Wenchuan County Research Articles

First report of sweet cherry root rot caused by Fusarium solani in China.

Sweet cherry (Prunus avium L.) has become an economically important fruit in China. And its cultivation area has significantly expanded over the last three decades (Wang et al. 2020; Zhao et al. 2023). In July 2023, wilting of cherry trees was observed in a cherry plantation in Wenchuan County (31°51'N, 103°56'E, altitude: 1,510 m) in Sichuan Province and approximately 27% of the trees showed symptoms of root rot including soft roots, dark brown to black lesions, yellowing and wilted leaves, and a distinct yellow-brown core discoloration of the inner root core when cut in cross-section. To isolate the causal pathogens, six infected sweet cherry plants with rootstock 'Daqingye' from Cerasus pseudocerasus were randomly selected from the orchard and then the intertwined diseased and healthy roots (5mm× 5mm × 2mm) were washed with sterile water to remove surface soil. The root samples were surface sterilized with 75% ethanol for 30 seconds and NaClO for 30 seconds and washed three times with distilled water. The disinfected tissues were placed on potato dextrose agar (PDA) and incubated at 27°C in darkness for 5 days (Zhao et al. 2024). A total of nine fungal isolates with similar morphological characteristics were obtained. The colony obtained through single-spore purification displays a red reverse side and a concentric ring pattern on the front, with a sparse surface. Macroconidia were relatively slender with a curve, like sickle shape, 0 to 3 septate measuring (25.8 to 46.1) μm× (4.2 to 7.5) μm, respectively (n=20). The morphological characteristics were consistent with the description of Fusarium spp. (Li et al. 2021). Among these isolates, only HB5 was selected for additional molecular identification. Three target genes, including the internal transcribed spacer (ITS), partial translation elongation factor 1-alpha (TEF), and RNA polymerase second largest subunit (RPB2) were amplified using the primers ITS1/ITS4, TEF1-728/FTEF1-re, and fRPB2-5F/fRPB2-7r, respectively (Groenewald et al. 2013; Carbone and Kohn 1999; Reeb et al. 2004). Sequences of HB5 was deposited in GenBank (ITS, PP388208; TEF, PP580036; RPB2, PP580035). A BLAST search revealed high similarity to those of F. solani sequences with 99%, 100% and 100% respectively (MN013858.1, JF740846.1, OR371902.1), and a multilocus phylogenetic tree was generated to represent the molecular identification results. Pathogenicity studies were conducted on the rootstocks from 'Daqingye' of Cerasus pseudocerasus in 1 liter plastic flowerpots. The seedlings were incubated in a constant temperature incubator at 25°C with a humidity level of 65% for two weeks. Following the growth of green leaves, 200ml (1x106 spores/ml) of spore suspensions were poured into pots. After 4 weeks of inoculation, the same symptoms of the inoculated plants were observed consistent with those shown in the field , while control plants were inoculated with distill water with asymptomatic. The inoculated pathogen was confirmed both morphologically and molecularly as described earlier, thereby fulfilling Koch's postulates. It has been reported that Fusarium solani has been reported to cause root rot in various plants in China, including Actinidia sppt, Zanthoxylum bungeanum, Fragaria×ananassa Duch (Song et al.2022; Li et al. 2023; Zhao et al. 2024). To our knowledge, this is the first report of Fusarium solani causing root rot in sweet cherry (Prunus avium). We here also report the severity and outbreak of this disease, which has been found in other regions in recent years and may become prevalent. Further research on disease management strategies is urgently needed to protect sweet cherry production.

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

ABSTRACT The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

Scraping Effect of Dam-Overtopping Debris Flow—A Case Study of Chutou Gully ‘8.20’ in Miansi Town, Wenchuan County

ABSTRACT After the May 12 earthquake in Wenchuan, numerous debris flow disasters occurred due to heavy rainfall. After the earthquake, disaster prevention and mitigation projects, such as the sand dam, played an essential role in safety. However, when the outflow of solid material exceeds the designed storage capacity of the sand dam, the debris flow overtopping the dam intensifies the scraping effect on the source material of the gully channel. This paper takes the ”8.20” debris flow in the Chutou gully as the research object. A transport mode of the debris flow after dam overtopping is studied through comprehensive field investigation, theoretical analysis, and indoor model tests. A quantitative evaluation model is established for the scraping depth and scraping volume of dam-overtopping debris flow. The study results showed that once the debris flow overtops a dam, the loose source carried by the channel is involved in the entire migration process, enlarging the fluid scale and intensifying the damage along the channel. The results agree with the measurements and observations, with maximum relative errors of 5.81% and 14.70%, respectively. This shows model validity. Furthermore, the test results show that the debris flow scarping phenomenon in the γ=[1.4 t/m3, 1.6 t/m3] interval is the most significant. Overall, this study will help improve future engineering decisions, design strategies, and finding the best design solutions to minimize the risk of dam-overtopping debris flow disasters.

Dynamic susceptibility assessment of debris flow hazard after a strong earthquake, Wenchuan County, Sichuan, China

Dynamic susceptibility assessment of debris flow hazard after a strong earthquake, Wenchuan County, Sichuan, China

Prediction of rainfall-induced debris flow using Random Forests and Bayesian Optimization in Yingxiu Town, Wenchuan County, China

Prediction of rainfall-induced debris flow using Random Forests and Bayesian Optimization in Yingxiu Town, Wenchuan County, China

A scenario-based robust approach for joint planning of multi-blood product logistics and multi-casualty type evacuation

This paper presents a two-stage hybrid robust programming model that integrates multiple blood products scheduling and multiple casualty types evacuation under facility disruptions risk and casualty number uncertainty. The model seeks to maximize the rescue efficiency and minimize the total operations cost. Under a set of disruption scenarios, we adopt a scenario-based robust method to address disruption risks at temporary medical centres and two robust uncertainty sets to deal with uncertain casualty numbers. We propose a proximal bundle algorithm to solve large-scale instances of the proposed hybrid robust model approximately. Extensive numerical experiments show that: (i) the trade-off between model robustness and solution robustness can help the decision-makers determine an appropriate risk-aversion weight; (ii) compared with the corresponding stochastic and scenario-based robust models, the hybrid robust model can obtain more robust solutions with a slight increase in cost; (iii) the proximal bundle algorithm can produce near-optimal solutions within reasonable computational times; (iv) some model parameters have significant impact on the total cost, which can help decision-makers set the appropriate parameters to achieve the desired outcome. Finally, we also use the real data of the 2008 Wenchuan County Earthquake in Sichuan Province, China, to illustrate the application of the model.

Prediction of debris flow blocking river hazard chain by the numerical simulation: the Cutou catchment, Wenchuan County, China

Prediction of debris flow blocking river hazard chain by the numerical simulation: the Cutou catchment, Wenchuan County, China

Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area’s disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation’s findings mostly agreed with the survey’s actual findings. The study’s findings demonstrated that: (1) the FCNN model’s AUC value was 0.910, while the I-AHP model’s AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened.

Granular risk assessment of earthquake induced landslide via latent representations of stacked autoencoder

Earthquake-induced landslides are ubiquitous on slopes in terrestrial environments, which can pose a serious threat to local communities and infrastructures. Data-driven landslide assessments play a crucial role in preventing future landslide occurrences and recurrences. We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an end-to-end learning strategy to obtain a central latent vector with reduced dimensionality. The multivariate landslide dataset was used as both the input and output to train the stacked autoencoder algorithm. Subsequently, in the central latent vector of the stacked autoencoder, the Fuzzy C-means clustering algorithm was applied to cluster the landslides into various groups with different risk levels, and the intervals for each group were computed using the granular computing approach. An empirical case study in Wenchuan County, Sichuan, China, was conducted. A comparative analysis with other state-of-the-art approaches including Density-based spatial clustering of applications with noise (DBSCAN), K-means clustering, and Principal Component Analysis (PCA), is provided and discussed. The experimental results demonstrate that the proposed approach using a stacked autoencoder integrated with fuzzy information granulation provides superior performance compared to those by other state-of-the-art approaches, and is capable of studying deep patterns in earthquake-induced landslide datasets and provides sufficient interpretation for field engineers.

First Report of Epicoccum nigrum Causing Brown Leaf Spot of Sweet Cherry (Prunus avium) in China

Sweet cherry (Prunus avium L.) has become an important economic fruit in China, mainly produced in Shandong Province. In recent years, the planting area of Aba Prefecture in Sichuan Province has increased. In June 2022, sweet cherry brown leaf spot was found in a cherry plantation (100ha) in Wenchuan County (30°54′50.21″N, 103°24′49.10″E), with an incidence of 50 - 70%. The symptoms appeared as brown circular spots on the leaf, gradually expanding until multiple lesions coalesced to form large irregular brown spots; eventually entire leaves were killed. To isolate the causal pathogens, 10 diseased trees were randomly selected from an orchard, one diseased leaf was taken from each tree, and samples (4×4 mm2) were cut from the border between diseased and healthy tissues of 10 diseased leaves, surface sterilized with 75% ethanol for 30 sec, washed three times with sterilized water, dried on sterilized filter paper and placed on potato dextrose agar (PDA). After 5d at 25℃, five morphologically similar colonies were obtained, colony appears yellow fluffy and released a large amount of red-orangepigment. Microscopy revealed circular to ovoid, verrucose, and multicellular conidia measuring 20×25 μm diameter (n = 30) were produced on the mycelia. The morphological characteristics were consistent with the description of Epicoccum nigrum (Lima et al 2011). To further identify the strains, the internal transcribed spacer (ITS), β-tubulin, and RNA polymerase second largest subunit (RPB2) gene regions were amplified with ITS1/ITS4 , Bt2a/Bt2b, and 5f2/7cr (White et al. 1990; Glass and Donaldson 1995; Sung et al. 2007), respectively. BLAST analysis revealed that the ITS, β-tubulin, and RPB2 sequences were 99.2%, 100% and 99.6% homologous, with those of E. nigrum (KU204750.1, OL782123.1, and MW602294.1), respectively. The sequences of the five isolates were identical; and those of representative strain TY3 were deposited in GenBank (ITS, OP410968; β-tubulin, OR502448; RPB2, OP484927). Maximum likelihood phylogenetic analyses were performed for the combined data set with ITS , β-tubulin and RPB2 using MEGA6 under the Tamura–Nei model (Tamura et al. 1993). Isolate TY3 clustered with E. nigrum type strain CBS 505.85. The pathogenicity of TY3 was tested on 10 sweet cherry trees aged 3 years (there were about 50 leaves per plant). Five plants were sprayed with 50 mL of spore suspension (1×105 spores/mL), while the controls (Five plants) were sprayed with 50 mL of sterile water. All plants were in closed plastic bags to maintain high humidity, placed in a greenhouse, and incubated at 25℃with a 12-h photoperiod. Twelve days after inoculation, 35% of the inoculated leaves showed lesions; that were consistent with those observed in the field, and the control group was asymptomatic. To confirm Koch´s postulates, two isolates were taken from the margins of leaf lesions and both were confirmed to be E. nigrum based on morphological observations and molecular identification using ITS β-tubulin, and RPB2 sequences. This is the first report of brown leaf spot caused by E. nigrum on P. avium in China. This discovery needs to be considered in developing and implementing disease management programs in sweet cherry production.

Dual Path Attention Network (DPANet) for Intelligent Identification of Wenchuan Landslides

Currently, the application of remote sensing technology in landslide identification and investigation is an important research direction in the field of landslides. To address the errors arising from the inaccurate extraction of texture and location information in landslide intelligent recognition, we developed a new network, the dual path attention network (DPANet), and performed experiments in a typical alpine canyon area (Wenchuan County). The results show that the new network recognizes landslide areas with an overall accuracy (OA) and pixel accuracy (PA) of 0.93 and 0.87, respectively, constituting an overall improvement of 4% and 18% compared to the base pyramid scene parsing network (PSPNet). We applied our knowledge of the landslide image features to other areas in the upper reaches of the Minjiang River to enrich the landslide database for this region. Our evaluation of the results shows that the proposed network framework has good robustness and can accurately identify some complex landslides, providing an excellent contribution to the intelligent recognition of landslides.

Exploring the relationship between risk perception and public disaster mitigation behavior in geological hazard emergency management: a research study in Wenchuan county

Modern disaster emergency management emphasizes the collaborative participation of multiple parties. Exploring the relationship between risk perception and mitigation behaviors of the public is essential to develop the effectiveness of public disaster reduction and improve the performance of emergency management. Based on risk perception theory, a research framework on the relationship between risk perception and mitigation behaviors was constructed by selecting two phases of emergency management: pre-disaster preparation and mid-disaster emergency response. The results showed that self-efficacy positively influenced the pre-disaster mitigation behaviors and emergency evacuation behavior. Perceived severity only positively influenced the emergency evacuation behavior in mid-disaster. Additionally, pre-disaster evacuation drills positively influenced the emergency evacuation behavior and the behavior of asking for help from government departments in mid-disaster. The results indicate that pre-disaster mitigation behaviors can have a significant impact on mid-disaster behaviors only if pre-disaster mitigation behaviors provide clear guidance for the emergency phase of the disaster. Therefore, pre-disaster mitigation activities should be carried out strategically with the aim of improving the public’s emergency self-help capabilities in disasters, which provides theoretical and practical guidance for improving the effectiveness of public disaster mitigation and improving the government’s disaster emergency management system.

Hydrometeorological-modeling-based analysis and risk assessment of a torrential rainfall flash flood in a data deficient area in Wenchuan County, Sichuan Province, China

Hydrometeorological-modeling-based analysis and risk assessment of a torrential rainfall flash flood in a data deficient area in Wenchuan County, Sichuan Province, China

Application of remote sensing monitoring to the spatiotemporal variation in debris flow activity in the catastrophic Wenchuan seismic area

Debris flows are typical compounded geohazards with long-term activity in earthquake-stricken areas. Numerous debris flows were simultaneously induced by subsequent extreme precipitation and caused serious property losses and casualties. Monitoring the dynamic variation in debris flow activity and its decay process over time is key for debris flow risk management and mitigation of the impact of debris flows. In this study, the spatiotemporal variation in debris flow activity of the Wenchuan seismic area over the past ten years was monitored based on multiperiod satellite data and field monitoring. The hillslope landslide sediment delivery to the channel gradually decreased, and the area with higher sediment transfer potential gradually changed from southern Wenchuan County to the northern area, which caused the area with high susceptibility to debris flows to gradually move towards northern Wenchuan County. Impacted by the decrease in available materials and coarsening of channel materials, the power of rainfall runoff required for driving debris flows gradually increased. This resulting debris flow is more likely to occur in this basin with a larger catchment area, longer channel length, and higher elevation difference. The changes in the debris flow activity index indicated that the debris flow activity evolves with an impulse type, and it may last for decades. The decay processes of the debris flow activity were summed up in three periods, i.e., high-activity period (2008–2015), unstable period (2015–2035), and recession period (after 2035), based on the dynamic evolution of material, hydrodynamics, and the coefficient of debris flow activity. Simultaneously, the active characteristics of debris flows in different periods were revealed, which is of great significance to the risk management of debris flows in the next period.

Thrixspermumtaeniophyllum (Orchidaceae, Epidendroideae), a new species from southwest China, based on molecular and morphological evidence.

Thrixspermumtaeniophyllum is described as a new orchid species from Wenchuan County, Sichuan Province of southwest China. It is morphologically similar to T.japonicum, but it differs from the latter in having branched stems, slightly fleshy strap-shaped leaves, longer inflorescences with 3-6 flowers and a capitate gynandrium with a lip-shaped mouth opening. Its species status is also supported by molecular phylogenetic analyses, based on nuclear ribosome internal transcribed spacer (nrITS) and three chloroplast DNA fragments (matK, psbA-trnH and trnL-F), which showed distinct systematic boundaries from the most morphologically similar T.japonicum and their morphological relatives T.saruwatarii and T.pygmaeum.

Risk Assessment of Single-Gully Debris Flow Based on Dynamic Changes in Provenance in the Wenchuan Earthquake Zone: A Case Study of the Qipan Gully

After the Wenchuan earthquake on 12 May 2008, a huge amount of loose deposits was generated on the mountain surface in the earthquake zone, and vegetation was severely damaged, providing a rich source of material for debris flow, greatly increasing the danger. For many years, researchers have mainly considered the recovery of slope vegetation in assessing the risk of debris flow post-earthquake. However, field investigations have found that large amounts of the dynamic reserve materials in the gully have an important impact on the risk. Thus, based on field survey data, this paper takes the Qipan gully in Wenchuan County as an object and uses multi-source and multi-scale monitoring methods (Landsat series, Quickbird, and Unmanned Air Vehicle) to analyze and statistically study the provenance of the slope and gully both pre- and post- the earthquake. By comprehensively using game theory combination weighting and the cloud model, a dynamic risk assessment model for debris flow was constructed to evaluate the risk of debris flow from 2005 to 2019. The results show that the slope provenance post-earthquake was 7.7 times that of pre-earthquake, and by 2019 the slope provenance had recovered to the pre-earthquake level. Based on the statistical estimation of the amount of debris flow outbreak and the dredging of the blocking dam recorded in relevant data, the dynamic provenance of debris flow had decreased by about 781.3 × 104 m3 by 2019. Compared with considering slope provenance only, the assessment result of debris flow risk considering both slope and gully provenance is more realistic. The results are expected to provide reference and guidance for dynamic assessment of the risk of debris flow faced by increasing projects in high-seismic-intensity mountainous areas and to ensure the safety of people’s lives and property effectively.

Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area.

Research on Physical and Mental State and Influence of the Elderly in Disaster Areas under the Background of Reverse Hollowing

Major natural disasters exert far-reaching and enduring negative impacts on affected regions, particularly on vulnerable demographics such as the rural elderly, who often have limited external resilience. This paper uses Wenchuan County in Sichuan Province as a case study. Following severe natural disasters, Wenchuan County has begun to exhibit a unique phenomenon of reverse hollowing, distinct from other common regions. In this context, the physical and mental health of the elderly is uniquely affected, with each aspect influencing the other. The physical condition of the elderly in disaster-stricken areas displays a bifurcation trend, showing a stark contrast between pre- and post-disaster states. Despite economic hardship, frequent geological disasters, and strained relationships between local cadres and groups, life satisfaction among the elderly is generally high. However, their psychological state reveals marked regional differences and an unbalanced distribution. Most elderly individuals in the affected areas are empty-nesters, typically experiencing a strong sense of loneliness. Based on these observations, the paper offers several suggestions for addressing these issues.

Prediction of landslide susceptibility in Wenchuan County based on pixel-level samples

Prediction of landslide susceptibility in Wenchuan County based on pixel-level samples

Influences of social environment and psychological cognition on individuals’ behavioral intentions to reduce disaster risk in geological hazard-prone areas: An application of social cognitive theory

In light of the increasing frequency of geological disasters and the socialization of disaster risk reduction (DRR), boosting individuals’ behavioral intentions to engage in DRR is critical to the task of fostering sustainable disaster mitigation in society. Exploring the effects of the interaction between individuals’ social environment and psychological cognition on their DRR behavioral intentions is one of our most pressing concerns. This study developed an influencing factor model based on social cognitive theory, which included social trust and subjective norms as social environment factors and self-efficacy, outcome expectations, and risk perception as personal cognition factors. Cross-sectional survey data were collected by distributing 181 questionnaires to residents of Wenchuan County, Sichuan Province. Structural equation modeling was employed to investigate the factors influencing individuals’ DRR behavioral intentions. The following primary key hypothesis testing results were obtained: (1) social environment (social trust and subjective norms) had a positive effect on personal cognition (self-efficacy); (2) social environment (social trust and subjective norms) and personal cognition (self-efficacy) significantly and positively affected individuals’ DRR behavioral intentions; and (3) individuals’ intentions to engage in DRR behaviors had a significant positive impact on their actual DRR behaviors. Three strategies for improving individuals’ DRR behavioral intentions and behaviors are discussed based on previously proposed hypothetical paths, including facilitating the establishment of a trustworthy DRR environment, enhancing individuals’ perception of DRR ability, and narrowing the gap between intentions and behaviors. Theoretical guidance and practical suggestions are provided for socializing risk mitigation and strengthening disaster risk management capabilities, which can help policymakers better understand the influencing factors and pathways associated with improving individuals’ behavioral intentions to reduce disaster risk.