Rainfall Thresholds For Landslide Occurrence Research Articles

Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment, Central Java, Indonesia

Satellite rainfall products for landslide early warning prediction have been spotlighted by several researchers, in the last couple of decades. This study investigates the use of TRMM and ERA-Interim data, for the determination of rainfall thresholds and the prediction of precipitation, respectively, to be used for landslide early warning purposes at the Bogowonto catchment, Central Java, Indonesia. A landslide inventory of 218 landslides for the period of 2003–2016 was compiled, and rainfall data were retrieved for the landslide locations, as given by 6 ground stations, TRMM, and ERA-Interim data. First, rainfall data from the three different sources was compared in terms of correlation and extreme precipitation indices. Second, a procedure for the calculation of rainfall thresholds for landslide occurrence was followed consisting of four steps: i) the TRMM-based rainfall data was reconstructed for selected dates and locations characterized by landslide occurrence and non-occurrence; ii) the antecedent daily rainfall was calculated for 3, 5, 10, 15, 20 and 30 days for the selected dates and locations; iii) two-parameter daily rainfall-antecedent rainfall thresholds were calculated for the aforementioned dates; after analysis of the curves the optimum number of antecedent rainfall days was selected; and (iv) empirical rainfall thresholds for landslide occurrence were determined. The procedure was repeated for the entire landslide dataset, differentiating between forested and built-up areas, and between landslide occurrence in four temporal periods, in relation to the monsoon. The results indicated that TRMM performs well for the detection of very heavy precipitation and can be used to indicate the extreme rainfall events that trigger landslides. On the contrary, as ERA-Interim failed to detect those events, its applicability for LEWS remains limited. The 15-day antecedent rainfall was indicated to mostly affect the landslide occurrence in the area. The rainfall thresholds vary for forested and built-up areas, as well as for the beginning, middle and end of the rainy season.

Probabilistic Rainfall Thresholds for Landslide Occurrences in Bhutan

Several works have been conducted all across the globe determining rainfall thresholds in context of intensity-duration thresholds. Such results provide results in terms of occurrence or non-occurrence of landslides. This paper determines probabilities for various rainfall parameters causing landslides using a Bayesian approach along Phuentsholing Thimphu Highway in Chukha Dzongkhag of Bhutan. For Bhutan Himalayas, probabilistic rainfall thresholds for landslide initiation has not yet been attempted. The probabilities determined can be used for early warning systems considering it is a major trade route with India

Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall–duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall–duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004–2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3–30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data and the addition of more landslide data. These can also be used as an early warning system especially along the Phuentsholing–Thimphu Highway to prevent any disruptions of trade.

Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan

Bhutan is highly prone to landslides, particularly during the monsoon season. Several landslides often occur along the Phuentsholing–Thimphu highway, a very important infrastructure for the country. Worldwide, empirical rainfall thresholds represent a widely used tool to predict the occurrence of rainfall-induced landslides. Nevertheless, no thresholds are currently designed and proposed for any region in Bhutan. In this work, we define empirical cumulated event rainfall–rainfall duration thresholds for the possible initiation of landslides using information on 269 landslides that occurred between 1998 and 2015 along the 90-km highway stretch between the towns of Phuentsholing and Chukha, in southwestern Bhutan, and daily rainfall measurements obtained from three rain gauges. For this purpose, we apply a consolidated frequentist method and use an automatic tool that identifies the rainfall conditions responsible for the failures and calculates thresholds at different exceedance probabilities and the uncertainties associated with them. Analyzing rainfall and landslide data, we exclude from the analysis all the landslides for which the rainfall trigger is not certain, so we reduce the number of landslides from 269 to 43. The calculated thresholds are useful to identify the triggering conditions of rainfall-induced landslides and to predict the occurrence of the failures in the study area, which is, to date, poorly studied. These rainfall thresholds might be implemented in an early warning system, in order to reduce the risk posed by these phenomena to the population living and traveling along the investigated road stretch.

Rainfall thresholds for landslide occurrence: systematic underestimation using coarse temporal resolution data

Rainfall thresholds for landslides occurrence derived in real applications tend to be lower than the ones one would obtain using exact data. This letter shows how the use of coarse temporal resolution rainfall data causes a systematic overestimation of the duration of the triggering rainfall events that directly contributes to thresholds underestimation. A numerical experiment is devised to quantify this systematic effect for the relevant case of power-law depth/intensity–duration thresholds. In the examined conditions, i.e., the frequentist method at 5% non-exceedance probability level, ~ 70% underestimation of the scale parameter and ~ 60% overestimation of the shape parameter of the thresholds is to be expected using daily resolution rainfall data, but the exact quantification depends on the specific characteristics of each study case. The underestimation increases as the temporal resolution becomes larger than the expected minimal duration of the triggering events. Under operational conditions, sensitivity analyses based on the methods and datasets of interest are advised.

Impact of climate change on landslides frequency: the Esino river basin case study (Central Italy)

Researchers have long attempted to determine the amount of rainfall needed to trigger slope failures, yet relatively little progress has been reported on the effects of climate change on landslide initiation. Indeed, some relationships between landslides and climate change have been highlighted, but sign and magnitude of this correlation remain uncertain and influenced by the spatial and temporal horizon considered. This work makes use of statistically adjusted high-resolution regional climate model simulations, to study the expected changes of landslides frequency in the eastern Esino river basin (Central Italy). Simulated rainfall was used in comparison with rainfall thresholds for landslide occurrence derived by two observation-based statistical models (1) the cumulative event rainfall–rainfall duration model, and (2) the Bayesian probabilistic model. Results show an overall increase in projected landslide occurrence over the twenty-first century. This is especially confirmed in the high-emission scenario representative concentration pathway 8.5, where according to the first model, the events above rainfall thresholds frequency shift from ~ 0.025 to ~ 0.05 in the mountainous sector of the study area. Moreover, Bayesian analysis revealed the possible occurrence of landslide-triggering rainfall with a magnitude never occurred over the historical period. Landslides frequency change signal presents also considerable seasonal patterns, with summer displaying the steepest positive trend coupled to the highest inter-model spread. The methodological chain here proposed aims at representing a flexible tool for future landslide-hazard assessment, applicable over different areas and time horizons (e.g., short-term climate projections or seasonal forecasts).

A tool for the automatic calculation of rainfall thresholds for landslide occurrence

Empirical rainfall thresholds are commonly used to forecast landslide occurrence in wide areas. Thresholds are affected by several uncertainties related to the rainfall and the landslide information accuracy, the reconstruction of the rainfall responsible for the failure, and the method to calculate the thresholds. This limits the use of the thresholds in landslide early warning systems. To face the problem, we developed a comprehensive tool, CTRL–T (Calculation of Thresholds for Rainfall-induced Landslides−Tool) that automatically and objectively reconstructs rainfall events and the triggering conditions responsible for the failure, and calculates rainfall thresholds at different exceedance probabilities. CTRL−T uses a set of adjustable parameters to account for different morphological and climatic settings. We tested CTRL−T in Liguria region (Italy), which is highly prone to landslides. We expect CTRL−T has an impact on the definition of rainfall thresholds in Italy, and elsewhere, and on the reduction of the risk posed by rainfall-induced landslides.

Regional rainfall thresholds for landslide occurrence using a centenary database

Abstract. This work proposes a comprehensive method to assess rainfall thresholds for landslide initiation using a centenary landslide database associated with a single centenary daily rainfall data set. The method is applied to the Lisbon region and includes the rainfall return period analysis that was used to identify the critical rainfall combination (cumulated rainfall duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds are assessed and calibrated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, these thresholds may be used with acceptable confidence up to 50 km from the rain gauge. The rainfall thresholds obtained using linear and potential regression perform well in ROC metrics. However, the intermediate thresholds based on the probability of landslide events established in the zone between the lower-limit threshold and the upper-limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

Implications of climate change on landslide hazard in Central Italy

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003–2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from regional climate model projections for the 14-year calibration period 2002–2015, and for the 40-year projection period 2010–2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to forcethe Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms of variations (or lack of variations) in the rainfall thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect our results to contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards.

A review of the recent literature on rainfall thresholds for landslide occurrence

The topic of rainfall thresholds for landslide occurrence was thoroughly investigated, producing abundance of case studies at different scales of analysis and several technical and scientific advances. We reviewed the most recent papers published in scientific journals, highlighting significant advances and critical issues. We collected and grouped all the information on rainfall thresholds into four categories: publication details, geographical distribution and uses, dataset features, threshold definition. In each category, we selected descriptive information to characterize each one of the 115 rainfall threshold published in the last 9 years. The main improvements that stood out from the review are the definition of standard procedures for the identification of rainfall events and for the objective definition of the thresholds. Numerous advances were achieved in the cataloguing of landslides too, which can be defined as one of the most important variables, together with rainfall data, for drawing reliable thresholds. Another focal point of the reviewed articles was the increased definition of thresholds with different exceedance probabilities to be employed for the definition of warning levels in landslide early warning systems. Nevertheless, drawbacks and criticisms can be identified in most part of the recent literature on rainfall thresholds. The main issues concern the validation process, which is seldom carried out, and the very frequent lack of explanations for the rain gauge selection procedure. The paper may be used as a guide to find adequate literature on the most used or the most advanced approaches followed in every step of the procedure for defining reliable rainfall thresholds. Therefore, it constitutes a guideline for future studies and applications, in particular in early warning systems. The paper also aims at addressing the gaps that need to be filled to further enhance the quality of the research products in this field. The contribution of this manuscript could be seen not only as a review of the state of the art, but also an effective method to disseminate the best practices among scientists and stakeholders involved in landslide hazard management.

Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India

The Indian Himalayan locale has been essentially influenced by the increase in the frequency of landslide events. Out of 0.42 million km2 of India’s landmass prone to landslides, 42% falls in the North East Himalaya, especially Darjeeling and Sikkim. The harm due to landslides is massive, causing loss of life, property and agricultural land, thus initiating a dire need for formulating strategies to minimize its impact. There have been many attempts to establish rainfall thresholds on global, regional and local scales which compare analysis at various levels. Rainfall thresholds anticipate landslide occurrence and help in issuing a warning to civil authorities and the general population. However, empirical relations defining the relationship between landslide occurrences and rainfall events in Kalimpong remain unattended. In this paper, rainfall thresholds for landslide occurrence have been ascertained for Kalimpong area of Darjeeling Himalayas, in the Indian province of West Bengal. A threshold for landslide occurrences which describes intensity–duration threshold was estimated using the power law equation. The relationship for the study area is I = 3.52 D−0.41 (I is rainfall intensity (mm/h) and D is duration (h)). Results show that events with a rainfall intensity of 0.95 mm/h with a duration of 24 h have a high risk of slide initiation in this region. It also demonstrates that for 10- and 20-day antecedent rainfall, an intensity of 88.37 and 133.5 mm is required for landslide occurrence in this region. Such data would help in implementing early warning systems that focus on rainfall thresholds and forecasting rainfall measurements. Rainfall thresholds for landslide initiation in Kalimpong can be enhanced with more precipitation and landslide data as and when available.

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.

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-a-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.

Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach

Various methods have been proposed in the literature to predict the rainfall conditions that are likely to trigger landslides in a given area. Most of these methods, however, only consider the rainfall events that resulted in landslides and provide deterministic thresholds with a single possible output (landslide or no‐landslide) for a given input (rainfall conditions). Such a deterministic view is not always suited to landslides. Slope stability, in fact, is not ruled by rainfall alone and failure conditions are commonly achieved with a combination of numerous relevant factors. When different outputs (landslide or no‐landslide) can be obtained for the same input a probabilistic approach is preferable. In this work we propose a new method for evaluating rainfall thresholds based on Bayesian probability. The method is simple, statistically rigorous, and returns a value of landslide probability (from 0 to 1) for each combination of the selected rainfall variables. The proposed approach was applied to the Emilia‐Romagna Region of Italy taking advantage of the historical landslide archive, which includes more than 4000 events for which the date of occurrence is known with daily accuracy. The results show that landsliding in the study area is strongly related to rainfall event parameters (duration, intensity, total rainfall) while antecedent rainfall seems to be less important. The distribution of landslide probability in the rainfall duration‐intensity shows an abrupt increase at certain duration‐intensity values which indicates a radical change of state of the system and suggests the existence of a real physical threshold.