甘肃陇南武都区泥石流易发性评价

文章分析了甘肃陇南市武都区泥石流形成的自然环境背景、发育特征及易发性。通过野外实地考察,查明了泥石流的发育情况,在此基础上,采用模糊物元可拓方法对泥石流的易发性进行了评价。分析表明,研究区的泥石流具有分布密度高、冲沟及坡面泥石流成片发育、北岸泥石流较南岸发育且粘性泥石流所占比例大于南岸的发育特征;选取岩性、沟床比降、山坡坡度、完整系数、发育程度、降水、断层密度7个因子构建泥石流易发性评价指标体系。通过易发性评价,研究区104条泥石流沟中,66条为高易发性,占总数的63.5%;32条为中等易发性,占总数的30.8%;6条为低易发性,占总数的5.7%。     

Rainfall-triggering response patterns of post-seismic debris flows in the Wenchuan earthquake area

Several giant debris flows occurred in southwestern China after the Wenchuan earthquake, causing serious casualties and economic losses. Debris flows were frequently triggered after the earthquake. A relatively accurate prediction of these post-seismic debris flows can help to reduce the consequent damages. Existing debris flow prediction is almost based on the study of the relationship between post-earthquake debris flows and rainfall. The relationship between the occurrence of post-seismic debris flows and characteristic rainfall patterns was studied in this paper. Fourteen rainfall events related to debris flows that occurred in four watersheds in the Wenchuan earthquake area were collected. By analyzing the rainfall data, characteristics of rainfall events that triggered debris flows after the earthquake were obtained. Both the critical maximum rainfall intensity and average rainfall intensity increased with the time. To describe the critical conditions for debris flow initiation, intensity–duration curves were constructed, which shows how the threshold for triggering debris flows increased each year. The time that the critical rainfall intensities of debris flow occurrences return to the value prior to the earthquake could not be estimated due to the absent rainfall data before the earthquake. Rainfall-triggering response patterns could be distinguished for rainfall-induced debris flows. The critical rainfall patterns related to debris flows could be divided on the basis of antecedent rainfall duration and intensity into three categories: (1) a rapid triggering response pattern, (2) an intermediate triggering response pattern, and (3) a slow triggering response pattern. The triggering response patterns are closely related to the initiation mechanisms of post-earthquake debris flows. The main difference in initiation mechanisms and difference in triggering patterns by rainfall is regulated by the infiltration process and determined by a number of parameters, such as hydro-mechanical soil characteristics, the thickness of the soil, and the slope gradient. In case of a rapid triggering response rainfall pattern, the hydraulic conductivity and initial moisture content are the main impact factors. Runoff erosion and rapid loading of solid material is the dominant process. In case of a rainfall pattern with a slow triggering response, the thickness and strength of the soil, high hydraulic conductivity, and rainfall intensity are the impact factors. Probably slope failure is the most dominant process initiating debris flows. In case of an intermediate triggering response pattern, both debris flow initiation mechanisms (runoff erosion and slope failure) can play a role.     

Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily

A certain number of studies have been carried out in recent years that aim at developing and applying a model capable of assessing water erosion of soil. Some of these have tried to quantitatively evaluate the volumes of soil loss, while others have focused their efforts on the recognition of the areas most prone to water erosion processes. This article presents the results of a research whose objective was that of evaluating water erosion susceptibility in a Sicilian watershed: the Naro river basin. A geomorphological study was carried out to recognize the water erosion landforms and define a set of parameters expressing both the intensity of hydraulic forces and the resistance of rocks/soils. The landforms were mapped and classified according to the dominant process in landsurfaces affected by diffuse or linear water erosion. A GIS layer was obtained by combining six determining factors (bedrock lithology, land use, soil texture, plan curvature, stream power index and slope-length factor) in unique conditions units. A geostatistical multivariate approach was applied by analysing the relationships between the spatial distributions of the erosion landforms and the unique condition units. Particularly, the density of eroded area for each combination of determining factors has been calculated: such function corresponds, in fact, to the conditional probability of erosion landforms to develop, under the same geoenvironmental conditions. In light of the obtained results, a general geomorphologic model for water erosion in the Naro river basin can be depicted: cultivated areas in clayey slopes, having fine-medium soil texture, are the most prone to be eroded; linear or diffuse water erosion processes dominate where the topography is favourable to a convergent or divergent runoff, respectively. For each of the two erosion process types, a susceptibility map was produced and submitted to a validation procedure based on a spatial random partition strategy. Both the success of the validation procedure of the susceptibility models and the geomorphological coherence of the relationships between factors and process that such models suggest, confirm the reliability of the method and the goodness of the predictions.     

Regional debris flow susceptibility analysis based on principal component analysis and self-organizing map: a case study in Southwest China

A method was developed to analyze the susceptibilities of 541 regional basins affected by debris flows at the Wudongde Dam site in southwest China. Determining susceptibility requires information on source material quantity and occurrence frequency. However, the large number of debris flows can hinder the individual field investigation in a each small basin. Factors that may trigger debris flows can be identified using remotely sensed interpretation information. Susceptibility analysis can then be conducted based on these factors. In this study, SPOT5 satellite imagery, digital elevation models (DEM), a lithology distribution map, and rainfall monitoring data were used to identify 12 debris flow trigger factors: basin relief ratio, slope gradient in the initiation zone, drainage density, downslope curvature of the main channel, vegetation coverage, main channel aspect, topographic wetness index, Melton’s ruggedness number, lithology, annual rainfall, form factor, and cross-slope curvature of the transportation zone. Principal component analysis was used to obtain the eight principal components of these factors that contribute to susceptibility results. Then, a self-organizing map method was adopted to analyze the principal components, which resulted in a debris flow susceptibility classification. Field validation of 26 debris flow basins was used to evaluate the errors of the susceptibility classification, as well as assess the causes of such errors. The study found that principle component analysis and self-organizing map methodologies are good predictors of basin susceptibility to debris flows.     

北京市泥石流易发区降雨预警阈值研究

泥石流灾害合理的雨量预警阈值不仅与历史泥石流灾害发生时的降雨量有关,且与研究区域的气候、地形地貌、地质、植被等密切相关。论文采用雨场分割法和GIS技术研究了影响泥石流启动的降雨和地质背景两大因素,在对北京市泥石流灾害易发分区的基础上,结合北京地区已发生的82起泥石流的易发性分区和雨量值,提出了不同泥石流易发等级条件下的雨量预警阈值。研究成果已经在2015年7月17日北京房山区西区沟泥石流预警中成功应用,为泥石流区域预警预报提供了一种新的思路。     

GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area,Northwest Covilhã, Portugal

Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.     

Influence of the sediment delivery ratio index on the analysis of silting and break risk in the Plasencia reservoir (Central System,Spain)

The random forest method was used to generate susceptibility maps for debris flows, rock slides, and active layer detachment slides in the Donjek River area within the Yukon Alaska Highway Corridor, based on an inventory of landslides compiled by the Geological Survey of Canada in collaboration with the Yukon Geological Survey. The aim of this study is to develop data-driven landslide susceptibility models which can provide information on risk assessment to existing and planned infrastructure. The factors contributing to slope failure used in the models include slope angle, slope aspect, plan and profile curvatures, bedrock geology, surficial geology, proximity to faults, permafrost distribution, vegetation distribution, wetness index, and proximity to drainage system. A total of 83 debris flow deposits, 181 active layer detachment slides, and 104 rock slides were compiled in the landslide inventory. The samples representing the landslide free zones were randomly selected. The ratio of landslide/landslide free zones was set to 1:1 and 1:2 to examine the results of different sample ratios on the classification. Two-thirds of the samples for each landslide type were used in the classification, and the remaining 1/3 were used to evaluate the results. In addition to the classification maps, probability maps were also created, which served as the susceptibility maps for debris flows, rock slides, and active layer detachment slides. Success and prediction rate curves created to evaluate the performance of the resulting models indicate a high performance of the random forest in landslide susceptibility modelling.     

GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.     

Effects of strong ground motion on the susceptibility of gully type debris flows

Typhoon Herb in 1996 caused widespread debris flows in central Taiwan. The 7.3 Chi-Chi earthquake on September 21, 1999, which also took place in central Taiwan, induced many landslides in the region. These landslides turned into debris flows when Typhoon Toraji struck Taiwan in 2001. This research selects three regions which suffered a ground motion class of 5, 6, and 7 on the Richter scale during the Chi-Chi earthquake as study areas. Air photos from 1997 and 2001 of these regions are used to map the gully-type debris flows that took place after Typhoons Herb and Toraji, respectively. The gullies adjacent to the debris flow, but without a trace of debris flows, are also mapped as the non-debris flow data. The topography, hydrogeology, and rainfall factors – where debris flow occurred and in which there was no occurrence of debris flows in these gullies were retrieved from DTM, geological maps, and iso-countour maps, and of rainfall through GIS processing. These characteristic are introduced into a probabilistic neural network to build a predicting model for the probability of the occurrence of debris flows. Three series of cross analyses are conducted to compare the probability of the occurrence of debris flows of the same dataset predicted by different prediction models. The results reveal that the susceptibility of debris flows was elevated after the Chi-Chi earthquake struck. The upsurge of susceptibility was more obvious for the regions that received a higher class of ground motion.     

浙江省永嘉县泥石流特征及成因分析

分别叙述了永嘉县坡面泥石流和沟谷泥石流的发育分布特征,并分别对其成因进行了分析。阐述了泥石流的形成条件和影响因素,初步分析了坡面泥石流的形成过程及沟谷泥石流的形成方式和侵蚀特征。     

沟岸侧蚀对泥石流形成和运动过程的影响

沟岸被侧蚀掉的松散物质会通过动量交换将能量传递给龙头,从而影响泥石流的形成和运动过程。前人建立了许多模型来研究泥石流的侵蚀过程对泥石流形成和运动过程的影响,但是模型中大多以底蚀作用为前提条件。通过侧蚀模型和底蚀模型两种水槽实验的对比,针对泥石流的形成和运动过程展开研究。实验发现侧蚀作用更有利于泥石流的形成和运动,泥石流的龙头高度和速度都有波动特征,但侧蚀作用使得这种波动特征更加明显。侧蚀作用使得泥石流的龙身速度更快于龙头速度,龙身颗粒源源不断地堆积于龙头,使得龙头有较大的高度和附加坡降,因此,侧蚀条件下龙头的速度更快。     

A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy

The aim of this study is to analyze the susceptibility conditions to gully erosion phenomena in the Magazzolo River basin and to test a method that allows for driving the factors selection. The study area is one of the largest (225 km²) watershed of southern Sicily and it is mostly characterized by gentle slopes carved into clayey and evaporitic sediments, except for the northern sector where carbonatic rocks give rise to steep slopes. In order to obtain a quantitative evaluation of gully erosion susceptibility, statistical relationships between the spatial distributions of gullies affecting the area and a set of twelve environmental variables were analyzed. Stereoscopic analysis of aerial photographs dated 2000, and field surveys carried out in 2006, allowed us to map about a thousand landforms produced by linear water erosion processes, classifiable as ephemeral and permanent gullies. The linear density of the gullies, computed on each of the factors classes, was assumed as the function expressing the susceptibility level of the latter. A 40-m digital elevation model (DEM) prepared from 1:10,000-scale topographic maps was used to compute the values of nine topographic attributes (primary: slope, aspect, plan curvature, profile curvature, general curvature, tangential curvature; secondary: stream power index; topographic wetness index; LS-USLE factor); from available thematic maps and field checks three other physical attributes (lithology, soil texture, land use) were derived. For each of these variables, a 40-m grid layer was generated, reclassifying the topographic variables according to their standard deviation values. In order to evaluate the controlling role of the selected predictive variables, one-variable susceptibility models, based on the spatial relationships between each single factor and gullies, were produced and submitted to a validation procedure. The latter was carried out by evaluating the predictive performance of models trained on one half of the landform archive and tested on the other. Large differences of accuracy were verified by computing geometric indexes of the validation curves (prediction and success rate curves; ROC curves) drawn for each one-variable model; in particular, soil texture, general curvature and aspect demonstrated a weak or a null influence on the spatial distribution of gullies within the studied area, while, on the contrary, tangential curvature, stream power index and plan curvature showed high predictive skills. Hence, predictive models were produced on a multi-variable basis, by variously combining the one-variable models. The validation of the multi-variables models, which generally indicated quite satisfactory results, were used as a sensitivity analysis tool to evaluate differences in the prediction results produced by changing the set of combined physical attributes. The sensitivity analysis pointed out that by increasing the number of combined environmental variables, an improvement of the susceptibility assessment is produced; this is true with the exception of adding to the multi-variables models a variable, as slope aspect, not correlated to the target variable. The addition of this attribute produces effects on the validation curves that are not distinguishable from noise and, as a consequence, the slope aspect was excluded from the final multi-variables model used to draw the gully erosion susceptibility map of the Magazzolo River basin. In conclusion, the research showed that the validation of one-variable models can be used as a tool for selecting factors to be combined to prepare the best performing multi-variables gully erosion susceptibility model.     

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.     

"5·12" 地震后汶川县泥石流特征与演化分析

2008年“5·12”汶川地震极大地改变了震区泥石流的特征,不仅增强了泥石流的活动性,同时也使得震区在相当长的时间内都要面临泥石流的威胁。本文基于前人大量的研究成果,并利用遥感解译结合现场调查等手段,分析了汶川县泥石流沟道纵坡降、沟壑密度、两岸坡度等基本发育特征;进而分析了地震前后汶川县降雨分布及泥石流相关降雨参数变化特征。结果显示,流域内泥石流沟的沟壑密度在0.2~4之间,属于微度土壤侵蚀区域,泥石流的沟床纵坡降偏大,有利于泥石流的发生;泥石流流域内斜坡坡度多为30°~40°,有利于灾害的发生;震后汶川县年均降雨量增加了5.17%,降雨多集中在7~9月份,降雨量由南及北逐渐降低;震后泥石流的降雨阈值在2008~2013年呈现缓慢回升的趋势,但2019年又有所下降,预计恢复到震前水平尚需要一定时间;同时震后汶川县泥石流物源丰富,震后物源量呈现“震荡式衰减”的演化趋势,但体量仍然很大,对泥石流仍需坚持监测预警工作。     

基于分形理论和模型试验的沟道物源动储量评价模型

汶川地震后,大量松散固体物源堆积在沟道中,使沟道泥石流发生的概率激增。准确的计算泥石流沟道物源的动储量一直是泥石流物源统计的难点。文章以七盘沟下游主沟段沟道物源为研究对象,在实地勘查、资料收集的基础上,以室内模型试验为研究手段,引入分形理论将复杂的土体粒度成分用分维值定量描述,研究不同沟道堆积体在不同降雨作用下的侵蚀规律,建立以降雨强度和分维度为双影响因子的动储量评价模型。研究表明:粗粒土不易起动,但在充足的水动力条件下,侵蚀作用会成倍放大;上细下粗土发生泥石流时侵蚀变化和总的侵蚀规模较小,这种粒序分布形式有益于沟道的稳定;上粗下细土与粗粒土的侵蚀现象类似,但发生大规模泥石流的降雨阈值低于粗粒土;沟道物源中,侵蚀作用效应的排序为:溯源侵蚀>下切侵蚀>侧缘侵蚀>潜蚀;文章所拟合的公式适用于宽缓型沟道泥石流,对于窄陡型沟道泥石流存在一定的局限性。     

Assessment of the effects of vegetation on soil erosion risk by water: a case of study of the Batta watershed in Tunisia

Soil erosion by water is a serious environmental problem which affects particularly the agriculture of developing countries. Due to specific factors, such as high rainfall intensity, steep slopes and vegetation scarcity, Tunisia is prone to soil erosion. Taking this into account, the main objective of this study was to estimate the soil erosion risk in the Batta watershed in Tunisia using qualitative and quantitative erosion model with remote sensing data and geographic information system (GIS). Moreover, a developed method that deals with evaluating the impact of vegetation on soil erosion by water is also applied. This method used multi-temporal satellite images with seasonal variability and the transformed soil adjusted vegetation index (TSAVI) which is appropriate in arid and semi-arid areas. For both erosion models, the results show that a large area of the Batta watershed is seriously affected by erosion. This potentially high risk is due especially to severe slopes, poor vegetation coverage and high soil erodibility in this watershed. Furthermore, the use of multi-temporal satellite images and vegetation index show that the effect of vegetation is a significant factor to protect the soil against erosion. The risk is especially serious in the summer season, but it decreases with the growth of vegetation cover in spring. Erosion model, combined with a GIS and remote sensing, is an adequate method to evaluate the soil erosion risk by water. The findings can be used by decision makers as a guideline to plan appropriate strategies for soil and water conservation practices.     

An empirical approach for evaluation of the potential of debris flow occurrence in mountainous areas

Debris flows can occur relatively suddenly and quickly in mountainous areas, resulting in major structural damage and loss of life. The establishment of a model to evaluate the occurrence probability of debris flows in mountainous areas is therefore of great value. The influence factors of debris flows are very complex; they can basically be divided into background factors and triggering factors. Background factors include the mechanical characteristics of geo-materials, topography and landscape, and soil vegetation; and triggering factors include hydrological and rainfall conditions, and human activities. By assessing the dynamic characteristics of debris flows in mountainous areas, some important influence factors are selected here for analysis of their impacts on the occurrence probability of debris flow. A mathematical model for evaluation of the occurrence probability of debris flows is presented and combined with probability analysis. Matlab software is used for the numerical implementation of the forecasting model, and the influences of rainfall, lithology and terrain conditions on the occurrence probability of debris flows are analyzed. Finally, the presented model is applied to forecast the occurrence probability of debris flows in the mountainous area around Qingping Town; the simulation results show that many loose landslide deposits and heavy rainfall are the key factors likely to trigger debris flows in this region.     

Wildfire impacts on the processes that generate debris flows in burned watersheds

Every year, and in many countries worldwide, wildfires cause significant damage and economic losses due to both the direct effects of the fires and the subsequent accelerated runoff, erosion, and debris flow. Wildfires can have profound effects on the hydrologic response of watersheds by changing the infiltration characteristics and erodibility of the soil, which leads to decreased rainfall infiltration, significantly increased overland flow and runoff in channels, and movement of soil. Debris-flow activity is among the most destructive consequences of these changes, often causing extensive damage to human infrastructure. Data from the Mediterranean area and Western United States of America help identify the primary processes that result in debris flows in recently burned areas. Two primary processes for the initiation of fire-related debris flows have been so far identified: (1) runoff-dominated erosion by surface overland flow; and (2) infiltration-triggered failure and mobilization of a discrete landslide mass. The first process is frequently documented immediately post-fire and leads to the generation of debris flows through progressive bulking of storm runoff with sediment eroded from the hillslopes and channels. As sediment is incorporated into water, runoff can convert to debris flow. The conversion to debris flow may be observed at a position within a drainage network that appears to be controlled by threshold values of upslope contributing area and its gradient. At these locations, sufficient eroded material has been incorporated, relative to the volume of contributing surface runoff, to generate debris flows. Debris flows have also been generated from burned basins in response to increased runoff by water cascading over a steep, bedrock cliff, and incorporating material from readily erodible colluvium or channel bed. Post-fire debris flows have also been generated by infiltration-triggered landslide failures which then mobilize into debris flows. However, only 12% of documented cases exhibited this process. When they do occur, the landslide failures range in thickness from a few tens of centimeters to more than 6 m, and generally involve the soil and colluvium-mantled hillslopes. Surficial landslide failures in burned areas most frequently occur in response to prolonged periods of storm rainfall, or prolonged rainfall in combination with rapid snowmelt or rain-on-snow events.     

A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed,Morocco

Gully erosion is an important environmental issue with severe impacts. This study aimed to characterize gully erosion susceptibility and assess the capability of information value (InfVal) and frequency ratio (FR) models for its spatial prediction in Ourika watershed of the High Atlas region of Morocco. These two bivariate statistical methods have been used for gully erosion susceptibility mapping by comparing each data layer of causative factor to the existing gully distribution. Weights to the gully causative factors are assigned based on gully density. Gullies have been mapped through field surveys and Google earth high-resolution images. Lithofacies, land use, slope gradient, length-slope, aspect, stream power index, topographical wetness index and plan curvature were considered predisposing factors to gullying. The digitized gullies were randomly split into two parts. Sixty-five percent (65%) of the mapped gullies were randomly selected as training set to build gully susceptibility models, while the remaining 35% cases were used as validation set for the models’ validation. The results showed that barren and sparse vegetation lands and slope gradient above 50% were very susceptible to gully erosion. The ROC curve was used for testing the accuracy of the mentioned models. The analysis confirms that the FR model (AUC 80.61%) shows a better accuracy than InfVal model (AUC 52.07%). The performance of the gully erosion susceptibility map constructed by FR model is greater than that of the map produced by InfVal model. The findings proved that GIS-based bivariate statistical methods such as frequency ratio model could be successfully applied in gully susceptibility mapping in Morocco mountainous regions and in other similar environments. The produced susceptibility map represents a useful tool for sustainable planning, conservation and protection of land from gully processes.     

A comparison of logistic regression-based models of susceptibility to landslides in western Colorado,USA

The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %).