Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias,NW Spain)

Given its geological and climatic conditions and its rugged orography, Asturias is one of the most landslide prone areas in the North of Spain. Most of the landslides occur during intense rainfall episodes. Thus, precipitation is considered the main triggering factor in the study area, reaching average annual values of 960 mm. Two main precipitation patterns are frequent: (i) long-lasting periods of moderate rainfall during autumn and winter and (ii) heavy short rainfall episodes during spring and early summer. In the present work, soil moisture conditions in the locations of 84 landslides are analysed during two rainfall episodes, which represent the most common precipitation patterns: October–November 2008 and June 2010. Empirical data allowed the definition of available water capacity percentages of 99–100% as critical soil moisture conditions for the landslide triggering. Intensity-duration rainfall thresholds were calculated for each episode, considering the periods with sustained high soil moisture levels before the occurrence of each analysed landslide event. For this purpose, data from daily water balance models and weather stations were used. An inverse relationship between the duration of the precipitation and its intensity, consistent with published intensity-duration thresholds, was observed, showing relevant seasonal differences.     

Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate

The Tropical Rainfall Measuring Mission (TRMM) is a joint space mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. In this study, the daily rainfall from TRMM has been utilized to simulate the soil moisture content up to 30 cm vertical soil profile of at an interval depth of 15 cm by using the HYDRUS 1D numerical model for the three plots. The simulated soil moisture content using ground-based rainfall and TRMM-derived rainfall measurements indicate an agreeable goodness of fit between the both. The Nash–Sutcliffe efficiency using ground-based and TRMM-derived rainfall was found in the range of 0.90–0.68 and 0.70–0.40, respectively. The input data sensitivity analysis of precipitation combined with different irrigation treatment indicates a high dependency of soil moisture content with rainfall input. The overall analysis reveals that TRMM rainfall is promising for soil moisture prediction in absence of ground-based measurements of soil moisture.     

Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (central Italy)

Rainfall thresholds represent the main tool for the Italian Civil Protection System for early warning of the threat of landslides. However, it is well-known that soil moisture conditions at the onset of a storm event also play a critical role in triggering slope failures, especially in the case of shallow landslides. This study attempts to define soil moisture (estimated by using a soil water balance model) and rainfall thresholds that can be employed for hydrogeological risk prevention by the Civil Protection Decentrate Functional Centre (CFD) located in the Umbria Region (central Italy). Two different analyses were carried out by determining rainfall and soil moisture conditions prior to widespread landslide events that occurred in the Umbria Region and that are reported in the AVI (Italian Vulnerable Areas) inventory for the period 1991?C2001. Specifically, a ??local?? analysis that considered the major landslide events of the AVI inventory and an ??areal?? analysis subdividing the Umbria Region in ten sub-areas were carried out. Comparison with rainfall thresholds used by the Umbria Region CFD was also carried out to evaluate the reliability of the current procedures employed for landslide warning. The main result of the analysis is the quantification of the decreasing linear trend between the maximum cumulated rainfall values over 24, 36 and 48?h and the soil moisture conditions prior to landslide events. This trend provides a guideline to dynamically adjust the operational rainfall thresholds used for warning. Moreover, the areal analysis, which was aimed to test the operational use of the combined soil moisture?Crainfall thresholds showed, particularly for low values of rainfall, the key role of soil moisture conditions for the triggering of landslides. On the basis of these results, the Umbria Region CFD is implementing a procedure aimed to the near real-time estimation of soil moisture conditions based on the soil water balance model developed ad hoc for the region. In fact, it was evident that a better assessment of the initial soil moisture conditions would support and improve the hydrogeological risk assessment.     

Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets

An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.     

Response of a loess landslide to rainfall: observations from a field artificial rainfall experiment in Bailong River Basin,China

Rainfall-induced landslides are a significant hazard in many areas of loess-covered terrain in Northwest China. To investigate the response of a loess landslide to rainfall, a series of artificial rainfall experiments were conducted on a natural loess slope, located in the Bailong River Basin, in southern Gansu Province. The slope was instrumented to measure surface runoff, pore water pressure, soil water content, earth pressure, displacement, and rainfall. The hydrological response was also characterized by time-lapse electrical resistivity tomography. The results show that most of the rainfall infiltrated into the loess landslide, and that the pore water pressure and water content responded rapidly to simulated rainfall events. This indicates that rainfall infiltration on the loess landslide was significantly affected by preferential flow through fissures and macropores. Different patterns of pore water pressure and water content variations were determined by the antecedent soil moisture conditions, and by the balance between water recharge and drainage in the corresponding sections. We observed three stages of changing pore water pressure and displacement within the loess landslide during the artificial rainfall events: Increases in pore water pressure initiated movement on the slope, acceleration in movement resulting in a rapid decrease in pore water pressure, and attainment of a steady state. We infer that a negative pore water pressure feedback process may have occurred in response to shear-induced dilation of material as the slope movement accelerated. The process of shear dilatant strengthening may explain the phenomenon of semi-continuous movement of the loess landslide. Shear dilatant strengthening, caused by intermittent or continuous rainfall over long periods, can occur without triggering rapid slope failure.     

基于原位监测的浅层黄土斜坡水分运移规律分析

降雨诱发的浅层黄土滑坡是黄土高原重要的地质灾害类型之一.斜坡水分空间分布和变化趋势是导致斜坡失稳的重要因素,但基于此的剖面监测数据较少.依托延安黄土地质灾害野外观测基地,选择典型黄土斜坡,在坡面布设两条5m深的水分探测纵剖面,观测在降雨过程中斜坡水分的空间特征.监测结果显示:1)降雨引起的土壤含水率变化深度有限,与降雨...     

浙江地区引发滑坡的降雨强度-历时关系

由于独特的地理位置,复杂的地质、地形和气候背景,浙江成为中国降雨型滑坡（土体和岩体滑动,也包括泥石流和崩塌等）最频发的地区之一。为评价浙江地区的滑坡灾害,本文对该地区1990年至2003年雨量站记录的降雨数据进行了详细分析,确定了引发土体滑坡-泥石流的降雨强度―历时下限。     

降雨及库水位联合作用下秭归八字门滑坡稳定性预测

针对三峡库区库水位调控方案和极端降雨情况,对秭归县八字门滑坡稳定性分析设置了10种计算工况,采用SEEP/W软件模拟该滑坡在降雨入渗及库水位联合作用下的暂态渗流场,并利用SLOPE/W软件,将暂态孔隙水压力分布用于该滑坡的极限平衡分析中,确定不同工况下（不同降雨强度）的滑坡稳定性系数,据此采用降雨量对该滑坡进行失稳预测。研究认为：150 mm/d以上的降雨量对该滑坡影响较大,降雨入渗具有滞后性;在相同的降雨量情况下,1 d的降雨强度比5 d的连续降雨对滑坡体的稳定性影响更明显;在水位从175 m降至145 m的过程中,临界雨量为100 mm/d时,滑坡就可能失稳;水位从145 m升至175 m的过程中和175 mm稳定水位时,当临界雨量为200 mm/d时,滑坡才可能失稳,即水位骤降过程中滑坡失稳概率大。不同工况下的滑坡土体含水率分析结果表明,降雨影响的主要是上部土体,下部土体含水率受控于地下水位,即降雨更容易引起浅层滑坡与局部滑坡     

Rainfall induced debris flows in pyroclastic deposits, Campania (southern Italy)

A combination of empirical and physically based hydrological models has been used to analyze historical data on rainfall and debris-flow occurrence in western Campania, to examine the correlation between rainfall and debris-flow events.

Rainfall data from major storms recorded in recent decades in western Campania were compiled, including daily series from several rain gauges located inside landslide areas, supplemented by hourly rainfall data from some of the principal storms.

A two-phase approach is proposed. During phase 1, soil moisture levels have been modelled as the hydrological balance between precipitation and evapotranspiration, on a daily scale, using the method of Thornthwaite [Geograph. Rev. 38 (1948) 55].

Phase 2 is related to the accumulation of surplus moisture from intense rainfall, leading to the development of positive pore pressures. These interactions take place on an hourly time scale by the “leaky barrel” (LB) model described by Wilson and Wiezoreck [Env. Eng. Geoscience, 1 (1995) 11]. In combination with hourly rainfall records, the LB model has been used to compare hydrological effects of different storms. The critical level of retained rain water has been fixed by the timing of debris-flow activity, related to recorded storm events.

New rainfall intensity–duration thresholds for debris-flow initiation in western Campania are proposed. These thresholds are related to individual rain gauge and assume a previously satisfied field capacity condition. The new thresholds are somewhat higher than those plotted by previous authors, but are thought to be more accurate and thus need less conservatism.     

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.     

Extraction of soil moisture from RADARSAT-1 and its role in the formation of the 6 December 2008 landslide at Bukit Antarabangsa,Kuala Lumpur

Active microwave has a huge potential in the estimation of soil moisture especially over large areas where the meteorological observations are seldom. The large contrast in dielectric constant between different types of soil is considered as the main factor for measuring the moisture content. This study is aimed at the extraction of soil moisture over the areas of Bukit Antarabangsa, Malaysia using active microwave remote sensing technique in order to examine the impact of moisture content dynamically on landslides occurrence, which have been a basic challenge that threaten Bukit Antarabangsa area, particularly in falling of monsoon seasons. This study addressed a specific event that took place in 6 December 2008 due to a very high level of precipitation that resulted in a raise in ground water table causing the occurrence of landslide. One Radarsat-1 image acquired in July 2008 before the landslide was used for generating the moisture content map. The resultant moisture content map showed a reasonable distribution of the moisture concentrated over the forest areas which has previous records landslides. Moreover, it was found that the previous landslide events were within the high moisture zone indicating the presence of high moisture content. Subsequently, three moisture maps were extracted from Landsat-7 ETM+, which were then used for validation process. A statistically based validation technique was used by calculating area under the curve that correlates the high moisture values of three images. In order to validate the Landsat-7 ETM+ moisture content, monthly rainfall data was plotted against the high moisture values derived from three Landsat-7 images. The validation result indicated an acceptable compatibility. The spatial relation between high moisture areas in Landsat-7 ETM+ images along the year resulted in a good fitting in the high–low moisture distribution areas with sensitivity ranged of 60–70 %. Finally, the moisture content map generated by Radarsat-1 was validated using a landslide inventory map. The resultant validation produced an area under curve of 0.704 (70 %).     

基于TRMM卫星雷达降雨的流域陆面水文过程

利用热带降雨观测计划(TRMM)卫星雷达降雨数据驱动分布式陆面水文模型,研究流域尺度陆面水文过程,评估该数据在水文模拟与预报等研究领域的性能。通过与实测雨量资料比较,验证TRMM卫星雷达降雨数据的质量。分别将TRMM卫星雷达降雨与观测降雨作为耦合模型的气象输入,模拟和研究淮河流域1998~2003年的陆面水文过程时空变化。结果表明,TRMM卫星雷达降雨数据能够很好地描述降雨的时空分布,利用TRMM降雨模拟的结果与利用观测降雨模拟的结果精度相当;模拟流量与实测资料基本吻合。卫星雷达降雨数据在陆面水文过程研究中具有广泛的应用前景。     

中国大陆流域分区TRMM降水质量评价

根据中国境内2 257个气象站点1998-2013年逐日降水资料,结合流域分区,采用探测准确性、相关系数以及相对误差等指标,对热带降水测量（TRMM）降水精度和一致性进行系统评价。结果表明:① TRMM日降水准确性从东南沿海向西北内陆递减;② 气象站点年均降水日数显著大于TRMM年均降水日数;③ 西北片区以外气象站点降水量和TRMM降水量在月尺度和年尺度上均具有较好的相关关系;④ 各流域年均TRMM面降水量均高于气象站点面降水量,且TRMM面降水量相对误差雨季较小,枯季较大;⑤ 各流域TRMM面降水量与气象站点面降水量演变趋势基本一致,南方各流域年降水量均呈减少趋势,北方各流域年降水量均呈增加趋势,全国尺度上年降水量呈微弱的减少趋势。     

雨水渗透与香港滑坡灾害

本文分析了滑坡与降雨的关系，暴雨是激发滑坡的主要原因，绝大多数的滑坡都是由于短期强烈暴雨所致，而且这些滑坡事件与暴雨的峰值几乎同时发生；前期雨量对边坡稳定也有一定的影响，坡积土的含水量是仅次于短期雨暴影响边坡稳定的重要因素。     

土壤湿度遥感估算同化研究综述

土壤湿度是影响气候的至关重要的变量之一。利用数据同化方法反演大规模高精度土壤湿度数据是目前土壤水分研究的一个重要方向。结合国内外土壤湿度遥感估算研究现状,总结了土壤水分同化算法主要应用进程,梳理了目前实现土壤水分反演且应用广泛的陆面过程模型,Noah模型、通用陆面过程模型CLM、简单生物圈模型Si B2、北方生产力模拟模型BEPS,介绍了大范围卫星土壤水分数据集,包括陆面同化系统数据集、ASCAT数据集、AMSR-E数据集及SMOS数据集,最后探讨了遥感土壤水分同化过程中存在的问题及发展方向。     

Modeling the mudslide aftermath of the 2007 Southern California Wildfires

An investigation is made of the influence of fire-caused deforestation on precipitation-triggered, shallow landslide susceptibilities in southern California, using a scalable and extensible geo-fluid model (SEGMENT), that accounts for soil mechanics, root distribution, and relevant hydrologic processes. SEGMENT is applicable to variable regional topography, soil thickness profiles, and vegetation coverage. In this study, for southern California following the 2007 wildfires, three experiments were performed with rainfall recurrence periods chosen to be 2, 10, and 25 years. These intervals correspond to 24-h storm rainfall totals of 17, 25.4, and 33 mm. The model generated landslide stability maps that identified three areas of high instability. These unstable regions are located in the San Fernando Valley, the San Gabriel Mountains, and the Santa Monica Mountains. In each case, the vegetation cover had been severely burned during the preceding 2007 wildfires. The model results showed that burning from wildfires is a major destabilizing factor for southern California. Burn sites are more prone to landslides than vegetated slopes because the soil more readily exceeds its critical moisture content. Severe droughts in a future warming climate are expected to increase the likelihood of more frequent and intense wild fires. Higher temperatures combined with decreased total rainfalls facilitate more intense landslides, including devastating mudslides, following heavy precipitation. Finally, the model is designed to assist in developing timely mitigation measures for post-fire, storm-triggered landslides.     

Forest cover change trajectories and their impact on landslide occurrence in the tropical Andes

Tropical mountain regions are prone to landslide hazards. Given the current land pressure with increasing occupation of steep uplands, landslide hazards are expected to increase in the near future. Understanding the factors that control landslide hazards is therefore essential. Rare event logistic regression allows us to perform a robust detection of landslide controlling factors. This technique is here applied to the tropical Andes to evaluate the impact of dynamic land cover changes on landslide occurrences. Land cover change trajectories (i.e. dynamic evolution of land cover through time) were specifically included in the probabilistic landslide analysis. While natural physical processes such as slope undercutting by rivers and failure of oversteepened slopes are important in this tropical mountainous site, landslides are increasingly associated with human activities. The data show that land cover trajectories are associated with landslide patterns. In this humid mountainous site, forest degradation does not lead to a measurable increase in landslide occurrence. However, few years after forests are converted to pastures, a rapid decline of slope stability is observed. Land cover conversion from forest to pasture permanently reduces slope stability. It is assumed that major changes in soil properties and hydrology induced by the vegetation conversion play a role in accelerating landslide hazards.     

康县城关镇刘家坎十字坡滑坡形成机制与稳定性分析

基于滑坡区地质环境,通过区域地质调查,大比例尺平面图测绘,滑坡微地貌和变形破坏特征测量籍调查,室内岩土体试验等手段,对康县十字坡滑坡的特征和形成机制进行了系统研究。研究成果表明,该滑坡为小型碎石堆积土滑坡,降雨、不合理的人工活动及滑坡区高陡的地形是诱发滑坡变形的主要因素。在认识滑坡形成机制的基础上,采用多种极限平衡计算方法,并结合滑坡所处的地质环境,评价该滑坡在天然、降雨、地震及降雨和地震耦合作用下的稳定性,为该滑坡的治理提供理论指导。     

Assessing landslide vulnerability from soil characteristics—a GIS-based analysis

Hilly regions are prone to landslides that cause heavy losses of life and properties every year. A number of researches and analyses are carried out in the GIS environment to identify landslide vulnerability in the region. The important conditioning factors identified by the researchers are slope, geological, geomorphologic features, and land use coupled with triggering factors like rainfall and a few of the anthropogenic activities. Soil forms the uppermost part of the earth crust, and it is expected that various soil characteristics like depth, surface texture, depth texture, soil erosion, hydraulic conductivity, stoniness, etc., play significant roles in causing landslide in the area. These factors have been ignored so far by most researchers while identifying landslide hazard-prone areas. This paper attempts to assess the vulnerability status in parts of East Sikkim, India, by integrating the influence of the various soil attributes. A composite index called soil stability value was determined by aggregating the weights assigned to different soil parameters. Finally, based on the soil stability values, the study area was classified into least vulnerable, moderately vulnerable, and most vulnerable zones of landslide occurrences. Comparison between the vulnerability zones and the actual landslide occurrences yielded a 90% agreement with the density of landslides in the most vulnerable zone, demonstrating the efficacy of soil characteristics as potential indicators of landslide events.     

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County,North Carolina

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.