Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

Rainfall-induced landslides (RILs) have been a source of social and economic disruption in the mountainous Baguio area in northern Philippines. Prolonged heavy rainfall usually happens during tropical cyclone and southwest monsoon activity. A pragmatic approach to RIL mitigation is to develop rainfall-based early warning. We implemented a modified regression method to derive the empirical minimum intensity (I)–duration (D) threshold I = 6.46 D ^?0.28 and a normalized ID threshold NI = 0.002 D ^?0.28 for rainfall duration ranging between 24 and 264 h. Using a separate data set to evaluate the applicability of the threshold, 93% of the landslide-triggering rainfall events fell above the derived threshold. RILs also occurred when 24-h rainfall was 0.02–28% of the mean annual precipitation or after accumulating at least 500 mm of rainfall from the onset of the rainy season. The thresholds may be further refined as more landslide data become available in the future.     

Geometrical characteristics of earthquake-induced landslides and correlations with control factors: a case study of the 2013 Minxian,Gansu, China, <Emphasis Type="Italic">Mw</Emphasis> 5.9 event

Geometric parameters are useful for characterizing earthquake-triggered landslides. This paper presents a detailed statistical analysis on this issue using the landslide inventory of the 2013, Minxian, China Mw 5.9 earthquake. Based on GIS software and a 5-m resolution DEM, geometric parameters of 635 coseismic landslides (with areas larger than 500 m²) were obtained, including height, length, width, reach angle (arc tangent of the height-length ratio), and aspect ratio (length-width ratio). The fitting relationship of height and length from these data is H = 0.6164L + 0.4589, with an average reach angle of 31.65°. The landslide aspect ratios concentrate in the range of 1.4～2.6, with an average of 2.11. According to the plane geometric shapes and aspect ratios, the landslides are classified into four categories: transverse landslide (LA1, L/W ≤ 0.8), isometric landslide (LA2, 0.8 < L/W ≤ 1.2), longitudinal landslide (LA3, 1.2 < L/W ≤ 3), and elongated landslide (LA4, L/W > 3). Statistics of these four types of landslides versus ten classified control factors (elevation, slope angle, slope aspect, curvature, slope position, distance to drainages, lithology, seismic intensity, peak ground acceleration, and distance to seismogenic fault) are used to examine their possible correlations and the landslide-prone areas, which would be helpful to the landslide disaster mitigation in the affected area.     

A new region-based preparatory factor for landslide susceptibility models: the total flux

This paper presents a new region-based preparatory factor, total flux (TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides.     

Field monitoring of groundwater responses to heavy rainfalls and the early warning of the Kualiangzi landslide in Sichuan Basin,southwestern China

The Kualiangzi landslide was triggered by heavy rainfalls in the “red beds” area of Sichuan Basin in southwestern China. Differing from other bedrock landslides, the movement of the Kualiangzi landslide was controlled by the subvertical cracks and a subhorizontal bedding plane (dip angle < 10°). The ingress of rainwater in the cracks formed a unique groundwater environment in the slope. Field measurement for rainfall, groundwater movement, and slope displacement has been made for the Kualiangzi landslide since 2013. The field monitoring system consists of two rainfall gauges, seven piezometers, five water-level gauges, and two GPS data loggers. The equipments are embedded near a longitudinal section of the landslide, where severe deformation has been observed in the past 3 years. The groundwater responses to four heavy rainfall events were analyzed between June 16 and July 24 in 2013 coincided with the flood season in Sichuan. Results showed that both of the water level and the pore-water pressure increased after each rainfall event with delay in the response time with respect to the precipitation. The maximum time lag reached 35 h occurred in a heavy rainfall event with cumulative precipitation of 127 mm; such lag effect was significantly weakened in the subsequent heavy rainfall events. In each presented rainfall event, longer infiltration period in the bedrock in the upper slope increased the response time of groundwater, compared to that of in the gravels in the lower slope. A translational landslide conceptual model was built for the Kualiangzi landslide, and the time lag was attributed to the gradual formation of the uplift pressure on the slip surface and the softening of soils at the slip surface. Another important observation is the effect on the slope movement which was caused by the water level (H _w) in the transverse tension trough developed at the rear edge of the landslide. Significant negative correlation was found for H _w and the slope stability factor (F _s), in particular for the last two heavy rainfall events, of which the drastic increase of water level caused significant deterioration in the slope stability. The rapid drop (Δ?=?22.5 kPa) of pore-water pressure in the deep bedrock within 1 h and the large increase (Δ?=?87.3 mm) of surficial displacement were both monitored in the same period. In the end, a four-level early warning system is established through utilizing H _w and the displacement rate D _r as the warning indicators. When the large deformation occurred in flood season, the habitants at the leading edge of the landslide can be evacuated in time.     

Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia

Bogotá is located in the central Andean region of Colombia, which is frequently affected by landslide processes. These processes are mostly triggered during the rainy season in the city. This fact remarks the importance of determining what rain-derived parameters (e.g. intensity, antecedent rain, daily rain) are better related with the occurrence of landslides. For this purpose, the linear discriminant analysis (LDA), a technique derived from multivariate statistics, was used. The application of this type of analysis led to obtain simple mathematical functions that represent the probability of occurrence of landslides in Bogotá. The functions also allow to identify the most relevant variables derived from records of rainfall linked to the generation of landslides. A proof of concept using the proposed methodology was done using historic rainfall data from a 9-km² area of homogenous climatology and geomorphology in the south part of Bogotá. Landslides needed to be grouped for the LDA. Each one of these grouping categories represents landslides that occurred in similar geomorphologic conditions. Another set of events with no landslides was generated synthetically. Results of the proof of concept show that rainfall parameters such as normalized rainfall intensity I _MAP, normalized daily rainfall R _MAP and rainy-days normal RDN have the best statistical correlation with the landslides observed in the zone of analysis.     

Developing an algorithm for automated geometric analysis and classification of landslides incorporating LiDAR-derived DEM

Amending landslides inventories is immensely important to policy and decision makers alike. Sliding creates geometric shapes on the Earth’s surface. This study presents the utilization of LiDAR high-resolution digital elevation model (DEM) in the Alborz Mountains, Iran to refurbish the existing landslide inventory dataset by implementing the proposed algorithm. The method consists of the automated derivation of landslide geometry (length, width, and area) followed by classification of landslide types considering length, width and flow direction. This study has used the trapezoidal rule for numerical integration to develop the proposed algorithm. The landslides were then classified into four types (very long, long, very wide, and wide) based on slope, length, and width. This geometric classification of landslides is based on the geographical coordinates, slope angle (θ), length (L), and width (W), and further failure flow direction. A total of 95 landslides were updated from the existing inventory database. The proposed method was verified and evaluated by field observations; and 14 samples were tested to determine the relative error. The results demonstrated that the mean percentage relative error is 0.496% in length and width and 0.008% in area, related to the GIS analysis. The accuracy performance of determining the landslide’s type is 92%. The purposefulness of this algorithm is to increase the accuracy performance of landslides geometry analysis and automated measurements associated with the usual GIS platforms such as ArcGIS.     

Rainfall-triggered landslides in an unsaturated soil: a laboratory flume study

Extreme and/or prolonged rainfall events frequently cause landslides in many parts of the world. In this study, infiltration of rainfall into an unsaturated soil slope and triggering of landslides is studied through laboratory model (flume) tests, with the goal of obtaining the triggering rainfall intensity–duration (I–D) threshold. Flume tests with fine sand at two different relative densities (34 and 48%) and at slope angle of 56.5° are prepared, and rainfall (intensity in the range of 18 to 64 mm/h) is applied via a mist sprinkler system to trigger landslides. Soil water characteristic curve and hydraulic conductivity function of the fine sand are also presented. In flume tests, suction in the soil is measured with tensiometers, the progress of wetting front with time and deformations in the soil are also measured. Some of the findings of this study are: for the fine sand used in this study (a) the failure mechanism is infinite-slope type (mostly translational), and the failure surface is generally coincident with the wetting front or is in its vicinity, (b) the deformations leading to a landslide occurred abruptly, (c) both relatively high-intensity–short-duration rainfalls and relatively low-intensity–long duration rainfalls triggered landslides, (d) the shape of the I–D threshold is demonstrated to be a bilinear relation in log intensity–log duration plot, (e) below a certain rainfall intensity landslides are not triggered, (f) the effect of relative density of the soil on the I–D threshold is demonstrated by physical laboratory tests (as the relative density of the soil increases, the triggering rainfall intensity–duration threshold moves to larger rainfall events). The results of this study could be useful for accurate numerical modeling of rainfall-triggered landslides.     

Evaluating the susceptibility of landslide landforms in Japan using slope stability analysis: a case study of the 2016 Kumamoto earthquake

This study analyzed 267 landslide landforms (LLs) in the Kumamoto area of Japan from the database of about 0.4 million LLs for the whole of Japan identified from aerial photos by the National Research Institute for Earth Science and Disaster Resilience of Japan (NIED). Each LL in the inventory is composed of a scarp and a moving mass. Since landslides are prone to reactivation, it is important to evaluate the sliding-recurrence susceptibility of LLs. One possible approach to evaluate the susceptibility of LLs is slope stability analysis. A previous study found a good correlation (R ² = 0.99) between the safety factor (F _s ) and slope angle (α) of F _s  = 17.3α ^?0.843. We applied the equation to the analysis of F _s for 267 LLs in the area affected by the 2016 Kumamoto earthquake (M _j  = 7.3). The F _s was calculated for the following three cases of failure: scarps only, moving mass only, and scarps and moving mass together. Verification with the 2016 Kumamoto earthquake event shows that the most appropriate method for the evaluation of LLs is to consider the failure of scarps and moving mass together. In addition, by analyzing the relationship between the factors of slope of entire landslide and slope of scarp for LLs and comparing the results with the Aso-ohashi landslide, the largest landslide caused by the 2016 Kumamoto earthquake, we also found that morphometric analysis of LLs is useful for forecasting the travel distance of future landslides.     

Exploiting historical rainfall and landslide data in a spatial database for the derivation of critical rainfall thresholds

Critical rainfall thresholds for landslides are powerful tools for preventing landslide hazard. The thresholds are commonly estimated empirically starting from rainfall events that triggered landslides in the past. The creation of the appropriate rainfall–landslide database is one of the main efforts in this approach. In fact, an accurate agreement between the landslide and rainfall information, in terms of location and timing, is essential in order to correctly estimate the rainfall–landslide relationships. A further issue is taking into account the average moisture conditions prior the triggering event, which reasonably may be crucial in determining the sufficient amount of precipitation. In this context, the aim of this paper is exploiting historical landslide and rainfall data in a spatial database for the derivation of critical rainfall thresholds for landslide occurrence in Sicily, southern Italy. The hourly rainfall events that caused landslides occurred in the twentieth century were specifically identified and reconstructed. A procedure was proposed to automatically convert rain guages charts recorded on paper tape into digital format and then to provide the cumulative rainfall hyetograph in digital format. This procedure is based on a segmentation followed by signal recognition techniques which allow to digitalize and to recognize the hyetograph automatically. The role of rainfall prior to the landslide events was taken into account by including in the analysis the rainfall occurred 5, 15 and 30 days before each landslide. Finally, cumulated rainfall duration thresholds for different exceedance probability levels were determined. The obtained thresholds resulted in agreement with the regional curves proposed by other authors for the same area; antecedent rainfall turned out to be particularly important in triggering landslides.     

Shear wave velocity imaging of landslide debris deposited on an erodible bed and possible movement mechanism for a loess landslide in Jingyang,Xi’an,China

The South Jingyang Plateau, with a total area of 70 km², is located in Shaanxi Province, China. Since 1976, more than 50 landslides of different types have occurred repeatedly on the edge slopes of the plateau due to the start of diversion irrigation on the plateau, resulting in great loss of lives and property. To better understand the initiation and movement mechanisms of these loess landslides, we surveyed them and carried out a detailed investigation of a large landslide in the Xihetan area. Our field survey results revealed that although most of these landslides had a long runout with high mobility, most of the landslide materials originating from the edge slopes may have been in an unsaturated state when the landslide occurred. This suggests that the materials at the toe of the edge slope as well as on the travel path along the river terrace might have played a key role in landslide movement. To examine how the materials on the travel path were involved in the landsliding, we used a multichannel surface wave technique and surveyed shear wave velocity (V _s ) profiles of the landslide deposits. We also examined the internal geometry of the deposits that outcropped on the right-side slope of the landslide foot. The longitudinal profile of V _s along the direction of movement showed that terrace deposits near the toe of the edge slope may have been sheared upward, indicating that at the toe, the surface of rupture might be located inside the terrace deposits. The V _s contours showed an A-shaped fold within the landslide deposits in the middle part of the travel path and became greater in the most distal toe part. The V _s profile across the deposits showed a U-shaped belt, in which the soil layers have smaller V _s . This belt may be the boundary between the sliding landslide debris and terrace deposits. The observed internal geometry of the landslide deposits indicates that a sliding surface developed within the sandy layer underlying the gravel layer. Therefore, we inferred that after failure, the displaced landslide materials overrode and sheared the terrace deposits along its main sliding direction, resulting in the formation of thrust folds within the terrace deposits, and greater V _s on the distal toe part of the landslide.     

A novel fuzzy <Emphasis Type="Italic">K</Emphasis>-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.     

Landslide dam formation susceptibility analysis based on geomorphic features

Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H _L/W _R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.     

Rainfall-induced landslide in the active frontal fold–thrust belt of Northwestern Himalaya,Jammu: dynamics inferred by geological evidences and Ground Penetrating Radar

Landslides are the most established geological hazards in the frontal fold–thrust belt of Northwestern Himalaya comprising of Siwaliks and Murree strata. The continuous rainfall from 2 to 6 September, 2014 caused a massive landslide at village Sadal in Udhampur district of Jammu and Kashmir state. The landslide occurred in the early morning of September 6, 2014, destroying entire Sadal habitation comprising 45 houses, and killing 41 people and more than 500 domestic animals. Google earth images of pre and post-landslide events along with the field measurements show the kinematics of upper and lower parts of the slide. Horizontal and vertical components of displacement and mode of failure suggest the landslide as of complex nature. The shallow subsurface geophysical imaging through Ground Penetrating Radar (GPR) survey shows the failure plane composed of friable mudstone bed underlain by massive mudstone and overlain by cross-bedded sandstone. The depth of debris material above the failure plane ranges from 6 m at Site S1a-b to 10 m at Site-S2b and 20 m at Site S3a. The velocity analysis of Site-3 shows four thick layers represented from bottom to surface by L1—sandstone (V?=?0.16 m/ns, travel time?=?356.36 ns), L2—mudstone (V?=?0.17 m/ns, travel time?=?288.48 ns), L3—massive mudstone (V?=?0.19 m/ns, travel time 220.68 ns), and L4—cross-laminated sandstone (V?=?0.20 m/ns, travel time?=?77.58 ns) overlaying the failure plane. The study shows the landslide occur along the western limb of a fold identified during the present work. We mapped an old landslide on the same limb which shows 5–6 m-thick subsurface debris material with thick rock fragments involved in the landslide process. The detailed geological and geophysical investigations suggest that both the landslides were triggered by extreme rain fall events.     

Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region,Garhwal Himalayas,India

Majority of landslides in the Indian sub-continent are triggered by rainfall. Several attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity-duration and antecedent rainfall models on global, regional and local scales for the occurrence of landslides. However, in the context of the Indian Himalayas, the rainfall thresholds for landslide occurrences are not yet understood fully. Neither on regional scale nor on local scale, establishing such rainfall thresholds for landslide occurrences in Indian Himalayas has yet been attempted. This paper presents an attempt towards deriving local rainfall thresholds for landslides based on daily rainfall data in and around Chamoli-Joshimath region of the Garhwal Himalayas, India. Around 128 landslides taken place in last 4 years from 2009 to 2012 have been studied to derive rainfall thresholds. Out of 128 landslides, however, rainfall events pertaining to 81 landslides were analysed to yield an empirical intensity–duration threshold for landslide occurrences. The rainfall threshold relationship fitted to the lower boundary of the landslide triggering rainfall events is I?=?1.82 D ^?0.23 (I?=?rainfall intensity in millimeters per hour and D?=?duration in hours). It is revealed that for rainfall events of shorter duration (≤24 h) with a rainfall intensity of 0.87 mm/h, the risk of landslide occurrence in this part of the terrain is expected to be high. Also, the role of antecedent rainfall in causing landslides was analysed by considering daily rainfall at failure and different period cumulative rainfall prior to failure considering all 128 landslides. It is observed that a minimum 10-day antecedent rainfall of 55 mm and a 20-day antecedent rainfall of 185 mm are required for the initiation of landslides in this area. These rainfall thresholds presented in this paper may be improved with the hourly rainfall data vis-à-vis landslide occurrences and also data of later years. However, these thresholds may be used in landslide warning systems for this particular region of the Garhwal Himalayas to guide the traffic and provide safety to the tourists travelling along this pilgrim route during monsoon seasons.     

Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (P) and runoff (Q) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed P and Q datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (λ) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median λ values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to λ and it improved consistently as λ changed from 0.2 to 0.03.     

The formation and development of debris flows in large watersheds after the 2008 Wenchuan Earthquake

The Wenchuan earthquake has caused abundance of loose materials supplies for debris flows. Many debris flows have occurred in watersheds in area beyond 20 km², presenting characteristics differing from those in small watersheds. The debris flows yearly frequency decreases exponentially, and the average debris flow magnitude increases linearly with watershed size. The rainfall thresholds for debris flows in large watersheds were expressed as I?=?14.7 D ^?0.79 (2 h?<?D?<?56 h), which is considerably higher than those in small watersheds as I?=?4.4 D ^?0.70 (2 h?<?D?<?37 h). A case study is conducted in Ergou, 39.4 km² in area, to illustrate the formation and development processes of debris flows in large watersheds. A debris flow develops in a large watershed only when the rainfall was high enough to trigger the wide-spread failures and erosions on slope and realize the confluence in the watershed. The debris flow was supplied by the widely distributed failures dominated by rill erosions (14 in 22 sources in this case). The intermittent supplying increased the size and duration of debris flow. While the landslide dam failures provided most amounts for debris flows (57 % of the total amount), and amplified the discharge suddenly. During these processes, the debris flow velocity and density increased as well. The similar processes were observed in other large watersheds, indicating this case is representative.     

Quantifying land use influences on event-based flow frequency,timing, magnitude,and rate of change in an urbanizing watershed of the central USA

There is a need for research that advances understanding of flow alterations in contemporary watersheds where natural and anthropogenic interactions can confound mitigation efforts. Event-based flow frequency, timing, magnitude, and rate of change were quantified at five-site nested gauging sites in a representative mixed-land-use watershed of the central USA. Statistically independent storms were paired by site (n = 111 × 5 sites) to test for significant differences in event-based rainfall and flow response variables (n = 17) between gauging sites. Increased frequency of small peak flow events (i.e., 64 more events less than 4.0 m³ s^?1) was observed at the rural–urban interface of the watershed. Differences in flow response were apparent during drier periods when small rainfall events resulted in increased flow response at urban sites in the lower reaches. Relationships between rainfall and peak flow were stronger with decreased pasture/crop land use and increased urban land use by approximately 20%. Event-based total rainfall explained 40–68% of the variance in peak flow (p < 0.001). Coefficients of determination (r²) were negatively correlated with pasture/crop land use (r² = 0.92; p = 0.007; n = 5) and positively correlated with urban land use (r² = 0.90; p = 0.008; n = 5). Significant differences in flow metrics were observed between rural and urban sites (p < 0.05; n = 111) that were not explained by differences in rainfall variables and drainage area. An urban influence on flow timing was observed using median time lag to peak centroid and time of maximum precipitation to peak flow. Results highlight the need to establish manageable flow targets in rapidly urbanizing mixed-land-use watersheds.     

<Emphasis Type="Italic">Landslides</Emphasis>: a review of achievements in the first 5 years (2004–2009)

The international journal Landslides was initiated in April 2004. It is the core project (IPL-C100) of the International Programme on Landslides, a joint initiative of the International Consortium on Landslides and the United Nations and other global organizations. The aims of Landslides are to promote landslide sciences, technology, and capacity building and strengthen global cooperation for landslide risk reduction within the United Nations International Strategy for Disaster Reduction. This paper presents an analysis of the first 5 years of Landslides, the study methods employed, the types and major causes of landslides, the number of different contributors per country, and the “times cited” per issue and most frequently cited papers and briefly discusses some of these. Strategies for future development of the journal involve obtaining input and suggestion from researchers and readers worldwide.     

Coseismic landslides triggered by the 8th August 2017 <Emphasis Type="Italic">M</Emphasis><Subscript>s</Subscript> 7.0 Jiuzhaigou earthquake (Sichuan,China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification

On 8th August 2017, a magnitude M_s 7.0 earthquake struck the County of Jiuzhaigou, in Sichuan Province, China. It was the third M_s ≥?7.0 earthquake in the Longmenshan area in the last decade, after the 2008 M_s 8.0 Wenchuan earthquake and the 2013 M_s 7.0 Lushan earthquake. The event did not produce any evident surface rupture but triggered significant mass wasting. Based on a large set of pre- and post-earthquake high-resolution satellite images (SPOT-5, Gaofen-1 and Gaofen-2) as well as on 0.2-m-resolution UAV photographs, a polygon-based interpretation of the coseismic landslides was carried out. In total, 1883 landslides were identified, covering an area of 8.11 km², with an estimated total volume in the order of 25–30?×?10⁶ m³. The total landslide area was lower than that produced by other earthquakes of similar magnitude with strike-slip motion, possibly because of the limited surface rupture. The spatial distribution of the landslides was correlated statistically to a number of seismic, terrain and geological factors, to evaluate the landslide susceptibility at regional scale and to identify the most typical characteristics of the coseismic failures. The landslides, mainly small-scale rockfalls and rock/debris slides, occurred mostly along two NE-SW-oriented valleys near the epicentre. Comparatively, high landslide density was found at locations where the landform evolves from upper, broad valleys to lower, deep-cut gorges. The spatial distribution of the coseismic landslides did not seem correlated to the location of any known active faults. On the contrary, it revealed that a previously-unknown blind fault segment—which is possibly the north-western extension of the Huya fault—is the plausible seismogenic fault. This finding is consistent with what hypothesised on the basis of field observations and ground displacements.     

Rheological properties of clayey soils originating from flow-like landslides

Flow-like landslides in clayey soils represent serious threats for populations and infrastructures and have been the subject of numerous studies in the past decade. However, despite the rising need for landslide mitigation with growing urbanization, the transient mechanisms involved in the solid-fluid transition are still poorly understood. One way of characterizing the solid-fluid transition is to carry out rheometrical tests on clayey soil samples to assess the evolution of viscosity with the shear stress. In this study, we carried out geotechnical and rheometrical tests on clayey samples collected from six flow-like landslides in order to assess if these clayey soils exhibit similar characteristics when they fluidize (solid-fluid transition). The results show that (1) all tested soils except one exhibit a yield-stress fluid behavior that can be associated with a bifurcation in viscosity (described by the critical shear rate \( \dot{\gamma_c} \)) and in shear modulus G; (2) the larger the amplitude of the viscosity bifurcation, the larger the associated drop in G; and (3) the water content (w) deviation from the Atterberg liquid limit (LL) seem a key parameter controlling a common mechanical behavior of these soils at the solid-fluid transition. We propose exponential laws describing the evolution of the critical shear stress τ_c, the critical shear rate \( \dot{\gamma_c} \), and the shear modulus G as a function of the deviation w-LL.