Special Issue "Analysis and Prediction of Rainfall-Induced Landslides in a Changing Environment"

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Dr. Stefano Luigi Gariano
E-Mail Website
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
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Dr. Samuele Segoni
E-Mail Website
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. Guido Rianna
E-Mail Website
Guest Editor
Regional Models and Geo-Hydrological Impacts (REMHI) Division, CMCC Foundation Euro-Mediterranean Center on Climate Change, 73100 Lecce, Italy
Interests: slope stability; hydrological modeling; water balance; evapotranspiration modeling; hydro-thermal modeling; landslides; evaporation; unsaturated soil
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Rainfall-induced landslides are frequent and widespread natural phenomena that cause damage to humans and goods worldwide. About 90% of the landslides that caused casualties worldwide are triggered by rainfall. Therefore, the prediction of rainfall-induced landslides constitutes a key scientific question with significant social implications.

To model the relationship between rainfall and slope stability and to predict the occurrence of landslides, two approaches are generally adopted. The first, “physically-based”, attempts to determine the influence of water (rainfall, infiltration, superficial flow) on slope stability by modelling its effects in terms of pore water pressure and related balance between shear stress and resistance. In this regard, numerical models are employed, and a notable number of detailed data is commonly required. The second approach, “empirical”, is based on a statistical–probabilistic analysis of rainfall time series and of past dates of known occurrences of slope movement. In this context, rainfall thresholds are the most commonly used empirical tools to predict the possible occurrence of a single landslide or a population of landslides within a homogeneous geo-environmental setting in a given study area.

This Special Issue will collect contributions about recent research advances and/or well-documented applications in the prediction of rainfall-induced landslides. Contributions regarding the definition and the application of both empirical and physically-based methods and procedures to single phenomena or a population of landslides are welcome. Given the strong relationship between rainfall and landslides, variations in rainfall regimes are supposed to have effects on slope stability and on landslide characteristics. Therefore, contributions regarding the evaluation of the impact of observed and expected climatic and environmental (e.g. land use/cover) changes on landslide activity, frequency, and distribution are also welcome. Finally, we encourage the submission of contributions concerning operative applications, with the validation and performance evaluation of the models.

Dr. Stefano Luigi Gariano
Dr. Samuele Segoni
Dr. Guido Rianna
Guest Editors

Manuscript Submission Information

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Keywords

  • Rainfall-Induced Landslides
  • Predictive Approaches
  • Landslide Analysis
  • Landslide Hazard
  • Climate Change
  • Extreme Rainfall Events
  • Land Use Change
  • Adaptation Strategies
  • Disaster Risk Reduction

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Published Papers (4 papers)

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Research

Article
Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover
Geosciences 2020, 10(6), 217; https://doi.org/10.3390/geosciences10060217 - 03 Jun 2020
Cited by 5 | Viewed by 1065
Abstract
In June 2009 and September 2014, the Styrian Basin in Austria was affected by extreme events of heavy thunderstorms, triggering thousands of landslides. Since the relationship between intense rainfall, land cover/land use (LULC), and landslide occurrences is still not fully understood, our objective [...] Read more.
In June 2009 and September 2014, the Styrian Basin in Austria was affected by extreme events of heavy thunderstorms, triggering thousands of landslides. Since the relationship between intense rainfall, land cover/land use (LULC), and landslide occurrences is still not fully understood, our objective was to develop a model design that allows to assess landslide susceptibility specifically for past triggering events. We used generalized additive models (GAM) to link land surface, geology, meteorological, and LULC variables to observed slope failures. Accounting for the temporal variation in landslide triggering, we implemented an innovative spatio-temporal approach for landslide absence sampling. We assessed model performance using k-fold cross-validation in space and time to estimate the area under the receiver operating characteristic curve (AUROC). Furthermore, we analyzed the variable importance and its relationship to landslide occurrence. Our results showed that the models had on average acceptable to outstanding landslide discrimination capabilities (0.81–0.94 mAUROC in space and 0.72–0.95 mAUROC in time). Furthermore, meteorological and LULC variables were of great importance in explaining the landslide events (e.g., five-day rainfall 13.6–17.8% mean decrease in deviance explained), confirming their usefulness in landslide event analysis. Based on the present findings, future studies may assess the potential of this approach for developing future storylines of slope instability based on climate and LULC scenarios. Full article
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Article
Spatial Landslide Risk Assessment at Phuentsholing, Bhutan
Geosciences 2020, 10(4), 131; https://doi.org/10.3390/geosciences10040131 - 07 Apr 2020
Cited by 13 | Viewed by 1548
Abstract
Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where [...] Read more.
Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extended to other areas around the investigated area. The tool can serve as an indicator for the civil authorities to analyze the risk posed by landslides due to the rapid infrastructure development in the region. Full article
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Article
Determination of Rainfall Thresholds for Landslide Prediction Using an Algorithm-Based Approach: Case Study in the Darjeeling Himalayas, India
Geosciences 2019, 9(7), 302; https://doi.org/10.3390/geosciences9070302 - 10 Jul 2019
Cited by 17 | Viewed by 1927
Abstract
Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been [...] Read more.
Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system. Full article
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Article
Landslides in the Mountain Region of Rio de Janeiro: A Proposal for the Semi-Automated Definition of Multiple Rainfall Thresholds
Geosciences 2019, 9(5), 203; https://doi.org/10.3390/geosciences9050203 - 08 May 2019
Cited by 18 | Viewed by 1995
Abstract
In 2011 Brazil experienced the worst disaster in the country’s history. There were 918 deaths and thousands made homeless in the mountainous region of Rio de Janeiro State due to several landslides triggered by heavy rainfalls. This area constantly suffers high volumes of [...] Read more.
In 2011 Brazil experienced the worst disaster in the country’s history. There were 918 deaths and thousands made homeless in the mountainous region of Rio de Janeiro State due to several landslides triggered by heavy rainfalls. This area constantly suffers high volumes of rain and episodes of landslides. Due to these experiences, we used the MaCumBa (Massive CUMulative Brisk Analyser) software to identify rainfall intensity–duration thresholds capable of triggering landslides in the most affected municipalities of this region. More than 3000 landslides and rain data from a 10-year long dataset were used to define the thresholds and one year was used to validate the results. In this work, a set of three thresholds capable of defining increasing alert levels (moderate, high and very high) has been defined for each municipality. Results show that such thresholds may be used for early alerts. In the future, the same methodology can be replicated to other Brazilian municipalities with different datasets, leading to more accurate warning systems. Full article
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