Rainfall-induced landslides in Hulu Kelang area, Malaysia

Hulu Kelang is known as one of the most landslide-prone areas in Malaysia. The area has been constantly hit by landslide hazards since 1990s. This paper provides an insight into the mechanism of rainfall-induced landslide in the Hulu Kelang area. Rainfall patterns prior to the occurrences of five selected case studies were first analyzed. The results showed that daily rainfall information is insufficient for predicting landslides in the area. Rainfalls of longer durations, i.e., 3–30 days prior to the landslides should be incorporated into the prediction model. Numerical simulations on a selected case study demonstrated that both matric suction and factor of safety decreased steadily over time until they reached the lowest values on the day of landslide occurrence. Redistribution of infiltrated rainwater in the soil mass could be a reason for the slow response of failure mechanism to rainfall. Based on 21 rainfall-induced landslides that had occurred in the area, three rainfall thresholds were developed as attempts to predict the occurrence of rainfall-induced landslide. The rainfall intensity–duration threshold developed based on the local rainfall conditions provided a reasonably good prediction to the landslide occurrence. The cumulative 3- versus 30-day antecedent precipitation index threshold chart was capable of giving the most reliable prediction with the limiting threshold line for major landslide yielded a reliability of 97.6 %.     

Rainfall duration and debris-flow initiated studies for real-time monitoring

A rainfall-induced debris flow warning is implemented employing real-time rain gauge data. The pre-warning for the time of landslide triggering derives the threshold or critical rainfall from historical events involving regional rainfall patterns and geological conditions. In cases of debris flow, the time taken cumulative runoff, to yield abundant water for debris triggering, is an important index that needs monitoring. In gathered historical cases, rainfall time history data from the nearest rain gauge stations to debris-flow sites connected to debris flow are used to define relationships between the rainfall intensity and duration. The effects by which the regional rainfall patterns (antecedent rainfall, duration, intensity, cumulative rainfall) and geological settings combine together to trigger a debris-flow are analyzed for real-time monitoring. The analyses focused on 61 historical hazard events with the timing of debris flow initiation and rainfall duration to burst debris-flow characteristics recorded. A combination of averaged rainfall intensity and duration is a more practical index for debris-flow monitoring than critical or threshold rainfall intensity. Because, the outburst timing of debris flows correlates closely to the peak hourly rainfall and the forecasting of peak hourly rainfall reached in a meteorological event could be a valuable index for real-time debris-flow warning.     

Rainfall-triggering response patterns of post-seismic debris flows in the Wenchuan earthquake area

Several giant debris flows occurred in southwestern China after the Wenchuan earthquake, causing serious casualties and economic losses. Debris flows were frequently triggered after the earthquake. A relatively accurate prediction of these post-seismic debris flows can help to reduce the consequent damages. Existing debris flow prediction is almost based on the study of the relationship between post-earthquake debris flows and rainfall. The relationship between the occurrence of post-seismic debris flows and characteristic rainfall patterns was studied in this paper. Fourteen rainfall events related to debris flows that occurred in four watersheds in the Wenchuan earthquake area were collected. By analyzing the rainfall data, characteristics of rainfall events that triggered debris flows after the earthquake were obtained. Both the critical maximum rainfall intensity and average rainfall intensity increased with the time. To describe the critical conditions for debris flow initiation, intensity–duration curves were constructed, which shows how the threshold for triggering debris flows increased each year. The time that the critical rainfall intensities of debris flow occurrences return to the value prior to the earthquake could not be estimated due to the absent rainfall data before the earthquake. Rainfall-triggering response patterns could be distinguished for rainfall-induced debris flows. The critical rainfall patterns related to debris flows could be divided on the basis of antecedent rainfall duration and intensity into three categories: (1) a rapid triggering response pattern, (2) an intermediate triggering response pattern, and (3) a slow triggering response pattern. The triggering response patterns are closely related to the initiation mechanisms of post-earthquake debris flows. The main difference in initiation mechanisms and difference in triggering patterns by rainfall is regulated by the infiltration process and determined by a number of parameters, such as hydro-mechanical soil characteristics, the thickness of the soil, and the slope gradient. In case of a rapid triggering response rainfall pattern, the hydraulic conductivity and initial moisture content are the main impact factors. Runoff erosion and rapid loading of solid material is the dominant process. In case of a rainfall pattern with a slow triggering response, the thickness and strength of the soil, high hydraulic conductivity, and rainfall intensity are the impact factors. Probably slope failure is the most dominant process initiating debris flows. In case of an intermediate triggering response pattern, both debris flow initiation mechanisms (runoff erosion and slope failure) can play a role.     

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.     

坡面泥石流降雨预报模型

中国东部地区的地质灾害多以坡面泥石流的形式发生,预测预报坡面泥石流的发生对于开展防灾减灾具有重要意义。地形、地质和降雨三大条件是影响坡面泥石流发生的主要条件。通过选择同样地质条件和基本相同降雨条件的区域,研究影响坡面泥石流发生的地形条件,并得出可以用于坡面泥石流预报的坡面泥石流地形条件。结果表明:地形条件由坡面坡度因子、泥石流上部因子、泥石流侧面因子和临空面因子组成;较大的地形条件T对应较大的泥石流发生可能性;降雨条件由泥石流发生前的降雨量与1 h降雨量组成;得出了由地形条件T和降雨条件R组成的坡面泥石流预报条件P,P值越大,坡面泥石流发生的可能性越大。预报条件P可以预报坡面泥石流的发生。     

Landslide and debris flow initiated characteristics after typhoon Morakot in Taiwan

Typhoon Morakot brought extreme rainfall and initiated numerous landslides and debris flows in southern Taiwan in August of 2009. The purpose of this study is to identify the extreme rainfall-induced landslide frequency-area distribution in the Laonong River Basin in southern Taiwan and debris flow-initiated conditions under rainfall. Results of the analysis show that debris flows were initiated under high cumulative rainfall and long rainfall duration or high rainfall intensity. The relationship of mean rainfall intensity and duration threshold could reflect debris flow initiation characteristics under high rainfall intensity in short rainfall duration conditions. The relationship of cumulative rainfall and duration threshold could reflect debris flow initiation characteristics under high cumulative rainfall in long rainfall duration. Defining rainfall events by estimating rainfall parameters with different methodologies could reveal variations among intermittent rainfall events for the benefit of issuing debris flow warnings. The exponent of landslide frequency-area distribution induced by Typhoon Morakot is lower than that induced by the Chi-Chi earthquake. The lower exponent of landslide frequency-area distribution can be attributed to the transportation and deposition areas of debris flow that are included in the landslide area. Climate change induced high rainfall intensity and long duration of precipitation, for example, Typhoon Morakot brought increased frequency of debris flow and created difficulty in issuing warnings from rainfall monitoring.     

Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain)

Landslide hazard in a region limited to data from a regional scale about triggering factors is assessed via cross tabulation between determining factors and landslides with recent activity. Firstly, landslide susceptibility was evaluated and validated through a bivariate statistical method between the previously identified stability conditioning factors and the mapped landslides. In this way, the most susceptible areas for assessing landslide hazards were selected. The main problem to solve in this type of research is the landslide activity. For this purpose, several techniques were applied: news reports, differential interferometric synthetic aperture radar, digital photogrammetry, light detection and ranging, photointerpretation, and dendrochronology. Both the strong and weak points of these techniques are also mentioned. The landslide return period was computed via the association between landslide activity and triggering factors, in this case annual rainfall. Finally, landslide hazard was mapped solely based on landslides with recent activity and their computed return period. The relationship between landslide occurrence and triggering factors shows that, according to both the considered assumptions and the observations made, deep-seated landslides are triggered or reactivated together with superficial landslides once every 18 years, while superficial landslides as flows or falls occur once every 5 years. The results show that there is generally a low landslide hazard in the study zone, especially when compared to landslide susceptibility. This means that landslides are mainly dormant from a natural evolution point of view, but could be reactivated as a result of geomorphological, climate, or human changes. In any case, the landslide hazard is successfully assessed, with a prediction of a 6% annual probability of a high hazard in 5% of the area, intersecting with the main infrastructures of the region; thus, control strategies are justified in order to avoid damage in extraordinary rainfall periods.     

汶川地震强震区地震诱发滑坡与后期降雨诱发滑坡控制因子耦合分析

本文以汶川地震强震区北川县典型研究区为例,利用高分辨率航片、SPOT5卫星图像对北川县典型研究区进行了512地震之后和924降雨之后诱发的滑坡解译,解译结果显示:512地震诱发滑坡1999个,924强降雨诱发滑坡828个,924强降雨导致原有地震滑坡面积扩大的滑坡150个。研究表明:地震和强降雨都是诱发滑坡的动力成因,924强降雨诱发的滑坡面积是512地震诱发滑坡面积的1/4倍,强降雨诱发滑坡的数量增加了41.4%; 强降雨不仅诱发新的滑坡,而且促使原来地震滑坡复活,并扩大其面积,强降雨导致地震诱发的滑坡面积扩大了原面积的68.7%。同时,在遥感解译数据基础之上,开展地震诱发滑坡与降雨诱发滑坡规模对比和控制因子耦合分析及地震与降雨耦合灾害链模式研究,为进一步分析研究地震灾区滑坡的产生、发展趋势、危险性和风险评价等预测预报提供科学依据,也为汶川震区恢复重建中的减灾防灾提供决策参考。     

An empirical approach for evaluation of the potential of debris flow occurrence in mountainous areas

Debris flows can occur relatively suddenly and quickly in mountainous areas, resulting in major structural damage and loss of life. The establishment of a model to evaluate the occurrence probability of debris flows in mountainous areas is therefore of great value. The influence factors of debris flows are very complex; they can basically be divided into background factors and triggering factors. Background factors include the mechanical characteristics of geo-materials, topography and landscape, and soil vegetation; and triggering factors include hydrological and rainfall conditions, and human activities. By assessing the dynamic characteristics of debris flows in mountainous areas, some important influence factors are selected here for analysis of their impacts on the occurrence probability of debris flow. A mathematical model for evaluation of the occurrence probability of debris flows is presented and combined with probability analysis. Matlab software is used for the numerical implementation of the forecasting model, and the influences of rainfall, lithology and terrain conditions on the occurrence probability of debris flows are analyzed. Finally, the presented model is applied to forecast the occurrence probability of debris flows in the mountainous area around Qingping Town; the simulation results show that many loose landslide deposits and heavy rainfall are the key factors likely to trigger debris flows in this region.     

Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy

We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.     

降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模

提高降雨型滑坡危险性预警精度和空间辨识度具有重要意义.以江西宁都县1980—2001年156个降雨型滑坡为例,首先基于传统的EE-D（early effective rainfall-rainfall duration）阈值法计算不同降雨诱发滑坡的时间概率级别;然后以各级别临界降雨阈值曲线对应的时间概率为因变量,并以对应的前期有效降雨量（early effective rainfall,EE）和降雨历时（D）为自变量,采用逻辑回归拟合出上述因变量与自变量之间的非线性关系,得到降雨诱发滑坡的连续概率值;之后对比C5.0决策树和多层感知器的滑坡易发性预测性能;最后利用降雨诱发滑坡的连续概率值与易发性图相耦合以实现连续概率滑坡危险性预警.结果显示:（1）宁都降雨型滑坡连续概率值的逻辑回归方程为1/P=1+e4.062+0.747 4×D-0.079 44×EE,其拟合优度为0.983;（2）2002—2003年的20处用于连续概率阈值测试的降雨型滑坡大都落在连续概率值大于0.7的区域,只有4处落在小于0.7的区域;（3）C5.0决策树预测滑坡易发性的精度显著高于多层感知器;（4）近5年的4次降雨型滑坡的连续概率危险性值都在0.8以上,且高和极高预警区的面积较传统滑坡危险性分区更小.可见连续概率滑坡危险性预警法相较于传统危险性分区法具有更高的预警精度和空间辨识度,且通过叠加滑坡易发性图及其临界降雨阈值可开展实时滑坡危险性预警制图.      

基于Logistic回归及前期有效雨量的降雨诱发型滑坡预测方法

滑坡预测预报一直是国内外滑坡灾害研究的重点。且多数滑坡均属降雨诱发型。为此将降雨诱发型滑坡的研究列为重中之重。但是,降雨与滑坡发生的关系不仅密切,而且非常复杂。除降雨外,还涉及到地质、气象、水文、土壤等多个学科。因此,至今尚未总结出一种对降雨诱发型滑坡进行定量预测预报的成熟方法。文章将Logistic回归模型与前期有效降雨量结合,形成一套对降雨诱发型滑坡进行定量预测预报的方法。并以长江三峡地区为例进行了检验,效果较好。     

浅层滑坡诱发沟谷泥石流的地形和降雨条件

2011年贵州省望谟县打易镇的大范围浅层滑坡诱发的沟谷泥石流提供了研究这类泥石流地形和降雨条件的机会。在地质条件一致和小区域内的降雨条件基本一致的情况下,地形条件就是这些泥石流暴发与否的唯一决定因素。对比一些重要的地形因素与泥石流暴发的关系,得出了由流域面积、沟床纵比降和25°~45°山坡坡度面积比组成的泥石流综合地形因子T。在地形因子T的基础上,研究获得了由前期降雨量、1 h降雨强度、年平均降雨量等组成的降雨因子R。由地形因子T和降雨因子R获得的临界条件P可以判断该区域的泥石流暴发。由于研究工作部分基于泥石流的形成机理,研究成果还可用于其他区域的泥石流形成预测,为泥石流的预测预报提供了一个较好的方法。     

Hydrometeorological thresholds for landslide initiation and forest operation shutdowns on the north coast of British Columbia

Debris flows and debris avalanches are the most widespread and hazardous types of landslides on the British Columbia north coast. Triggered by heavy rain, they pose risks to forestry workers in sparsely developed regions. The scarcity of long-term quality rain gauges and the lack of weather radar information create significant challenge in predicting the timing of landslides, which could be used to warn and, when necessary, evacuate forestry personnel. Traditional methods to relate rainfall antecedents and rainfall intensity to known landslide dates have proven to be unsatisfactory in this study due to extreme spatial variability of rainfall, enhanced by the orographic effect and the scarcity of rain gauges in a very large area. This has led to an integration of meteorological variables in a landslide advisory system that classifies three types of approaching storms by the 850-mbar wind speed and direction, the occurrence of subtropical moisture flow, and the existence of a warm layer characterized by high thickness values of the 500- to 1,000-mbar pressure levels. The storm classification was combined with a 4-week antecedent rainfall and the 24-h rainfall measured near or in the watershed where logging operations are taking place. This system, once implemented, is thought to reduce loss of life, injury, and economic losses associated with forestry works in the study area.     

The January 10, 1997 Pozzano landslide, Sorrento Peninsula, Italy

From 1997 to 1999, a huge number of slides, often turning into extremely rapid debris-earth flows, repeatedly affected the late Quaternary volcaniclastic deposits mantling the carbonate slopes of Campania region, Italy. The Sorrento Peninsula was the epicentral district of the 1997 regional slope-instability crisis. Some hundred shallow mass movements took place during January 1997 in this area. These were the last episode of a long series of slope failure events dating back to mid-18th century. Results from geological and geomorphologic surveys are presented. Landslide mechanism and triggering factors are analysed for the most important mass movement, which occurred during the January 9-11, 1997, regional event. On January 10, 1997, at about 8:15 PM, a rainfall-induced debris slide-debris flow occurred at Pozzano (province of Naples), mainly affecting the 79 AD pyroclastic products. Following a J-path, the landslide destroyed a private house and invaded the State Road no. 145. This event resulted in four deaths, 22 persons injured and road closure for about 2 months. There was less than 200 mm of rainfall in the 72-h period prior to the landslide, although intense precipitation had occurred during a preceding 4-month period. However, the slope failure event was not preceded by an extreme short-term antecedent rainfall, as already noticed in previous landslides of this type in Campania. Finally, following a preliminary geotechnical characterization of volcaniclastic soils, a slope-stability back analysis was carried out, which adopted the classical infinite slope scheme. This analysis gave further evidence of the role played by pore pressure in reducing the overall shear strength of pyroclastic soils.     

The March 25 and 29, 2016 landslide-induced debris flow at Clapar,Banjarnegara, Central Java

The Clapar landslide induced debris flow consisted of the Clapar landslide occurred on 24 March 2017 and the Clapar debris flow occurred on 29 March 2017. The first investigation of the Clapar landslide induced debris flow was carried out two months after the disaster. It was followed by UAV mapping, extensive interviews, newspaper compilation, visual observation and field measurements, and video analysis in order to understand chronology and triggering mechanism of the landslide induced debris flow in Clapar. The 24 March 2016 landslide occurred after 5 hours of consecutive rainfall (11,2 mm) and was affected by combination of fishponds leak and infiltration of antecedent rain. After five days of the Clapar landslide, landslide partially mobilized to form debris flow where the head scarp of debris flow was located at the foot of the 24 March 2016 landslide. The Clapar debris flow occurred when there was no rainfall. It was not generated by rainstorm or the surface erosion of the river bed, but rather by water infiltration through the crack formed on the toe of the 24 March 2016 landslide. Supply of water to the marine clay deposit might have increased pore water pressure and mobilized the soil layer above. The amount of water accumulated in the temporary pond at the main body of the 24 March 2016 landslide might have also triggered the Clapar debris flow. The area of Clapar landslide still shows the possibility of further retrogression of the landslide body which may induce another debris flow. Understanding precursory factors triggering landslides and debris flows in Banjarnegara based on data from monitoring systems and laboratory experiments is essential to minimize the risk of future landslide.     

The formation and evolution of a coastal alluvial fan in eastern Taiwan caused by rainfall-induced landslides

The formation and evolution of a coastal fan in eastern Taiwan associated with a sequence of rainfall-induced landslides during the 2009–2013 period are explored in this study. The evolution of these landslides is mainly attributed to the head-cutting process initiated by Typhoon Parma in October 2009. During the attack of Typhoon Megi in October, 2010, a subaerial coastal fan with a surface slope of 8.9° was formed after the mobilization of the rainfall-induced landslides. The geomorphic features both in the steep gully and on the coastal fan were categorized as the sequence of granular debris flows and sheet floods. Severe fan toe erosion occurred thereafter due to the wind-wave forcing. Even if the variations of both the cumulative rainfalls and the drainage areas are one or two orders of magnitude among devastating fan-forming landslides worldwide, the mean annual precipitation and the basin ruggedness index (Melton ratio) are effective indicators to normalize the rainfall threshold and to characterize the fan surface slope, respectively. Severe catastrophic landslides generally occur when the normalized cumulative rainfalls with respect to mean annual precipitation are greater than 0.1. The fan slope generally increases with the increasing Melton ratio for the catchment. Uchiogi’s empirical model is applicable for predicting the rainfall-induced area ratio of newly generated landslides. In this case study, the relationship of the fan area to the total landslide area follows a linear regressive curve when the ratio of landslide area with respect to the drainage area exceeds 0.0056.     

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.     

Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?

Predicting rainfall-induced landslides hinges on the quality of the rainfall product. Satellite rainfall estimates or rainfall reanalyses aid in studying landslide occurrences especially in ungauged areas, or in the absence of ground-based rainfall radars. Quality of these rainfall estimates is critical; hence, they are commonly crosschecked with their ground-based counterparts. Beyond their temporal precision compared to ground-based observations, we investigate whether these rainfall estimates are adequate for hindcasting landslides, which particularly requires accurate representation of spatial variability of rainfall. We developed a logistic regression model to hindcast rainfall-induced landslides in two sites in Japan. The model contains only a few topographic and geologic predictors to leave room for different rainfall products to improve the model as additional predictors. By changing the input rainfall product, we compared GPM IMERG and ERA5 rainfall estimates with ground radar–based rainfall data. Our findings emphasize that there is a lot of room for improvement of spatiotemporal prediction of landslides, as shown by a strong performance increase of the models with the benchmark radar data attaining 95% diagnostic performance accuracy. Yet, this improvement is not met by global rainfall products which still face challenges in reliably capturing spatiotemporal patterns of precipitation events.

     

Landslide hazard and cascading effects following the extreme rainfall event on Madeira Island (February 2010)

Heavy rainfall on February 20, 2010, triggered numerous shallow rapid landslides across Madeira Island, a Portuguese archipelago in the North Atlantic. Two days after the extreme rainfall event, a field campaign was started which involved describing and mapping a variety of landslide types and the related losses at 120 different locations throughout the Island. Most of the failures started as debris slides or avalanches at high elevations and transformed into debris flows which rushed downslope into populated coastal areas. Over half of the mapped landslides were located in the central and southern area of the island. A further 1,257 landslide locations were revealed in these areas using remote sensing data which were then assembled in a spatial database. Due to anthropogenic influences caused by urban development and population expansion, the event demonstrated the increased vulnerability of the island??s infrastructure. In order to mitigate future losses, it is important to quantify the typical preparatory factors which contribute to rainfall-induced landslides. This increases our understanding of the hazards and associated risks. The analysis shows that based on their spatial frequency, distribution and in the context of the drainage system, three main factors contribute to the triggering of the landslides due to the heavy rainfall event in February 2010: the characteristic soil type, the land cover and the slope gradient. It can now be recognized that the distribution of landslides is highly dependent on the temporal and spatial distribution of these factors. Furthermore, the anthropogenic impact on the extent of the hazard becomes obvious due to poor settlement planning and drainage system modification.