Early warning system for shallow landslides using rainfall threshold and slope stability analysis

A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent(xvariable) vs. daily rainfall(y-variable) provided the best fit to the data with a threshold equation of y = 80.7-0.1981 x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is～20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model(PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety(FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions.

A GIS tool for infinite slope stability analysis(GIS-TISSA)

Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard assessment.Shallow landslide occurrence in soil and regolith covered slopes are often modeled using the infinite slope model,which characterizes the slope stability in terms of a factor of safety(FS) value.Different approaches have been followed to also assess and propagate uncertainty through such models.Haneberg(2004) introduced the use of the First Order Second Moment(FOSM) method to propagate input uncertainty through the infinite slope model,further developing the model and implementing it in the PISA-m software package(Haneberg,2007).Here we present an ArcPy implementation of PISA-m algorithms,which can be run from ESRI ArcMap in an entirely consistent georeferenced framework,and which we call "GIS Tool for Infinite Slope Stability Analysis"(GIS-TISSA).Users can select between different input options,e.g.,following a similar input style as for PISA-m,i.e., using an ASCII.csv parameters input file,or providing each input parameter as a raster or constant value,through the program graphic user interface.Analysis outputs can include FS mean and standard deviation estimates,the probability of failure(FS <1), and a reliability index(RI) calculation for FS.Following the same seismic analysis approach as in PISA-m, the Newmark acceleration can also be done,for which raster files of the mean,standard deviation,probability of exceedance,and RI are also generated.Verification of the code is done by replicating the results obtained with the PISA-m code for different input options,within a 10-5 relative error.Monte Carlo modeling is also applied to validate GIS-TISSA outputs,showing a good overall correspondence.A case study was performed for Kannur district,Kerala,India,where an extensive landslide databa se for the year 2018 was available.81.19% of the actual landslides fell in zones identified by the model as unstable.GIS-TISSA provides a user-friendly interface,particularly for those users familiar with ESRI ArcMap,that is fully embedded in a GIS framework and which can then be used for further analysis without having to change software platforms and do data conversions.The ArcPy toolbox is provided as a.pyt file as an appendix as well as hosted at the weblink:https://pages.mtu.edu/~toommen/GeoHazard.html.

A composite fall-slippage model for cliff recession in the sedimentary coastal cliffs

A composite fall-slippage model is proposed in this study for the Tertiary sedimentary coastal cliffs of Varkala in the western coastal tract of Peninsular India which are retreating landwards due to the combination of several factors.The fall model in the present study accounts both spring seepage and wave action,resulting in undercutting and this fall affects only the topmost laterite and the just below sandstone in the cliff.Slippage in this area affects all the litho-units and hence the geologic characteristics of all the litho-units are considered for developing the slippage model.This mathematically derived model can be used in other cliffs exhibiting the same morphology as well as the one controlled by the same influencing factors.This model differs from other models in incorporating multi-lithounits as well as multi-notches.Varkala cliffs form a part of the aspiring geopark in the Global Geopark Network and hence a study on the cliff recession is a pressing requirement.

InSAR as a tool for monitoring hydropower projects:A review

This paper provides a review of using Interferometric Synthetic Aperture Radar(InSAR),a microwave remote sensing technique,for deformation monitoring of hydroelectric power projects,a critical infrastructure that requires consistent and reliable monitoring.Almost all major dams around the world were built for the generation of hydropower.InSAR can enhance dam safety by providing timely settlement measurements at high spatial-resolution.This paper provides a holistic view of different InSAR deformation monitoring techniques such as Differential Synthetic Aperture Radar Interferometry(DInSAR),Ground-Based Synthetic Aperture Radar(GBInSAR),Persistent Scatterer Interferometric Synthetic Aperture Radar(PSInSAR),Multi-Temporal Interferometric Synthetic Aperture Radar(MTInSAR),QuasiPersistent Scatterer Interferometric Synthetic Aperture Radar(QPSInSAR)and Small BAseline Subset(SBAS).PSInSAR,GBInSAR,MTInSAR,and DInSAR techniques were quite commonly used for deformation studies.These studies demonstrate the advantage of InSAR-based techniques over other conventional methods,which are laborious,costly,and sometimes unachievable.InSAR technology is also favoured for its capability to provide monitoring data at all times of day or night,in all-weather conditions,and particularly for wide areas with mm-scale precision.However,the method also has some disadvantages,such as the maximum deformation rate that can be monitored,and the location for monitoring cannot be dictated.Through this review,we aim to popularize InSAR technology to monitor the deformation of dams,which can also be used as an early warning method to prevent any unprecedented catastrophe.This study also discusses some case studies from southern India to demonstrate the capabilities of InSAR to indirectly monitor dam health.

Remote sensing for energy resources:Introduction

Energy resources are critical for human existence.The energy that we use is obtained from the Earth,its atmosphere,or the Sun.Some of these energy resources we obtain from the subsurface through the mining or extraction process,like gas,coal,oil,geothermal,and uranium.Furthermore,we also harness from the surface like wind,solar,and tidal.Geoscientists and geological engineers play a critical role in developing energy resources,maintaining its operation,and evaluating their environmental impacts.In recent years,the advancement in satellite and aerial remote sensing is a tool that is widely used by geoscientists and engineers to address various aspects of energy resources.The application of remote sensing to energy resources,starting from reconnaissance to detailed studies,utilizing multispectral to hyperspectral and radar to LiDAR techniques,have gained significant importance.Some remote sensing applications include identifying potential source of fuels used in the energy.