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.     

细沟形成对坡面产流产沙过程的影响

为探明细沟形成条件下不同土壤坡面水沙变化特性,利用室内模拟降雨试验,分析了塿土和黄绵土在细沟发育及已有细沟条件下的水沙过程变化规律。结果表明,塿土产流快,易形成细沟,细沟发育有明显的规律性,在坡面上呈平行状分布,细沟密度与坡度密切相关,细沟存在会延长产流时间;黄绵土产流慢,较难形成细沟,细沟发育随机性大。细沟发育对产流过程没有明显影响,土壤入渗快慢是两种土壤产流过程存在差异的主要原因。细沟发育程度加剧,会使塿土含沙量的增加速度加快,达到稳定含沙量的时间提前,坡面已有细沟只对稳定含沙量有明显影响,细沟密度增加会使稳定含沙量提高;黄绵土含沙量的变化主要取决于细沟发育过程中的随机性,当细沟侵蚀以崩塌作用为主时,含沙量会出现急剧增加,即使雨强较小,也会产生严重的土壤侵蚀。     

利用REE示踪法研究坡面侵蚀过程

次降雨条件下坡面侵蚀形态的演变过程和细沟的发生发展过程是土壤侵蚀规律研究中的重点和难点。利用REE示踪法,采取沿坡面垂直分层布设的新的试验方法,通过室内模拟降雨试验,对坡面侵蚀演变过程进行了探索性研究。结果表明:降雨初期坡面侵蚀以面蚀为主,细沟出现后,坡面侵蚀加剧;随着降雨时间的延续,累积面蚀量和细沟侵蚀量逐渐增加,但后者的增加速率大于前者,面蚀占总侵蚀量的比率随降雨时间呈曲线形式逐渐递减,细沟侵蚀则逐渐增加;试验结束时细沟侵蚀量为面蚀量的2～4倍。本研究为定量区分和研究坡面侵蚀过程中面蚀和细沟侵蚀量,面蚀向细沟侵蚀的转变以及细沟侵蚀发生、发育提供了新的思路和解决途径。     

Application of a distributed erosion model for the assessment of spatial erosion patterns in the Lushi catchment,China

A grid-based erosion model is developed by integrating the distributed hydrological model, BTOPMC, with the modified USLE to estimate soil erosion and sediment outflow during single storms. The possible sheet, rill, channel erosion types, and sediment transport processes are considered within each grid under the model structure. Instead of representing the sheet erosion and rill erosion separately, the classic USLE method is modified to simulate the lumped sheet–rill erosion during storms. In the modification, the runoff ratio and a relevant correction coefficient are brought into the R-factor which improves the model’s applicability in predicting erosion during single storms. Instead of representing a grid with a unique erosion type, a channel component is assumed to exist in each grid, and its width varies with the upstream contributing area of the grid. This assumption avoids the problems that are caused by the difference between the channel widths in the upstream area and the downstream area if the grid is simply recognized as a channel grid. It also enables the model to be applicable in simulating soil erosion and sediment outflow from a large catchment. Through a case study in the Lushi catchment, China, the results show an overall satisfactory accuracy for the selected events. Moreover, by analyzing the spatial distribution of soil erosion or deposition, the erosion-prone areas are identified for the prioritization purpose.     

黄土坡面侵蚀产沙时空演变的REE示踪技术研究

室内交叉布设不同的稀土氧化物,通过人工模拟次降雨,在同一试验条件下,对坡面侵蚀沿顺坡方向和深度方向的演变过程同时展开研究。结果表明:REE示踪技术对定量研究土壤侵蚀具有较高的精度;降雨前期,片蚀与细沟侵蚀发育程度基本相当;后期细沟侵蚀占据坡面侵蚀的主导地位,其侵蚀平均加速度和平均侵蚀率分别是片蚀的15倍、9倍;试验结束,细沟侵蚀占据坡面总侵蚀的90%;本试验条件下,坡面下1/3区域为侵蚀活跃带。     

An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS

Remote sensing data and Geographical Information System (GIS) has been integrated with the weighted index overlay (WIO) method and E ₃₀ model for the identification and delineation of soil erosion susceptibility zones and the assessment of rate of soil erosion in the mountainous sub-watershed of River Manimala in Kerala (India). Soil erosion is identified as the one of the most serious environmental problems in the human altered mountainous environment. The reliability of estimated soil erosion susceptibility and soil loss is based on how accurately the different factors were estimated or prepared. In the present analysis, factors that are considered to be influence the soil erosion are: land use/land cover, NDVI, landform, drainage density, drainage frequency, lineament frequency, slope, and relative relief. By the WIO analysis, the area is divided into zones representing low (33.30%), moderate (33.70%), and high (33%) erosion proneness. The annual soil erosion rate of the area under investigation was calculated by carefully determining its various parameters and erosion for each of the pixels were estimated individually. The spatial pattern thus created for the area indicates that the average annual rate of soil erosion in the area was ranging from 0.04 mm yr⁻¹ to 61.80 mm yr⁻¹. The high soil erosion probability and maximum erosion rate was observed in areas with high terrain alteration, high relief and slopes with the intensity and duration of heavy precipitation during the monsoons.     

人工掏挖坡面侵蚀微地貌演化及其水力学特性分析

人工掏挖是黄土高原地区夏闲地翻耕时广为应用的耕作方式,为探明其坡面侵蚀过程,利用三维激光扫描仪及ArcGIS软件,阐明其在人工模拟间歇降雨下坡面微地貌、侵蚀产沙及水力学参数演变规律。结果表明:①坡面侵蚀历经溅蚀-片蚀、断续细沟、连续细沟3个阶段;降雨截止时细沟平面密度、平均沟深、最大沟长和最大沟深分别增至初始的1.42倍、2.24倍、15.5倍和2.43倍。②地表糙度随降雨历时推移从1.706呈近似线性趋势减小至1.488;累积降雨量达80 mm之前,径流量、含沙量随地表糙度的减小增加缓慢,但之后随地表糙度减小增加剧烈。③降雨过程中流速呈现波动增加趋势,水流由层流快速过渡至紊流,但始终保持为缓流,水流阻力呈波动下降,且形态阻力一直居于主导地位。人工掏挖耕作坡面在雨强1.5 mm/min、降雨量80 mm以内可起到蓄水保土作用,但在连续强降雨下也更易引起细沟侵蚀。     

A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China

Forest fire is known as an important natural hazard in many countries which causes financial damages and human losses; thus, it is necessary to investigate different aspects of this phenomenon. In this study, performance of four models of linear and quadratic discriminant analysis (LDA and QDA), frequency ratio (FR), and weights-of-evidence (WofE) was investigated to model forest fire susceptibility in the Yihuang area, China. For this purpose, firstly, a forest fire locations map was prepared implementing MODIS satellite images and field surveys. Then, it was classified into two groups including training (70%) and validation (30%) by a random algorithm. In addition, 13 forest fire effective factors were prepared and used such as slope degree, slope aspect, altitude, Topographic Wetness Index (TWI), plan curvature, land use, Normalized Difference Vegetation Index (NDVI), annual rainfall, distance from roads and rivers, wind effect, annual temperature, and soil texture. Using the training dataset and effective factors, LDA, QDA, FR, and WofE models were applied and forest fire susceptibility maps were prepared. Finally, area under the curve (AUC) of receiver operating characteristics (ROC) was implemented for investigating the performance of the models. The results depicted that WofE had the best performance (AUC = 82.2%), followed by FR (AUC = 80.9%), QDA (AUC = 78.3%), and LDA (AUC = 78%), respectively. The results of this study showed the high contribution of altitude, slope degree, and temperature. On the other hand, it was seen that slope aspect and soil had the lowest importance in forest fire susceptibility mapping. From the AUC results, it can be concluded that FR, WofE, LDA, and QDA had acceptable performance and could be used for forest fire susceptibility mapping at the regional scale.     

Landslide susceptibility assessment in the Anfu County,China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.     

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.     

The RUSLE erosion index as a proxy indicator for debris flow susceptibility

Debris flows represent dangerous occurrences in many parts of the world. Several disasters are documented due to this type of fast-moving landslides; therefore, natural-hazard assessment of debris flows is crucial for safety of life and property. To this aim, much current work is being directed toward developing geotechnical-hydraulic models for the evaluation of debris flow susceptibility. A common base for such current models is parameterization of background predisposing and triggering factors such as inherent characteristics of geo-materials, topography, landscape and vegetation cover, rainfall regime, human activities, etc. which influence the occurrence of these processes on slopes. The same factors are also taken into account in soil erosion prediction models. Consequently, it seems worth investigating the effectiveness of the soil erosion index as debris flows susceptibility indicator. To this aim, a logistic regression analysis was carried out between the erosion index assessed by means of the Revised Universal Soil Loss Equation (RUSLE) model and the inventory of debris flows that have occurred in an area in Sicily (Southern Italy). Model assumptions were verified and validated by means of a series of statistical tools. Different possible scenarios were also evaluated by considering hypothetical changes in soil erosion rate under different rain erosivity conditions. Notwithstanding the rough approximations in model data collection, the outcomes appear encouraging.     

A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models

The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning.     

Rill erosion: Exploring the relationship between experiments,modelling and field observations

Predicting rill erosion rates for a given discharge and slope minimally requires a model for rill hydraulics that allows the prediction of hydraulic parameters and a model for sediment detachment. Several relationships that describe rill hydraulics and/or sediment detachment within an eroding rill have been proposed and are incorporated into state of the art soil erosion models.In this paper a critical review of the theoretical concepts that are underpinning current rill flow and sediment detachment models is made in the light of recent experimental results. Approaches to define detachment–hydraulics relationship are generally based on developments in alluvial river hydraulics. However, experimental evidence to support the use of these concepts in models of rill erosion is scarce and recent experimental findings suggest that the basic assumptions used to model rill erosion are to some extent flawed. An analysis of empirically collected data on rill hydraulics conclusively shows that the empirical Manning equation does not hold for rill flow and should therefore not be used in rill erosion models. An empirical power law relationship relating velocity to discharges is much better in agreement with available experimental data, both for soils with and without rock fragments. In the absence of vegetation residue and/or other macroscopic, immobile elements such as rock fragments, total shear stress and unit length shear force can be used to predict soil detachment. The use of unit length shear force has the advantage that no information about rill geometry is necessary.The evidence for sediment load and rill flow detachment interaction is somewhat conflicting: the presence of a heavy sediment load appears to restrict rill flow detachment, but the exact form of the relationship between detachment rate and sediment load remains unclear. The effect of the presence of a sediment load on flow detachments under natural conditions is also limited by the nature of the detachment and transporting capacity relationships: on a rectilinear hillslope, transporting capacity increases much more rapidly with discharge than detachment capacity.We propose modifications to the theoretical formulations used in rill erosion models so that they are in better agreement with experimental evidence. Finally, we illustrate the potential of simplified models and conclude that the combination of empirical equations for flow detachment and rill hydraulics leads to results that are consistent with empirical data relating rill erosion rates to topography.     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.     

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

几种土壤的细沟侵蚀过程及其影响因素

通过间隔为11h的二次人工模拟降雨和冲蚀槽试验研究了10种土壤的侵蚀过程,发现在第2次降雨中大多数土壤出现了细沟侵蚀形式。采样微形态观察发现,在第1次降雨中形成结皮的土壤在第2次降雨中出现了细沟侵蚀;产流产沙观测发现所有出现细沟侵蚀的土壤在第1次降雨中径流含沙量逐渐减少,而第2次降雨中有一个先剧增后减少的过程。土壤理化分析及多元逐步回归分析结果表明,土壤颗粒组成、有机质含量、铁铝含量等因素对细沟侵蚀的影响表现不明显,而与土壤团聚体有关结构指标、细沟侵蚀、产流产沙速率及总量呈显著相关关系。分析表明,团聚体分散度、崩解速率与渗透系数之比两个指标能较好地预测细沟侵蚀发生的可能性,同时也能很好地预测侵蚀产沙量。     

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.     

An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin,southwestern Iran

Soil erosion by water is a significant problem in arid and semi-arid areas of large parts of Iran. Water erosion is one of the most effective phenomena that leads to decreasing soil productivity and pollution of water resources; especially, in the Mazayjan watershed in the southwest of Fars Province gully erosion contributes to the sediment dynamics in a significant way. Consequently, the intention of this research is to identify the different types of soil erosion processes acting in the area and to assess the process dynamics in an integrative way. Therefore, we applied GIS and satellite image analysis techniques to derive input information for the numeric models. For sheet and rill erosion the Unit Stream Power-based Erosion Deposition Model (USPED) was utilized. The spatial distribution of gully erosion was assessed using a statistical approach, which used three variables (stream power index, slope, and flow accumulation) to predict the spatial distribution of gullies in the study area. The eroded gully volumes were estimated for a 7-year period by fieldwork and Google Earth high-resolution images. Finally the gully retreat rates were integrated into the USPED model. The results show that the integration of the SPI approach to quantify gully erosion with the USPED model is a suitable method to qualitatively and quantitatively assess water erosion processes. The application of GIS and stochastic model approaches to spatialize the USPED model input yields valuable results for the prediction of soil erosion in the Mazayjan catchment. The results of this research help to develop an appropriate management of soil and water resources in the southwestern parts of Iran.     

Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand

A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved.