High Susceptibility Areas Research Articles

Understanding Chemical And Non-Chemical Vulnerability Of Private Well Owners: Results From The Survey Of The Health Of Wisconsin

Background: Groundwater contamination of private wells is widespread. Despite this, regulators in Wisconsin and across the United States estimate only 10% of private well owners regularly test their wells. Consequently, they are particularly vulnerable to exposure and human health risks from groundwater contamination, but little empirical evidence exists to understand risks. Methods: Using baseline and follow-up data from the Survey of the Health of Wisconsin, a population based household examination survey, we examined barriers to private well testing behaviors and patterns as markers of population vulnerability. We also examined variation in testing and treatment behaviors amongst individuals in high vs. low groundwater susceptibility areas in the state. Results: From 2008-2013, SHOW identified 726 households using private wells. Approximately 45% of had at least 1 child less than 20 years living in the home. Prevalence of private well testing was low (about 10%). Barriers to testing include costs, perceived safety and limited understanding of where and how to get testing done. Approximately 40% of participants (37.8%) do not know their well depth. An estimated 57% of the participants indicate they do not use any type of water treatment. Among individuals who do treat, use of a water softener was most prominent and only about 3% report using a whole house reverse osmosis system. Discussion: Results show important opportunities for increasing awareness and reducing vulnerability of private well owners. Regular testing of private well water is a known and effective method for detecting contaminants and identifying strategies for reducing exposures. Increased targeted outreach to vulnerable sub-populations could greatly aid efforts to prevent potential health risks from groundwater contamination among private well owners across the state.

Susceptibility assessment of debris flows using the analytic hierarchy process method − A case study in Subao river valley, China

Many debris flows have occurred in the areas surrounding the epicenter of the Wenchuan earthquake. Susceptibility assessment of debris flows in this area is especially important for disaster prevention and mitigation. This paper studies one of the worst hit areas, the Subao river valley, and the susceptibility assessment of debris flows is performed based on field surveys and remote sensing interpretation. By investigating the formation conditions of debris flows in the valley, the following assessment factors are selected: mixture density of landslides and rock avalanches, distance to the seismogenic fault, stratum lithology, ground roughness, and hillside angle. The weights of the assessment factors are determined by the analytic hierarchy process (AHP) method. Each of the assessment factors is further divided into five grades. Then, the assessment model is built using the multifactor superposition method to assess the debris flow susceptibility. Based on the assessment results, the Subao river valley is divided into three areas: high susceptibility areas, medium susceptibility areas, and low susceptibility areas. The high susceptibility areas are concentrated in the middle of the valley, accounting for 17.6% of the valley area. The medium susceptibility areas are in the middle and lower reaches, most of which are located on both sides of the high susceptibility areas and account for 45.3% of the valley area. The remainders are classified as low susceptibility areas. The results of the model are in accordance with the actual debris flow events that occurred after the earthquake in the valley, confirming that the proposed model is capable of assessing the debris flow susceptibility. The results can also provide guidance for reconstruction planning and debris flow prevention in the Subao river valley.

Magnetometric exploration in intrusive basic with evidence of gold in northern region of the Camaquã Sedimentary Basin (RS)

AbstractThis study involved magnetometric exploration in the region of São Sepé (RS), southern Brazil, to evaluate the potential for gold mineralization in a gabbro using evidence previously obtained from the geochemical exploration of stream sediments. The gabbro named Santa Catarina is intruded in the Metamorphic Complex Vacacaí, a metassedimentary sequence where several gold deposits were recognized. In this study, nine lines of land magnetometry were performed with 1.200m in length each, a total of 10.800m and 720 reading stations. The results suggest the presence of filonian mineralization in fractures, some of which are subject to weathering action, releasing and transporting gold by water action and depositing it along the stream sediments downstream from the gabbro. The analytical signal map outlines areas of high magnetic susceptibility within its boundaries, coinciding with structural lineaments in the NS and EW directions, associated with the last of the four stages of regional deformation, possibly responsible for both fracturing and opening of spaces in the gabbro, as by remobilization of sulfide and gold recognizably described in several gold deposits within the Vacacaí Metamorphic Complex.

Community participation in flood mapping in the Amazon through interdisciplinary methods

Community participation is an increasing issue in risk and disaster management. This paper argues that interdisciplinary methods are necessary for mapping flooding areas. On the one hand, hazards can be quantified and mapped through deductive approaches and methods from the natural sciences. On the other hand, hazards can also be described from the population perspective at a local level using social sciences methods. The methods are successfully mixed with a geographical information system environment. In the first case, important inputs include topographic and slope data for geomorphological mapping. In the second case, timeline methodology can indicate temporally the most severe floods, and a participatory map construction gives the population the opportunity to point out the affected areas. These methods were applied in a case study in the lower Amazon River Region in the urban area of the Santarem municipality. High-resolution images and remote sensing were essential tools. Results show that nine neighborhoods have high and moderate susceptibility areas to riverine floods. Another nine neighborhoods are affected by flash floods. Flood mapping is the first step in risk mapping for the sustainability of regional planning.

Erratum to: Application of landslide hazard scenarios at annual scale in the Niraj River basin (Transylvania Depression, Romania)

The main objective of the study was to determine the landslide hazard in the drainage basin of the Niraj River taking into account as far as possible the dichotomous relationship between space and time. A broad inventory of the Romanian approaches concerned with natural hazard prediction reveals a clear preference for spatial analysis, while the temporal scale was almost totally neglected. To fill this gap, the proposed methodological approach combines GIS techniques for quantitative analysis with statistical analysis and detailed observation in the field, both directly and indirectly through remote sensing. Niraj River drainage basin is extended over an area of 658 km2 in the east-central part of Transylvania Depression, one of Romania’s major geographic units. Due to the substrate composition, consisting essentially of marls, clays and sands, this area is often affected by rotational and shallow landslides causing important material damages. The first step of this approach was to develop a spatial model for determining the susceptibility to landslide occurrence related to several causative factors: lithology, geomorphology, structural, hydroclimatic, seismic, land use and anthropogenic factors. In order to develop the temporal side of the prediction, two subsidiary objectives were assumed. The first one, to obtain a more detailed database which contains also the temporal moments of the landslides activation or reactivation. Secondly, to realise a statistical analysis of cumulative rainfall, over a period of 90 days prior to the time of each sliding event, which allows us to identify the correlation between cumulative rainfall which had triggered recorded landslides (beginning with the year 2005) and the landslide occurrence moments. On this basis, the main results obtained were: (1) the identification the recurring interval of each value of precipitation amount; (2) the assessment of the temporal probability of landslide occurrence, accepting the assumption that the precipitation amount which led to landslide activation in the past will have the same effects in the future; and (3) the development of four scenarios of landslide occurrence, starting from the events of May 2005, April 2006, July 2010 and February 2013, which were used to determine the annual probability of landslide occurrence under similar precipitation conditions. Finally, by identifying the correlation curves between the probability of landslides and time, taking into account the susceptibility classes, additional scenarios were determined for representative years in the future (2021, 2050, 2071 and 2100). The probability values were used to create correlation curves for each susceptibility class, the corresponding mathematical expressions facilitating the computation of annual probability. The results from the study area highlight the fact that there is an acceleration trend of landslide processes in the high susceptibility area.

Development and assessment of an erosion and landslide predictive model for the coastal region of the State of São Paulo, Brazil

A área de estudo é a Unidade de Gerenciamento de Recursos Hídricos número 11 (UGRHI-11), com declives acentuados, muito dissecados e relevo ondulado, localizada na última parte de remanescente contínuo da Mata Atlântica no Estado de São Paulo (Brasil). Este artigo apresenta um novo modelo de previsão para a identificação de áreas suscetíveis à erosão e deslizamentos de terra na região da UGRHI-11, combinando ferramentas geotécnicas e trabalho de campo. A fim de avaliar a metodologia, foi realizada uma análise multicritério utilizando o software IDRISI Andes. As áreas que são mais suscetí- veis à erosão estão localizadas em Apiaí, Barra do Chapéu, Barra do Turvo, Cajati, Eldorado, Itaóca, Itapirapuã Paulista e Ribeira. 128 ocorrências de deslizamentos observados em pesquisas de campo em Ribeira de Iguape Vale do Rio foram plotados no mapa de suscetibilidade deslizamento de terra. Dez ocorrências estavam situadas em áreas classificadas como baixa suscetibilidade a deslizamentos de terra; cinquenta e seis ocorrências em áreas de susceptibilidade moderada, cinquenta e cinco em áreas de elevada susceptibilidade e sete áreas estavam situadas em áreas de elevada susceptibilidade a deslizamentos de terra. Os dados de campo mostraram que os mapas de suscetibilidade a erosão e deslizamento de terra, em escala 1:250.000, forneceram previsões confiáveis.

A susceptibility mapping model of earthquake-triggered slope geohazards based on geo-spatial data in mountainous regions

The main purpose of this paper is to develop a susceptibility mapping model of earthquake-trigged slope geohazards based on geo-spatial data in mountainous region. A rapid-response mapping model of slope geohazards triggered by earthquakes that can be widely applied in practice can be constructed by a simple method of weighted superposition of related accessible factors. Based on site investigations into geological disasters in Wenchuan County caused by the Wenchuan earthquake on 12 May 2008, the main influencing factors of the earthquake triggering slope geohazards were studied, including peak ground acceleration (PGA) and 6 geo-spatial data factors: distance from streams, distance from highway, slope gradient, slope position, normalised difference vegetation index (NDVI) and micro-landforms. The order of the relative importance of each factor was determined by regression analysis, then the weight of each factor contributing to slope geohazards was calculated by using the analytic hierarchy process (AHP). The susceptibility mapping model of slope geohazards was derived from the map overlaying principle in ArcGIS platform, and finally, the results were re-classified to obtain the map concerning susceptibility areas of slope geohazards. The results, by comparing the geohazards points with the field survey, proved that over 63% geohazards points were located in high and very high susceptibility areas, while only about 14% geohazards points were located in very low and low susceptibility areas. The model showed a relative agreement between the points on the susceptibility mapping and onsite points of slope geohazards. Above all, predictions about the slope geohazards at meizoseismal areas of earthquakes occurring in mountainous regions can be based on geo-spatial data; so the model could be applied to study other areas which may be prone to the slope geohazards during the earthquakes.

Assessment of terrain susceptibility to thermokarst lake development along the Qinghai–Tibet engineering corridor, China

Thermokarst lakes have been developing recently along the Qinghai–Tibet engineering corridor in association with increased human activity and persistent climatic warming. Based on field observations, we assessed the susceptibility of terrain to the development of thermokarst lakes between the Chumaerhe River and Fenghuoshan mountain pass. A susceptibility map of the region was created in a geographic information system by assessing seven controlling factors, ranked using the analytic hierarchy process. The resulting susceptibility values ranged between 0.1 and 0.66. These susceptibility values were divided into four classes (high, moderate, low, and lowest) according to the mutagenesis point method. Areas with values between 0.1 and 0.16 were considered to have the ‘lowest’ susceptibility, while those between 0.26 and 0.66 were considered to have ‘high’ susceptibility. Using SPOT-5 satellite data, we determined that the high-susceptibility areas contained approximately 91 % of the total thermokarst lake area in the study region, and 88 % of the number of lakes. The moderate, low, and lowest level areas accounted for about 52 % of the study region, but only contained 9 % of the total lake area and 12 % of the lakes. Finally, relations between the area of the thermokarst lakes and the main controlling factors, e.g., ground ice content, ground temperature, vegetation type, and altitude were discussed.

Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran.

Visceral leishmaniasis (VL) is a potentially fatal vector-borne zoonotic disease, which has become an increasing public health problem in the north-western part of Iran. This work presents an environmental health modelling approach to map the potential of VL outbreaks in this part of the country. Radial basis functional link networks is used as a data-driven method for predictive mapping of VL in the study area. The high susceptibility areas for VL outbreaks account for 36.3% of the study area and occur mainly in the north (which may affect the neighbouring countries) and South (which is a warning for other provinces in Iran). These parts of the study area have many nomadic, riverside villages. The overall accuracy of the resultant map was 92% in endemic villages. Such susceptibility maps can be used as reconnaissance guides for planning of effective control strategies and identification of possible new VL endemic areas.

Geophysical Survey in Sub‐Saharan Africa: magnetic and Electromagnetic Investigation of the UNESCO World Heritage Site of Songo Mnara, Tanzania

ABSTRACTMagnetometry and Slingram electromagnetic surveys were conducted at the UNESCO World Heritage Site of Songo Mnara, Tanzania, as part of a multinational programme of investigation to examine the uses of space within and outside of this stonetown. The town was a major Islamic trading port during the fourteenth and fifteenth centuries. The surveys detected significant evidence for the containment of activities within the town walls, and previously unknown anthropogenic activity was revealed between the existing coral rag buildings, as well as within the open areas inside the town. Over 40 areas of magnetic disturbance were identified that corresponded directly with areas of high magnetic susceptibility in the Slingram electromagnetic in‐phase responses. On excavation many of these anomalies were found to correlate with wattle and daub structures, indicating a hitherto unidentified population, and the location of the anomalies also suggests a potentially deliberate delineation of space within the open areas of the stonetown. The combined results of the three geophysical data sets indicate that there are clear delineations in the use of space within Songo Mnara. This, coupled with the presence of industrial activities and evidence of more ephemeral occupation, neither of which had previously been recorded at the site, indicates that the pre‐existing town plan is in need of significant reappraisal. The current plan, based upon the remains of extant and collapsed coral buildings, can now be updated to incorporate the more ephemeral aspects of Swahili sites, including activity areas, and notably, the homes of the ‘hidden majority’ of the population. The results establish the benefit of a combined approach at these sites, and demonstrate that further invasive and non‐invasive exploration is required in order to fully exploit the significance of the role of geophysical techniques in understanding Swahili towns. © 2014 The Authors. Archaeological Prospection published by John Wiley & Sons Ltd.

Pan-Atlantic analysis of the overlap of a highly migratory species, the leatherback turtle, with pelagic longline fisheries

Large oceanic migrants play important roles in ecosystems, yet many species are of conservation concern as a result of anthropogenic threats, of which incidental capture by fisheries is frequently identified. The last large populations of the leatherback turtle, Dermochelys coriacea, occur in the Atlantic Ocean, but interactions with industrial fisheries could jeopardize recent positive population trends, making bycatch mitigation a priority. Here, we perform the first pan-Atlantic analysis of spatio-temporal distribution of the leatherback turtle and ascertain overlap with longline fishing effort. Data suggest that the Atlantic probably consists of two regional management units: northern and southern (the latter including turtles breeding in South Africa). Although turtles and fisheries show highly diverse distributions, we highlight nine areas of high susceptibility to potential bycatch (four in the northern Atlantic and five in the southern/equatorial Atlantic) that are worthy of further targeted investigation and mitigation. These are reinforced by reports of leatherback bycatch at eight of these sites. International collaborative efforts are needed, especially from nations hosting regions where susceptibility to bycatch is likely to be high within their exclusive economic zone (northern Atlantic: Cape Verde, Gambia, Guinea Bissau, Mauritania, Senegal, Spain, USA and Western Sahara; southern Atlantic: Angola, Brazil, Namibia and UK) and from nations fishing in these high-susceptibility areas, including those located in international waters.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic

This paper presents an expert knowledge-based approach to landslide susceptibility mapping in an effort to overcome the deficiencies of data-driven approaches. The proposed approach consists of three generic steps: (1) extraction of knowledge on the relationship between landslide susceptibility and predisposing factors from domain experts, (2) characterization of predisposing factors using GIS techniques, and (3) prediction of landslide susceptibility under fuzzy logic. The approach was tested in two study areas in China — the Kaixian study area (about 250km2) and the Three Gorges study area (about 4600km2). The Kaixian study area was used to develop the approach and to evaluate its validity. The Three Gorges study area was used to test both the portability and the applicability of the developed approach for mapping landslide susceptibility over large study areas. Performance was evaluated by examining if the mean of the computed susceptibility values at landslide sites was statistically different from that of the entire study area. A z-score test was used to examine the statistical significance of the difference. The computed z for the Kaixian area was 3.70 and the corresponding p-value was less than 0.001. This suggests that the computed landslide susceptibility values are good indicators of landslide occurrences. In the Three Gorges study area, the computed z was 10.75 and the corresponding p-value was less than 0.001. In addition, we divided the susceptibility value into four levels: low (0.0–0.25), moderate (0.25–0.5), high (0.5–0.75) and very high (0.75–1.0). No landslides were found for areas of low susceptibility. Landslide density was about three times higher in areas of very high susceptibility than that in the moderate susceptibility areas, and more than twice as high as that in the high susceptibility areas. The results from the Three Gorge study area suggest that the extracted expert knowledge can be extrapolated to another study area and the developed approach can be used in large-scale projects. Results from these case studies suggest that the expert knowledge-based approach is effective in mapping landslide susceptibility and that its performance is maintained when it is moved to a new area from the model development area without changes to the knowledge base.

Spatial Prediction of Earthquake-Induced Secondary Landslide Disaster in Beichuan County Based on GIS

In earthquake-stricken area, with the occurrence of aftershocks, heavy rainfall and human activity, the earthquake-induced secondary landslide disaster will threaten people’s life and property in a very long period. So, it makes secondary landslide became a research hotspots that draw much attention. The forecasting of natural disaster is considered as a most effective way to prevention or mitigation disaster and the spatial prediction is the base work of landslide disaster research. The aim of this study is to analyze the landslide prediction, taking the case of Beichuan County. Six factors affecting landslide occurrence have been taken into account, including elevation, slope, lithology, seismic intensity, distance to roads and rivers. The correlations of landslide distribution with these factors is calculated, the multiple regression and neural network model are applied to landslide spatial prediction and mapping. The model calculates result is ultimately categorized into four classes. It shows that the high and very high susceptibility areas most distribute in Qushan, Chenjiaba towns, etc., along the rivers and the roads around the area of Longmenshan fault. The precision accuracy using multiple regression models is about 73.7% and the neural network model can be up to 81.28%. It can be concluded that in this study area, the neural network model appears to be more accurate in landslide spatial prediction.

Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)

Landslides are one of the most widespread natural hazards that cause damage to both property and life every year, and therefore, the spatial distribution of the landslide susceptibility is necessary for planning future developmental activities. In this paper the artificial neural network (ANN) technique is tested for developing a landslide susceptibility map in Turbolo River catchment, North Calabria, South Italy.Landslides were mapped through air-photo interpretation and field surveys, by identifying both the landslide depletion zones (DZs) and accumulation zones (AZs); and relevant geo-environmental thematic layers pertaining to landslide predisposing factors were generated using air-photo interpretation, field surveys and Geographic Information System (GIS) tools. Ten predisposing factors were related to the occurrence of landslide: lithology, faults, land use, drainage network, and a series of topographic factors: elevation, slope, aspect, plan curvature, topographic wetness index (TWI) and stream power index (SPI).In order to evaluate and validate landslide susceptibility, the DZs were divided in two groups using a random partition strategy. The first group (training set) was used to prepare the susceptibility map, employing a back-propagation learning algorithm in the Idrisi Taiga software. The second group (testing set) was used to validate the landslide susceptibility model, using the confusion matrix and the receiver operating characteristic (ROC) curve.The susceptibility map was classified into five susceptibility classes: very low, low, moderate, high, and very high. About 46% of the study area falls in high to very high susceptible classes and most of the DZs mapped (87.3%) occur in the same classes.The validation results showed satisfactory agreement between the susceptibility map and the DZs locations; over 85% of the DZs of the validation set are correctly classified falling in high and very high susceptibility areas. Also, the ROC curve had shown an area under curve (AUC) value of 0.90 which demonstrates the robustness and good reliability of the landslide susceptibility model. According to these results, we conclude that the map produced by the artificial neural network is reliable and the methodology applied in the study produced high performance, and satisfactory results, which may be useful for land planning policy.

SU‐E‐J‐60: In Vivo Management of Suspectibility‐Induced Distortions for Intracranial MRI Guided Therapies at 3 Tesla

Purpose: To assess, in vivo, subject‐dependent susceptibility‐induced shifts along the frequency encode direction, and develop a strategy for correcting such distortions for applications in need of high spatial accuracy. Methods: Distortion maps of the brain were created for 5 volunteers using data from a 16‐echo multiple gradient echo (MFGRE) sequence, acquired on a 3 Tesla scanner for R2* mapping applications. Both an R2* map and a proton resonance frequency (PRF) map was calculated using a single peak autoregressive moving average (ARMA) model, and aliased frequencies were unwrapped with a compressed sensing type non‐convex optimization method, using the assumption that sharp changes in the field are sparse. A map of susceptibility induced shifts is then calculated with knowledge of the frequency gradient, which scales with field of view and receiver bandwidth. The need for distortion correction is then determined either visually or automatically based on a threshold. If distortion correction is necessary, a smoothed version of the distortion map can be corrected to subvoxel resolution using conventional image deformation techniques. Results: Due to the high frequency encoding gradient of MFGRE sequences (±125 kHz over 25.6 cm FOV in our protocol), susceptibility‐induced distortions within the brain were less than 0.7 mm. However, the distortion map and correction method was applied to sequences with lower bandwidth in the same slice locations. Most distortions occurred in high susceptibility areas near the sinuses, with the optic chiasm shifting by approximately one pixel on low bandwidth, high resolution images. Conclusion: The creation of field and distortion maps for assessment and correction of distortion was performed using data from an MFGRE sequence. The additional R2* information from the same sequence provides an added benefit over dedicated field mapping sequences. With proper design of imaging protocols, distortions in the brain can be adequately contained for therapy planning purposes. Ken‐Pin Hwang is an employee of General Electric Healthcare.

Comprehensive fuzzy evaluation model and evaluation of the karst collapse susceptibility in Zaozhuang Region, China

Karst collapses in Zaozhuang area are the typical karst collapses induced by groundwater over-pumping in north China, which lead to severe economic loss and negative societal influence. Based on the comprehensive analysis of all natural influencing factors and human influencing factors associated with karst collapse in the Zaozhuang Shili Spring Area, China, this paper establishes karst collapse susceptibility evaluation model by means of fuzzy mathematic method, in which the Quantitative theory method was firstly applied in determination of the factor weights. The karst collapse susceptibility evaluation model with 14 evaluation indicators can be effectively used for quantitative evaluation on the karst collapse susceptibility. So, an analysis and evaluation on the karst collapse susceptibility of Zaozhuang Shili Spring Area is completed by means of karst collapse susceptibility evaluation model. The karst collapse area of Zaozhuang region is divided into very high susceptibility area, high susceptibility area, medium susceptibility area and low susceptibility area, which account for about 6.3, 5.2, 10.4 and 78.1 % of the total karst area and the karst collapse susceptibility evaluation map of Zaozhuang Shili Spring Area is completed. The results above will be helpful for the strategic planning and decision-making processes associated with exploitation of karst water resources and with prevention and control of karst collapse hazards of this region.

Automated T2* measurements using supplementary field mapping to assess cardiac iron content

To develop and evaluate an algorithm that automatically identifies high-susceptibility areas and excludes them from T(2) * measurements in the left ventricle (LV) for myocardial iron measurements. An autoregressive moving average (ARMA) model was implemented on multigradient echo scans of 24 patients (age range 3-45 years, 10 male/14 female). Voxels with relatively high susceptibility (>3 Hz/mm) were flagged and deselected from the T(2) * calculations for iron quantification. The mean, standard deviation, and coefficient of variation (CoV) of the ARMA-defined region were compared to the CoV of four distinct regions of the LV and the entire LV using a Student's t-test (α = 0.05). The CoV of T(2) * values obtained by the ARMA method are comparable with that in the interventricular septum (IS), where susceptibility was the lowest (CoV = 0.31). The ARMA method provides a greater area (51.9 ± 13.7% of the LV) to measure T(2) * than that using the IS alone (21.1 ± 3.4%, P < 0.0001). Areas where low susceptibility are measured corroborate with areas reported in previous studies that investigated T(2) * variations throughout the LV. An automated method to measure T(2) * relaxation in the LV with minimal effects from susceptibility has been developed. Variability is reduced while covering more regions for cardiac T2 * calculation.

Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan

This study develops a model for evaluating the hazard level of landslides at Alishan Forestry Railway, Taiwan, by using logistic regression with the assistance of a geographical information system (GIS). A typhoon event-induced landslide inventory, independent variables, and a triggering factor were used to build the model. The environmental factors such as bedrock lithology from the geology database; topographic aspect, terrain roughness, profile curvature, and distance to river, from the topographic database; and the vegetation index value from SPOT 4 satellite images were used as variables that influence landslide occurrence. The area under curve (AUC) of a receiver operator characteristic (ROC) curve was used to validate the model. Effects of parameters on landslide occurrence were assessed from the corresponding coefficient that appears in the logistic regression function. Thereafter, the model was applied to predict the probability of landslides for rainfall data of different return periods. Using a predicted map of probability, the study area was classified into four ranks of landslide susceptibility: low, medium, high, and very high. As a result, most high susceptibility areas are located on the western portion of the study area. Several train stations and railways are located on sites with a high susceptibility ranking.

Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China)

Debris flow susceptibility assessment is the premise of risk assessment. In this paper, Sichuan Province is chosen as a study area, where debris flow disasters happen frequently. Information value model is applied to calculate the information values of seven environmental factors, namely elevation, slope, aspect, flow accumulation, vegetation coverage, soil type and land-use type. Geographic information system technology is used to analyze the comprehensive information values so as to determine the debris flow susceptibility. The results show that the northeast, the central and the south of Sichuan are the most hazardous regions, which display a zonal distribution feature from the southeast to the south. From the validation results, 7.53 % of the total area suffers from high susceptibility and 19.97 % suffers from very high susceptibility. However, 80 % of the debris flows are concentrated in two regions. The actual occurrence ratios of debris flows of the high-susceptibility and very high-susceptibility areas are 4.95 and 2.14, respectively.

Periglacial debris-flow initiation and susceptibility and glacier recession from imagery, airborne LiDAR, and ground-based mapping

Climate changes in the Pacific Northwest, USA, may cause both retreat of alpine glaciers and increases in the frequency and magnitude of storms delivering rainfall at high elevations absent significant snowpack, and both of these changes may affect the frequency and severity of destructive debris flows initiating on the region9s composite volcanoes. A better understanding of debris-flow susceptibility on these volcanoes’ slopes is therefore warranted. Field mapping and remote sensing data, including airborne light detection and ranging (LiDAR), were used to locate and characterize initiation sites of six debris flows that occurred during an “atmospheric river” event (warm wet storm) on Mount Rainier, Washington, in November 2006, and data from prior studies identified six more debris flows that occurred in 2001–2005. These 12 debris flows had initiation sources at the heads of 17 gullies distributed over seven distinct initiation zones near the termini of glaciers, and all debris-flow initiation sites were located within areas exposed by glacier retreat in the past century. Gully locations were identified by their steep walls and heads on a 1-m digital elevation model (DEM) from LiDAR data collected in 2007–2008. Gullies in which debris flows initiated were differentiated from numerous non-initiating gullies primarily by the greater upslope contributing areas of the former. Initiation mechanisms were inferred from pre- and post-2006 gully width measurements from aerial photos and the LiDAR DEM, respectively, field observations of gully banks, and elevation changes calculated from repeated LiDAR, and these data indicate that debris flows were initiated by distributed sources, including bank mass failures, related to erosion by overland flow of water. Using gully-head initiation sites for debris flows that occurred during 2001–2006, a data model was developed to explore the viability of the method for characterization of debris-flow initiation susceptibilities on Mount Rainier. The initiation sites were found to occupy a restricted part of the four-dimensional space defined by mean and standard deviation of simulated glacial meltwater flow, slope angle, and minimum distance to an area of recent (1994–2008) glacier retreat. The model identifies the heads of most gullies, including all sites of known debris-flow initiation, as high-susceptibility areas, but does not appear to differentiate between areas of varying gully-head density or between debris-flow and no-debris-flow gullies. The model and field data, despite limitations, do provide insight into debris-flow processes, as well as feasible methods for mapping and assessment of debris-flow susceptibilities on periglacial areas of the Cascade Range.