A spatiotemporal probabilistic modelling of storm‐induced shallow landsliding using aerial photographs and logistic regression

Landslides constitute one of the major natural hazards that could cause significant losses of life and property. Mapping or delineating areas prone to landsliding is therefore essential for land‐use activities and management decision making in hilly or mountainous regions. A landslide hazard map can be constructed by a qualitative combination of maps of site conditions, including geology, topography and geomorphology, by statistical methods through correlating landslide occurrence with geologic and geomorphic factors, or by using safety factors from stability analysis. A landslide hazard map should provide information on both the spatial and temporal probabilities of landsliding in a certain area. However, most previous studies have focused on susceptibility mapping, rather than on hazard mapping in a spatiotemporal context. This study aims at developing a predictive model, based on both quasi‐static and dynamic variables, to determine the probability of landsliding in terms of space and time. The study area selected is about 13 km² in North Lantau, Hong Kong. The source areas of the landslides caused by the rainstorms of 18 July 1992 and 4–5 November 1993 were interpreted from multi‐temporal aerial photographs. Landslide data, lithology, digital elevation model data, land cover, and rainfall data were digitized into a geographic information system database. A logistic regression model was developed using lithology, slope gradient, slope aspect, elevation, slope shape, land cover, and rolling 24 h rainfall as independent variables, since the dependent variable could be expressed in a dichotomous way. This model achieved an overall accuracy of 87·2%, with 89·5% of landslide grid cells correctly classified and found to be performing satisfactorily. The model was then applied to rainfalls of a variety of periods of return, to predict the probability of landsliding on natural slopes in space and time. It is observed that the modelling techniques described here are useful for predicting the spatiotemporal probability of landsliding and can be used by land‐use planners to develop effective management strategies. Copyright © 2003 John Wiley & Sons, Ltd.     

Prediction of rainfall‐induced shallow landslides in the Loess Plateau,Yan'an,China, using the TRIGRS model

In this work, a transient rainfall infiltration and grid‐based regional slope‐stability model (TRIGRS) was implemented in a case study of Yan'an City, Northwest China. In this area, widespread shallow landslides were triggered by the 12 July 2013 exceptional rainstorm event. A high‐resolution DEM, soil parameters from in‐situ and laboratory measurements, water table depths, the maximum depth of precipitation infiltration and rain‐gauge‐corrected precipitation of the event, were used as inputs in the TRIGRS model. Shallow landslides triggered on the same day were used to evaluate the modeling results. The summarized results are as follows: (i) The characteristics and distribution of thirty‐five shallow landslides triggered by the 12 July 2013 rainfall event were identified in the study area and all were classified as shallow landslides with the maximum depth, area and volume less than 3 m, 200 m² and 1000 m³, respectively, (ii) Four intermediate factor of safety (F_S) maps were generated using the TRIGRS model to represent the scenarios 6, 12, 18 and 24 hours after the storm event. The area with F_S < 1 increased with the rainfall duration. The percentage of the area with F_S < 1 was 0.2%, 3.3%, 3.8% and 5.1% for the four stages, respectively. Twenty‐four hours after the rainstorm, TRIGRS predicted that 1255 grid cells failed, which is consistent with the field data. (iii) TRIGRS generated more satisfactory results at a given precipitation threshold than SINMAP, which is ideal for landslide hazard zoning for land‐use planning at the regional scale. Comparison results showed that TRIGRS is more useful for landslide prediction for a certain precipitation threshold, also in the regional scale. (iv) Analysis of the responses of loess slope prone to slope failure after different precipitation scenarios revealed that loess slopes are particularly sensitive to extended periods of heavy precipitation. Copyright © 2016 John Wiley & Sons, Ltd.     

GIS‐based regional landslide susceptibility mapping: a case study in southern California

Landslides threaten lives and property throughout the United States, causing in excess of $2 billion in damages and 25–50 deaths annually. In regions subjected to urban expansion caused by population growth and/or increased storm intensities caused by changing climate patterns, the economic and society costs of landslides will continue to rise. Using a geographic information system (GIS), this paper develops and implements a multivariate statistical approach for mapping landslide susceptibility. The presented susceptibility maps are intended to help in the design of hazard mitigation and land development policies at regional scales. The paper presents (a) a GIS‐based multivariate statistical approach for mapping landslide susceptibility, (b) several dimensionless landslide susceptibility indexes developed to quantify and weight the influence of individual categories for given potential risk factors on landslides and (c) a case study in southern California, which uses 11 111 seismic landslide scars collected from previous efforts and 5389 landslide scars newly digitized from local geologic maps. In the case study, seven potential risk factors were selected to map landslide susceptibility. Ground slope and event precipitation were the most important factors, followed by land cover, surface curvature, proximity to fault, elevation and proximity to coastline. The developed landslide susceptibility maps show that areas classified as having high or very high susceptibilities contained 71% of the digitized landslide scars and 90% of the seismic landslide scars while only occupying 26% of the total study area. These areas mostly have ground slopes higher than 46% and 2‐year, 6‐hour precipitation greater than 51 mm. Only 12% of digitized landslides and less than 1% of recorded seismic landslides were located in areas classified as low or very low susceptibility, while occupying 42% of the total study region. These areas mostly have slopes less than 27% and 2‐year, 6‐hour precipitation less than 41 mm. Copyright © 2007 John Wiley & Sons, Ltd.     

Comparison of the return period for landslide-triggering rainfall events in Japan based on standardization of the rainfall period

The intensity of rainfall events with potential to cause landslides has varying temporal characteristics. In this study, the time at which the 72-h accumulated rainfall reached its maximum was used to standardize the period of rainfall measurement. The proposed standardization of the rainfall period was used in conjunction with the return level of rainfall intensity, obtained from intensity–duration–frequency curves, to investigate rainfall intensity anomalies associated with 10 hazardous rainfall events that triggered numerous landslides at the regional scale in Japan. These landslides included shallow landslides in volcanic and non-volcanic areas, as well as deep-seated landslides. The rainfall events that triggered the shallow landslides were divided into two types: downpours that repeatedly reached close to the 100-year return level within approximately 3–4 h, and accumulated rainfall that reached close to 200–400 mm over longer time intervals but within 72 h. Lithological differences seemed unrelated to the differences between the two types of shallow-landslide-triggering rainfall; however, precipitation >1000 mm was necessary to trigger deep-seated landslides. Although the characteristics of the hyetographs differed markedly among the landslide-triggering rainfall events, all the landslides could have been triggered when the mean rainfall intensity reached the 100-year rainfall level during the standardized period. Thus, the landslide trigger can be evaluated indirectly based on the increase in the return level of the mean rainfall intensity, which could provide a means for estimating the time of landslide occurrence.     

Predicting global landslide spatiotemporal distribution： Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing a preliminary real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components： surface landslide susceptibility and a real-time space-based rainfall analysis system （http：//trmm.gsfc.nasa.gov）. First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics （digital elevation topography, slope, soil types, soil texture, land cover classification, etc.） using a GIS weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this work is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This preliminary system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world.     

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.     

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.     

A new method for spatio-temporal prediction of rainfall-induced landslide

1 Introduction The landslides influences on the human society have become an environment difficult problem not able to be neglected, and according to the priority of harms, harms of landslides are only smaller than those from earthquakes in all sorts of natural hazards[1]. Landslide is part of rock mass, soil mass or their compound mass slides downward along a certain slid- ing surface under the actions of inner and external dy- namics, and it is one severe instability phenomenon of rock and s…     

Landslide incidence zonation in the Rio Mendoza valley,Mendoza Province,Argentina

This paper presents a landslide incidence zonation map showing the percentage of underlying material involved in mass‐movement processes in the Rio Mendoza valley, Argentina. The landslide incidence zonation map was derived from an inventory map of landslides and reveals that many areas of the Rio Mendoza valley are implicated in this kind of process. A correlation has been found between the occurrence of landslides, earthquakes, and rainfall. The relation between lithology and landslides is clear: areas covered by friable sedimentary and volcanic rocks of the Choiyoi Group are prone to debris ?ows and complex landslides. The slope map has been ranked and a general relation between slope and type of event is shown. Falls commonly develop in high‐angle slopes. Copyright © 2004 John Wiley & Sons, Ltd.     

A time probabilistic approach to seismic landslide hazard estimates in Iran

Understanding where seismically induced landslides are most likely to occur is crucial in land use planning and civil protection actions aimed at reducing property damage and loss of life in future earthquakes. For this purpose an approach proposed by Del Gaudio et al. [1] has been applied to the whole Iranian territory to provide the basis to assess location and temporal recurrence of conditions of seismic activation of slope failures, according to the Newmark's model [2]. Following this approach, occurrence probabilities for different levels of seismic shaking in a time interval of interest (50 years) were first obtained through a standard hazard estimate procedure. Then, empirical formulae in the form proposed by Jibson et al. [3] and calibrated for the main seismogenic Iranian regions were used to evaluate the slope critical acceleration (Ac)_x for which a prefixed probability exists that, under seismic shakings, Newmark's displacement D_N exceeds a threshold×corresponding to landslide triggering conditions. The obtained (Ac)_x values represent the minimum slope resistance required to limit the probability of landslide seismic triggering within the prefixed value. A map reporting the spatial distribution of these values gives comparative indications on regional different exposure of slopes to shaking capable of inducing failures and provides a reference for hazard estimate at local scale. The obtained results show that the exposure to landslide seismic induction is maximum in the Alborz Mountains region, where critical accelerations up to ∼0.1 g are required to limit the probability of seismic triggering of coherent type landslides within 10% in 50 years.     

Terrain attributes of earthquake‐ and rainstorm‐induced landslides in orogenic mountain Belt,Taiwan

Landsliding induced by earthquakes and rainstorms in montane regions is not only a sculptor for shaping the landscape, but also a driver for delivering sediments and above‐ground biomass downstream. However, the terrain attributes of earthquake‐ and rainstorm‐induced landslides are less discussed comprehensively in Taiwan. As part of an island‐wide inventory, we here compare and contrast the landslide terrain attributes resulting from two catastrophic events: the Chi‐Chi earthquake (M _w = 7.6, September 1999) and typhoon Morakot (rainfall >2500 mm, August 2009). Results show that the earthquake‐induced landslides are relatively small, round‐shaped and prone to occur primarily in middle and toe of slopes. In contrast, the rainstorm‐induced landslides are larger, horseshoe‐shaped and preferentially occurring in slope toes. Also, earthquake‐induced landslides, particularly large landslides, are usually found at steeper gradients, whereas rainstorm‐induced landslides aggregate at gradients between 25° and 40°. Lithologic control plays a secondary role in landsliding. From an island‐wide perspective, high landslide density locates in the region of earthquake intensity ≥ VI or one‐day rainfall ≥600 mm day^?1. Through the landslide patterns and their terrain attributes, our retrospective approach sheds light on accessing the historical and remote events for close geophysical investigations. Finally, we should bear in mind that the landslide location, size, and terrain attributes varying with triggers may affect the landscape evaluation or biogeochemical processes in landslide‐dominated regions. Copyright © 2017 John Wiley & Sons, Ltd.     

Modelling catchment‐scale shallow landslide occurrence and sediment yield as a function of rainfall return period

A model‐based method is proposed for improving upon existing threshold relationships which define the rainfall conditions for triggering shallow landslides but do not allow the magnitude of landsliding (i.e. the number of landslides) to be determined. The SHETRAN catchment‐scale shallow landslide model is used to quantify the magnitude of landsliding as a function of rainfall return period, for focus sites of 180 and 45 km² in the Italian Southern Alps and the central Spanish Pyrenees. Rainfall events with intensities of different return period are generated for a range of durations (1‐day to 5‐day) and applied to the model to give the number of landslides triggered and the resulting sediment yield for each event. For a given event duration, simulated numbers of landslides become progressively less sensitive to return period as return period increases. Similarly, for an event of given return period, landslide magnitude becomes less sensitive to event duration as duration increases. The temporal distribution of rainfall within an event is shown to have a significant impact on the number of landslides and the timing of their occurrence. The contribution of shallow landsliding to catchment sediment yield is similarly quantified as a function of the rainfall characteristics. Rainfall intensity–duration curves are presented which define different levels of landsliding magnitude and which advance our predictive capability beyond, but are generally consistent with, published threshold curves. The magnitude curves are relevant to the development of guidelines for landslide hazard assessment and forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Establishing a threshold for rainfall-induced landslides by a kinetic energy–duration relationship

Many investigators have attempted to define the threshold of landslide failure, that is, the level of the selected climatic variable above which a rainfall-induced landslide occurs. Intensity–duration (I–d) relationships are the most common type of empirical thresholds proposed in the literature for predicting landslide occurrence induced by rainfall. Recent studies propose the use of the kinetic power per unit volume of rainfall (J m⁻² mm⁻¹) to quantify the threshold of landslides induced by rainfall. In this paper, the relationship between rainfall duration and kinetic power corresponding to landslides triggered by rain was used to propose a new approach to define the threshold for predicting landslide occurrence. In particular, for the first time, a kinetic power per unit volume of rainfall–duration relationship is proposed for defining the minimum threshold needed for landslide failure. This new method can be applied using commonly used relationship for estimating the kinetic power per unit volume of rainfall and a new equation based on the measured raindrop size distribution. The applicability of this last method was tested using the data of rainfall intensity, duration and median volume diameter for 51 landslides in Taiwan. For the 51 landslides, the comparison between the measured pairs' kinetic power–duration and all selected relationships demonstrated that the equation based on the measured raindrop size distribution is the best method to define the landslide occurrence threshold, as it is both a process-oriented approach and is characterized by the best statistical performance. This last method has also the advantage to allow the forecasting of landslide hazard before the end of the rainfall event, since the rainfall kinetic power threshold value can be exceeded for a time interval less than the event duration.     

基于Newmark模型的概率地震滑坡危险性分析方法研究

本文以天水地区为研究区,结合地震潜在震源区模型和Newmark位移预测方程,采用概率地震危险性分析方法,计算了该地区50年超越概率10%水平下的Newmark位移。同时,根据天水地区50年超越概率10%下的阿里亚斯烈度,并结合Newmark位移与阿里亚斯烈度的关系式,计算了天水地区在遭受50年超越概率10%下的阿里亚斯烈度影响时,潜在滑坡体产生的Newmark位移分布。通过比较上述两种方法得到的天水地区不同Newmark位移的分布特征,本文认为二者虽然存在较大差异,但其空间分布特征均能反映天水地区每个场点处的相对滑坡危险性。对滑坡危险性水平进行分区的结果显示,天水地区60%以上的区域具有高地震滑坡危险性,50%以上的区域具有甚高地震滑坡危险性。本文的研究结果可以作为天水地区地震危险性及风险评估的参考资料,也可以作为天水地区城市规划、土地使用规划、地震应急准备以及其它公共政策制定的参考资料。      

Unique landslides (loess slide-flows) induced by an extreme rainstorm in 2018 on the Loess Plateau: A new geological hazard and erosion process

Currently, the vegetation has recovered well in most areas of the Loess Plateau in China, and soil erosion has significantly decreased. However, the heavy rainfall event in July 2018 triggered many instances of a unique type of loess landslides(i.e., slide-flows) on the gully-slopes with vegetation recovery in the Nanxiaohegou Basin on the Loess Plateau. This rainfall event was unusual and was a persistent heavy rainfall. The accumulated rainfall from 24 June to 10 July was 232.2 mm, which compr...     

Testing a model for predicting the timing and location of shallow landslide initiation in soil‐mantled landscapes

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever‐expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km² catchment a near‐annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an in?nite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process‐based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and ?ve in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide‐producing storms. Landslide and soil production parameters were ?xed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were ?rst calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to de?ne whether a rainstorm was a signi?cant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identi?ed the 1982 storm as the only landslide‐producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identi?ed the three events, but also predicted that two other landslide‐producing events should have occurred. This performance is signi?cantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non‐producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to signi?cant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

潜在地震滑坡危险区区划方法

不同地区地震活动的强度和频率是不同的.基于地震危险性分析的地震滑坡危险研究在综合了地震烈度、位置、复发时间等因素的基础上,考虑了地震动峰值加速度时空分布的特点,可以有效地应用于潜在地震滑坡危险区区划.以汶川地震灾区为研究对象,根据研究区的地质构造、地震活动特点等划分出灾区的潜在震源区,对该区进行地震危险性分析,并在此基础上采用综合指标法做出基于地震危险性分析的地震滑坡危险性区划.所得地震滑坡危险性区划按照滑坡危险程度分为高危险、较高危险、较低危险和低危险四级,表示未来一段时间内研究区在遭受一定超越概率水平的地震动作用下,不同地区地震滑坡发生的可能程度. 本文给出的地震滑坡危险性区划结果中,汶川地震滑坡崩塌较发育的汶川、北川、茂县等部分区域均处于高危险或较高危险区域;在对具有较高DEM精度的北川擂鼓镇地区所作的地震滑坡危险性区划中,汶川地震中实际发生的地震滑坡灾害与地震滑坡危险区划结果表现出较好的一致性.对区域范围而言,基于地震危险性分析的地震滑坡区划,可为初期阶段的土地规划使用及重大工程选址提供参考.     

Quantifying rainfall controls on catchment‐scale landslide erosion in Taiwan

Landslide erosion is a dominant hillslope process and the main source of stream sediment in tropical, tectonically active mountain belts. In this study, we quantified landslide erosion triggered by 24 rainfall events from 2001 to 2009 in three mountainous watersheds in Taiwan and investigated relationships between landslide erosion and rainfall variables. The results show positive power‐law relations between landslide erosion and rainfall intensity and cumulative rainfall, with scaling exponents ranging from 2·94 to 5·03. Additionally, landslide erosion caused by Typhoon Morakot is of comparable magnitude to landslide erosion caused by the Chi‐Chi Earthquake (M_W = 7·6) or 22–24 years of basin‐averaged erosion. Comparison of the three watersheds indicates that deeper landslides that mobilize soil and bedrock are triggered by long‐duration rainfall, whereas shallow landslides are triggered by short‐duration rainfall. These results suggest that rainfall intensity and watershed characteristics are important controls on rainfall‐triggered landslide erosion and that severe typhoons, like high‐magnitude earthquakes, can generate high rates of landslide erosion in Taiwan. Copyright © 2012 John Wiley & Sons, Ltd.     

Temporal and spatial variation of infilling processes in a landslide scar in a steep mountainous region,Japan

The duration of the soil‐depth recovery needed for reoccurrence of shallow colluvial landslides at a given site in humid regions is much longer than the return period of rainfall needed to generate sufficient pore water pressure to initiate a landslide. Knowledge of the rate of change in soil depth in landslide scars is therefore necessary to evaluate return intervals of landslides. Spatial variation in sediment transport at the Kumanodaira landslide scar in central Japan was investigated by field observations. Spatial distribution of the rate of change in soil depth was estimated using sediment transport data and geographic information system (GIS) analysis. Observations revealed that the timing of sediment transport differed for shallow and deep soil layers. Near‐surface sediment transport (mostly dry ravel and some shallow soil creep at depths ≤0·05 m) measured in sediment traps was active in winter and early spring and was affected by freezing–thawing; soil creep of subsoil (i.e. >0·05 m), monitored by strain probes, was active in summer and autumn when precipitation was abundant. Near‐surface sediment flux was estimated by a power law function of slope gradient. Deeper soil creep was more affected by relative location to the landslide scar, which influences soil depth, than by slope gradient. Our study indicated that the rate of soil‐depth recovery is high just below the head scarp of the landslide. Abrupt changes in the longitudinal slope topography immediately above, within and just below the head scarp became smoother with time due to degradation proximate to the landslide head scarp and flanks, as well as aggradation just below the head scarp. Copyright © 2014 John Wiley & Sons, Ltd.     

A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall-triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select from a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide-triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity–duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of 177 reported landslides across the continental United States and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity–duration storm thresholds, but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.