Natural Hazards Research最新文献

The paper tries to analyze the impacts of erosion on the livelihood and vulnerability statusof the char dwellers of Assam, India. The study employs both quantitative and qualitative methodologies, choosing one district from each of Assam's agro-climatic zones across the Brahmaputra valley as a representative of the state's char regions. As a qualitative tool, the study uses the participatory rural appraisal (PRA) technique and as quantitative tool the study uses Vulnerability as Uninsured Exposure to Risk (VER) econometric model.394 char households were surveyed through a semi-structured schedule. For each village selected for the study, a combined social-resource map was created using the PRA method in order to determine the severity of the erosion issue in the char regions. The VER model is used to empirically examine the relationship between char land erosion and the well-being of char inhabitants. The study reveals that erosion of the char land has serious, detrimental impacts on the livelihood and economic well-being of the char residents and thereby make the char dwellers vulnerable. The study makes recommendations of both structural and non-structural adaptation practices to minimize the effects of erosion on char dwellers livelihood.

Understanding the spatiotemporal distribution of forest fires and future predictions is very important for management strategies. To identify the present status of forest fires in the Kingdom of Thailand and their risk in the future, ten-year forest fire data were used, and a forest fire hotspot was prepared. A geospatial technique was used in the study to characterize the parameters of forest fires in the country and identify future forest fire risk areas. Most of the forest fires in the country were found to be seasonal. Deciduous forests in higher elevations and on moderate slopes were most vulnerable to forest fire. The level of aridity, soil moisture, temperature, precipitation, vegetation status, and topography influenced the spatiotemporal distribution of forest fires in the country. Greater than 50% of fire risks were observed in 22 administrative divisions, and 17 of the 209 protected areas are also in the high-risk category. The final forest fire hotspot map can be used in policy development and successful management strategies. A better monitoring strategy should be used in the fire hotspot areas as a precautionary measure to minimize the anthropogenic causes of forest fires.

The study aimed to investigate the potential glacial lakes in response to climate change and the associated risk of glacial lake outburst floods (GLOFs). Remote sensing data and GIS techniques were utilized to analyze glacial lakes, employing empirical models to estimate their area, volume, and depth. The Normalized Difference Water Index (NDWI) was applied to detect changes in glacial lakes using Sentinel imagery. The findings revealed a notable increase in both the number and surface area of glacial lakes over the past two decades. Specifically, the number of glacial lakes rose from 101 in 2000 to 162 in 2020, while their combined surface area expanded from 9.72 km² to 12.36 km² during the same period. Among these lakes, 31 were identified as Potentially Dangerous Glacial Lakes (PDGLs), with 6 located in Chitral, 16 in Swat, and 9 in Upper Dir. Two lakes were classified as high potential glacial lakes, with depths estimated at 41.86 ​m and 30.43 ​m. Continued monitoring of these glacial lakes and their susceptibility to GLOFs is crucial in the face of ongoing climate change. Long-term planning and adaptation strategies are necessary to safeguard the well-being and safety of communities residing in these vulnerable regions. By understanding the evolving characteristics of these lakes, researchers and policymakers can better prepare for and mitigate the impacts of GLOFs on downstream communities and infrastructure.

People with disabilities are among the most vulnerable groups in building fires. According to the U.S. Fire Administration, an estimated 700 home fires involve people with physical disabilities each year. In parallel, the National Fire Protection Association estimates that 11% of civilian fire deaths were people with disabilities. Despite these statistics, the current body of literature shows few studies focused on the evacuation of disabled people. To bridge this knowledge gap, this paper presents findings on the evacuation processes of wheelchair users in a low-rise apartment (dormitory) building. More specifically, we simulate 1–3 wheelchair users in a dormitory building at our home institution via 327 simulations to examine evacuation time as well as identify structural aids and barriers. As a byproduct of this research, a new dynamic structural ranking system of egress components is proposed for wheelchair users, and a series of suggestions for structural modifications to improve the egressibility of the simulated building are provided.

Accommodating uncertainty stands as one of the most salient challenges in the development of soil erosion assessment tools. We presented a novel approach integrating the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model and Bayesian Belief Networks (BBNs) to assess soil erosion in a region of western Iran. The soil erosion status was reckoned based on the nine factors of MPSIAC. We utilized BBNs to produce a causal model for soil erosion, with output probabilities being validated through re-evaluation and sensitivity analysis. We identified erosion types, geological formations, run-off, soil erodibility, soil permeability, soil characteristics, and precipitation intensity as the main determinants of soil erosion. A significant, positive correlation existed between the erosion rate derived from MPSIAC and BBNs model in all land-use/covers over the work units. Overall, this study highlighted the potential of BBNs as a supportive tool for soil erosion prediction as well as a relatively simple and updatable soil erosion model for dealing with the diagnostic, scenario, and sensitivity analysis. Considering the increasing incidence of soil erosion, the BBNs model proposed in this study can be extended to a variety of ecosystems that are subject to soil erosion and changes in the probability of its causal factors.

Evaluation of landslide susceptibility is essential to planning of land and space utilization. For this purpose, the paper presents a case study from Fugu County, Shaanxi Province, China. Firstly, the geological environment and current state of landslides in Fugu County were investigated. Then, slope, aspect, terrain relief, curvature, lithology, land type, and normalized difference vegetation index (NDVI) were considered as the landslide susceptibility condition factors, and the correlation between these carried out by using Multicollinearity Analysis method. Next, landslide and non-landslide samples were divided into training samples and testing samples according to the sample ratios of 8/2, 7/3, 6/4, and 5/5, respectively. The landslide susceptibility mapping was carried out by using Random Forest (RF) model and Frequency Ratio coupled with Random Forest (FR-RF) model, respectively. Lastly, the landslide density (LD), landslide frequency ratio (LFR), the area under the curve (AUC) of the receiver operator, and other indicators were used to validate the rationality, accuracy, and performance of the landslide susceptibility maps produced from different models and ratios. The results indicated that all maps are reasonable, except the map when ratio is 5/5. For each map, regardless of ratios, the LD and LFR are the greatest in the zones classed as having a very high susceptibility, followed by those with a high, moderate, low, and very low classes.

In the Random Forest (RF) model, when the training test set is not at the same time its in the area of extremely high sensitivity of LD and the size of the FR value respectively 7/3 (201.026) ​> ​8/2 (154.440) ​> ​6/4 (93.696) >5/5 (136.364) and 7/3 (4.806) ​> ​8/2 (3.692) ​> ​6/4 (3.260) ​> ​5/5 (2.240); in the Frequency Ratio coupled with Random Forest (FR-RF) model, Inall the training test sets the size of the proportion of LD and FR value respectively 7/3 (145.693) ​> ​6/4 (127.151) ​> ​5/5 (122.857) ​> ​8/2 (113.263) and 7/3 (3.334) ​> ​6/4 (3.073) ​> ​5/5 (2.811) ​> ​8/2 (2.592). What else, from the comparison of ROC curves, when ratio is 7/3, the accuracy of the two models is higher than that of other ratios. Similarly, the results of the ensemble model (A combination of two models with different learning abilities.) are not more reasonable than the results of the single model, which reflects that the combination of a weaker learner model (Frequency Ratio model here) with a stronger learner model (Random Forest model here) can diminish the performance of the stronger model.

Drought and flood disasters occur frequently in the Poyang Lake basin during the flood season, making it significantly important to monitor the lake's flood season area using time-series remote sensing. This research employed multisource remote sensing datasets from Landsat, ALOS, and Sentinel-1 across the time span of 2000–2022. Using the MNDWI method and adaptive global threshold segmentation, the flood season area of Poyang Lake was extracted. The spatiotemporal variation characteristics of the flood season area were analysed, and correlations with precipitation and temperature were assessed. Moreover, this study explored the characterization of its response to drought and flood events. The results revealed a strong fluctuation in the lake during the flood season, with the maximum and minimum area differences exceeding 3000 ​km², and spatial changes were mainly concentrated in the southwestern lake region. There is a significant positive correlation between area changes and precipitation and a significant negative correlation with temperature. By analysing the response characteristics of the flood season area changes to drought and flood events, in years when the flood season area of Poyang Lake exceeds 4500 ​km², extreme flood disasters usually occur. Areas between 3900 ​km² and 4500 ​km² are prone to floods, areas between 2000 ​km² and 3000 ​km² are prone to drought events, and areas below 2000 ​km² typically experience extreme drought disasters.

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, the country's efforts in planning and managing flood risks remain insufficient, especially in the vulnerable Mohana-Khutiya River. Therefore, this study focused on the Mohana-Khutiya River and utilizes the Maximum Entropy (MaxEnt) model to comprehensively map flood susceptibility and fill crucial gaps in flood risk assessments. This study employed a combination of 10 geospatial environmental layers and field-based past flood inventory to implement the MaxEnt machine learning model for flood susceptibility modeling. The available past flood data were divided into two sets, with 75% allocated for model construction and the remaining 25% for model validation. This study demonstrated that the proximity of the river had a significant impact (33.1%) on the occurrence of the flood. Surprisingly, the amount of annual precipitation throughout the year exhibited no detectable contribution to the flood event in the study site. About 4.9% area came under the high flood susceptible zone followed by 12.75 % in the moderate zone and 82.34% in the low-risk zone. The model exhibited excellent performance with an Area Under Curve (AUC) value of 0.935 and a low standard deviation of 0.018, indicating accurate predictions and consistent precision. These results highlight the model's reliability and its significance for developing disaster management policy by local government in the study site. Future research should refine the MaxEnt model by including more variables, validating against observed flood events, and exploring integration with other flood modeling approaches.

Identification of potential flood risk areas is crucial to reduce flood damage for the frequently flooded and low-lying South Asian developing countries. The present study has prepared district-level (District: second administrative unit of the country) flood risk map for the flood-prone Rangpur Division (Division: first administrative unit) of Bangladesh using the multi-criteria decision analysis along with the application of analytical hierarchy process (AHP) method. Eight physical factors such as elevation, slope, distance from river, drainage density, land cover, rainfall, height above nearest drainage (HAND), and topographic wetness index (TWI), and six social factors such as population density, dependent population, disabled population, female population, agriculture dependent population, and literacy have been assessed to create a final risk map. The flood risk map is divided into five risk zones: very low, low, moderate, high, and very high. Integration of the social factors along with the physical factors reflects the insight of the vulnerability and increases the authenticity of the generated risk map. This study found that 62.46% area of the Rangpur Division resides under the moderate to very high-risk zone of flooding. Using ROC (receiver operating characteristic)-AUC (area under the curve) curve, the risk map is validated with a score of 0.83 from the flood inventory map of 2020 generated from the Sentinel 1 image. This risk map will guide policymakers to easily identify the vulnerable area for flood hazards and suitable areas for development activities necessary to attain sustainable development.

Categorization of landslide susceptibility holds great significance in hilly regions as it is one of the regularly occurring natural hazards that brings massive devastation to life as well as property. Detection of such landslide susceptible areas is regarded as a useful input for policymakers who plan various developmental activities in those areas. The present study considers Pabbar Catchment, located in the state of Himachal Pradesh, India as the study area and prepares a Landslide Susceptibility Map (LSM) for it using the Frequency Ratio (FR) technique. Eleven geo-morphological aspects of the catchment called ‘causative factors’ were used in thematic form for building the LSM. Being a quantitative method, FR functioned satisfactorily as the prediction accuracy came out as 0.825 in the Area Under Curve (AUC) of the Receiver Operation Characteristics (ROC) process. Approximately 7.48% of the geographical area from the catchment falls under the ‘very high’ landslide susceptible zone, 37.31% under the ‘high’ category, whereas 35.34% of the area comes under the ‘moderate’ susceptible zone. The results shall be advantageous for similar kinds of investigations as well as for planning and development authorities.