Investigation on the Failure Mechanism of Rainfall-Induced Long-Runout Landslide at Upputhode, Kerala State of India

The main objective of the study is to estimate the shear resistance mobilized on the slope surface under large deformation and to identify the failure mechanism of the landslide through the simulation model. The field investigations were carried out using Geophysical tests, and the laboratory tests were conducted to identify the engineering properties of the soil with weathering characteristics of the parent rock. The residual shear strength parameters from Torsional ring shear tests were used in LS-RAPID numerical simulation software to study the mechanism of the landslide. The critical pore water pressure ratio (ru = 0.32) required for the initiation of a landslide was obtained. The increase in pore water pressure reduces the soil matric suction and thereby results in the reduction of the shear strength of the soil. The progressive failure mechanism and the three landslide processes (initiation, run out and deposition) are investigated. The velocity of the moving landslide mass in the role of demolishing the building is studied and helps in finding suitable remedial measures for the nearby building. The empirical rainfall threshold based on the antecedent rainfall was developed and revealed that either a high daily rainfall intensity of 142 mm without any antecedent rainfall, or an antecedent rainfall of 151 mm for a cumulative period of 5 days with even continuous normal rainfall can initiate landslide.

Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

Towards the effective use of Artificial Neural Networks for accessing rainfall thresholds for rainfall-induced landslides, a study based on in-situ and satellite merged rainfall data

<p>Rainfall-induced landslides have caused destruction and deaths in South America. Accessing its triggers can help researchers and policymakers to understand the nature of the events and to develop more effective warning systems. In this research, triggering rainfall for rainfall-induced landslides is evaluated. The soil moisture effect is indirectly represented by the antecedent rainfall, which is an input of the ANN model. The area of the Rolante river basin, in Rio Grande do Sul state, Brazil, is chosen for our analysis. On January 5<sup>th</sup>, 2017, an extreme rainfall event caused a series of landslides and debris flows in this basin. The landslide scars were mapped using satellite imagery. To calculate the rainfall that triggered the landslides, it was necessary to compute the antecedent rainfall that occurred within the given area. The use of satellite rainfall data is a useful tool, even more so if no gauges are available for the location and time of the rainfall event, which is the case. Remote sensing products that merge the data from in situ stations with satellite rainfall data are increasingly popular. For this research, we employ the data from MERGE (Rozante et al., 2010), that is one of these products, and is focused specifically on Brazilian gauges and territory. For each 12.5x12.5m raster pixel, the rainfall is interpolated to the points and the rainfall volume from the last 24h before the event is accumulated. This is added as training data in our Artificial Neural Network (ANN), along with 11 terrain attributes based on ALOS PALSAR (ASF DAAC, 2015) elevation data and generated by using SAGA GIS. These attributes were presented and analyzed in Lucchese et al. (2020). Sampling follows the procedure suggested in Lucchese et al. (2021, in press). The ANN model is a feedforward neural network with one hidden layer consisting of 20 neurons. The ANN is trained by backpropagation method and cross-validation is used to ensure the correct adjustment of the weights. Metrics are calculated on a separate sample, called verification sample, to avoid bias. After training, and provided with relevant information, the ANN model can estimate the 24h-rainfall thresholds in the region, based on the 2017 event only. The result is a discretized map of rainfall thresholds defined by the execution of the trained ANN. Each pixel of the resulting map should represent the volume of rainfall in 24h necessary to trigger a landslide in that point. As expected, lower thresholds (30 - 60 mm) are located in scarped slopes and the regions where the landslides occurred. However, lowlands and the plateau, which are areas known not to be prone to landslides, show higher rainfall thresholds, although not as high as expected (75 - 95 mm). Mean absolute error for this model is 16.18 mm. The inclusion of more variables and events to the ANN training should favor achieving more reliable outcomes, although, our results are able to show that this methodology has potential to be used for landslide monitoring and prediction.</p>

Definition of 3D rainfall thresholds for operative LEWS

<p>Rainfall induced landslide is one of the most common hazards worldwide and it is responsible every year of huge losses, both economic and social. <br>Because of the high impact of this kind of natural hazard, the forecasting of the meteorological condition associated with the initiation of landslide has become paramount in the recent years and several papers addressing this issue have been published.<br>When working over large areas, the definition of rainfall thresholds is the most used approach, since it requires few data that can be easily retrieved: landslide triggering date and location and rainfall recording associated to landslide events.<br>The intensity-duration threshold is the most used approach and it showed over the time its potential to be implemented in an operative landslide early warning system (LEWS), but literature papers showed that this approach is affected by a main drawback, i.e., the high number of false positives (events that are not capable of triggering landslides are classified as landslide triggering events).<br>To overcome this problem several authors tried to combine these thresholds with other parameters and recently one of the most promising approach is the use of the antecedent soil moisture condition, but this parameter is note very easily available for large areas and it is difficult to retrieve it in real time, so as it can be used in a LEWS.<br>In our work we used antecedent rainfall to simulate the progressive saturation of the soil and then the soil moisture condition associated with the initiation of landslides.<br>In a given area the total rainfall recorded by each rain gauge over a defined period of time prior the landslide is considered and used to define a parameter named MeAR (Mean Antecedent Rainfall), which represent the mean rainfall of the area over a given time interval, as recorded by all the active rain gauges.<br>The MeAR parameter has been coupled with classical I-D thresholds to define 3D thresholds, where the conditions associated with the initiation of a landslide are defined by a portion of a 3D space, instead of a portion of a 2D plane. This approach has been tested in Emilia-Romagna region (Italy) and it resulted the possibility of reducing false positives from 30% up to 80% on different areas.</p>

Enhancing Flood Hazard Assessments in Coastal Louisiana Through Coupled Hydrologic and Surge Processes

Traditional coastal flood hazard studies do not typically account for rainfall-runoff processes in quantifying flood hazard and related cascading risks. This study addresses the potential impacts of antecedent rainfall-runoff, tropical cyclone (TC)-driven rainfall, and TC-driven surge on total water levels and its influence in delineating a coastal flood transition zone for two distinct coastal basins in southeastern Louisiana (Barataria and Lake Maurepas watersheds). Rainfall-runoff from antecedent and TC-driven rainfall along with storm surge was simulated using a new rain-on-mesh module incorporated into the ADCIRC code. Antecedent rainfall conditions were obtained for 21 landfalling TC events spanning 1948–2008 via rain stations. A parametric, TC-driven, rainfall model was used for precipitation associated with the TC. Twelve synthetic storms of varying meteorological intensity (low, medium, and high) and total rainfall were utilized for each watershed and provided model forcing for coastal inundation simulations. First, it was found that antecedent rainfall (pre-TC landfall) is influential up to 3 days pre-landfall. Second, results show that antecedent and TC-driven rainfall increase simulated peak water levels within each basin, with antecedent rainfall dominating inundation across the basin's upper portions. Third, the delineated flood zones of coastal, transition, and hydrologic show stark differences between the two basins.