High-energy storm events induce hazards that promote damage and destruction of property and infrastructure. Defining high-risk areas is therefore fundamental to prioritise management actions. This work presents the application of an approach to identify hotspots of storm impact at a regional scale (tens to hundreds of kilometres). The Coastal Risk Assessment Framework Phase 1 (CRAF1) is a hotspot selection method based on a coastal index that combines the potential hazard (i.e. overwash and erosion), the exposure (based on land use) and the vulnerability (based on socio-economic data) along each kilometre of the coast to assess the risk level. The suitability of the approach was tested on the southeastern coast of the Gulf of Cadiz (South Spain). CRAF1 was applied considering a morphological worst-case scenario and events of 10/50/100-year return period. The region shows a high overwash and erosion hazard level. Nevertheless, a relatively low number of risk hotspots were identified due to the low level of occupation in the study area. Comparison against available information of previous overwash and erosion events proved the reliability of the method to identify hotspots at a regional scale, even in a coastal area with high alongshore variability (geomorphology, wave exposure and tidal range). The results support the utility of the tool for coastal managers to prioritise and support risk reduction plans. Furthermore, the method presents two aspects that enlarge its potential applicability: (1) it is relatively easy to apply at a regional scale, and (2) it can be updated with new data to test different scenarios (e.g. sea-level rise).
Urmia Lake, the largest saline lake in Iran and the Middle East, in the northwest of Iran, has shrunk over the past decades. The reduced water level has increased the area of dry land around the lake allowing new environmental hazard such as sand dunes encroachment, particularly on the western side of the lake. New land has emerged as a consequence of lake shrinkage, and this new land is a major sediment source for the creation of sand dunes around the lake. This shrinking of the lake has created emerging lands. These lands play a major role in creating sand dunes around the lake. There are five terrain types that could contribute sediment to the dunes, and it is the main aim of this research to identify the contributions to the dunes of each terrain type. Fifteen surface samples were collected from the five most erodible terrain types, and eight samples were collected from the dunes both downwind and upwind from the lake, and major element components were measured using X-ray fluorescence. According to the Besler classification, all samples are in the saline class. Also, the chemical index of alteration values in all samples were less than 50, indicating weak weathering. Based on multivariate statistical analysis, suitable tracers were selected and were imported to the sourcing equations. Quantification of uncertainty and the creation of two new fingerprinting models for aeolian sediments based on both Bayesian and GLUE procedures were used. The highest proportion comes from the salty and puffy lands (44.2%) followed by salty polygon land (23.5%), clay-salty areas, puffy-flaky lands (7.01%), the terminus of the fine sandy alluvial fan (13.2%) and clay-salty abandoned lands (12.1%). It is concluded that if land managers use these results, they can more efficiently decrease the hazards posed by dune formation, reactivation and migration through implementation of soil conservation on the affected lands around the dried lake.
The Poás volcano is an active volcano of Costa Rica with intense tectonic activity in its flanks. Historically, the volcano has presented strong, surficial earthquakes provoking many landslides with associated casualties and immense economic impacts. One example is the Cinchona earthquake in 2009 (Mw 6.2 and 4.6 km depth). We aim to determine a landslide zonation according to seismic data and a geomorphic assessment in the NW sector of the Poás volcano based on a combination of qualitative methods and morphometric parameters. The results estimate the possible outcome of a 6.8 Mw earthquake. The susceptibility mapping and models showed a positive relationship between this methodology and others previously developed for the area that surrounds the Poás volcano as well as a correspondence with the landslides caused by the Cinchona earthquake in 2009. The final coseismic landslides susceptibility zoning indicated that 52% of the area is prone to landslides. Furthermore, there is a relationship between the areas with high exposure to landslides with conical volcanic geomorphologies, active faulting structural and/or tectonic geomorphological units with slopes greater than 15°. The proposed zoning can be useful for land use planning and by civil protection entities to orientate quick response and reduce the impact of future landslides. Moreover, this method can be applied in active tectonic and volcanic areas worldwide.
The haor region of Bangladesh is exposed to a variety of natural hazards such as flash floods, seasonal floods, droughts, riverbank erosion, embankment breach due to climate change, which impacts the haor people's lives and livelihoods. Haor households are attempting to diversify their livelihood activities to protect themselves from such extreme climate events. The study’s aim to evaluate the various livelihood strategies adopted by haor households, and multinomial logistic regression is employed to identify the factors influencing their decision to pursue more eco-friendly and sustainable livelihood strategies. A multi-stage stratified random sampling technique was used to collect primary data from 300 haor households in Kishoreganj, Netrokona, and Sunamganj districts, with 100 from each district. We provide inimitable insight into the analysis to understand how livelihood resources, livelihood strategies, and livelihood outcomes are closely tied in the framework for sustainable rural livelihoods. The study classified a household’s economic activities into five distinct categories, together with crop farming. Among the livelihood options, crop plus livestock rearing is the most productive livelihood strategy for haor households. The findings revealed that the household’s head age and education, dependency ratio, land holdings, household assets value, access to credit, annual income, membership of any organization, home to road, market, and haor distances, communication during the dry season, duration of waterlogged, and agro-ecology of the haor ecosystem all have a significant impact on the choice of higher returning livelihood diversification strategies. Policies should attempt to promote the significant determinants of livelihood strategies choice, as well as should ensure livelihood assets, a strong infrastructure, and minimize natural hazards, in order to transform the local context and enable poor households to build more profitable livelihood strategies.
Disasters can have substantial impacts on people’s livelihoods in developing countries. Further, if the need for livelihood interventions is ignored or delayed, the crisis may trigger unexpected harmful consequences in the affected households in the aftermath. Therefore, restoring livelihoods should remain a priority in the post-disaster recovery process. However, such recoveries in rural contexts and developing countries, like Nepal, are complex as the livelihood restoration process is affected by serious spatial, socio-economic, and political factors. We employed qualitative research methods in four highly affected districts in the 2015 Nepal Earthquake (7.8 M w ) to examine post-disaster livelihoods recovery. Our paper critically assesses the humanitarian response based on the narratives and lived experiences of affected households. The findings show that humanitarian assistance was crucial in addressing several unmet needs of disaster-affected rural households in resource-poor settings in Nepal. However, the interventions were generally fragmented, insufficient, neoliberal led (forcing market dependencies), and largely business-as-usual in their orientation. Previous studies in Nepal paid insufficient attention to the goods provided to affected households in the name of recovery. Therefore, our paper scrutinises selected humanitarian objects, such as power tillers, and unpacks their political economy and effectiveness in local contexts. Further, our findings show that some livelihood policies reinforced the gap between the haves and have-nots, thereby reproducing pre-disaster inequalities in the post-disaster field.
Most losses from earthquakes are associated with fully collapsed buildings. So, determining the seismic risk of buildings is essential for building occupants in active earthquake zones. Unfortunately, current methods used to estimate the risk state of large building stocks are insufficient for reliable, fast, and accurate decision-making. In addition, the risk classifications of buildings after major natural disasters depend entirely on the experience of the technical team of engineers. Therefore, the decision on risk distributions of building stocks before and after hazards requires more sustainable and accurate methods that include other means of technological advancement. In this study, the building characteristics dominating the seismic risk outcome were determined using a database of 543 masonry buildings. Later, for the first time in the literature, a new, fast and accurate seismic evaluation method is proposed. The proposed method is thoroughly associated with detailed evaluation results of structures with the help of machine learning algorithms. This study utilized an approach in which six machine learning algorithms work together (i.e., Logistic Regression, Decision Tree, Random Forest, K-Mean Clustering, Support Vector Machine, and Ensemble Learning Method). As a result of the analysis of these algorithms, the correct prediction rates for the learning database (i.e., 434 buildings) and the test database (i.e., 109 buildings) of the proposed method were determined as approximately 96.67% and 95%, respectively. Lastly, machine learning algorithms trained by structures with known after seismic risk results are developed. The proposed method managed to classify risk states with the accuracy of 84.6%.
The Indian Himalayan region is highly susceptible to landslides because of its complex geology, rugged topography, steep slopes augmented by seismo-tectonic activities and heavy rainfalls, and often causes life losses with huge economic damages. Therefore, landslide susceptibility zonation (LSZ) mapping provides an effective solution for the end-users to estimate the vulnerability level and determine potential consequences. To date, different methodological frameworks have been implemented in terms of spatial modelling and predict future landslide locations for meeting these needs. Hence, it is necessary and meaningful to conduct a review of the current state of the studies addressed to LSZ mapping in the Indian Himalayan region. Based on this, the present paper reviews 144 research articles published in the last decade (2010–2020) to understand the recent trends, techniques and practices adopted by researches. Along with the review process, some critical points are emphasized with short- and long-term visions based on the issues discussed by various researchers; thereby, we try to ensure that this review work presents a more general deliberation of LSZ mapping which may also be relevant for global practitioners. At the same time this review also serves as a relevant database for scientist and researchers working in the field of landslide particularly in the Himalayan region.
As the weak structure of geotechnical engineering, frozen rock joints determine the stability of geotechnical engineering in frozen region. To study the influencing factors of the peak shear displacement of frozen rock joints, three groups of tuff coupling joints (upper and lower joints completely coincide) are selected in Jiangluling Tunnel, Qinghai Province, China. The joint surface morphology is obtained by three-dimensional scanning technology. The joints are frozen at – 35 ℃ for 12 h before direct shear test. The test results show that there are three main shear failure patterns of frozen rock joints, which are mainly determined by the opening degree of joints. The peak shear displacement decreases with the increase in joints surface roughness and opening degree, and increases with the increase in normal stress. By analyzing the characteristics of peak shear displacement under different opening degrees and normal stress, the influencing factors of peak shear displacement and shear strength of frozen rock joints are discussed. On this basis, the expression of peak shear displacement of frozen rock joints is proposed. It provides a theoretical basis for quantitative study on shear deformation of frozen rock joints. The study provides technical support for geotechnical engineering construction and rock slope instability prevention in frozen region.
Drought is one of the most frequent and devastating natural disasters. Based on future climate scenarios and land use/land cover (LULC) patterns, the copula framework was employed to calculate the probabilities of meteorological and hydrological drought risks for the next 30 years (2021–2050) in the Wanquan River Basin, meanwhile, the relationship between hydrological and meteorological droughts was revealed by correlation analysis and cross-wavelet transform (XWT). The results are as follows: (1) In the next 30 years, the risk of intra-seasonal meteorological drought (short-term drought) in the WRB is high at a probability of 40–70%, while the risk of inter-seasonal meteorological drought is relatively small at a probability of close to 30%; (2) compared with meteorological drought, the risk of intra-seasonal hydrological drought is small, but the probability of inter-seasonal hydrological drought (medium- or long-term drought) is 30–50%, and the risk of hydrological drought in the upstream is greater than that in the downstream; (3) the future meteorological and hydrological droughts in the WRB are significantly and positively correlated, and that hydrological drought lags behind meteorological drought.
Volcanic ash fallout is a recurrent environmental disturbance of flora ash deposits from the Icelandic volcano Eyjafjöll (2010) over large areas are responsible for several impacts on ecological processes, agricultural production, and human health in Western Europe. This study assessed the ash fall effects from the subject volcano on the surrounding flora as well as vegetative recovery at two different sites (Scotland and southern Sweden). For this purpose, we analyzed Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by Plants (FPAR) data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). The cited biophysical variables were most strongly influenced by ash cloud fallout, with the lowest maxima recorded for both sites during the 2010 eruption year. To confirm this impact, a statistical study with climate indicators was performed. The results showed a significant correlation between LAI and precipitation (R² = 0.63, p-value = 0.0022) at site 1 (Scotland), while a weak non-significant correlation (R² = 0.2248, p-value > 0.5) was observed at site 2. However, climatic data from both sites showed low correlations (R² < 50%) with NDVI vegetation indicator. Despite the heavy rainfall and heat recorded during this period, our statistical results show us that ash cloud fallout affects vegetative development.
Flash floods are the most hazardous types of floods, specifically in urban areas, since they impose huge financial damages and tremendous life loss. A flood risk map is required to identify the most vulnerable areas of a city for better management of this phenomenon. This research aims at achieving the risk and vulnerability maps of the city of Sanandaj, Kurdistan Province, Iran, using the geographical information system and fuzzy analytic hierarchy process through the assessment of several conditioning factors. The parameters slope, elevation, profile curvature, drainage density, land use, and distance from waterways were applied to prepare a hazard of flood map of Sanandaj city. Also, effective information layers, including slope, elevation, permeability index, distance from hospitals, distance from fire stations, distance from trails, distance from refueling centers, distance from roads, and distance from historical monuments, were considered for designing a flood vulnerability map. By multiplying the hazard of flood occurrence map and the flood vulnerability map, the flood risk map is obtained. The designed flash flood risk map demonstrated that about 30% of the city’s areas were classified as the most prone to flash floods. The results indicate that the flash flood is a serious concern in the city of Sanandaj, which should be considered by the city’s officials and authorities. The designed flood risk map can be used as an appropriate management tool for reducing or avoiding flood risks.
Cherry trees are one of Chile’s most important specialty crop activities. Its commercial orchards have an extensive spatial distribution between the 31° S and 48° S, spreading from semiarid to tundra climates, but the trees appear primarily in the Mediterranean climate. Different extreme weather events, such as frosts, precipitation, and high temperatures, affect this crop at different phenological stages, especially in bloom, ripening, and floral differentiation. Based on a high-resolution climatic-gridded dataset of daily temperature and precipitation data, we defined an integrated risk index (RI) representing the frequency of occurrence of the events throughout the plant development period and considering each type of risk affecting each concrete phenological stage. High RI values indicate high climatic risk. The RI follows a meridional pattern influenced by elevation, with higher values in the highest elevations between 36° S and 40° S, sensitive to the simultaneous occurrence of frosts and precipitation events. The northern coast exhibited the lowest risk values, while a general gradient from low values in coastal areas to higher ones in inland elevated zones revealed an altitudinal pattern. Low-risk areas have a sparse distribution of crops, which can be explained by several factors restricting cherry cultivation such as soil limitations, high slopes, lack of productive support infrastructure, and competition with other profitable forestry and agricultural activities in the north and forest production in the south. These results will help to improve climate impact assessments for production systems, which can be conducted by following an easy-to-understand tool.
The Sinai Peninsula in Egypt is highly vulnerable to flash flooding due to its huge variation in relief and erratic rainfall. However, assessing flood risk in the Sinai is a difficult challenge due to the almost complete lack of accurate flood observations and relevant drainage basin characteristics. Therefore, this study evaluates the flood hazard in the Sinai Peninsula through multi-criteria analysis of morphometric characteristics of the drainage patterns, which can be easily inferred from readily available remote sensing data and geographical analysis tools. From a digital elevation model with a spatial resolution of 30 m, 112 catchments are identified, each characterized by twenty morphometric parameters, grouped into three categories: geometry, drainage network and relief. The importance of the morphometric parameters on flooding is assessed with the analytical hierarchy process. Normalized weights for each parameter and category, obtained from pairwise comparison matrices, allow to derive a flood sensitivity index and create a map showing different degrees of flash flood hazard. The results show that basins characterized as highly sensitive to flooding generally have high values for all three morphometric parameter categories: geometry, flow network and relief. About 17% of the Sinai appears to be very highly sensitive to flooding, 39% highly sensitive and 34% moderately sensitive. The very highly flood-prone basins are all located in the southern Sinai mountain ranges, while the highly flood-prone basins are mainly found along the outer edges of the southern Sinai mountain ranges and in some sub-basins of wadi El-Aris. Comparison with case studies reported in other publications and the media shows a strong agreement, indicating that the proposed methodology is reliable and accurate. Based on the obtained flash flood sensitivity map, management plans are proposed to reduce the risk of flash flooding and to protect major cities and roads by installing side channels and culverts to collect and drain the excess water from areas most affected prone to flooding.
This study highlights the importance of the effects of spatial tourism. Using panel data of 135 Wenchuan earthquake-affected counties from 2008 to 2018, this study employs the dynamic spatial Durbin model to examine the spatial effects of tourism development on post-disaster economic resilience and compares the differences in the tourism-growth nexus between severe disaster-affected counties and general disaster-affected counties. The empirical results show that tourism development contributes to economic resilience for general disaster-affected counties, which supports the tourism-led growth hypothesis, whereas there is an inverted U-shaped relationship for severely disaster-affected counties. Furthermore, the spatial spillover effects of tourism are insignificant. Plausible explanations for these results are discussed, and policy suggestions are provided.
Forest fire is one of the main issues of forest ecosystems around the world which has resulted in loss of biodiversity, forest degradation, soil erosion, and greenhouse gas emission. Ironically, the information on the forest fire regime and its pattern are still lacking in the Himalayan region. In this study, Moderate-Resolution Imaging Spectroradiometer active fire data products from 2001 to 2020 have been analysed for understanding the forest fire trends and its hotspots patterns during the active fire season (February to June). About 1347 average fire counts/year were recorded in six natural vegetations with the highest number of fires observed during the year 2012 (n = 3096) and minimum in 2011 (n = 210). Mann–Kendall trends analysis for the spatial and temporal pattern of fires indicated that there is a significant increase of forest fires towards higher elevation. Forest fire hotspot analysis using fire radiative power, fire frequency, and fire density showed that Uttarakhand is the most forest fire-prone state as compared to other north-western Himalayan states. It is also revealed that the May month has a higher number of fire counts and the evergreen needle forests have higher fire frequencies amongst the forest types. The forest fires were found to be more influenced by land surface temperature as compared to rainfall. The outcomes in this study on the temporal and spatial patterns for forest fire can be used for forest fire modelling.
For people in earthquake-poverty areas, taking effective disaster preparedness is the key to reduce the loss of life and property. Based on the survey data of 327 residents in Wenchuan and Lushan County, Sichuan earthquake-stricken areas in 2019, the research analyzed the influence of peer effects on disaster preparedness and its mechanism by using instrumental variable method. The results show that: (1) the level of disaster preparedness of residents in the earthquake-stricken areas still needs to be improved, with the highest level of important items placement preparation and the lowest level of disaster insurance preparedness; (2) convergence effect is the key path for peer household to influence other families' disaster preparedness; (3) the general public learn from opinion leader to take disaster Insurance preparedness, important items placement preparation and house reinforcement preparation. Based on this, it is recommended to select model residents who are well prepared for disaster preparedness. Establishing a platform for communication between opinion leaders and the general public as well as preventing inaction of opinion leaders. The findings of this study can provide a reference for improving disaster prevention and mitigation policies.
Increased temperature rates have the potential to change the rainfall regime in a given region, as well as to intensify its extreme events, which may lead to significant and negative socioeconomic and environmental impacts on urban populations. However, knowledge about the extent of changes in rainfall rates in Rio de Janeiro City (RJC) remains incipient; thus, it is necessary applying indices climate change to help better understanding this phenomenon. The aim of the current study is to investigate changes in rainfall distribution and increase in the number of extreme rainfall events in RJC. Daily rainfall data deriving from fifteen weather stations distributed in RJC were analyzed in the RclimDex software and Mann–Kendall test. The analysis has shown increased rainfall rates from the beginning of the series to approximately the first ten years of study. Total rainfall rate has decreased after this period. Rainfall intensity in almost all seasons has decreased after 2005; this outcome has indicated reduced annual rainfall rate and number of wet days. However, there was prevalence of positive trends in daily rainfall rates (Rx1day) and in total rainfall of five consecutive days (Rx5day). The increased number of extreme rainfall events in RJC can cause sudden inundations, floods, runoffs and river overflows with potential to cause landslides and human death due to irregular occupation of hills and slopes.
Climate catastrophe insurance is an important tool for advancing China's comprehensive disaster prevention and mitigation efforts and an important part of modernizing China's national emergency management capabilities. Based on the understanding of the definition of catastrophe and China's climate catastrophe, this paper systematically analyzes the main problems and challenges faced by China's climate catastrophe risk management and elaborates on the characteristics of the current opportunities for the development of China's climate catastrophe insurance. The paper then summarizes the development features of international catastrophe insurance systems, compares the features of the Shenzhen and Ningbo pilots of catastrophe insurance in China, and proposes key focus points for the meteorological department to participate in climate catastrophe insurance. Finally, this paper proposes measures to enhance climate catastrophe insurance in China in future from the development of international catastrophe insurance and China's climate catastrophe pilot work. Firstly, consider the whole process of comprehensive disaster prevention and mitigation concept and play the role of the government and the market and other multi-body, to explore the construction of the catastrophe insurance system. Secondly, establish a special or comprehensive catastrophe insurance fund. Thirdly, promote the formation of public–private partnership sharing mechanism. Fourthly, the government should provide appropriate legal and policy support. Fifthly, use the market mechanisms to reduce government pressure on public finances in catastrophe insurance.
The uncertainty in the empirical ground motion prediction models (GMMs) for any region depends on several parameters. In the present work, we apply an artificial neural network (ANN) to design a GMM of peak ground acceleration (PGA) for Kachchh, Gujarat, India, utilizing independent input parameters viz., moment magnitudes, hypocentral distances, focal depths and site proxy (in terms of average seismic shear-wave velocity from the surface to a depth of 30 m (Vs30)). The study has been performed using a PGA dataset consisting of eight engineering seismoscope records of the 2001 Mw7.7 Bhuj earthquake and 237 strong-motion records of 32 significant Bhuj aftershocks of Mw3.3–5.6 (during 2002–2008) with epicentral distances ranging from 1.0 to 288 km. We apply a feed-forward back propagation ANN method with 8 hidden nodes, which is found to be optimal for the selected PGA database and input–output mapping. The standard deviation of the error has been utilized to examine the performance of our model. We also test the ground motion predictability of our ANN model using real recordings of the 2001 Bhuj mainshock, two Mw5.6 Kachchh aftershocks and the 1999 Mw6.4 Chamoli mainshock. The standard deviation of PGA prediction error estimates in log10 units is found to be ± 0.2554. Also, the model predictability of our ANN model suggests a good prediction of the PGA for earthquakes of Mw5.6–7.7, which are occurring in Kachchh, Gujarat, India.
This paper uses the difference-in-differences method to assess how home prices change in the periods after major hurricanes in Miami-Dade County and links these changes to risk-perception determinants that affect market participants’ determination of the prices they are willing to accept or pay. The empirical results shed light on the effects of how risk-perception determinants, affected by different heuristics, can be important contributors to housing market dynamics caused by the physical effects of extreme weather events. We find that perception determinants related to hurricane intensity have a larger effect on housing prices than those related to hurricane frequency. More recent and stronger hurricane experiences also have a strong association with decreased housing prices. We find no evidence of a perception factor leading to underestimating chronic risks. The findings identify important nuances related to the housing market and contribute to enhancing coastal housing market stability from uncertainty about extreme weather by suggesting sensitive post-disaster policy reactions based on the effects of risk-perception factors.
Forest fire not only seriously affects the stability of forest ecosystem, but also threatens the safety of human life and property. It is very important to evaluate and forecast the risk of forest fire. This paper takes Conghua District of Guangzhou City as the research area. Based on Landsat 8 OLI/TIRS data, Sentinel-1 data, nighttime light data, DEM data, the seven evaluation factors of fuel moisture content, land surface temperature, altitude, slope, proximity to river, proximity to road and proximity to settlement are calculated. The forest fire risk assessment index system is constructed by analytic hierarchy process. Data from 326 historical active fires obtained by NASA-FIRMS website are used to verify. The area under the ROC curve (AUC) is used to estimate the performance of the forest fire risk map. The result shows that the value of AUC is 0.791, indicating that the forest fire risk assessment index system has good applicability to Conghua District of Guangzhou City, and it can provide scientific reference for the establishment of forest fire risk assessment index system in other areas.
High-frequency dynamic monitoring of flood disaster using remote sensing technology is crucial for accurate decision-making of disaster prevention and relief. However, the current trade-off between spatial and temporal resolution of remote sensing sensors limits their application in high-frequency dynamic monitoring of flood disaster. To deal with this challenge, in this study, we presented an approach to conduct high-frequency dynamic monitoring of flood disaster based on remote sensing data cube with high spatial and temporal resolution. The presented approach included two steps: a, removing the cloudy areas in original MODIS data to construct the cloud-free MODIS data cube by using the information provided by GPM rainfall data; b, fusing the cloud-free MODIS data cube and Landsat-8 data by using the spatiotemporal data fusion algorithm to construct the high spatiotemporal resolution (Landsat-like) data cube. The approach was tested by conducting high-frequency dynamic monitoring of flood disaster occurred in Henan Province, PR China. Our study had three main results: (1) the presented cloud removal algorithm in the first step was able to retain flood information and performed well in removing clouds during consecutive rainy days. The differences between cloud-free MODIS data cube and original MODIS data were small and the cloud-free MODIS data cube could be used for constructing high spatiotemporal resolution data cube. (2) Our presented approach could be used to conduct high-frequency dynamic monitoring of flood disaster. (3) Testing results showed that there were two floods occurred in the study area from July 17, 2021, to October 16, 2021; the first flood occurred from July 17, 2021, to September 15, 2021, with maximum affected area of 668.36 km²; the second flood occurred from September 18, 2021, to October 16, 2021, with maximum affected area of 303.88 km². Our study provides a general approach for high-frequency monitoring of flood disaster.
Coastal erosion is a natural process that constantly reconfigures beaches, either in a landward or seaward direction. This study aims to assess the situation, causes, and impacts of coastal erosion on selected communities in Lagos State, Nigeria. Three local government areas (LGAs) that share boundaries with the coast (Ibeju-Lekki, Eti-Osa and Badagry-West LGAs) were purposively sampled. Google Earth Pro images were acquired from Landsat, Copernicus, Maxar technologies, and TerraMetrics for 1984, 1999 and 2021 to analyze shoreline movement dynamics using Digital Shoreline Analysis Software. A structured questionnaire was used to elicit information on the communities' perceptions. We used the convenience sampling method to administer 670 samples of the questionnaire at a 95.52% response rate. Images analysis revealed that Eti-Osa has the highest erosion distance (98.54 m) at an erosion rate of 8.88 m/year, while Badagry-West LGA has the least erosion distance (25.82 m) at an erosion rate of −2.31 m/year. This confirms the respondents’ perception of coastline erosion and accretion which varies significantly across communities at X² (18) = 378.54, p < 0.05. The perceived leading causes of coastal erosion include heavy rainfall, intense storms, sea-level rise, developmental activities, and the anger of the gods, and they vary significantly across the communities at communities (X² (36) = 135.46, p < 0.05). The malevolent effects of this phenomenon, which include loss of homes and properties, groundwater quality impairment, relocation of settlement, and a threat to tourism, also vary significantly across communities. These communities are characterized by a low level of resilience to coastal erosion, hence susceptible to impending effects of climate change.
A skewed grey cloud ordered clustering model is proposed to overcome the problem that the existing grey clustering assessment results are inconsistent with objective reality due to the greyness, fuzziness, and randomness of information in the process of agricultural drought loss assessment. The skewed grey cloud model is built by split-combination possibility function, which is based on the normal grey cloud model. The grey cloud constraint interval is solved using the outer envelope curve and expectation curve of the skewed grey cloud, and the classification weight of the index is derived using the Gini coefficient. Then, the grey cloud constraint interval and the weight coefficient of comprehensive decision measure are combined to establish the ordered clustering criterion of skewed grey cloud. The degree of agricultural drought loss in Henan Province is examined from 2006 to 2019, and the assessment results are compared to those of the grey clustering model, normal grey cloud clustering model and comprehensive index method. The results reveal that the skewed grey cloud model’s evaluation results are more in line with the actual scenario of agricultural drought in Henan Province, showing the model’s applicability and usefulness.
Machine learning-based methodologies have depicted remarkable performance in digital processing of remote sensing imagery. In this work, we propose an integration of hazard susceptibility and vulnerability assessment in flood risk mapping using a CNN—based methodological framework. For this reason, we used nine predictor variables and a flood inventory from past flood events in a part of Tuscany region to train the model. Following a successful learning procedure, the performance of the proposed model was evaluated on a test dataset and depicted a promising prediction accuracy (95%). The analysis of the flood susceptibility map indicated that 4.7 and 2% of the entire study area depict very high and high susceptibility to future flood occurrences, respectively, corresponding to total areas of 44.06 and 19.33 km². Flood risk map depicts those land cover categories that will be severely affected in a future flood event. Among them, a large part of Livorno and a few industrial buildings were highlighted as areas of very high risk.
It would be beneficial to consider the results of the long-term evaluation of natural disasters in the decision-making process for disaster damage reduction/prevention. However, disaster evaluation is a complex and time-consuming process depending on different factors such as data type and data period. In this study, a new approach is proposed to determine the risk groups of the provinces in Turkey according to the disaster types (avalanche, landslide, rockfall, and flood) at regional and national scales. Disaster data between 1950 and 2020 were evaluated by considering the number of disasters in the provinces. The obtained data were subjected to cluster analysis, and then, the cluster groups were converted into risk classes. Finally, the risk weight ratios of the provinces and regions were calculated and thematically mapped by integrating them with GIS methods. According to the results, when four disaster types were considered together, Trabzon is the riskiest province on a provincial basis and the Black Sea is the riskiest region on a regional basis in Turkey. Additionally, the results of the study show that cluster analysis offers an effective solution for the evaluation of long-term large datasets. Furthermore, it was found that the new approach, which is used to minimize the errors that may be caused by surface area differences, makes a significant contribution to the evaluation process. This new approach will make a positive contribution to the analysts at the stage of giving priority to disasters and establishing protective and preventive policies on a national and global scale.
Scientific risk assessment of dammed lakes is vitally important for emergency response
planning. In this study, based on the evolution process of the disaster chain, the logic topology structure of dammed lake risk was developed. Then, a quantitative risk assessment model of dammed lake using Bayesian network is developed, which includes three modules of dammed lake hazard evaluation, outburst flood routing simulation, and loss assessment. In the model, the network nodes of each module were quantified using statistical data, empirical model, logical inference, and Monte Carlo method. The failure probability of a dammed lake, and the losses of life and property were calculated. This can be multiplied to assess the risk a dammed lake imposes after the uniformization of each loss type. Based on the socio-economic development and longevity statistics of dammed lakes, a risk-level classification method for dammed lakes is proposed. The Baige dammed lake, which emerged in China in 2018, was chosen as a case study and a risk assessment was conducted. The obtained results showed that the comprehensive risk index of Baige dammed lake is 0.7339 under the condition without manual intervention, identifying it as the extra-high level according to the classification. These results are in accordance with the actual condition, which corroborates the reasonability of the proposed model. The model can quickly and quantitatively evaluate the overall risk of a dammed lake and provide a reference for decision-making in a rapid emergency response scenario.
Natural disasters have always threatened the lives of humans and other creatures. One of the significant challenges for quickly responding to an earthquake is the need for precise and comprehensive information. Given that part of the environmental infrastructure is destroyed, quickly acquiring the required information is a serious challenge. Due to the ubiquity of smartphones, which have sensing, processing, and communication capabilities, this paper proposes CrowdBIG, a crowdsourcing-based architecture for information acquisition from the disaster environment. CrowdBIG architecture consists of four layers: sensing, fog, cloud, and application. Given that the reliability of crowdsourcing systems is dependent on the quality of user data, detecting malicious users, as well as scoring, and selecting useful users are of great importance. The CrowdBIG system is equipped with a reputation management component, which contains two sub-components: malicious user detection and user scoring. To evaluate the CrowdBIG system, first, we validate the information acquisition and dissemination workflow of the system using a scenario-based method. We then simulate the disaster environment through several well-known scenarios. The results show that CrowdBIG can detect malicious users appropriately. The CrowdBIG system can also score non-malicious users reasonably based on their usefulness and information completeness rates. The simulation results reveal that the reliability of the CrowdBIG system is 92%. Finally, the usability evaluation survey shows that more than 80% of the participants rated the usability of the proposed information-gathering tool as good or excellent.
The Huaihe River Basin (HRB) is located in the climate transition zone between north and south of china, where cold and warm air flows are easily encountered, resulting in frequent extreme precipitation events occurred in this area. In this study, in order to extract the spatiotemporal distribution characteristics of extreme precipitation in the HRB, the optimal edge distribution functions were used to fit the precipitation series to obtain the extreme precipitation threshold, and then six indicators were used to describe the spatiotemporal distribution characteristics of extreme precipitation. The results show that the number of occurrences and the amount of precipitation in the HRB are generally greater in the southern part than in the northern part, but the intensity of precipitation in the eastern coastal areas is greater than in the inland areas. The Weibull function has the best fitting effect on both the precipitation and precipitation intensity series in the five zones of the HRB. As the cumulative probability increases, the area with the largest precipitation amount is Zone 1, and the area with the largest precipitation intensity is Zone 3. The spatial variation trends of extreme precipitation and intensity-based extreme precipitation in the HRB are roughly the same. The area with more extreme precipitation is in the southwest of the basin, while the area with higher precipitation intensity is on the eastern coast of the basin. The number of extreme precipitation occurrences has a decreasing trend in most of the basin, and the precipitation amount also has a decreasing trend, but the precipitation intensity has an increasing trend in the southern and northern parts of the basin. Both the start date and end date of extreme precipitation have an increasing trend, indicating that the occurrence time of extreme precipitation has a tendency to delay. This study can provide an important reference for the prevention of extreme precipitation disasters in the HRB.
Recent crisis events and disasters, such as the 2021 flood disaster in Germany highlight the fact, that the management of multi-risks remains a challenge for risk managers and decision-makers even in highly developed countries. Nevertheless, over the past decade, new and innovative methods for data collection, analyses and communication have emerged and are increasingly being utilised in all phases of disaster risk management, e.g., from the domain of earth observation, artificial intelligence (AI) and social media. However, there are still gaps between scientific research, operative civil protection, and political decision-making. These gaps can only be bridged by closer collaboration and exchange.
The increasing severity and frequency of disasters across the USA is revealing a landscape that is not entirely prepared to cope with these exposures. Resilience as a socio-ecological concept has become progressively more important as a means of assessing and mitigating these losses. Technological advances and planning have improved many outcomes, but all populations have not experienced the benefits. In this paper, we focus on the shortcomings of current resilience measures in capturing neighborhood disparities. Much like vulnerability and sustainability, local disparities will have a deleterious impact on the community as a whole. We use the Baseline Resilience Indicators for Communities (BRIC) framework and downscale the index using neighborhood-level Census data (tracts) and variations in household access to community resources. These added variables represent the variation of resilience indicators across a community and capture cross-scale relationships that exist between county and Census tract characteristics. We apply scaled variables in the Pensacola Bay Watershed to demonstrate cross-scaled interactions in the Florida panhandle. Potential modifications and applications of the concepts are also discussed.
At a time, when the five riparian countries have renewed consultation with each other about the future of the Caspian Sea, it is appropriate to propose a state of the art of the potential natural threats to the regional environment. We present a critical review of geological, meteorological–climatological and hydrological hazards and disasters illustrated by many examples from within the Caspian drainage basin. Our work is set in the frame of an analysis of the factors contributing to the scale of the disasters. A brief overview of the mitigation measures in place and their future development is also included underlining the current limited warning systems (especially transboundary) despite improvements. While analysing past disasters is an essential source of information on which to base new mitigation, current and future conditions have poor or even no analogue in the past. Even though it clearly turns out that earthquakes are certainly the most deadly hazard, Caspian Sea level changes are by far causing the largest economical impact and affect the largest area and thus population. This review has also highlighted the need to create a Caspian database of natural hazards and disasters.
Typhoons usually bring natural disasters and economic losses. Satellite-based vegetation monitoring approaches largely improve our understanding of monitoring and assessing the area damaged by typhoons at high temporal and spatial scales, but it is still unclear which approach could achieve a greater robustness in assessing typhoon damages. From August 27 to September 8, 2020, three typhoons of Bavi, Maysak, and Haishen successively passed through the Northeast China, covering Liaoning, Jilin, and Heilongjiang provinces, and caused great damages to local vegetation and crops. Here, we employed two top-recognized approaches, i.e., the Normalized Difference Infrared Index (NDII) and the Disturbance Index (DI) derived from the moderate-resolution imaging spectroradiometer, to assess impacts three typhoons of Northeast China on the local vegetation and crops. With the help of Google Earth high-resolution images, this study demonstrated that the DI-based assessment gave a more accurate performance with an overall accuracy of 83% compared with NDII in typhoon-induced damages. DI was therefore used for spatial monitoring and assessment of three typhoon impacts. The DI-based results revealed that the damaged area of vegetation and crops in Northeast China was over 1.24 × 10⁵ km², including croplands, forests, and grasslands with the damaged area of 4.74 × 10⁴ km² (38.23% of total damaged area), 3.41 × 10⁴ km² (27.5%), and 0.12 × 10⁴ km² (0.97%), respectively. The damaged proportions were 14.13%, 13.19%, and 3.11% accounting for croplands, forests, and grasslands, respectively, of entire Northeast China. This study proves that DI-based vegetation damage assessment has more potential in large-scale monitoring of typhoon damages.
This paper introduces four advanced intelligent algorithms, namely kernel logistic regression, fuzzy unordered rule induction algorithm, systematically developed forest of multiple decision trees and random forest (RF), to perform the landslide susceptibility mapping in Jian’ge County, China, as well as well study of the connection between landslide occurrence and regional geo-environment characteristics. To start with, 262 landslide events were determined, and the proportion of randomly generated training data is 70%, while the proportion of randomly generated validation data is 30%, respectively. Then, through the comprehensive consideration of local geo-environment characteristics and relevant studies, fifteen conditioning factors were prepared, such as slope angle, slope aspect, altitude, profile curvature, plan curvature, sediment transport index, topographic wetness index, stream power index, distance to rivers, distance to roads, distance to lineaments, soil, land use, lithology and NDVI. Next, frequency ratio model was utilized to identify the corresponding relations for conditioning factors and landslides distribution. In addition, four data mining techniques were conducted to implement the landslide susceptibility research and generated landslide susceptibility maps. In order to examine and compare model performance, receiver operating characteristic curve was brought for judging accuracy of those four models. Finally, the results indicated that a traditional model, namely RF model, acquired the highest AUC value (0.859). Last but gained a lot of attention, the results can provide references for land use management and landslide prevention.
The paper considers accidents triggered by the impacts of debris flows on the infrastructure. The information collected by the author in the database of technological and natural–technological accidents and emergency situations (NTES) occurred in the Russian Federation from 1991 to 2020 is analyzed. The concept of emergency situation is used to collect data. The majority of accidents are caused by debris flows of rain origin. The classification of NTES due to debris flows is proposed based on the type of the infrastructure disrupted and cascading accidents. Eight types of NTES are revealed. Linear structures are most exposed and vulnerable to these impacts. The most affected are facilities of the transport infrastructure, as well as power lines, pipelines, lines of communication, and other lifelines. An average annual frequency of occurrences of these accidents and disruptions was estimated; areas most at risk were identified; seasonal variation of events was investigated. These accidents occur most frequently (once every 2–6 years) in Sakhalin and the North Caucasus debris flow provinces. The proportion of accidents triggered by debris flows in the total number of NTES is relatively small (about 2%); however, they cause the large damage and social problems creating emergency situations not only in the disaster areas but also in other areas connected by damaged linear structures. The majority of accidents occur during the warm season: from May to August in the North Caucasus and from June to October in the Pacific and Eastern regions.
During an earthquake sequence, there are often multiple recurring landslides. Understanding the spatial distribution of the landslides triggered by the first earthquake can help us predict the landslide susceptibility for subsequent shakes over a short term. This study used two landslide inventories from the Lombok earthquake sequence in Indonesia in 2018 to construct a short-term secondary disaster prediction model and an overall spatial prediction model using four machine learning algorithms. The average accuracy of the positive samples predicted by the prediction model was 7.1% lower than that of the short-term model. The highest accuracy of the overall prediction model was 14.9% higher, on average, and the area under the ROC curve (AUC) score was 8.1% higher, on average, but the corresponding probability thresholds were lower. The reason for this difference is that, in the short-term prediction model, since most of the landslides in the first landslide inventory were prone to fail two or more times due to the effect of multiple earthquakes, the prediction results have a high positive rate. This feature of the short-term prediction model makes it suitable for landslide rescue guidance in a sequence of earthquakes. In contrast, the overall prediction model can better represent the spatial distribution of the earthquake-triggered landslides in the area.
Flood is one of the most common natural disasters, which also triggers other natural disasters such as erosion and landslides. Flood damage can be minimised by ensuring optimum design of drainage infrastructure and other flood management tasks, which depends largely on reliable estimation of flood quantiles. This study investigates flood quantile estimation in ungauged catchments using a kriging-based regional flood frequency analysis (RFFA) technique. Three main research objectives are addressed in this study. Firstly, kriging-based RFFA models are developed using 558 catchments from eastern Australia in the range of frequent to rare flood quantiles (2, 5, 10, 20, 50 and 100 years of average recurrence intervals (ARIs)). Secondly, a validation of the models by adopting a leave-one-out (LOO) validation technique is undertaken to identify the best and the worst performing catchments across eastern Australia. Finally, a detailed comparison is made for the kriging-based RFFA technique with a generalised least-squares-based quantile regression technique, known as ‘RFFE model 2016’ using the same dataset to evaluate whether there are general patterns of the performance in different catchments. The study shows that for eastern Australia (a) the developed kriging-based RFFA model is a viable alternative for flood quantile estimation in ungauged catchments, (b) the 10-year ARI model Q 10 performs best among the six quantiles, which is followed by the models Q 5 and Q 20 , and (c) the kriging-based RFFA model is found to outperform the ‘RFFE model 2016’.
Many landslides have been reactivated along the banks of the Three Gorges Reservoir (TGR) in China since 2003, many of which were slow-moving landslides. Normally, these landslides do not occur suddenly, but accelerations during short periods may occur, and they can still cause damage to buildings. The initiation of slow-moving landslides depends not only on the hydraulic characteristics of the sliding body, but also on the mechanical properties of the sliding zone soil. In this study, the Sifangbei landslide in the TGR was selected as a case study to analyse the residual strength of the sliding zone and the long-term monitoring data. The analysis of the long-term monitoring data indicated that the periodic landslide deformation reactivation was affected by seasonal rainfall and annual reservoir water-level fluctuations. Soil samples along the sliding zone collected from the front and middle of the landslide were tested using a ring shear test to study the influence of the water content and shear rate on the residual strength values. The results showed that an increase in water content can weaken the shear strength of sandy clay and clay. The sandy clay with fewer montmorillonite minerals and more sand particles had higher mechanical properties than the clay with more montmorillonite minerals and fewer sand particles. The damaging effect of the increased water content on the sandy clay was mostly reflected in the residual friction angle, while the damaging effect of the increased water content on the clay was mostly reflected in the residual cohesion. An increase in the shear rate had a positive effect on the shear strength of the sandy clay and clay. For the sandy clay with high particle friction, the shear mode changed from turbulent flow to slippage as large particles on the sliding surface broke into smaller particles when the shear rate reached a high speed (v ≥ 0.5 mm min −1). The shear strength under different scenarios revealed the mechanism of slow-moving landslide reactivation.
Many bays worldwide are susceptible to coastal hazards such as storm surges, river floods, and tsunamis. Because most previous studies have focused on one or two of the above-mentioned hazards, in this study, we assess coastal vulnerability based on all three hazards. To accommodate the increase in the number of cases in multihazard analysis, an efficient method based on an estimated overflow volume without computing for inundation is proposed. Subsequently, the method is validated via a comparison with inundation simulation. It is shown that when the free overflow is dominant, the result yielded by the method is consistent with that of the inundation simulation. Using Tokyo Bay as the study area, an efficient method is applied to multihazard vulnerability assessment. By comparing the overflow volume maps and maximum anomaly distribution along the coast for four types of hazards, we investigate the characteristics of different types of hazards and identify the differences between single and multiple hazards. Furthermore, we compare the differences between superposing and concurrent computation methods for multiple hazards. It is discovered that the linear superposing method tends to overestimate the total water elevation in coastal regions; however, in the coast, where the superposing method underestimates multihazard anomalies, dike upgrades must be considered.
The Maduo earthquake caused a large number of sand liquefaction features to form in the surface deformation zone. Structural stress, shaking, and hydraulic fracturing, as well as animal activities, provided migration channels for the liquefied sand. Based on the different flow paths that produced the sand features, the sand liquefaction was classified into four different morphologies. The first morphology was formed by liquefied sand being ejected from the large cracks developed in the main surface rupture area. The second morphology was characterized by the en echelon distribution of sand ejected from small shear fractures developed outside of the main surface rupture zone. The third morphology was characterized by the sand below a weak layer being liquefied by vibration and hydraulic fracturing and ejected, forming independent sand cones and circular pits. The fourth morphology was characterized by mouse holes providing pathways for the eruption of liquefied sand.
Access to detailed data sets which can enable detailed hydraulic modelling of the river around a bridge structure is not always possible and may require extensive surveys. It is important for infrastructure managers to decide whether additional data availability that may increase the accuracy of scour risk assessments may be worthwhile. In this regard, this paper aims to examine the scour risk assessment of bridges under different model resolutions by using two commonly used scour risk assessment procedures for railway bridges in the UK and investigate the sensitivity of a number of hydraulic parameters used in the scour prediction equations. These procedures are applied to four case study railway bridges coupled with four data availability scenarios capturing different levels of topographical and hydrological data available for the bridge/river site. The results show that the estimations of hydraulic parameters based on the simple empirical equations recommended by EX2502 for data scarcity conditions have a tendency to cause a significant disparity compared to the estimates from 1D and 2D HEC-RAS models. Particularly 2D models with bathym-etric representation of river can provide more reliable results and improve the accuracy of scour risk assessments, especially for bridges that are close to the thresholds distinguishing different scour risk categories, i.e. medium to high risk. Sensitivity analysis of hydraulic parameters suggests that the most influential parameter that causes significant variations in total scour depth and scour risk is average velocity, followed by mean flow depth, mean floodplain depth, and mean floodplain width.
Morphometric indices from high-resolution DEMs can contribute to the estimation of flash flood susceptibility in mountainous areas. We have screened 25 morphometric indices commonly used in literature, and based on a correlation matrix, selected those which showed the strongest relationship with flash flood generation: area ( A ), drainage texture (Rt), drainage density (Dd), elongation ratio (Re), form factor (Ff), lemniscate method ( k ), Gravelius coefficient (GC), forested area (Fa) and relief ratio (Rr). Among them Dd, Rt and Rr had a direct impact on flash flood generation, while A , Re, Fa, Ff, k and GC are in inverse relationship with the intensity of flash floods. Our summary map shows the prioritization of the watersheds on a scale of 0 to 9. The flash flood susceptibility ranking was empirically verified using hydrological data (20-year water regime obtained from 14 official stream gauges). Our conclusions only partially agree with former observations which may be explained by the particular lithology and morphology of the Mecsek Mountains. Since the lower sections of the watersheds are urbanized, for optimal watershed management more detailed GIS analyses of anthropogenic controls on flash flood hazard are needed in the future.
Uneven ground deformations resulting from excessive groundwater exploitation have been causing problems in metropolitan cities worldwide. Metro Manila and its adjoining provinces have been recognized as undergoing ground deformations due to excessive groundwater pumping, meeting the continued population growth rate demands. Previous studies have identified widespread subsidence using advanced Differential Interferometric Synthetic Aperture Radar (D-InSAR) techniques, but with insufficient archived SAR data, which suffered from the extended perpendicular and temporal baselines covering long periods from 1993 to 2011. This study presents a Sentinel-1 Permanent Scatterer InSAR (PS-InSAR) application from 2015 to 2019 as a continued effort to monitor ground deformations caused by groundwater extraction and recharge in and around Metro Manila. The results revealed that several areas manifest apparent subsidence and uplift within the image’s footprint. The line-of-sight (LOS) subsidence rates in Manila, Caloocan, Malabon, Navotas, and Valenzuela are between 1 and 2 cm/year, except for some coastal communities. Other parts of the region are detected to be experiencing an uplift of 0.1 to 1 cm/year. LOS subsidence rates exceeding 4 cm/year were recorded in the adjoining provinces of Metro Manila, specifically in Bulacan, Cavite, and Laguna. These observations are consistent when correlated with groundwater levels during the same period. Moreover, a high correlation was observed with coefficients of determination (R²) > 0.95 in Makati, Bagumbayan (in Quezon City), and Dasmariñas (in Cavite). With the results obtained, a better understanding of these subtle ground deformations affecting various areas in the Philippines can help mitigate possible disasters and damages by this geohazard.
Susceptibility mapping is an effective means of preventing debris flow disasters. However, previous studies have failed to solve spatial heterogeneity well, especially at the regional scale. The main objective of this study is to solve the spatial heterogeneity of regional-scale debris flow susceptibility (DFS) mapping by establishing a geographic information system (GIS)-based processing framework. The framework was realized by integrating the determination factor (DFactor) model with machine learning models. The DFactor model established different combinations of evaluation factors in each local region and clarified the differing contributions of influencing factors to DFS. To test the feasibility of the framework, the support vector machine (SVM) and two-dimensional convolutional neural network (CNN) were integrated with the DFactor model (DFactor-SVM and DFactor-CNN) to evaluate DFS in Jilin Province, China. The individual models (SVM and CNN) were also used to map the DFS for comparison with the integrated models. For debris flow modeling, 868 debris flow samples were collected and randomly divided into two datasets: 70% of the samples were used for training and the result was used for verification. The results of the receiver operating characteristic curve showed that the integrated models performed better. The DFactor-CNN model had the highest predictive accuracy, followed by the DFactor-SVM, CNN and SVM models. In general, the GIS-based processing framework maximizes the contribution of the influencing factors to debris flows and enhances the prediction ability of models. Furthermore, it provides a reliable means to predict debris flows at the regional scale.
In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.
Natural hazards and urbanization put enormous pressure on cities and affect their sustainable development. Against the backdrop of the increasingly prominent urban disease, a comprehensive urban vulnerability assessment has a positive effect on improving the quality of urbanization. In this study, prefecture-level cities in Southwest China were taken as the study area, and their vulnerability was studied dynamically from 2010 to 2019. The four major systems were integrated, and a comprehensive vulnerability evaluation index system was constructed. Game theory and TOPSIS were combined to minimize the impact of subjective factors on the results. The results indicate that the overall vulnerability of Southwest China shows an irregular downward trend and strong spatial heterogeneity. There are 5 cities with very high vulnerability and 1 with very low vulnerability in 2010, accounting for 15.15% and 3.03% of the total number of cities, respectively. In contrast, the results for 2019 are the opposite, with 1 city with very high vulnerability and 5 cities with very low vulnerability. The better the economic situation, the lower the urban vulnerability. And there is a non-strictly negative correlation between urban vulnerability and urban size. When the difference in size between two cities is large enough, the vulnerability of the larger city is significantly lower. Leading development cities such as Chengdu and Chongqing have low vulnerability, while high vulnerability is mainly distributed in cities with poor infrastructure. On the basis of results, the research can be regarded as reference for urban management and coordinated development.
Rockfall is a natural process of mountain slope evolution that can endanger human settlements adjacent to rock cliffs. The Morro do Moreno Hill is a granite inselberg with steep faces adjoining a densely populated area located in the Vila Velha city (State of Espírito Santo, southeastern Brazil). A large number of fragmented blocks are found amid vegetation cover in the foothills. In this study, rockfall danger was evaluated by means of 3D numerical simulations to reproduce trajectories followed by blocks coupled with field evidences. In addition, the rockfall risk rating system for settlements—R³S², which is used for rockfall risk analysis of populated areas, has been applied. Only the SE and SW-facing slopes were analyzed, as they were considered the most critical. Numerical simulation revealed block trajectories reaching the urban area with high kinetic energy. The application of the R³S² indicated high rockfall risk levels. In addition, an extensive talus slope deposit has been identified. Proactive actions have been proposed in order to assist local authorities. Finally, our findings demonstrated that block detachment from rock cliffs is governed by different predisposing factors.
Areas with a high density of large-scale gullies are frequent in regions of the crystalline basement of Southeastern Brazil, such as the Bação Complex. These gullies pose a high hazard for people and properties, and cause loss of agricultural land, among other impacts. Gullies originating as erosion by channelized runoff can be controlled in their first stages by ordinary containment practices. However, when erosive channels reach the groundwater, erosive processes conditioned by subsurface flows start acting, causing mass movements and their control becomes more difficult. Field monitoring shows that these mass movements occur not only in the rainy season, as expected, but also in the dry season. To understand the dynamics of mass movement in the evolution of these features, a representative unstable gully in this Complex was selected. As it is common in this region, this gully presents unstable slopes, especially due to slumps. Numerical simulations of saturated and unsaturated flow have shown that, in this region of high seasonality, the aquifer is anomalously recharged in two stages. Safety factor analysis by limit-equilibrium method indicates a condition near to instability, with slumps occurring during the rainy season, when the aquifer at the toe of the slope is recharged, and in the dry season, when the upper slope is recharged after a few months' lags due to thicker unsaturated zone. As these gullies are very unstable and difficult to access, a viable means of stabilization could be the sediment retention with gabion walls, backfilled with soil or inert materials.
Nowadays, preparing natural hazard maps has become essential to protect society and its infrastructure from future natural disasters. In this context, the multi-criteria semi-quantitative technique, the analytical hierarchy process (AHP) was integrated into the Geographic Information System (GIS) environment to produce a flood hazard map. The Lower Mahananda basin of India was chosen as the study area that annually suffers from damage from floods. The produced map was categorized into five classes as the zones of flood susceptibility, namely very high, high, moderate, low and very low. The corresponded zones were then examined using sensitivity analysis, and the results show there are very small differences in the spatial distribution of flood susceptible zones. The high flood hazard zones (18%) are located generally along river tracts in the lower portion of the study area that gets flooded more or less every year. The comparison found between the various flood susceptible zones, and the rise in the water level at the Mahananda conveys the accuracy and reliability of the applied methodology. Moreover, the settlement zones were identified within the high flood probable category and the sub-districts situated along the western margin have been found with inadequate medical facilities to combat the flood events. Therefore, the proposed methodology and resultant analysis of the work can be useful for local government authorities to prepare flood mitigation plans and strategies.