Empirical prediction of coseismic landslide dam formation

In this study we develop an empirical method to estimate the volume threshold for predicting coseismic landslide dam formation using landscape parameters obtained from digital elevation models (DEMs). We hypothesize that the potential runout and volume of landslides, together with river features, determine the likelihood of the formation of a landslide dam. To develop this method, a database was created by randomly selecting 140 damming and 200 non‐damming landslides from 501 landslide dams and > 60 000 landslides induced by the M_w 7.9 2008 Wenchuan earthquake in China. We used this database to parameterize empirical runout models by stepwise multivariate regression. We find that factors controlling landslide runout are landslide initiation volume, landslide type, internal relief (H) and the H/L ratio (between H and landslide horizontal distance to river, L). In order to obtain a first volume threshold for a landslide to reach a river, the runout regression equations were converted into inverse volume equations by taking the runout to be the distance to river. A second volume threshold above which a landslide is predicted to block a river was determined by the correlation between river width and landslide volume of the known damming landslides. The larger of these two thresholds was taken as the final damming threshold. This method was applied to several landslide types over a fine geographic grid of assumed initiation points in a selected catchment. The overall prediction accuracy was 97.4% and 86.0% for non‐damming and damming landslides, respectively. The model was further tested by predicting the damming landslides over the whole region, with promising results. We conclude that our method is robust and reliable for the Wenchuan event. In combination with pre‐event landslide susceptibility and frequency–size assessments, it can be used to predict likely damming locations of future coseismic landslides, thereby helping to plan emergency response. Copyright © 2014 John Wiley & Sons, Ltd.     

Hazard Assessment of Co-seismic Landslides Based on Information Value Method: A Case in 2018 MW6.6 Hokkaido Earthquake, Japan

An M_W6.6 earthquake occurred in eastern Hokkaido, Japan on September 6^th, 2018. Based on the pre-earthquake image from Google Earth and the post-earthquake image from high resolution (3 m) planet satellite, we manually interpret 9 293 coseismic landslides and select 7 influencing factors of seismic landslide, such as elevation, slope, slope direction, road distance, flow distance, peak ground acceleration (PGA) and lithology. Then, 9 293 landslide points are randomly divided into training samples and validation samples with a proportion of 7:3. In detail, the training sample has 6 505 landslide points and the validation sample has 2 788 landslide points. The hazard risk assessment of seismic landslide is conducted by using the information value method and the study area is further divided into five risk grades, including very low risk area, low risk area, moderate risk area high risk area and very high risk area. The results show that there are 7 576 landslides in high risk area and very high risk area, accounting for 81.52% of the total landslide number, and the landslide area is 22.93 km², accounting for 74.35% of the total area. The hazard zoning is in high accordance with the actual situation. The evaluation results are tested by using the curve of cumulative percentage of hazardous area and cumulative percentage of landslides number. The results show that the success rate of the information value method is 78.50% and the prediction rate is 78.43%. The evaluation results are satisfactory, indicating that the hazard risk assessment results based on information value method may provide scientific reference for landslide hazard risk assessment as well as the disaster prevention and mitigation in the study area.     

Landslide inventories and their statistical properties

Landslides are generally associated with a trigger, such as an earthquake, a rapid snowmelt or a large storm. The landslide event can include a single landslide or many thousands. The frequency–area (or volume) distribution of a landslide event quanti?es the number of landslides that occur at different sizes. We examine three well‐documented landslide events, from Italy, Guatemala and the USA, each with a different triggering mechanism, and ?nd that the landslide areas for all three are well approximated by the same three‐parameter inverse‐gamma distribution. For small landslide areas this distribution has an exponential ‘roll‐over’ and for medium and large landslide areas decays as a power‐law with exponent ‐2·40. One implication of this landslide distribution is that the mean area of landslides in the distribution is independent of the size of the event. We also introduce a landslide‐event magnitude scale m_L = log(N_LT), with N_LT the total number of landslides associated with a trigger. If a landslide‐event inventory is incomplete (i.e. smaller landslides are not included), the partial inventory can be compared with our landslide probability distribution, and the corresponding landslide‐event magnitude inferred. This technique can be applied to inventories of historical landslides, inferring the total number of landslides that occurred over geologic time, and how many of these have been erased by erosion, vegetation, and human activity. We have also considered three rockfall‐dominated inventories, and ?nd that the frequency–size distributions differ substantially from those associated with other landslide types. We suggest that our proposed frequency–size distribution for landslides (excluding rockfalls) will be useful in quantifying the severity of landslide events and the contribution of landslides to erosion. Copyright © 2004 John Wiley & Sons, Ltd.     

Controls of preferential orientation of earthquake- and rainfall-triggered landslides in Taiwan's orogenic mountain belt

Landsliding usually occurs on specific hillslope aspect, which may reflect the control of specific geo-environmental factors, triggering factors, or their interaction. To explore this notion, this study used island-wide landslide inventories of the Chi-Chi earthquake in 1999 (M_W = 7.6) and Typhoon Morakot in 2009 in Taiwan to investigate the preferential orientation of landslides and the controls of landslide triggers and geological settings. The results showed two patterns. The orientations of earthquake-triggered landslides were toward the aspect facing away from the epicenter in areas with peak ground acceleration (PGA) ≥ 0.6 g and landslide ratio ≥ 1%, suggesting that the orientations were controlled by seismic wave propagation. Rainfall-triggered landslides tended to occur on dip slopes, instead of the windward slopes, suggesting that geological settings were a more effective control of the mass wasting processes on hillslope scale than the rainfall condition. This study highlights the importance of the endogenic processes, namely seismic wave and geological settings, on the predesigned orientation of landslides triggered by either earthquake or rainfall, which can in turn improve our knowledge of landscape evolution and landslide prediction. © 2019 John Wiley & Sons, Ltd.     

An open-accessed inventory of landslides triggered by the MS 6.8 Luding earthquake,China on September 5, 2022

This study constructs a preliminary inventory of landslides triggered by the M_S 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images. The results show that this earthquake triggered at least 5 007 landslides, with a total landslide area of 17.36 ?km², of which the smallest landslide area is 65 ?m² and the largest landslide area reaches 120 747 ?m², with an average landslide area of about 3 500 ?m². The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault, and the area density and point density of landslides are 13.8%, and 35.73 ?km^?2 peaks with 2 ?km as the search radius. It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images. Based on the available post-earthquake remote sensing images, the number of landslides triggered by this earthquake is roughly estimated to be up to 10 000. This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region, and the prevention and control of landslide hazards in the seismic area.     

Application of a multi‐temporal,LiDAR‐derived,digital terrain model in a landslide‐volume estimation

Sediments produced by landslides are crucial in the sediment yield of a catchment, debris flow forecasting, and related hazard assessment. On a regional scale, however, it is difficult and time consuming to measure the volumes of such sediment. This paper uses a LiDAR‐derived digital terrain model (DTM) taken in 2005 and 2010 (at 2 m resolution) to accurately obtain landslide‐induced sediment volumes that resulted from a single catastrophic typhoon event in a heavily forested mountainous area of Taiwan. The landslides induced by Typhoon Morakot are mapped by comparison of 25 cm resolution aerial photographs taken before and after the typhoon in an 83.6 km² study area. Each landslide volume is calculated by subtraction of the 2005 DTM from the 2010 DTM, and the scaling relationship between landslide area and its volume are further regressed. The relationship between volume and area are also determined for all the disturbed areas (V_L = 0.452A_L^1.242) and for the crown areas of the landslides (V_L = 2.510A_L^1.206). The uncertainty in estimated volume caused by use of the LiDAR DTMs is discussed, and the error in absolute volume estimation for landslides with an area >10⁵ m² is within 20%. The volume–area relationship obtained in this study is also validated in 11 small to medium‐sized catchments located outside the study area, and there is good agreement between the calculation from DTMs and the regression formula. By comparison of debris volumes estimated in this study with previous work, it is found that a wider volume variation exists that is directly proportional to the landslide area, especially under a higher scaling exponent. Copyright © 2013 John Wiley & Sons, Ltd.     

Seismically-induced landslides in the Betic Cordillera (S Spain)

A database of seismically-induced landslides in the Betic Cordillera is presented. Data included were classified according to landslide typology. Most of them (≈80%) correspond to small size, disrupted landslides (including rock/earth falls and earth slides that disorganize as mass-movement progresses) and the remaining consist mainly of coherent landslides (slumps in soils and rock-slides). Deep seated induced landslides are uncommon in the study zone and have occurred only after the few events of large magnitude reported in the Cordillera. Data available show that events of small magnitude (M_w<5.0) can induce instabilities in the study zone for comparatively large distances (>10 km) when compared with available upper bound curves for maximum epicentral distances for seismic induced landslides, that concentrate along areas prone to landsliding, like river banks or slopes on soft materials, which points out the importance of the role of slope susceptibility on the occurrence of instabilities during earthquakes. Landslides in the database are then analyzed and a power-law relationship that relates earthquake size, measured as epicentral intensity (I_o), to maximum distance of induced landslide valid for the study zone is proposed. Although included data represent a clear partial and incomplete dataset, they show the landslide state of knowledge for this region.     

Analysing the Relationship Between Typhoon‐Triggered Landslides and Critical Rainfall Conditions

Many landslides are triggered by rainfall. Previous studies of the relationship between landslides and rainfall have concentrated on deriving minimum rainfall thresholds that are likely to trigger landslides. Though useful, these minimum thresholds derived from a log–log plot do not offer any measure of confidence in a landslide monitoring or warning system. This study presents a new and innovative method for incorporating rainfall into landslide modelling and prediction. The method involves three steps: compiling radar reflectivity data in a QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system during a typhoon (tropical hurricane) event, estimating rainfall from radar data and using rainfall intensity and rainfall duration as explanatory variables to develop a landslide logit model. Given the logit model, this paper discusses ways in which the model can be used for computing probabilities of landslide occurrence for a real‐time monitoring system or a warning system, and for delineating and mapping landslides. Copyright © 2007 John Wiley & Sons, Ltd.     

APPLICATION OF LOGISTIC REGRESSION MODEL FOR HAZARD ASSESSMENT OF EARTHQUAKE-TRIGGERED LANDSLIDES: A CASE STUDY OF 2017 JIUZHAIGOU(CHINA)MS7.0 EVENT

The M_S7.0 Jiuzhaigou earthquake in Sichuan Province of 8 August 2017 triggered a large number of landslides. A comprehensive and objective panorama of these landslides is of great significance for understanding the mechanism, intensity, spatial pattern and law of these coseismic landslides, recovery and reconstruction of earthquake affected area, as well as prevention and mitigation of landslide hazard. The main aim of this paper is to present the use of remote sensing images, GIS technology and Logistic Regression(LR)model for earthquake triggered landslide hazard mapping related to the 2017 Jiuzhaigou earthquake. On the basis of a scene post-earthquake Geoeye-1 satellite image(0.5m resolution), we delineated 4834 co-seismic landslides with an area of 9.63km². The ten factors were selected as the influencing factors for earthquake triggered landslide hazard mapping of Jiuzhaigou earthquake, including elevation, slope angle, aspect, horizontal distance to fault, vertical distance to fault, distance to epicenter, distance to roads, distance to rivers, TPI index, and lithology. Both landsliding and non-landsliding samples were needed for LR model. Centroids of the 4834 initial landslide polygons were extracted for landslide samples and the 4832 non-landslide points were randomly selected from the landslide-free area. All samples(4834 landslide sites and 4832 non-landslide sites)were randomly divided into the training set(6767 samples)and validation set(2899 samples). The logistic regression model was used to carry out the landslide hazard assessment of the Jiuzhaigou earthquake and the results show that the landslide hazard assessment map based on LR model is very consistent with the actual landslide distribution. The areas of Wuhuahai-Xiamo, Huohuahai and Inter Continental Hotel of Jiuzhai-Ruyiba are high hazard areas. In order to quantitatively evaluate the prediction results, the trained model calculated with the training set was evaluated by training set and validation set as the input of the model to get the output results of the two sets. The ROC curve was used to evaluate the accuracy of the model. The ROC curve for LR model was drawn and the AUC values were calculated. The evaluation result shows good prediction accuracy. The AUC values for the training and validation data set are 0.91 and 0.89, respectively. On the whole, more than 78.5% of the landslides in the study area are concentrated in the high and extremely high hazard zones. Landslide point density and landslide area density increase very rapidly as the level of hazard increases. This paper provides a scientific reference for earthquake landslides, disaster prevention and mitigation in the earthquake area.     

Analysis of historical landslide time series in the Emilia‐Romagna region,northern Italy

A catalogue of historical landslides, 1951–2002, for three provinces in the Emilia‐Romagna region of northern Italy is presented and its statistical properties studied. The catalogue consists of 2255 reported landslides and is based on historical archives and chronicles. We use two measures for the intensity of landsliding over time: (i) the number of reported landslides in a day (D_L) and (ii) the number of reported landslides in an event (S_event), where an event is one or more consecutive days with landsliding. From 1951–2002 in our study area there were 1057 days with 1 ≤ D_L ≤?45 landslides per day, and 596 events with 1 ≤ S_event ≤ 129 landslides per event. In the first set of analyses, we find that the probability density of landslide intensities in the time series are power‐law distributed over at least two‐orders of magnitude, with exponent of about ?2·0. Although our data is a proxy for landsliding built from newspaper reports, it is the first tentative evidence that the frequency‐size of triggered landslide events over time (not just the landslides in a given triggered event), like earthquakes, scale as a power‐law or other heavy‐tailed distributions. If confirmed, this could have important implications for risk assessment and erosion modelling in a given area. In our second set of analyses, we find that for short antecedent rainfall periods, the minimum amount of rainfall necessary to trigger landslides varies considerably with the intensity of the landsliding (D_L and S_event); whereas for long antecedent periods the magnitude is largely independent of the cumulative amount of rainfall, and the largest values of landslide intensity are always preceded by abundant rainfall. Further, the analysis of the rainfall trend suggests that the trigger of landslides in the study area is related to seasonal rainfall. Copyright © 2010 John Wiley & Sons, Ltd.     

基于不同核函数的2010年玉树地震滑坡空间预测模型研究

基于统计学习理论与地理信息系统(GIS)技术的地震滑坡灾害空间预测是一个重要的研究方向,其可以对相似地震条件下地震滑坡的发生区域进行预测.2010年4月14日07时49分(北京时间),青海省玉树县发生了M_w6.9级大地震,作者基于高分辨率遥感影像解译与现场调查验证的方法,圈定了2036处本次地震诱发滑坡,这些滑坡大概分布在一个面积为1455.3 km²的矩形区域内.本文以该矩形区域为研究区,以GIS与支持向量机(SVM)模型为基础,开展基于不同核函数的地震滑坡空间预测模型研究.应用GIS技术建立玉树地震滑坡灾害及相关滑坡影响因子空间数据库,选择高程、坡度、坡向、斜坡曲率、坡位、水系、地层岩性、断裂、公路、归一化植被指数(NDVI)、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为地震滑坡预测因子.以SVM模型为基础,基于线性核函数、多项式核函数、径向基核函数、S形核函数等4类核函数开展地震滑坡空间预测研究,分别建立了玉树地震滑坡危险性指数图、危险性分级图、预测结果图.4类核函数对应的模型正确率分别为79.87%,83.45%,84.16%,64.62%.基于不同的训练样本开展模型训练与讨论工作,表明径向基核函数是最适用于该地区的地震滑坡空间预测模型.本文为地震滑坡空间预测模型中核函数的科学选择提供了依据,也为地震区的滑坡防灾减灾工作提供了参考.     

Temporal correlations and clustering of landslides

This paper examines temporal correlations and temporal clustering of a proxy historical landslide time series, 2255 reported landslides 1951–2002, for an area in the Emilia‐Romagna Region, Italy. Landslide intensity is measured by the number of reported landslides in a day (D_L) and in an ‘event’ (S_event) of consecutive days with landsliding. The non‐zero values in both time series D_L and S_event are unequally spaced in time, and have heavy‐tailed frequency‐size distributions. To examine temporal correlations, we use power‐spectral analysis (Lomb periodogram) and surrogate data analysis, confronting our original D_L and S_event time series with 1000 shuffled (uncorrelated) versions. We conclude that the landslide intensity series D_L has strong temporal correlations and S_event has likely temporal correlations. To examine temporal clustering in D_L and S_event, we consider extremes over different landslide intensity thresholds. We first examine the statistical distribution of interextreme occurrence times, τ, and find Weibull distributions with parameter γ << 1·0 [D_L] and γ < 1·0 [S_event]; thus D_L and S_event each have temporal correlations, but S_event to a lesser degree. We next examine correlations between successive interextreme occurrence times, τ. Using autocorrelation analysis applied to τ, combined with surrogate data analysis, we find for D_L linear correlations in τ, but for S_event inconclusive results. However, using Kendall's rank correlation analysis we find for both D_L and S_event the series of τ are strongly correlated. Finally, we apply Fano Factor analysis, finding for both D_L and S_event the timings of extremes over a given threshold exhibit a fractal structure and are clustered in time. In this paper, we provide a framework for examining time series where the non‐zero values are strongly unequally spaced and heavy‐tailed, particularly important in the Earth Sciences due to their common occurrence, and find that landslide intensity time series exhibit temporal correlations and clustering. Many landslide models currently are designed under the assumption that landslides are uncorrelated in time, which we show is false. Copyright © 2010 John Wiley & Sons, Ltd.     

APPLICATION OF TWO SIMPLIFIED NEWMARK MODELS TO THE ASSESSMENT OF LANDSLIDES TRIGGERED BY THE 2008 WENCHUAN EARTHQUAKE

At present, with the wide application of the Newmark method, various Newmark empirical formulas with different ground motion parameters have been fitted by many researchers based on global strong-motion records. However, the existing study about the Wenchuan earthquake does not quantitatively evaluate the applicability of different Newmark models based on the actual landslides distribution. The aim of this paper is to present a comparison between observed landslides from the 2008 Wenchuan earthquake and predicted landslides using Newmark displacement method based on different ground motion parameters. The factor-of-safety map and critical acceleration(a_c)map in the study area are obtained by using the terrain data and geological data. The distribution of Arias intensity(I_a)and PGA in the study area is obtained by using the attenuation formulas of Arias intensity(I_a)and PGA, which is regressed by Wenchuan ground motion records. Based on the distribution of Arias intensity(I_a)and PGA parameters, we obtained the predicted locations of landslide using Newmark regression equations which are generated using global strong-motion records. The results shows that the assessment results can better reflect the macroscopic distribution characteristics of co-seismic landslides, most predicted landslide cells are distributed on the two sides of the Beichuan-Yingxiu Fault, especially the Pengguan complex rock mass in the hanging wall. The abilities to predict landslide occurrence of the two Newmark simplified models are different. On the whole, the evaluated result of simplified model based on parameter I_a is better than that based on PGA parameter. The GFC values obtained by the Newmark model of I_a and PGA parameters are 65.7% and 34.9%respectively. The evaluated result based on I_a can better reflect the macro distribution of coseismic landslides. The Ls_Pred value based on the Newmark model of parameter I_a is 26.5%, and the Ls_Pred value based on the Newmark model of PGA parameter is 10.3%. However the total area of predicted landslides accounts for 2.4% of the study area, which indicates that the predicted landslide cells are greater than the observed landslide cells. This reminds us that depending on the current input of shear strength and ground-motion parameters, we can only conduct landslide hazard assessment in macro areas, the ability to predict landslide can be improved using more accurate topographic data and input parameters.     

The Spatial Distribution and Attribute Parameter Statistics of Landslides Triggered by the May 12th, 2008, MW7.9 Wenchuan Earthquake

A complete landslide inventory and attribute database is the importantly fundamental for the study of the earthquake-induced landslide. Substantial landslides were triggered by the M_W7.9 Wenchuan earthquake on May 12^th, 2008. Google Earth images of pre- and post-earthquakes show that 52 194 co-seismic landslides were recognized and mapped, with a total landslides area of 1 021 km².Based on the statistics,we assigned all landslide parameters and established the co-seismic landslides database, which includes area, length, and width of landslides, elevation of the scarp top and foot edge, and the top and bottom elevations of each located slope. Finally, the spatial distribution and the above attribute parameters of landslides were analyzed. The results show that the spatial distribution of the co-seismic landslides is extremely uneven. The landslides that mainly occur in a rectangular area (a width of 30 km of the hanging wall of the Yingxiu-Beichuan fault and a length of 120 km between Yingxiu and Beichuan) are obviously controlled by surface rupture, terrain, and peak ground acceleration. Meanwhile, a large number of small landslides (individual landslide area less than 10 000 m²)contribute less to the total landslides area. The number of landslides larger than 10 000 m² accounts for 38.7% of the total number of co-seismic landslides, while the area of those landslides account for 88% of the total landslides area. The 52 194 co-seismic landslides are caused by bedrock collapse that usually consists of three parts:source area, transport area, and accumulation area. However, based on the area-volume power-law relationship, the resulting regional landslide volume may be much larger than the true landslide volume if the landslide volume is calculated using the influenced area from each landslide.     

M5'模型树算法和无人机航拍影像在震后崩塌滑坡分析中的应用——以鲁甸M_S6.5地震为例

利用高分辨率无人机航拍影像,结合基本地质资料,分析了影响2014年8月3日鲁甸M_S6.5地震震后崩塌滑坡分布的主要因素,使用M5'模型树算法建立了崩塌滑坡密度与其影响因子间的分段线性模型,并检验了该模型的预测性能。结果表明,地震诱发的崩塌滑坡分布受断层距、岩土体结构强度、坡度、植被条件等的影响,其中,断层距、岩土体结构强度及坡度等为主要影响因素;崩塌滑坡易发生在结构破裂区及坡度为38°~50°的区域,其分布密度随断层距的增加而减小;利用M5'模型树算法建立的模型体现出崩塌滑坡分布与其影响因子间复杂的非线性关系,模型检验结果显示,理论模型与实际关联函数间的相关系数达到0.88,因此,可利用该模型预测地震诱发的崩塌滑坡的分布。     

Teleseismic Lg of Semipalatinsk and Novaya Zemlya Nuclear Explosions Recorded by the GRF (Gräfenberg) Array: Comparison with Regional Lg (BRV) and their Potential for Accurate Yield Estimation

—?A comparison of regional and teleseismic log rms (root-mean-square) L g amplitude measurements have been made for 14 underground nuclear explosions from the East Kazakh test site recorded both by the BRV (Borovoye) station in Kazakhstan and the GRF (Gräfenberg) array in Germany. The log rms L g amplitudes observed at the BRV regional station at a distance of 690?km and at the teleseismic GRF array at a distance exceeding 4700?km show very similar relative values (standard deviation 0.048 magnitude units) for underground explosions of different sizes at the Shagan River test site. This result as well as the comparison of BRV rms L g magnitudes (which were calculated from the log rms amplitudes using an appropriate calibration) with magnitude determinations for P waves of global seismic networks (standard deviation 0.054 magnitude units) point to a high precision in estimating the relative source sizes of explosions from L g-based single station data. Similar results were also obtained by other investigators (Patton, 1988; Ringdal et?al., 1992) using L g data from different stations at different distances.¶Additionally, GRF log rms L g and P-coda amplitude measurements were made for a larger data set from Novaya Zemlya and East Kazakh explosions, which were supplemented with m _b (L g) amplitude measurements using a modified version of Nuttli's (1973, 1986a) method. From this test of the relative performance of the three different magnitude scales, it was found that the L g and P-coda based magnitudes performed equally well, whereas the modified Nuttli m _b (L g) magnitudes show greater scatter when compared to the worldwide m _b reference magnitudes. Whether this result indicates that the rms amplitude measurements are superior to the zero-to-peak amplitude measurement of a single cycle used for the modified Nuttli method, however, cannot be finally assessed, since the calculated m _b (L g) magnitudes are only preliminary until appropriate attenuation corrections are available for the specific path to GRF.     

Spatial distribution analysis of landslides triggered by the 2013-04-20 Lushan earthquake,China

The 2013-04-20 Lushan earthquake(seismic magnitude Ms 7.0 according to the State Seismological Bureau)induced a large number of landslides.In this study,spatial characteristics of landslides are developed by interpreting digital aerial photography data.Seven towns near the epicenter,with an area of about 11.11 km2,were severely affected by the earthquake,and 703 landslides were identified from April 24,2013 aerial photography data over an area of 1.185 km2.About 55.56% of the landslide area was less than 1000 m2,whereas about 3.23 % was more than 10,000 m2.Rock falls and shallow landslides were the most commonly observed types in the study area,and were primarily located in the center of Lushan County.Most landslide areas were widely distributed near river channels and along roads.Five main factors were chosen to study the distribution characteristics of landslides:elevation,slope gradients,fault,geologic unit and river system.The spatial distribution of coseismal landslides is studied statistically using both landslide point density(LPD),defined as the number of landslides(LS Number)per square kilometer,and landslide area density(LAD),interpreted as the percentage of landslides area affected by earthquake.The results show that both LPD and LAD have strong positive correlations with five main factors.Most landslides occurred in the gradient range of 40°-50° and an elevation range of 1.0-1.5 km above sea level.Statistical results also indicate that landslides were mainly formed in soft rocks such as mudstone and sandstone,and concentrated in IX intensity areas.     

Distribution of Landslides in Baoshan City, Yunnan Province, China

Using Google Earth software as a platform, this study has established an integrated database of both old and new landslides in Baoshan City, Yunnan Province, China, and analyzed their development characteristics together with distribution rules, respectively. Based on the results, a total of 2 427 landslides occurred in the study area, including 2 144 new landslides and 283 old landslides, with a total area of about 104.8 km². The new landslides are mostly in small-scales with an area less than 10 000 m², while the area of individual old landslide is mostly larger than 10 000 m². By analyzing the relationship between the two types of landslides and eight impact factors (i.e., elevation, slope angle, slope aspect, slope position, lithology, fault, regional Peak Ground Acceleration (PGA), and average annual rainfall), the different individual influencing factors, distribution regularities and mechanisms of the two types of landslides are revealed. In detail, the main influencing factors of new landslides are elevation, slope angle, slope aspect, slope position, lithology, regional PGA and average annual rainfall, while the influencing factors of old landslides are mainly elevation, slope angle, and lithology. This study provides basic data and support for landslide assessment and further disaster reduction in Baoshan City. Besides, it also provides new constraints in deeply understanding the effect of different topographic and geological conditions, historical earthquakes, rainfall and other factors on the occurrence mechanisms of both new landslides and old landslides.     

On the interpolation of volumetric water content in research catchments

Digital Soil Mapping (DSM) is widely used in the environmental sciences because of its accuracy and efficiency in producing soil maps compared to the traditional soil mapping. Numerous studies have investigated how the sampling density and the interpolation process of data points affect the prediction quality. While, the interpolation process is straight forward for primary attributes such as soil gravimetric water content (θ_g) and soil bulk density (ρ_b), the DSM of volumetric water content (θ_v), the product of θ_g by ρ_b, may either involve direct interpolations of θ_v (approach 1) or independent interpolation of ρ_b and θ_g data points and subsequent multiplication of ρ_b and θ_g maps (approach 2). The main objective of this study was to compare the accuracy of these two mapping approaches for θ_v. A 23 ha grassland catchment in KwaZulu-Natal, South Africa was selected for this study. A total of 317 data points were randomly selected and sampled during the dry season in the topsoil (0–0.05 m) for θ_g by ρ_b estimation. Data points were interpolated following approaches 1 and 2, and using inverse distance weighting with 3 or 12 neighboring points (IDW3; IDW12), regular spline with tension (RST) and ordinary kriging (OK). Based on an independent validation set of 70 data points, OK was the best interpolator for ρ_b (mean absolute error, MAE of 0.081 g cm⁻³), while θ_g was best estimated using IDW12 (MAE = 1.697%) and θ_v by IDW3 (MAE = 1.814%). It was found that approach 1 underestimated θ_v. Approach 2 tended to overestimate θ_v, but reduced the prediction bias by an average of 37% and only improved the prediction accuracy by 1.3% compared to approach 1. Such a great benefit of approach 2 (i.e., the subsequent multiplication of interpolated maps of primary variables) was unexpected considering that a higher sampling density (∼14 data point ha⁻¹ in the present study) tends to minimize the differences between interpolations techniques and approaches. In the context of much lower sampling densities, as generally encountered in environmental studies, one can thus expect approach 2 to yield significantly greater accuracy than approach 1. This approach 2 seems promising and can be further tested for DSM of other secondary variables.