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Debris flow has always been a serious problem in mountainous areas. Accurate debris flow susceptibility (DFS) assessment and interpretable prediction results play an important role in the prevention and control of debris flow disasters. Some commonly used machine learning algorithms based on Boosting ensemble techniques were widely used in the study of geohazard susceptibility due to its excellent predictive ability. However, the Categorical Boosting (CatBoost) and Natural Gradient Boosting (NGBoost) have not yet been applied in the field of DFS assessment, and few geohazard studies systematically compare and research these boosting-based algorithms. Meanwhile, previous researches have mostly focused on comparing the predictive ability of algorithms, identifying the susceptibility zones of the entire study area, and ranking the importance of the indicators, but little thorough analysis of the relationship between the indicators and debris flow susceptibility on different types of construction land. The aims of this study were to explore the optimal boosting-based DFS model, and the distribution characteristics and change rules of DFS in the study area, so as to provide decision supports for debris flow disaster prevention and reduction. This was the first time that six boosting-based machine learning algorithms have been compared in the study of DFS assessment. After determining the optimal model, the change rules of indicators in the entire study area and two types of construction lands under different DFS levels were studied respectively. An eXplainable Artifcial Intelligence (XAI) method called SHapley Additive exPlantations (SHAP), combined with zonal statistics function in geographic information system (GIS) were adopted to explore how each indicator affects the occurrence of debris flows. The results showed that the CatBoost performed best and provided the most reasonable DFS result among six boosting-based models. We found that debris flows were more likely to occur along rivers and construction lands at low altitude. Rural areas faced more stronger pressure from rainfall and were featured by worse disaster-breeding environment than urban areas. This research enriches the application of machine learning in DFS assessment, explores the changing trends of indicators between different DFS levels, and provides suggestions for better debris flow disaster prevention and mitigation management. [ABSTRACT FROM AUTHOR]

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows. [ABSTRACT FROM AUTHOR]

Landslides are a severe geohazard around the world. When moving soil masses discharge into a large water body, a tsunami can be generated and exacerbate the devastating effects of the landslide as well as extend the affected area. In this study, based on on-site geological investigation and monitoring, a numerical depth-averaged, two-phase model is established for a hypothetical tsunami in the Suofengying reservoir induced by the potential Bianjiazhai landslide in China which has been previously identified as critical. The analysis of the simulation results shows that the maximum wave amplitude measured at gauge point closest to the landslide is 31 m, and the tsunami reaches the reservoir's dam about 66 s after the landslide initiation. The inundation map provides potential risk areas that could be affected if a landslide occurs with the anticipated characteristics. Under similar conditions, the research results will help guide reservoir operation and landslide-tsunami disaster prevention. Simultaneously, examining the sequence of events in the tsunami disaster chain facilitates the analysis of the fundamental physics governing the propagation of tsunamis within reservoirs. This analysis contributes to the prediction and prevention of landslide-induced tsunami disasters occurring along reservoir banks. [ABSTRACT FROM AUTHOR]

Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

In 1954, a devastating flood struck the entire Yangzi River basin, causing the first major natural disaster in the newly established People's Republic of China (PRC). The young regime had just embarked on its First Five-Year Plan, aimed at achieving rapid economic development and industrialization. The flood swept across several provinces, displaced populations, and submerged farmlands, posing both a new challenge and an opportunity for the regime to shape the socioeconomic landscape of the affected regions. This article uses a localized perspective to examine how the rural society of Hubei Province, the hardest-hit area, responded to the disaster. The central government's self-help doctrine delegated much power to local governments and communities. Local officials and rural cadres had considerable leeway in managing the relief campaign as they simultaneously followed the state macroeconomic policy of rural collectivization. The 1954 flood in Hubei Province serves as a case study that illustrates the complexities of disaster management in the early years of the PRC. [ABSTRACT FROM AUTHOR]

In recent decades, the Inner Mongolia Reach of the Yellow River (IMRYR) in China has experienced new characteristics in terms of ice conditions due to human activities and climate change. It is crucial to develop a comprehensive river ice model for this reach that reflects the changing physical characteristics of ice events. However, simulating river ice process in the IMRYR using numerical model is challenging due to the scarcity of observed data on river geometry, hydrology, meteorology, and river ice conditions. With latest field measurements, this study examined the spatiotemporal variations of ice conditions in the IMRYR. In the development of river ice model, this study tackled various challenges such as incomplete river geometry, insufficient tributary flow information, and the identification of ice bridge locations. By improving the one-dimensional river ice model, the study simulated the ice development process during the 2012–2013 ice flood season in the IMRYR. A comparative analysis was conducted between the simulated and observed water temperature, ice cover thickness, ice front locations, and flows, showing a good agreement. This study analyzed and determined the ice hydrodynamics, thermodynamics, and relevant river parameters, providing a deeper understanding of the intricate interactions and dynamics associated with the formation and behavior of river ice in the Yellow River. This research can provide scientific basis and methodological support for ice disaster prevention and control in the Yellow River, as well as insights and technical means for simulating comprehensive river ice processes in natural rivers and artificial channels. The Yellow River is one of the rivers in China that experiences frequent ice-related disasters, with the main occurrences happening in the Inner Mongolia Reach (IMR). In recent decades, the IMR has faced new challenges in ice evolution, including changing climatic conditions, severe channel siltation, and expanded ice-related channel storage, making the understanding of ice conditions more complex. The China Institute of Water Resources and Hydropower Research, commissioned by the Hydrographic Bureau of the Yellow River Conservancy Commission, conducted numerical simulations of the ice processes in the IMR. Based on the new characteristics of ice conditions in the Yellow River, a mathematical model was proposed to simulate the development of winter ice conditions in data-scarce regions. The one-dimensional river ice process model was enhanced to enable dynamic simulations of the ice processes in the IMR, and accurately capturing the evolution of winter water temperature, ice cover thickness, ice front progression, flow rate, and water level. This study provides valuable scientific basis for ice forecasting, ice control operations, and disaster prevention in the Yellow River. The improved model has been adopted by the Hydrographic Bureau of the Yellow River Conservancy Commission, and additional categories and sampling frequencies of hydrological and meteorological data have been tailored for the Inner Mongolia Reach considering specific data requirements for the model. [ABSTRACT FROM AUTHOR]

Background: As the largest group of healthcare professionals, nurses play an indispensable and crucial role in disaster response. The enhancement of nurses' disaster literacy is imperative for effective disaster emergency management. However, there is currently a lack of knowledge regarding nurses' disaster literacy. This study represents the first attempt to explore the key components and characteristics of disaster literacy among nurses. Methods: A qualitative descriptive design was employed, and the reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (refer to File S1). The purposive sampling method was utilized. Thirty-one rescue nurses from 31 medical institutions across 25 provinces and regions in China were recruited to participate in the study. The respondents were requested to share their experiences and insights regarding disaster rescue operations. Inductive content analysis was employed for data examination. Results: The results indicated that rescue nurses universally recognized that there was a pressing need to enhance the level of disaster literacy among nurses. The disaster literacy of nurses encompasses nine dimensions: physical and mental quality, disaster rescue general knowledge, professional and technical competence, professional ethics, teamwork, emotional ability, information literacy, leadership, and knowledge transformation. Conclusions: To ensure national sustainability, government departments, healthcare organizations, and hospital administrators can accurately evaluate the disaster literacy of individual clinical nurses, groups, and the workforce as a whole through nine dimensions, which also can provide evidence to support the development of precision strategies to strengthen the disaster literacy of nurses. [ABSTRACT FROM AUTHOR]

This paper aims to provide a design basis for engineering disaster prevention and mitigation by accurately describing the spatial distribution of soil liquefaction. A group of in-situ Cone Penetration Tests (CPT) was conducted to evaluate the seabed soil properties and liquefaction potential of the Yellow River Subaquatic Delta. The factor of safety against liquefaction (FSL) and liquefaction potential index (LPI) were utilized to investigate the liquefaction characteristics of seabed soils. The findings are as follows: (1) The delta is "near coarse and far fine," with sand and silt dominating the onshore direction and silt and silty clay dominating the offshore direction; (2) Silt, as a structured soil, has a maximum sensitivity of 10, and there are local liquefaction phenomena. The size of a Soil behavior type index can be used as the limit value of liquefaction discrimination in the region; (3) The FSL of the shallow layer is smaller, while that of the deep layer is generally larger. The FSL near the coast is smaller than that far from the coast, but its variation range is large, indicating that the shallow soil layer near the coast is more prone to liquefaction; (4) The LPI of the delta is less than 5, which is significantly affected by the wave cycle loads and geographical environment. Liquefaction can occur, but the probability of large-scale liquefaction is low. [ABSTRACT FROM AUTHOR]

In response to public complaints about the chaotic management of donations during the emergency, the site of the first identified COVID‐19 case, Wuhan (in central China), underwent a contingent governance transition. And given the public complaints about the disorganized management of donations during the early stage of emergency management in Wuhan in January 2020, local governments initiated a minor governance transition in philanthropy, involving multiple stakeholders such as public and private charities, as well as private enterprises. In contrast to the abundance of literature on governance actors such as the state, the market, and societal actors, this review discusses the changes in governance mechanisms using publicly available data from governmental websites, policy documents, and news reports. It examines a minor governance change in local philanthropy from hierarchical bureaucratic governance to embryotic non‐monocentric governance and to expedient collaborative/interactive governance. Moreover, it highlights the yet‐to‐be institutionalized characteristics of the newly emerging governance models in China, a country where the traditional mode of hierarchical governance is dominant. This article not only adds to the body of evidence on governance failures caused by monocentric bureaucracy, but it also contributes to the literature on public governance transition in China's philanthropic sector and the state‐society relations more broadly. [ABSTRACT FROM AUTHOR]

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas. [ABSTRACT FROM AUTHOR]

Advanced geological prediction plays an essential role in disaster prevention and safety guarantee. In this paper, by combining multiwavelet analysis with modified evidence reasoning, a new geological prediction method for surrounding rock in a tunnel considering the parameters' coupling effect was proposed. Based on the key drilling parameters' curves that were denoised in the No. 1 hole of the Xiaochuan tunnel in Gansu Province, China, the coupling effect of these parameters were analyzed and the power–speed ratio (PSR) concept was thereby proposed to deal with the denoised curves and obtain a PSR curve. Through continuous wavelet transform (CWT) of the PSR curve, the lithology and structure were preliminarily determined by sectional matching and optimization of multiple wavelet coefficient curves under the optimal scales and PSR curve. Further, the evidence reasoning was introduced to solve the uncertainty parts in the preceding result. To avoid misjudgment caused by the limitation of Dempster Shafer (D-S) theory in the case of complete conflict, it was modified, in which the basic probability assignment (BPA) was determined by the interval number, and a binary group was then defined by combining the traditional conflict coefficient with pignistic probability distance to measure the degree of conflict. The division result that settled by this method showed greater accuracy than tunnel seismic prediction (TSP) and the drilling core, also could give a clear division for both lithology and structure. Importantly, by applying the established method to Chengzhou tunnel, the result fitted the real condition well. This approach provides references for rock prediction in tunnels. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

In this paper, we explore what the travel restrictions during the COVID-19 pandemic reveal about the changing geographies of mobilities and the making of homo sacer, the latter constituted through differentiated control of mobilities. Implemented to protect U.S. public health, travel restrictions imposed on travelers from Mainland China during the early days of the pandemic exemplify how sovereign power that declares a state of emergency and creates bare life can be readily applied to groups of people who previously had privileged access to global mobility. In this sense, bare life does not refer to fixed and disadvantaged social categories but is rather contingently and contextually constituted, through the works of hybrid sovereign regimes. At the same time, however, these travelers are not reduced to a state of zero-agency but reside within a liminal space between soft and hard cosmopolitanisms, as they can still deploy agency and cosmopolitan capital to achieve certain degrees of mobility. By examining how Chinese travelers navigated various travel restrictions and the constantly changing policies to travel to the U.S., this paper explores new spaces of exception and forms of bare life, and argues that homo sacer is dynamically, relationally and recursively constructed, both through apparatus of control and the agency of travelers. [ABSTRACT FROM AUTHOR]

High-spatial-resolution urban buildings play a crucial role in urban planning, emergency response, and disaster management. However, challenges such as missing building contours due to occlusion problems (occlusion between buildings of different heights and buildings obscured by trees), uneven contour extraction due to mixing of building edges with other feature elements (roads, vehicles, and trees), and slow training speed in high-resolution image data hinder efficient and accurate building extraction. To address these issues, we propose a semantic segmentation model composed of a lightweight backbone, coordinate attention module, and pooling fusion module, which achieves lightweight building extraction and adaptive recovery of spatial contours. Comparative experiments were conducted on datasets featuring typical urban building instances in China and the Mapchallenge dataset, comparing our method with several classical and mainstream semantic segmentation algorithms. The results demonstrate the effectiveness of our approach, achieving excellent mean intersection over union (mIoU) and frames per second (FPS) scores on both datasets (China dataset: 85.11% and 110.67 FPS; Mapchallenge dataset: 90.27% and 117.68 FPS). Quantitative evaluations indicate that our model not only significantly improves computational speed but also ensures high accuracy in the extraction of urban buildings from high-resolution imagery. Specifically, on a typical urban building dataset from China, our model shows an accuracy improvement of 0.64% and a speed increase of 70.03 FPS compared to the baseline model. On the Mapchallenge dataset, our model achieves an accuracy improvement of 0.54% and a speed increase of 42.39 FPS compared to the baseline model. Our research indicates that lightweight networks show significant potential in urban building extraction tasks. In the future, the segmentation accuracy and prediction speed can be further balanced on the basis of adjusting the deep learning model or introducing remote sensing indices, which can be applied to research scenarios such as greenfield extraction or multi-class target extraction. [ABSTRACT FROM AUTHOR]

Held every four years, the International Workshop on Tropical Cyclone (IWTC) organized by the World Meteorological Organization has been a global leading conference in the field of tropical cyclone. In preparation for the 10th IWTC (IWTC-10) in December 2022, a summary of research advances of landfalling tropical cyclone (LTC) rainfall during past four years of 2019-2022 has been prepared. Some of the latest research advances has been summarized in Lamers et al. (2023), which reviewed the latest forecast and disaster prevention methods related to TC precipitation. As a supplement, this article mainly focuses on the recent advances in LTC asymmetric rainfall evolution mechanisms and forecast verification results over China. Some new findings have been made in the LTC inner-core size relationship with the asymmetric rainfall distribution. Some major advances focused on asymmetric microphysical characteristics in the TC rainbands. Current simulation and forecast performances of LTC precipitation have been analyzed, and different forecast error sources for rainfall during different landfall stages of TC were compared. To estimate the risk of TC rainfall hazards in China, a parameterized Tropical Cyclone Precipitation Model was reviewed as well in this article. [ABSTRACT FROM AUTHOR]

The Sichuan-Xizang Railway is an important part of the railway network in China, and geological disasters, such as mountain floods and landslides, frequently occur in this region. Precipitation is an important cause of these disasters; therefore, accurate simulation of the precipitation in this region is highly important. In this study, the descriptions for uncertain processes in the cloud microphysics scheme are improved; these processes include cloud droplet activation, cloud-rain autoconversion, rain accretion by cloud droplets, and the entrainment-mixing process. In the default scheme, the cloud water content of different sizes corresponds to the same cloud droplet concentration, which is inconsistent with the actual content; this results in excessive cloud droplet size, unreasonable related conversion rates of microphysical process (such as cloud-rain autoconversion), and an overestimation of precipitation. Our new scheme overcomes the problem of excessive cloud droplet size. The processes of cloudrain autoconversion and rain accretion by cloud droplets are similar to the stochastic collection equation, and the mixing mechanism of cloud droplets is more consistent with that occurred during the actual physical process in the cloud. Based on the new and old schemes, multiple precipitation processes in the flood season of 2021 along the Sichuan-Xizang Railway are simulated, and the results are evaluated using ground observations and satellite data. Compared to the default scheme, the new scheme is more suitable for the simulation of cloud physics, reducing the simulation deviation of the liquid water path and droplet radius from 2 times to less than 1 time and significantly alleviating the overestimation of precipitation intensity and range of precipitation center. The average root-mean-square error is reduced by 22%. Our results can provide a scientific reference for improving precipitation forecasting and disaster prevention in this region. [ABSTRACT FROM AUTHOR]

Rescues from building collapse accidents present a significant challenge for China's emergency rescue system. However, there are also many risk factors in a training scenario, which have been summarized in this study. A hierarchical indicator system for personnel safety was established, including 12 first-level indicators and 23s-level indicators. Then, an improved Grey-DEMATEL-ISM-MICMAC evaluation model was constructed to evaluate the level of risk. Influencing factor scores were determined according to the responses from the questionnaire survey. The influencing degree, influenced degree, centrality, and causality were identified, and the importance, relevance, and clustering of the various factors were obtained after making quantitative calculations. The results showed that the order of priority for solving the essential issues was safety education (A2), operating standards and proficiency (A10), equipment inspection (A4), equipment warehousing maintenance and records (A21). The solving of safety education was identified to be the most essential priority. The priority control order of direct causes was Scientific design and construction (A5), Potential fixed hazards in the facility (A12), Physical fitness of personnel (A1), Weather influences (A18), and Initiation efficiency of emergency plans (A20), and direct control measures for these five factors could achieve a relatively significant effect. [ABSTRACT FROM AUTHOR]

The dynamic effect is a very important issue widely debated by scholars when studying the genetic and disaster-causing mechanisms of earthquake-triggered landslides. First, the dynamic effect mechanism and phenomena of earthquake-triggered landslides were summarized in this paper. Then, the primary types of dynamic effects were further used to interpret the Mogangling landslide in Moxi Town of Luding County, China. A field investigation, remote sensing, numerical calculation and theoretical analysis were carried out to illustrate the failure mechanism of slope rock masses affected by earthquakes. The interaction between seismic waves and slope rock masses and the induced dynamic effect of slope rock masses were primarily accounted for in the analysis. The slope topography, rock mass weathering and unloading characteristics, river erosion, regional seismogenic structure, and rock mass structure characteristics were also discussed. The results showed that the formation of the Mogangling landslide was mainly related to the high amplification effect of seismic acceleration and back slope effects, interface dynamic stress effects, and double-sided slope effects of seismic waves caused by the catastrophic Ms 7.75 Moxi Earthquake in 1786. The principles for the site and route selection of large-scale infrastructure in the planning stage and the scientific prevention of seismic geological disasters were proposed on the basis of the dynamic effect of earthquake-induced landslides. [ABSTRACT FROM AUTHOR]

Studying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS‐weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix‐projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID‐19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID‐19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were "spindle‐shaped," meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was "wave‐shaped," indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies. [ABSTRACT FROM AUTHOR]

Monitoring and assessing coastal subsidence is crucial to mitigating potential disaster risks associated with rising sea levels. Nansha District in Guangzhou City, representing global coastal soft-soil urban areas, faces significant challenges related to ground subsidence. However, the current understanding of the status, causative factors, and risk (includes subsidence susceptibility and vulnerability) assessment of ground subsidence in Nansha District is unclear. To address this gap, we utilized the SBAS-InSAR technique, analyzing 49 Sentinel-1A images from December 2015 to June 2019, for systematic ground subsidence monitoring. Subsequently, we assessed subsidence risk using a comprehensive index method and a risk matrix. Our findings indicate that subsidence velocity primarily ranged from −40 to −5 mm/a, with a spatial pattern of increasing subsidence from inland to coastal areas. The cumulative subsidence process unfolded in four distinct stages. The genesis of land subsidence was linked to an endogenous geological context dominated by soft-soil deposition, influenced by external factors such as surface loading and groundwater extraction. High-risk zones were concentrated in key engineering development areas, transportation pipeline trunk lines, and densely populated regions, demanding special attention. This study provides a foundational resource for disaster prevention and control strategies in Nansha District and similar coastal cities. [ABSTRACT FROM AUTHOR]

Background: Health security is a critical issue which involves multiple dimensions. It has received increasing attention in recent years, especially in China. In order to improve the national health level, China has made many efforts, such as the "Healthy China 2030" plan proposed several years ago. However, due to the complexity of its national conditions and the difficulty of index design, the results of these efforts are not significant. Therefore, it is necessary to construct a new measurement index system. Methods: Based on the questionnaire of "Health China 2030", we have collected a total of 3,000 participants from all 31 provinces, autonomous regions, and municipalities in China. We used statistical methods such as multiple correspondence analysis and rank-ordered effect analysis to process the data. The balance index is constructed by a series of actions such as weight division, order calculation and ranking. Results: Through multiple correspondence analysis, we can find that there was a close relation in the correspondence space between the satisfaction degrees 1, 2, and 3, while a far distance from satisfaction degrees 4 and 5. There were four positive and four negative indices separately based on the average expected level and four clusters after ordinal rank cluster analysis. Generally speaking, there are no prominent discrepancies across gender and residential areas. Conclusions: We created and examined balanced indicators for health security in China based on the "Health China 2030" questionnaire. The findings of this study give insight into the overall situation of health security in China and indicate opportunities for improvement. [ABSTRACT FROM AUTHOR]

In 2022, prolonged periods of extreme heat waves fueled huge wildfires across the globe. While research has shown that past experience with wildfire increases homeowners' intentions to adopt fire mitigation activities, little is known about whether the impact of disaster can be long-lasting and represent a stable behavioral change. Thus, the current work explored whether Chinese homeowners' intentions to perform fire wise actions would change over time. In Study 1, we compared responses of residents in a fire-stricken area with those of residents in a nondisaster area three days after a massive wildfire. It was found that participants who had just experienced a wildfire reported a stronger intent to take fire mitigation on both self-report and behavioral measures. Study 2 involved a follow-up survey that took place in the same areas one month after the fire had occurred. The results showed that participants in these two areas demonstrated no differences in intentions to implement fire mitigation measures, which suggests that the impact of disasters decayed over time. Combined, these findings suggest that past experience with wildfire will likely give rise to stronger intention to engage in mitigation behavior but this effect may diminish with time ticking away. [ABSTRACT FROM AUTHOR]

Mining-induced disturbance to stress fields can lead to many engineering disasters such as rock bursts, roadway collapses, and coal and gas outbursts. The evolution characteristic of abutment pressure during the mining process is of great significance for prevention of engineering disasters. Based on a case study of No.12526E Panel in Xieqiao coalmine of Huainan mining area in China, this paper investigated the evolution characteristic of abutment pressure during the retreat mining process. A field measurement was performed using three-dimensional (3D) pressure sensor, and the abutment pressure was continuously monitored during the mining process. The monitoring results indicate that the abutment pressure curve can be divided into strong interference area and weak interference area. Further, the evolution characteristics of abutment pressure during the retreat mining process were simulated using a 3D discrete-element program. Periodic caving characteristics of rock strata were obtained, and the major causes of stress concentration and the bearing stress inflection point were analyzed. With the periodic caving of the overlying strata, the stress inflection point appears, and fracture cracks are generated and hinder stress recovery. Finally, the detailed dynamic evolution process of the abutment pressure as well as the deformation and caving characteristics of the overlying strata of the No.12526E Panel were analyzed. The coal and rock near the mined-out area undergo a sharp stress concentration and reduction, which would lead to damage to the rock mass near the mined-out area. [ABSTRACT FROM AUTHOR]

In order to scientifically evaluate the emergency management capabilities of major cities in China, this article conducts a comprehensive study on the input and output indicators of emergency response in each city. A two-stage network DEA model was used to construct an evaluation model that reflects the emergency management capacity of cities. A dataset containing emergency management data from 36 well-known cities in China was selected to effectively evaluate its performance, and the city that demonstrated the most effective input-output ratio in the field of emergency management was ultimately determined. The research results show that using a two-stage network DEA model as the foundation to construct an evaluation model that reflects urban emergency management capabilities can promote a wise combination of subjective and objective evaluations, and achieve scientific investment in urban emergency assets. [ABSTRACT FROM AUTHOR]

Urban waterlogging is a significant global issue. To achieve precisely control urban waterlogging and enhance our understanding of its causes, a novel study method was introduced. This method is based on a dynamic bidirectional coupling model that combines 1D-2D hydrodynamic and water quality simulations. The waterlogging phenomenon in densely populated metropolitan areas of Changzhi city, China, was studied. This study focused on investigating the process involved in waterlogging formation, particularly overflow at nodes induced by the design of the topological structure of the pipe network, constraints on the capacity of the underground drainage system, and the surface runoff accumulation. The complex interplay among these elements and their possible influences on waterlogging formation were clarified. The results indicated notable spatial and temporal variation in the waterlogging formation process in densely populated urban areas. Node overflow in the drainage system emerged as the key influencing factor in the waterlogging formation process, accounting for up to 71% of the total water accumulation at the peak time. The peak lag time of waterlogging during events with short return periods was primarily determined by the rainfall peak moment. In contrast, the peak time of waterlogging during events with long return periods was influenced by the rainfall peak moment, drainage capacity and topological structure of the pipe network. Notably, the access of inflow from both upstream and downstream segments of the pipe network drainage system significantly impacted the peak time of waterlogging, with upstream water potentially delaying the peak time substantially. This study not only provides new insights into urban waterlogging mechanisms but also provides practical guidance for optimizing urban drainage systems, urban planning, and disaster risk management. [Display omitted] • Introduces a method for accurately tracking urban multi-process drainage dynamics. • Analyzed waterlogging from node overflow and runoff, clarifying their interactions. • Unveils diverse waterlogging patterns and their underlying mechanisms. • Peak waterlogging times differ by up to 96 min across locations. • Upstream inflow into the main conduit delays downstream waterlogging peak times. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three series of pairwise rainfall and river flow were extracted to capture their dependence. • Pairs of rainfall and river flow were proportionally sampled from their bivariate joint distributions. • Expected annual damage was employed to characterize compound flood risk. • The relationship between flood damage and rainfall and river flow is positively stepped. • Future compound flood risk is expected to increase in most of the Yangtze River Basin. Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 ∼ 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % ∼ 66.13 % and 1.72 % ∼ 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % ∼ 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R 2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % ∼ 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % ∼ 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction. [ABSTRACT FROM AUTHOR]

We consider an integrated location–allocation and evacuation planning problem in a disaster context, where the effects of a disaster, including the uncertain capacities of relief facilities (rescue centers and distribution centers), uncertain demands for relief supplies and casualty treatment services, and uncertain availability of transportation links are characterized by a discrete scenario set. Instead of complete failures, we allow the disrupted relief facilities only lose part of their capacity. To deal with the uncertainties, we propose a two-stage recoverable robust optimization model, where the location decision of relief facilities, the allocation decision of delivering relief supplies from relief facilities to affected areas, the transfer decision of transporting casualties from affected areas to rescue centers etc are defined in two stages where the first-stage solution should be robust against the possible effects of a disaster that are revealed in the second stage, and the second-stage solution involves some recovery actions, which we term as multi-mitigation strategies: re-opening and re-operation, re-allocation, and relief supply sharing, to mitigate the effects. To solve the model to optimality, we develop a nested two-stage decomposition algorithm that iterates between a master problem considering only a subset of disaster scenarios solved by a Benders decomposition algorithm that incorporates some non-trivial acceleration strategies, and an adversarial separation problem that identifies disaster scenarios to enhance the worst-case recovery cost of the master problem. We introduce some warm-start techniques to accelerate the convergence of the solution algorithm. We conduct numerical studies on simulation instances to assess the performance of the solution algorithm, and analyze the robustness and recoverability of the model. We also conduct extensive numerical studies on realistic instances from Ya'an and Ganzi to demonstrate the benefits of accounting for recoverable robustness over a stochastic policy and a robust policy without recovery actions, the benefits of considering integrated optimization over sequential optimization, and the benefits of considering partial capacity loss and multi-mitigation strategies. • An integrated location–allocation and evacuation planning problem in disaster situations is studied. • A two-stage recoverable robust model with multi-mitigation strategies is presented. • An exact algorithm integrating Benders decomposition and row-and-column generation is devised. • Experiment results show the benefits of considering robustness and multi-mitigation strategies. • The performance of the model is showcased through two case studies in Ya'an and Ganzi, China. [ABSTRACT FROM AUTHOR]

Propagation from meteorological to hydrological drought events is crucial for understanding drought variation mechanisms and their consequences. Most previous studies were implemented using various standardized drought indices at month scale, which were difficult to reveal the physical processes and insufficient to characterize the comprehensive propagation characteristics (e.g., propagation time, duration, and severity) from meteorological to hydrological drought events. Furthermore, the impact mechanisms of underlying surfaces on drought propagation remained unclear. This study identified the paired relationships between meteorological and hydrological drought events and investigated their comprehensive propagation characteristics at the hydrologic response unit scale using the Soil and Water Assessment Tool (SWAT), run theory and paired analysis. The impact of underlying surfaces (e.g., land use, soil type, and slope) on drought propagation was further assessed using the Student's t -test at the 95% confident level. The upper middle Yuanjiang River Basin in China, which is prone to drought and exhibits diverse underlying surface conditions, was selected as the study area. Results indicate that the SWAT model optimally simulates the monthly streamflow with the Nash-Sutcliffe efficiency being ≥0.75 and the RMSE-observations standard deviation ratio being ≤0.50. Four main propagation types are identified, i.e., one-to-one, many-to-one, one-to-many, and many-to-many, in which the first two types are the dominant ones, accounting for 86.7% and 13.3% of total paired events, respectively. More than 85% of the study area exhibits a propagation time of ≤2 months, especially in the headwater area and the right bank of Lvzhijiang River. Drought duration and severity are amplified during the propagation process in 60.8% and 60.3% of the study area, respectively. Drought propagation time, duration, and severity are mainly controlled by soil and slope, and are slightly affected by land use type. The propagation time is quite short in areas with a high soil infiltration rate and steep slope, and the drought duration and severity are amplified in areas with a low soil infiltration rate and gentle slope. This study aims to provide deep insights on propagation from meteorological to hydrological drought considering various underlying surfaces, and further provides a scientific basis for the drought propagation mechanism and for drought disaster prevention and adaptation. • Drought propagation is analysed on time, duration, and severity by paired events. • Only 55% of meteorological drought events trigger hydrological drought events. • Areas with lower soil infiltration rates and gentler slopes show shorter duration and higher severity. • Drought propagation is impacted evidently by soil, followed by slope and land use. [ABSTRACT FROM AUTHOR]

Frequent occurrence of natural disasters has become one of the most serious challenges to human society. As rural communities are the forefront of disasters, enhancing the resilience of rural community is crucial to mitigate the impact of natural disasters and seek rapid recovery. This paper provides an analytical framework for changing trajectories of the resilience of rural communities after disasters. Taking the disaster area affected by the catastrophic debris flow in China as an example, we analyze the changing trajectories of rural community resilience after disaster in three villages with different damage levels from 2010 to 2020 based on the data from key person interviews, the disaster situation and development reports, village work records and farmers' questionnaires. We found that the changes of community resilience after disaster show obvious stages due to the difference in the intensity of effects of government intervention and community self-organization ability during disaster relief reconstruction period and self-recovery period. The resilience of communities with different disaster losses showed an increasing trend. However, during the disaster relief and reconstruction period, the resilience of the severely affected community was improved faster, followed by the most severely affected community, while the resilience of generally affected community was improved more slowly. During the self-recovery period, the resilience of the affected general community was improved faster than that of the other two communities. The evolution process of community resilience reflects the increasing role of self-organizational capacity in post-disaster community development, aligning with the overall global trend in rural community governance. In addition, there is a significant sequence in the improvement speed of different dimensions of community resilience at different stages after disasters. During the disaster relief and reconstruction period, the resilience of community management was improved faster and the resilience of environment was slower. During the self-recovery period, the economic resilience of community was improved faster and the social resilience was improved slower. Finally, some suggestions were put forward to strengthen natural disasters prevention and community resilience in disaster-prone areas. [Display omitted] • Provide an analytical framework for the changing trajectories of the resilience of rural communities after disaster. • Community self-organization ability play an important role in improving community resilience. • There are significant differences in resilience of communities with different disaster losses. • There is an obvious sequence in the improvement speed of different dimensions of community resilience after disaster. [ABSTRACT FROM AUTHOR]

An updated comprehension of landslide kinematics is a prerequisite for developing effective risk assessment and emergency management in the context of climate variability and anthropogenic disturbances. This study employs a multidisciplinary approach, taking the Outang landslide in China's Three Gorges Reservoir area as a case study. We examined the spatiotemporal surface processes and time- and subzone-dependent hydrometeorological factors from 2007 to 2021, particularly identifying rainfall regimes and reservoir water levels that trigger landslide movements. Given the uneven distribution of GNSS surface markers, we introduced weighted monthly mean displacement (D) and weighted marker frequency (f), enhancing the accuracy of our insights into distinct active subzones of the landslide. Preliminary results revealed July, September and June as months of higher susceptibility, as well as months with low water levels. The early-stage deformation control by reservoir water levels is being increasingly overshadowed by rainfall, due to extreme weather events and localized anti-slip measures. Long-term surface processes have also highlighted the transformation of landslide failure modes towards a compound pattern, from a predominantly retrogressive to a thrust-dominated pattern, and even overall translation coupled with localized rotation. Furthermore, the comparative analysis of normalized displacement/strain demonstrates the power and potential of integrating multi-source monitoring data. By revisiting the evolution processes and potential deformation mechanisms of the Outang landslide, we are getting closer to understanding more about the dynamics of giant landslides in reservoir areas, and illuminate the pathway towards enhanced risk management and emergency response strategies. • Revisiting of a case study brings new awakening to the kinematics and drivers of giant reservoir landslides. • Introducing weighted monthly mean displacement and weighted marker frequency to tackle spatial heterogeneity in subzones. • Precipitation has increasingly outperformed and even replaced reservoir water in controlling landslide deformation. • Comparative analysis of normalized surface displacement and subsurface strain illuminates the path towards robust early warning. [ABSTRACT FROM AUTHOR]

• Analyzes the spring drought in the cold region of Northeast China from 1980 to 2020. • Considering the impact of early spring snowfall, the spring drought was analyzed. • A novel cutoff radius approach for the PP information entropy model. Northeast China is an important grain-producing area, and spring is the sowing time for crops. Drought hinders crop germination and leads to crop failure. Spring drought analysis helps to understand the characteristics of regional spring drought and provides a reference for formulating disaster prevention and reduction measures. Considering the characteristics of seasonally frozen soil regions in northeastern China, sampling at a resolution of 0.5 degrees, accounting for the snow cover factors in the early spring, as well as precipitation, temperature, evapotranspiration, and soil moisture during the spring planting period, and uses the projection pursuit information entropy model to analyze the spatial and temporal variability of spring drought in Northeast China. In the model, a new method was proposed to calculate the cutoff radius by adjusting the radius size with the parameter. The optimal parameters were ultimately determined using the Shapiro-Wilk test, Kolmogorov-Smirnov test, and Epps-Pulley test. The results are as follows: drought is relatively severe in the eastern parts of Inner Mongolia, as well as in the northwest of Liaoning. The central and northern parts of Inner Mongolia, the northern and northeastern parts of Heilongjiang, the southeastern parts of Jilin, and the eastern parts of Liaoning are relatively humid, while the drought situation in other regions is relatively moderate. Regional spring drought shows an increasing trend, and most regions show statistical significance. Areas with high temperatures and low precipitation, as well as low snow cover in the early spring, are prone to spring drought. Moist regions are characterized by high precipitation, low evapotranspiration, and abundant snow resources. [ABSTRACT FROM AUTHOR]

• The drought indices SDCI, VHI and IDCI were constructed by four weighting methods. • Determination of weights was validated by comparing against scPDSI, SMCI and CY. • New classification schemes were executed by cumulative frequency curve of scPDSI. • Reliability of schemes was verified by drought affected area and frequencies. With the continuous development of remote sensing technology, an increasing number of drought indices are proposed to assess the drought process in different regions. However, most of the studies used the empirical coefficient method and standard drought classification scheme, without considering regional applicability, which may overestimate or underestimate drought intensity. Therefore, the GLDAS soil moisture dataset was firstly verified in the study area, Wei River basin, China, and then the response of precipitation accumulation to NDVI and the lagged effect were analyzed. Secondly, the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Integrated Drought Condition Index (IDCI) were constructed and the optimal weights of the drought indicators were determined against the scPDSI, SMCI and CY based on four weighting methods. Finally, the new classification schemes of the three indicators were executed based on the cumulative frequency curve of the self-calibrating Palmer Drought Severity Index (scPDSI) from June to September during the period 2000–2019, and the reliability of the new schemes was verified by comparing with drought frequency and drought affected area. Furthermore, the variabilities of drought frequency of the new classification scheme based on the three indicators were analyzed. The results showed that the correlation coefficients of 2-month accumulation of precipitation and NDVI produced the best value and no lagged effect was detected. The SDCI and VHI determined by the AHP weighting method and the IDCI determined by the entropy weighting method showed better performance. The new classified schemes of the three drought indicators demonstrated better performance than the original classified scheme with more similar frequency distributions to scPDSI and higher correlation coefficients against the drought affected area. Additionally, the three indicators based on the revised classification scheme all indicated a decreasing trend in the frequency of drought occurrence for all the different drought levels; the SDCI and VHI were better at capturing drought events than IDCI. Overall, the results of this study proved that the optimal weighting methods and new classification schemes could effectively improve the precision of drought assessment and may provide helpful information for agricultural drought disaster prevention. [ABSTRACT FROM AUTHOR]

• Determination of new engineering parameter as prediction indicator. • Regional minefield geological response and deformation mechanism are revealed. • A robust and cost-effective workflow for dynamic disaster identification. • Integration of traditional and new rockburst prediction indicators. Due to seismic load, complex geological and geo-mechanical conditions of rock mass in Steeply Inclined and Extremely Thick Coal Seam (SIETCS) mines, the prediction and prevention of dynamic disasters remain quite challenging. Traditionally, physical models and numerical simulations are widely practiced for disaster prediction at shallow depths which becomes difficult in SIETCS mines. The drilling tests and above models are generally time-consuming and uneconomic providing miniature coverage. Whilst, rockburst driven disasters require detailed regional geological understanding and mechanical behavior of rock mass. For this purpose, a non-invasive and cost-effective geophysical workflow covering regional-scale geology is introduced for accurate identification and prediction of rockburst in a SIETCS mine of northwestern China. Passive seismological in-situ observations were made for a period of twenty-four months to compute the seismic source parameters, map fracturing process, identify regional lineaments and calculate important rockburst prediction indicators. The spatiotemporal distribution of induced-microseismicity, three-states stress theory, and mapped fractures helped in the identification of three rockbursts, several high-energy tremors, and the prediction of two new rockburst-prone regions. The mapped fractures through the novel 'Seismo-Frac model' showed that for the simultaneous excavation in B3 + 6 and B2 + 1 coal seams subjected to repetitive episodes of seismic deformation, the shear-displacement accumulation, prying effects of rock pillar and roof coupled with dynamic stress are the primary mode of deformation. The mapped fractures and lineaments are unevenly distributed in the minefield with a maximum density of the fractures concentrated in the B3 + 6 coal seam and rock pillar. Furthermore, through conditional probability analysis, the prediction efficiency of source parameter averages (E and N), seismological parameters (b-value and Δb), and maximum potential magnitude (M m) was compared. A sharp-increase in source parameter averages while a sharp-decrease in the seismological parameters proved to be effective rockburst prediction indicators. This study also introduces decreasing occurrence frequency of fractures and lineaments as the new prediction indicators. This method, while a panacea for imaging the entire fracturing phenomenon, provides insights with widespread implications for academic researchers and industry practitioners to mitigate dynamic disasters in global underground engineering excavation. [ABSTRACT FROM AUTHOR]

• The integrated model has been used to simulate the compound flood. • The nonlinear superposition characteristics has been shown with the effect of sea level rise and typhoon wind intensity. • The multiple hazards significantly increases the inundation of compound flood, seawall defense can effectively reduce the inundation impact. Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m3, respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities. [ABSTRACT FROM AUTHOR]

• First systematic landslide inventory in the Bailu Tableland. • Combination of InSAR, UAV and landform interpretation for small landslide mapping. • ALOS-2 shows good ability to identify potential landslide in high vegetation areas. • Promoting the understanding of loess tableland landslide development and evolution. Loess landslides, predominantly triggered by human activity and precipitation, dominate the geomorphological evolution of extensive regions of the Chinese Loess Plateau. Tablelands constitute a large proportion of this Loess Plateau. Although the area has a long history of human occupation, the Bailu Tableland is relatively poorly studied. Recent landslide events along the edge of the tableland significantly affected lives and livelihoods. To develop a better understanding of geomorphological evolution and develop appropriate hazard management and mitigation strategies, we characterized recent landslide activity and determined whether there are discernable trends in activity. We used Unmanned Aerial Vehicle (UAV) observations, Interferometric Synthetic Aperture Radar (InSAR) monitoring, and Digital Terrain Analysis (DTA) to observe significant differences in the position, shape, and magnitude between old and new landslides. The geomorphological evolution of the tableland was initially driven by large landslides, while current and future morphological development is more likely affected by small landslide events. We demonstrate the successful application of InSAR to investigate potential landslides in well-vegetated loess landscapes and show that loess landslides tend to develop on slopes with high relative relief and high topographic wetness associated with gully erosion. Unlike landslides induced by groundwater variations in the Heifangtai and South Jingyang tablelands, precipitation and infrastructure construction form the main controls on landslide activity in the Bailu Tableland. We propose two geomorphological evolution models that describe loess tableland recession caused by landslides. The results from this research contribute to a better understanding of the reactivation behavior of landslides along loess tablelands, provide a scientific foundation for landslide hazard and risk management, and supplement understanding of the geomorphological evolution of loess tableland landscapes. [ABSTRACT FROM AUTHOR]

Waves are an oceanic phenomenon that involve intricate mechanisms of generation and dynamic processes. In the past, wave forecasting mainly primarily depended on complicated numerical models; however, these models are computationally expensive and time-consuming. As a result, different deep learning techniques have been tested. In the South China Sea, the generation and propagation of ocean waves are impacted by many factors, which are affected including the swell and typhoon as well as complex topography, making most of the deep learning methods limited in their accuracy. To address this issue, a Spatio-temporal network integrating multivariate attention mechanisms, MA-TrajGRU, is introduced and applied to forecast the ocean wave conditions during the typhoon process in the South China Sea. We simulated wave data for 2017–2021 using SWAN and validated it against buoy observations. The forecast data includes significant wave height (Hs) and mean wave period (Tm) with a spatial resolution of 0.1° and a temporal resolution of 1 h. Wave forecasting experiments were performed 24 h in advance to evaluate MA-TrajGRU. The errors of MA-ConvLSTM and MA-TrajGRU with the multivariate attention mechanism addition are lower with MAE of 0.136 m, MSE of 0.0582 m2, and PCC of 0.9555 for Hs; MAE of 0.2352 s, MSE of 0.1393 s2, and PCC of 0.9708 for Tm. We conducted experiments on wave forecasting during typhoon processes and observed that MA-TrajGRU performs much better than commonly used Spatio-temporal network models. Thus, MA-TrajGRU can forecast the spatial and temporal distribution of large-scale ocean waves in the South China Sea, especially during typhoons. Therefore, it has great potential for disaster prevention and mitigation in marine engineering. • A MA-TrajGRU model for wave conditions forecasting is developed. • Wave conditions (Hs and Tm) up to 24-h are forecasted based on data from SWAN simulations. • MA-TrajGRU responds better to extreme typhoon events. • The results of MA-TrajGRU model outperforms other commonly used models. [ABSTRACT FROM AUTHOR]