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Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 60197

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Dr. Samuele Segoni

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Guest Editor

Department of Earth Sciences, University of Firenze, Firenze, Italy
Interests: prediction and mapping of landslide hazards; physically based models for the triggering of shallow landslides; landslide susceptibility maps; rainfall thresholds for landslide triggering; regional-scale landslide early warning systems; civil protection; land planning; landslide risk assessment
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Dr. Stefano Luigi Gariano

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Guest Editor

CNR IRPI (Research Institute for Geo-Hydrological Protection - Italian National Research Council), Perugia, Italy
Interests: rainfall thresholds; landslide early warning systems; rainfall-induced landslides; rainfall analysis; landslide prediction; hydrology; geomorphology
Special Issues, Collections and Topics in MDPI journals

Dr. Ascanio Rosi

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Guest Editor

University of Firenze, Department of Earth Sciences
Interests: landslide prediction; rainfall thresholds; remote sensing; early warning system; rainfall data management; civil protection

Special Issue Information

Dear Colleagues,

Landslides are destructive processes causing casualties and damage worldwide. The majority of the landslides are triggered by intense and/or prolonged rainfall. Therefore, the prediction of the occurrence of rainfall-induced landslides is an important scientific and social issue. To mitigate the risk posed by rainfall-induced landslides, landslide early warning systems (LEWS) can be built and applied at different scales as effective non-structural mitigation measures. Usually, the core of a LEWS is constituted of a mathematical model that predicts landslide occurrence in the monitored areas. In the last decades, rainfall thresholds have become a widespread and well established technique for the prediction of rainfall induced landslides, and for the setting up of prototype or operational LEWS. A rainfall threshold expresses, with a mathematic law, the rainfall amount that, when reached or exceeded, is likely to trigger one or more landslides. Rainfall thresholds can be defined with relatively few parameters and are very straightforward to operate, because their application within LEWS is usually based only on the comparison of monitored and/or forecasted rainfall.

This Special Issue collects contributions about the recent research advances or well-documented applications of rainfall thresholds as well as other innovative methods for landslide prediction and early warning. Contributions regarding the description of a LEWS or single components of LEWS (e.g., monitoring approaches, forecasting models, communication strategies, and emergency management) are also welcome. We encourage, in particular, the submission of contributions concerning the definition and validation of rainfall thresholds, and their operative implementation in LEWS. Other approaches for the forecasting of landslides are also of interest, such as physically-based modelling, hazard mapping, and the monitoring of hydrologic and geotechnical indicators, especially when described in the framework of an operational or prototype early warning system.

Dr. Samuele Segoni,
Dr. Stefano Luigi Gariano,
Dr. Ascanio Rosi
Guest Editors

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Keywords

Early warning system
Rainfall induced landslides
Debris flows
Landslide
Hazard
Rainfall threshold
Landslide prediction
Forecasting
Rainfall event
Rainfall extremes

Published Papers (12 papers)

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Editorial

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5 pages, 215 KiB

Open AccessEditorial

Preface to the Special Issue “Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning”

by Samuele Segoni, Stefano Luigi Gariano and Ascanio Rosi

Water 2021, 13(3), 323; https://doi.org/10.3390/w13030323 - 28 Jan 2021

Cited by 3 | Viewed by 1529

Abstract

Landslides are frequent and widespread destructive processes causing casualties and damage worldwide [...] Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

Research

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15 pages, 3592 KiB

Open AccessArticle

Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool

by Galena Jordanova, Stefano Luigi Gariano, Massimo Melillo, Silvia Peruccacci, Maria Teresa Brunetti and Mateja Jemec Auflič

Water 2020, 12(5), 1449; https://doi.org/10.3390/w12051449 - 19 May 2020

Cited by 28 | Viewed by 4394

Abstract

Rainfall-triggered shallow landslides represent a major threat to people and infrastructure worldwide. Predicting the possibility of a landslide occurrence accurately means understanding the trigger mechanisms adequately. Rainfall is the main cause of slope failures in Slovenia, and rainfall thresholds are among the most-used tools to predict the possible occurrence of rainfall-triggered landslides. The recent validation of the prototype landslide early system in Slovenia highlighted the need to define new reliable rainfall thresholds. In this study, several empirical thresholds are determined using an automatic tool. The thresholds are represented by a power law curve that links the cumulated event rainfall (E, in mm) with the duration of the rainfall event (D, in h). By eliminating all subjective criteria thanks to the automated calculation, thresholds at diverse non-exceedance probabilities are defined and validated, and the uncertainties associated with their parameters are estimated. Additional thresholds are also calculated for two different environmental classifications. The first classification is based on mean annual rainfall (MAR) with the national territory divided into three classes. The area with the highest MAR has the highest thresholds, which indicates a likely adaptation of the landscape to higher amounts of rainfall. The second classification is based on four lithological units. Two-thirds of the considered landslides occur in the unit of any type of clastic sedimentary rocks, which proves an influence of the lithology on the occurrence of shallow landslides. Sedimentary rocks that are prone to weathering have the lowest thresholds, while magmatic and metamorphic rocks have the highest thresholds. Thresholds obtained for both classifications are far less reliable due to the low number of empirical points and can only be used as indicators of rainfall conditions for each of the classes. Finally, the new national thresholds for Slovenia are also compared with other regional, national, and global thresholds. The thresholds can be used to define probabilistic schemes aiming at the operative prediction of rainfall-induced shallow landslides in Slovenia, in the framework of the Slovenian prototype early warning system. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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13 pages, 2984 KiB

Open AccessArticle

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

by Minu Treesa Abraham, Neelima Satyam, Sai Kushal, Ascanio Rosi, Biswajeet Pradhan and Samuele Segoni

Water 2020, 12(4), 1195; https://doi.org/10.3390/w12041195 - 22 Apr 2020

Cited by 24 | Viewed by 4891

Abstract

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. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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18 pages, 4437 KiB

Open AccessEditor’s ChoiceArticle

The Selection of Rain Gauges and Rainfall Parameters in Estimating Intensity-Duration Thresholds for Landslide Occurrence: Case Study from Wayanad (India)

by Minu Treesa Abraham, Neelima Satyam, Ascanio Rosi, Biswajeet Pradhan and Samuele Segoni

Water 2020, 12(4), 1000; https://doi.org/10.3390/w12041000 - 01 Apr 2020

Cited by 34 | Viewed by 6472

Abstract

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity–Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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17 pages, 2933 KiB

Open AccessArticle

Rainfall Event–Duration Thresholds for Landslide Occurrences in China

by Shuangshuang He, Jun Wang and Songnan Liu

Water 2020, 12(2), 494; https://doi.org/10.3390/w12020494 - 12 Feb 2020

Cited by 40 | Viewed by 4942

Abstract

A rainfall threshold for landslide occurrence at a national scale in China has rarely been developed in the early warning system for landslides. Based on 771 landslide events that occurred in China during 1998–2017, four groups of rainfall thresholds at different quantile levels of the quantile regression for landslide occurrences in China are defined, which include the original rainfall event–duration (E–D) thresholds and normalized (the accumulated rainfall is normalized by mean annual precipitation) (EMAP–D) rainfall thresholds based on the merged rainfall and the Climate Prediction Center Morphing technique (CMORPH) rainfall products, respectively. Each group consists of four sub-thresholds in rainy season and non-rainy season, and both are divided into short duration (<48 h) and long duration (≥48 h). The results show that the slope of the regression line for the thresholds in the events with long durations is larger than that with short durations. In addition, the rainfall thresholds in the non-rainy season are generally lower than those in the rainy season. The E–D thresholds defined in this paper are generally lower than other thresholds in previous studies on a global scale, and a regional or national scale in China. This might be due to there being more landslide events used in this paper, as well as the combined effects of special geological environment, climate condition and human activities in China. Compared with the previous landslide model, the positive rates of the rainfall thresholds for landslides have increased by 16%–20%, 10%–17% and 20%–38% in the whole year, rainy season and non-rainy season, respectively. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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24 pages, 4679 KiB

Open AccessEditor’s ChoiceArticle

Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

by Abhirup Dikshit, Raju Sarkar, Biswajeet Pradhan, Ratiranjan Jena, Dowchu Drukpa and Abdullah M. Alamri

Water 2020, 12(1), 267; https://doi.org/10.3390/w12010267 - 17 Jan 2020

Cited by 34 | Viewed by 5669

Abstract

Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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17 pages, 6946 KiB

Open AccessArticle

Using a Tank Model to Determine Hydro-Meteorological Thresholds for Large-Scale Landslides in Taiwan

by Guan-Wei Lin, Hsien-Li Kuo, Chi-Wen Chen, Lun-Wei Wei and Jia-Ming Zhang

Water 2020, 12(1), 253; https://doi.org/10.3390/w12010253 - 16 Jan 2020

Cited by 7 | Viewed by 3540

Abstract

Rainfall thresholds for slope failures are essential information for establishing early-warning systems and for disaster risk reduction. Studies on the thresholds for rainfall-induced landslides of different scales have been undertaken in recent decades. This study attempts to establish a warning threshold for large-scale landslides (LSLs), which are defined as landslides with a disturbed area more massive than 0.1 km². The numerous landslides and extensive rainfall records make Taiwan an appropriate area to investigate the rainfall conditions that can result in LSLs. We used landslide information from multiple sources and rainfall data captured by 594 rain gauges to create a database of 83 rainfall events associated with LSLs in Taiwan between 2001 and 2016. The corresponding rainfall duration, cumulative event rainfall, and rainfall intensity for triggering LSLs were determined. This study adopted the tank model to estimate conceptual water depths (S₁, S₂, S₃) in three-layer tanks and calculated the soil water index (SWI) by summing up the water depths in the three tanks. The empirical SWI and duration (SWI–D) threshold for triggering LSLs occurring during 2001–2013 in Taiwan is determined as SWI = 155.20 − 1.56D and D ≥ 24 h. The SWI–D threshold for LSLs is higher than that for small-scale landslides (SSLs), those with a disturbed area smaller than 0.1 km². The LSLs that occurred during 2015–2016 support this finding. It is notable that when the SWI and S₃ reached high values, the potential of LSLs increased significantly. The rainfall conditions for triggering LSLs gradually descend with increases in antecedent SWI. Unlike the rainfall conditions for triggering SSLs, those for triggering LSLs are related to the long duration–high intensity type of rainfall event. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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28 pages, 6586 KiB

Open AccessArticle

Empirical and Physically Based Thresholds for the Occurrence of Shallow Landslides in a Prone Area of Northern Italian Apennines

by Massimiliano Bordoni, Beatrice Corradini, Luca Lucchelli, Roberto Valentino, Marco Bittelli, Valerio Vivaldi and Claudia Meisina

Water 2019, 11(12), 2653; https://doi.org/10.3390/w11122653 - 16 Dec 2019

Cited by 38 | Viewed by 4003

Abstract

Rainfall thresholds define the conditions leading to the triggering of shallow landslides over wide areas. They can be empirical, which exploit past rainfall data and landslide inventories, or physicallybased, which integrate slope physical–hydrological modeling and stability analyses. In this work, a comparison between these two types of thresholds was performed, using data acquired in Oltrepò Pavese (Northern Italian Apennines), to evaluate their reliability. Empirical thresholds were reconstructed based on rainfalls and landslides triggering events collected from 2000 to 2018. The same rainfall events were implemented in a physicallybased model of a representative testsite, considering different antecedent pore-water pressures, chosen according to the analysis of hydrological monitoring data. Thresholds validation was performed, using an external dataset (August 1992–August 1997). Soil hydrological conditions have a primary role on predisposing or preventing slope failures. In Oltrepò Pavese area, cold and wet months are the most susceptible periods, due to the permanence of saturated or close-to-saturation soil conditions. The lower the pore-water pressure is at the beginning of an event, the higher the amount of rain required to trigger shallow failures is. physicallybased thresholds provide a better reliability in discriminating the events which could or could not trigger slope failures than empirical thresholds. The latter provide a significant number of false positives, due to neglecting the antecedent soil hydrological conditions. These results represent a fundamental basis for the choice of the best thresholds to be implemented in a reliable earlywarning system. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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22 pages, 4862 KiB

Open AccessArticle

Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding

by Elise Monsieurs, Olivier Dewitte, Arthur Depicker and Alain Demoulin

Water 2019, 11(11), 2202; https://doi.org/10.3390/w11112202 - 23 Oct 2019

Cited by 16 | Viewed by 2843

Abstract

Determining rainfall thresholds for landsliding is crucial in landslide hazard evaluation and early warning system development, yet challenging in data-scarce regions. Using freely available satellite rainfall data in a reproducible automated procedure, the bootstrap-based frequentist threshold approach, coupling antecedent rainfall (AR) and landslide susceptibility data as proposed by Monsieurs et al., has proved to provide a physically meaningful regional AR threshold equation in the western branch of the East African Rift. However, previous studies could only rely on global- and continental-scale rainfall and susceptibility data. Here, we use newly available regional-scale susceptibility data to test the robustness of the method to different data configurations. This leads us to improve the threshold method through using stratified data selection to better exploit the data distribution over the whole range of susceptibility. In addition, we discuss the effect of outliers in small data sets on the estimation of parameter uncertainties and the interest of not using the bootstrap technique in such cases. Thus improved, the method effectiveness shows strongly reduced sensitivity to the used susceptibility data and is satisfyingly validated by new landslide occurrences in the East African Rift, therefore successfully passing first transferability tests. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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16 pages, 6829 KiB

Open AccessArticle

Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

by Minu Treesa Abraham, Deekshith Pothuraju and Neelima Satyam

Water 2019, 11(10), 2113; https://doi.org/10.3390/w11102113 - 11 Oct 2019

Cited by 48 | Viewed by 9735

Abstract

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D^−0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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20 pages, 10162 KiB

Open AccessArticle

Numerical Runout Modeling Analysis of the Loess Landslide at Yining, Xinjiang, China

by Longwei Yang, Yunjie Wei, Wenpei Wang and Sainan Zhu

Water 2019, 11(7), 1324; https://doi.org/10.3390/w11071324 - 26 Jun 2019

Cited by 12 | Viewed by 4307

Abstract

The Panjinbulake loess landslide is located in the western part of the Loess Plateau, in Yining County, Xinjiang, China. It is characterized by its long runout and rapid speed. Based on a field geological survey and laboratory test data, we used the DAN-W dynamic numerical simulation software (Dynamic Analysis Of Landslides, Release 10, O. Hungr Geotechnical Research Inc., West Vancouver, BC, Canada) and multiple sets of rheological models to simulate the whole process of landslide movement. The best rheological groups of the features of the loess landslide process were obtained by applying the Voellmy rheological model in the debris flow area and applying the Frictional rheological model in the sliding source area and accumulation area. We calculated motion features indicating that the landslide movement duration was 22 s, the maximum movement speed was 20.5 m/s, and the average thickness of the accumulation body reached 5.5 m. The total accumulation volume, the initial slide volume and the long runout distance were consistent with the actual situation. In addition, the potential secondary disaster was evaluated. The results show that the DAN-W software and related model parameters can accurately simulate and predict the dynamic hazardous effects of high-speed and long runout landslides. Together, these predictions could help local authorities make the best hazard reduction measures and to promote local development. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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12 pages, 3096 KiB

Open AccessTechnical Note

Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

by Abhirup Dikshit, Raju Sarkar, Biswajeet Pradhan, Saroj Acharya and Kelzang Dorji

Water 2019, 11(8), 1616; https://doi.org/10.3390/w11081616 - 05 Aug 2019

Cited by 47 | Viewed by 6813

Abstract

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. Full article

(This article belongs to the Special Issue Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning)

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