Occurrence Of Rainfall-induced Shallow Landslides Research Articles

Deep learning forecast of rainfall-induced shallow landslides

Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts.

Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale

A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.

Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models

Physical models used to forecast the temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. Owing to the existing measuring technology and our knowledge of the physical laws controlling landslide initiation, model uncertainties are due to an inability to accurately quantify the model input parameters and rainfall forcing data. An uncertainty analysis of slope instability prediction provides a rationale for refining the geotechnical models. The Transient Rainfall Infiltration and Grid-based Regional Slope Stability-Probabilistic (TRIGRS-P) model adopts a probabilistic approach to compute the changes in the Factor of Safety (FS) due to rainfall infiltration. Slope Infiltration Distributed Equilibrium (SLIDE) is a simplified physical model for landslide prediction. The new code (SLIDE-P) is also modified by adopting the same probabilistic approach to allow values of the SLIDE model input parameters to be sampled randomly. This study examines the relative importance of rainfall variability and the uncertainty in the other variables that determine slope stability. The precipitation data from weather stations, China Meteorological Administration Land Assimilation System 2.0 (CLDAS2.0), China Meteorological Forcing Data set precipitation (CMFD), and China geological hazard bulletin are used to drive TRIGRS, SLIDE, TRIGRS-P and SLIDE-P models. The TRIGRS-P and SLIDE-P models are used to generate the input samples and to calculate the values of FS. The outputs of several model runs with varied input parameters and rainfall forcings are analyzed statistically. A comparison suggests that there are significant differences in the simulations of the TRIGRS-P and SLIDE-P models. Although different precipitation data sets are used, the simulation results of TRIGRS-P are more concentrated. This study can inform the potential use of numerical models to forecast the spatial and temporal occurrence of regional rainfall-induced shallow landslides.

Preliminary analysis on the impacts of the rhizosphere on occurrence of rainfall-induced shallow landslides

Rainfall-induced diffuse shallow landslides are one of the most critical natural hazards as they often evolve into highly destructive flow slides and debris flows. Vegetation is recognised to play a key role in landslide occurrence and is frequently invoked as a potential remedial measure for slope stabilisation at the catchment scale. The beneficial action of vegetation is generally associated with mechanical (root anchoring) and hydrological (suction generated by root water uptake) effects. There is indeed a third effect that has thus far been little explored. The rhizosphere, the portion of soil directly affected by plant roots, is characterised by hydraulic conductivity higher than the underlying soil horizons. This significantly affects hillslope hydrology by promoting lateral diversion of rainwater. This paper presents a case study in Scotland where the rhizosphere is demonstrated to play a major role in controlling shallow landslides. Field investigation and laboratory testing were carried out to characterise the hydraulic conductivity of the rhizosphere and deeper horizons. In turn, this formed the basis for the development of a physically based model for the slope. The model was first validated against its capability to simulate failure of two historical landslides and then exploited to demonstrate the beneficial effect of the rhizosphere. The lesson learned from this study is that shallow landslide hazard can be mitigated by enhancing the capacity of the rhizosphere to act as a natural lateral drainage. This implies that plants with root-system architecture that enhances lateral subsurface flow should be privileged when designing vegetation-based remedial measures.

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

ABSTRACTIn the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

Susceptibility Assessment of Rainfall-induced Shallow Landslides using a GIS-supported Deterministic Analysis – A Pilot Study

The landscape of Guwahati city (Assam) is characterized by a number of residual hills (eighteen) scattered in and around the main urban areas. The geology of all these isolated hills is the same, comprising an overburden of soil (weathered mantle) over bedrocks of porphyritic granite, quartzo-feldspathic gneiss, migmatites and schistose rocks. Shallow landslides in the form of soil slips are regular phenomena in these hills during the monsoons which are triggered by spells of high intensity rainfall. The Narakasur hill which lies in the heart of the city was selected as a small sample area for deterministic landslide susceptibility analysis to forecast the spatial occurrence of rainfall-induced shallow landslides. A grid-based slope-stability analysis was incorporated with the GIS spatial functions adopting the “infinite-slope” geometry to balance the resisting and the driving forces acting on the sliding mass. Landslide susceptibility is expressed by Safety Factor.Input data for the analysis includes topographic slope, soil thickness, water table depth and material strength properties. The effect of Safety Factor values, arising due to variation in material properties and water table conditions, was shown over the study area. Safety factors were calculated for three different water table depth scenarios assuming that soil is completely dry, completely saturated with water and an intermediate condition. Based on the calculated values of the Safety Factor the area was divided into stable, critical and unstable zones.Comparing the results with the shallow landslide inventory map, it has been observed that more than 70% agreement between predicted shallow landslide susceptibility and the inventory.

Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy

Empirical rainfall thresholds are tools to forecast the possible occurrence of rainfall-induced shallow landslides. Accurate prediction of landslide occurrence requires reliable thresholds, which need to be properly validated before their use in operational warning systems. We exploited a catalogue of 200 rainfall conditions that have resulted in at least 223 shallow landslides in Sicily, southern Italy, in the 11-year period 2002–2011, to determine regional event duration–cumulated event rainfall (ED) thresholds for shallow landslide occurrence. We computed ED thresholds for different exceedance probability levels and determined the uncertainty associated to the thresholds using a consolidated bootstrap nonparametric technique. We further determined subregional thresholds, and we studied the role of lithology and seasonal periods in the initiation of shallow landslides in Sicily. Next, we validated the regional rainfall thresholds using 29 rainfall conditions that have resulted in 42 shallow landslides in Sicily in 2012. We based the validation on contingency tables, skill scores, and a receiver operating characteristic (ROC) analysis for thresholds at different exceedance probability levels, from 1% to 50%. Validation of rainfall thresholds is hampered by lack of information on landslide occurrence. Therefore, we considered the effects of variations in the contingencies and the skill scores caused by lack of information. Based on the results obtained, we propose a general methodology for the objective identification of a threshold that provides an optimal balance between maximization of correct predictions and minimization of incorrect predictions, including missed and false alarms. We expect that the methodology will increase the reliability of rainfall thresholds, fostering the operational use of validated rainfall thresholds in operational early warning system for regional shallow landslide forecasting.

Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach

Abstract. Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. These models extend spatially the static stability models adopted in geotechnical engineering, and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the operation of the existing models lays in the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of rainfall-induced shallow landslides. For this purpose, we have modified the transient rainfall infiltration and grid-based regional slope-stability analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a probabilistic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, that is, the Frontignano (Italy) and the Mukilteo (USA) areas. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the message passing interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Spatial and temporal occurrence of rainfall-induced shallow landslides of flow type: A case of Sarno-Quindici, Italy

Rainfall-induced shallow landslides of flow type provide unstable masses which often travel long run-out distances with high velocities, thus posing a high societal risk when they affect large areas. Therefore, analysis of their spatial and temporal occurrence is relevant to landslide hazard assessment as the first step of the risk analysis. In order to address this issue, this paper outlines a multidisciplinary procedure that is applied to the May 1998 Sarno-Quindici landslides (southern Italy), whose spatial and temporal occurrence is not satisfactorily addressed in current literature. The spatial occurrence of the landslides is analysed using heuristic models for both the source and propagation areas. The temporal occurrence of the landslides is firstly compiled and then related to the cumulated rainfall, stratigraphy and hydraulic boundary conditions. Finally, the spatial and temporal occurrence of the main landslide triggering mechanism is modelled over the whole affected area by analysing the groundwater regime and slope stability conditions. The obtained results show that the spatial and temporal occurrence is strongly related to stratigraphy and hydraulic boundary conditions at both the slope and massif scales. They also highlight a suitable procedure for assessing the spatial and temporal occurrence of complex landslides over large areas.