports had the potential to negatively impact levels of substance use among youth across the archipelago. However, post-disaster substance use remains under-investigated in this context. The current study sought to identify risk and protective factors associated with substance use among Puerto Rican youth in the aftermath of Hurricane Maria. Methods: Cross-sectional, secondary data analyses were conducted using school-based survey data collected at all schools in Puerto Rico between February 1 and June 29, 2018 (5–9 months after Hurricane María). Social supports, substance use, and trauma symptoms were assessed. An ordinal regression analysis was conducted to identify student factors associated with greater likelihood of post-disaster substance use. Results: A total of 36,485 participants (50.7% female, grades 7–12), were included in an ordinal regression analysis that compared the likelihood of respondents endorsing high, low, or no substance use after Hurricane María based on reported adult social support, counselor/teacher social support, peer social support, ptsd symptomatology, and gender. Findings showed that, when compared to students that endorsed low or no substance use, those who reported having adult social support demonstrated a 58% reduction in odds (OR = 0.42, 95% CI: 0.34-0.53) of reporting high substance use after Hurricane María, while students who reported having teacher/counselor social support demonstrated a 21% reduction in odds (OR = 0.79, 95% CI: 0.69-0.89) of reporting high substance use. Additionally, those that reported having peer social support demonstrated a 31% increase in odds (OR = 1.31, 95% CI: 1.10 to 1.58) of reporting higher substance use, compared to those that reported low or no substance use. Conclusions: While social support was generally protective, prevention efforts to build positive family and community connections may be indicated. Evidence-based school screenings of substance use and trauma may help direct intervention to those most at risk for co-occurring issues. [ABSTRACT FROM AUTHOR]

In Nigeria, where rainfall plays a pivotal role in agriculture and disaster management, assessing the accuracy of satellite rainfall estimates at various thresholds is imperative. This study assesses the areal averages of nine satellite precipitation estimates (SPEs) against 48 ground-based raingauge observations in Nigeria. Employing categorical statistical metrics and compromise programming, the research assesses the performance of SPEs at seven rainfall thresholds. Results reveal that 82–94% of rainfall estimates align with ground truth, while 5–14% of rainy days are incorrectly detected by raingauges. Notably, SPEs tend to overestimate low rain between 1 mm and 5 mm d−1 and underestimate low heavy rain rates (10 mm ≤ rain < 20 mm), impacting flood monitoring and disaster preparedness. Global Precipitation Climatology Centre (CPCC), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series (TARCAT), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) emerge as more reliable SPEs, particularly in highland regions. This research underscores the importance of choosing appropriate satellite precipitation estimates for enhanced disaster preparedness and resource management across different Nigerian regions. [ABSTRACT FROM AUTHOR]

Global warming amplifies the risk of wind‐induced building damage in coastal cities worldwide. Existing numerical methods for predicting building damage under winds have been limited to virtual environments, given the prohibitive costs associated with establishing city‐scale window inventories. Hence, this study introduces a cost‐effective workflow for wind damage prediction of real built environments, where the window inventory can be established with the multi‐view street view image (SVI) fusion and artificial intelligence large model. The feasibility of the method is demonstrated based on two real‐world urban areas. Notably, the proposed multi‐view method surpasses both the single‐view and aerial image‐based methods in terms of window recognition accuracy. The increasing availability of SVIs opens up opportunities for applying the proposed method not only in disaster prevention but also in environmental and energy topics, thereby enhancing the resilience of cities and communities from multiple perspectives. [ABSTRACT FROM AUTHOR]

Geomorphic environments play a crucial role in influencing the preservation and characteristics of tsunami deposits. This paper introduces a global database of tsunami deposits, encompassing information on deposit locations, thematic data such as geomorphic environments and proxies and bibliographic details. Additionally, the database features maps incorporating environmental parameters and the precise locations of tsunami deposits. The primary utility of this database lies in assessing progress and identifying gaps in knowledge. It also involves analysing the relationship between environmental parameters and interpreting areas with varying probabilities of tsunami deposit preservation. The files are readily compatible with GIS software and can seamlessly integrate into spatial databases associated with tsunamis or other hazards. This contributes significantly to disaster risk management, enhancing preparedness and response efforts by providing a comprehensive historical dataset on tsunamis. Future applications of the database include the incorporation of modern deposits, boulders and new data from paleotsunami and historical studies. By enhancing data with thematic information, such as dating techniques and creating timelines, the database facilitates a more comprehensive understanding. The correlation between geomorphic environments and proxies aids in selecting sampling sites and identifying suitable proxies for analysis. Encouraging an open‐access approach, this database invites all interested researchers to include and modify additional information. The information compiled for this database serves multiple purposes: (1) assessing the global distribution of tsunami deposits; (2) identifying knowledge gaps in tsunami deposits; (3) guiding the selection of study areas for further research and finally; (4) enabling a meta‐analysis of the information gathered. [ABSTRACT FROM AUTHOR]

Introduction: The behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province. Methods: Based on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data. Results: The study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well. Discussion: The results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management. [ABSTRACT FROM AUTHOR]

Despite considerable efforts and progress in increasing resilience to natural hazards, the adverse socio-economic impacts of extreme weather events continue to increase globally. As climate change progresses, disaster risk management needs alignment with adaptation measures. In this perspective paper, we discuss emerging complications in disaster risk management during recent events from an interoperability perspective. We argue that a lack of interoperability between data and models, information and communication, and governance are barriers to successful integrated disaster risk management and climate adaptation. On this basis, we take a detailed look at the challenges involved and suggest that trans-disciplinary knowledge co-production is key to promoting interoperability between these components. Finally, we outline a framework for enabling knowledge co-production to enhance risk governance by improving ways of facilitating trans-disciplinary stakeholder engagement and draft a novel open-source federated data infrastructure, which allows stakeholders to consolidate and connect relevant data sources, models and information products. [ABSTRACT FROM AUTHOR]

In recent years, urban areas have been increasingly affected by more frequent and severe pluvial floods, attributed to climate change and urbanization. This trend is expected to continue in the future, underscoring the critical importance of flood warning and disaster management. However, pluvial flood forecasts on a communal level do not exist in practice, mainly due to the high computational run-times of high-resolution flood simulation models. Here, we showcase the capability of the hydrodynamic model RIM2D (Rapid Inundation Model 2D) to deliver highly detailed and localized insights into expected flood extent and impacts in very short computational processing times, enabling operational flood warnings. We demonstrate these capabilities using the case of the June 2023 torrential rain and resulting flood event in the city of Braunschweig, located in Lower Saxony, Germany. During this event, the city experienced intense rainfall of 60 liters per square meter within a short timeframe, resulting in widespread inundation, significant disruption to the residents' daily life, and substantial economic costs to the city. This study serves as a clear indication that different dimensions of flood consequences can be simulated at very high resolutions in extremely short computational times and that models such as RIM2D, along with the necessary hardware for their operation, have reached a level of maturity suitable for integration into operational early warning systems and impact-based forecasting systems for such floods. [ABSTRACT FROM AUTHOR]