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Rainfall-Induced Shallow Landslides Modeling and 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 December 2021) | Viewed by 27703

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Special Issue Editors

Prof. Pantaleone De Vita

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Guest Editor

Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse (DiSTAR), University of Naples Federico II, Complesso Universitario di Monte Sant’Angelo, Via Cinthia, Edificio L1, 80126 Naples, Italy
Interests: engineering geology; hydrogeology

Prof. Dr. Claudia Meisina

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Guest Editor

Department of Earth and Environmental Sciences, University of Pavia, Via Adolfo Ferrata 1, 27100 Pavia, Italy
Interests: engineering geological mapping; hazard assessment (subsidence, landslide, liquefaction); geological interpretation of satellite interferometric data
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Special Issue Information

Dear Colleagues,

Due to difficult spatial and temporal predictivity, as well as their usual high velocity and destructive potential, rainfall-induced shallow landslides pose a worrying threat to cities, settlements and lifelines developed along footslope areas of unstable soil-mantled slopes. Depending on such awareness, the scientific community has been working since the 1970’s on advancing the understanding of hydrological processes and mechanisms leading to slope instabilities in order to provide even more reliable approaches aimed at modelling their triggering in space and time. In such a framework, the complete reduction of risk appears not practicable, especially for most cases in which the delocalization of anthropic activities is not feasible and a coexistence with landslide risk is to be accepted. In these conditions, early warning systems, based on reliable approaches of rainfall and soil hydrological observations and modelling, appears the most promising way to reduce risk and increase societal resilience. A further step in this direction is the recognition of the temporal occurrence of hydrological processes, leading to landslide triggering, as a key to assess landslide temporal probability, namely landslide hazard.

Given this scientific framework, we would like to invite scientists involved in this topic to contribute to this Special Issue, which will be focused, in a broad sense, on the analysis and modeling of hydrological processes leading to slope instability of shallow landslides as well as on analyses aimed at the definition of warning based on rainfall or soil hydrological monitoring. Therefore, manuscripts dealing with case studies of analysis, monitoring and modelling of hydrological processes leading to landslide triggering, as well as studies aimed at the assessment of empirical and deterministic rainfall thresholds will be welcomed.

Prof. Pantaleone De Vita
Assoc. Prof. Claudia Meisina
Guest Editors

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Keywords

shallow landslides
slope hydrological processes
early warning systems
rainfall thresholds
landslide mechanism
hazard to shallow landslides

Published Papers (9 papers)

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Research

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16 pages, 37588 KiB

Open AccessArticle

Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS

by Sen Zhang, Qigang Jiang, Dongzhe Wu, Xitong Xu, Yang Tan and Pengfei Shi

Water 2022, 14(4), 524; https://doi.org/10.3390/w14040524 - 10 Feb 2022

Cited by 8 | Viewed by 1695

Abstract

The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model has been widely used to define rainfall thresholds for triggering shallow landslides. In this study, the rainfall intensity(I)-duration(D) thresholds for multiple slope units of an area in Pu’an County, Guizhou Province, China were defined based on TRIGRS. Given that TRIGRS is used to simulate the slope stability under the conditions of a given increasing sequence of I-D data, if the slope reaches instability at I = a, D = b, it will also become unstable in the case of I = a, D > b or I > a, D = b. To explore the effect of these I-D data with the same I or D values on the definition of I-D thresholds and the best method to exclude these data, two screening methods were used to exclude the I-D data that caused instability in the TRIGTS simulation. First, I-D data with the same I values when D values are greater than a certain limit value were excluded. Second, several D values were selected to exclude I-D data with the same I values for a slope unit. Then, an I value was selected to exclude I-D data with the same D values. After screening, two different I-D thresholds were defined. The comparison with the thresholds defined without screening shows that the I-D data with the same I or D values will reduce the accuracy of thresholds. Moreover, the second screening method can entirely exclude these data. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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31 pages, 8475 KiB

Open AccessArticle

Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

by Antonio Pasculli, Jacopo Cinosi, Laura Turconi and Nicola Sciarra

Water 2021, 13(6), 750; https://doi.org/10.3390/w13060750 - 10 Mar 2021

Cited by 12 | Viewed by 2405

Abstract

The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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17 pages, 6002 KiB

Open AccessArticle

Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events

by Yuan-Chang Deng, Jin-Hung Hwang and Yu-Da Lyu

Water 2021, 13(5), 732; https://doi.org/10.3390/w13050732 - 08 Mar 2021

Cited by 7 | Viewed by 2327

Abstract

In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation. The regional hydrogeological parameters were calibrated using a reverse-optimization analysis based on an RGA (Real-coded Genetic Algorithm) of the optimization techniques and an improved version of the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability) model. The 2009 landslide event in the Xiaolin area of Taiwan, associated with Typhoon Morakot, was used to test the real-time regional landslide-susceptibility system. The system-testing results showed that the system configuration was feasible for practical applications concerning disaster prevention and mitigation. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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27 pages, 26491 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Incorporating the Effects of Complex Soil Layering and Thickness Local Variability into Distributed Landslide Susceptibility Assessments

by Francesco Fusco, Benjamin B. Mirus, Rex L. Baum, Domenico Calcaterra and Pantaleone De Vita

Water 2021, 13(5), 713; https://doi.org/10.3390/w13050713 - 05 Mar 2021

Cited by 21 | Viewed by 2845

Abstract

Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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15 pages, 5862 KiB

Open AccessArticle

Framework of Emergency Response System for Potential Large-Scale Landslide in Taiwan

by Yuan-Jung Tsai, Fang-Tsz Syu, Chjeng-Lun Shieh, Chi-Rong Chung, Shih-Shu Lin and Hsiao-Yuan Yin

Water 2021, 13(5), 712; https://doi.org/10.3390/w13050712 - 05 Mar 2021

Cited by 4 | Viewed by 2958

Abstract

In order to lower the risks of large-scale landslides and improve community resilience in Taiwan, a long-term project has been promoted by the Soil and Water Conservation Bureau since 2017. In this study, methods to build an emergency response framework including hazard mapping and early warning system establishment were introduced. For hazard mapping, large-scale landslides were categorized into a landslide, debris flow, or landslide dam type based on the movement of unstable materials and topography. Each disaster type has different hazard zone delineation methods to identify the affected areas. After establishing the possible effected areas, early warning mechanisms, including warning value using rainfall as the indicator and evacuation procedures, should be created for emergency response. To set the warning value, analysis of the occurrence thresholds of previous existing large-scale landslides was conducted to determine the critical rainfall and further utilized to set the warning value considering the evacuation time for the locals. Finally, for integration with the current debris flow emergency response system, potential large-scale landslide areas were further divided into two types based on their spatial relationship with debris flows. For those overlapping with existing debris flow protected targets, the current emergency response system was upgraded considering the impact of large-scale landslides, while the others were suggested for use in building a new emergency response procedure. This integrated framework could provide a feasible risk avoidance method for local government and residents. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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24 pages, 13996 KiB

Open AccessEditor’s ChoiceArticle

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

by Mariano Di Napoli, Diego Di Martire, Giuseppe Bausilio, Domenico Calcaterra, Pierluigi Confuorto, Marco Firpo, Giacomo Pepe and Andrea Cevasco

Water 2021, 13(4), 488; https://doi.org/10.3390/w13040488 - 13 Feb 2021

Cited by 40 | Viewed by 5012

Abstract

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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21 pages, 4757 KiB

Open AccessArticle

Using Field-Based Monitoring to Enhance the Performance of Rainfall Thresholds for Landslide Warning

by Minu Treesa Abraham, Neelima Satyam, Maria Alexandra Bulzinetti, Biswajeet Pradhan, Binh Thai Pham and Samuele Segoni

Water 2020, 12(12), 3453; https://doi.org/10.3390/w12123453 - 09 Dec 2020

Cited by 26 | Viewed by 3646

Abstract

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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18 pages, 7644 KiB

Open AccessTechnical Note

A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models

by Maria Alexandra Bulzinetti, Samuele Segoni, Giulio Pappafico, Elena Benedetta Masi, Guglielmo Rossi and Veronica Tofani

Water 2021, 13(17), 2313; https://doi.org/10.3390/w13172313 - 24 Aug 2021

Cited by 4 | Viewed by 2521

Abstract

Distributed physically based slope stability models usually provide outputs representing, on a pixel basis, the probability of failure of each cell. This kind of result, although scientifically sound, from an operational point of view has several limitations. First, the procedure of validation lacks standards. As instance, it is not straightforward to decide above which percentage of failure probability a pixel (or larger spatial units) should be considered unstable. Second, the validation procedure is a time-consuming task, usually requiring a long series of GIS operations to overlap landslide inventories and model outputs to extract statistically significant performance metrics. Finally, if model outputs are conceived to be used in the operational management of landslide hazard (e.g., early warning procedures), the pixeled probabilistic output is difficult to handle and a synthesis to characterize the hazard scenario over larger spatial units is usually required to issue warnings aimed at specific operational procedures. In this work, a tool is presented that automates the validation procedure for physically based distributed probabilistic slope stability models and translates the pixeled outputs in warnings released over larger spatial units like small watersheds. The tool is named DTVT (double-threshold validation tool) because it defines a warning criterion on the basis of two threshold values—the probability of failure above which a pixel should be considered stable (failure probability threshold, FPT) and the percentage of unstable pixels needed in each watershed to consider the hazard level widespread enough to justify the issuing of an alert (instability diffusion threshold, IDT). A series of GIS operations were organized in a model builder to reaggregate the raw instability maps from pixels to watershed; draw the warning maps; compare them with an existing landslide inventory; build a contingency matrix counting true positives, true negatives, false positive, and false negatives; and draw in a map the results of the validation. The DTVT tool was tested in an alert zone of the Aosta Valley (northern Italy) to investigate the high sensitivity of the results to the values selected for the two thresholds. Moreover, among 24 different configurations tested, we performed a quantitative comparison to identify which criterion (in the case of our study, there was an 85% or higher failure probability in 5% or more of the pixels of a watershed) produces the most reliable validation results, thus appearing as the most promising candidate to be used to issue alerts during civil protection warning activities. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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17 pages, 5445 KiB

Open AccessTechnical Note

HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation

by Jacob L. Conrad, Michael D. Morphew, Rex L. Baum and Benjamin B. Mirus

Water 2021, 13(13), 1752; https://doi.org/10.3390/w13131752 - 25 Jun 2021

Cited by 10 | Viewed by 2526

Abstract

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems. Full article

(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)

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