Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India

Accurate rainfall estimates are required to forecast the spatio-temporal distribution of rain-triggered landslides. In this study, a comparison between rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region, the mountainous district of Idukki, along the Western Ghats of southwestern India, is carried out. Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM-Late (GPM IMERG-L) rainfall products were compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the actual rainfall. A conditional merging algorithm was applied to develop a product that combines the accuracy of rain gauges and the spatial variability of satellite precipitation data. Correlation Coefficient (CC) and Root Mean Squared Error (RMSE) were used to check the performance of the conditional merging process. An example from a station with the least favorable statistics shows the CC increasing from 0.589 to 0.974 and the RMSE decreasing from 65.22 to 20.01. A case scenario was considered that evaluated the performance of a landslide prediction model by relying solely on a sparse rain gauge network. Rainfall thresholds computed from both the conditionally merged GPM IMERG-L and the rain gauge data were compared and the differences indicated that relying solely on a discrete, sparse rain gauge network would create false predictions. A total of 18.7% of landslide predictions only were identified as true positives, while 60.7% was the overall false-negative rate, and the remaining were false-positives. This pointed towards the need of having a continuous data that is both accurate in measurement and efficient in capturing spatial variability of rainfall

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 (x-variable) vs. daily rainfall (y-variable) provided the best fit to the data with a threshold equation of y = 80.7–0.1981x. 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

Meteorite impact craters as hotspots for mineral resources and energy fuels: A global review

The ever-increasing recovery rate of natural resources from terrestrial impact craters over the last few decades across the globe offers new avenues for further exploration of mineral and hydrocarbon resources in such settings. As of today, 60 of the 208 terrestrial craters have been identified to host diverse resources such as hydrocarbons, metals and construction materials. The potential of craters as plausible resource contributors to the energy sector is therefore, worthy of consideration, as 42 (70%) of the 60 craters host energy resources such as oil, gas, coal, uranium, mercury, critical and major minerals as well as hydropower resources. Among others, 19 craters are of well-developed hydrocarbon reserves. Mineral deposits associated with craters are also classified similar to other mineral resources such as progenetic, syngenetic and epigenetic sources. Of these, the progenetic and syngenetic mineralization are confined to the early and late excavation stage of impact crater evolution, respectively, whereas epigenetic deposits are formed during and after the modification stage of crater formation. Thus, progenetic and syngenetic mineral deposits (like Fe, Ni, Pb, Zn and Cu) associated with craters are formed as a direct result of the impact event, whereas epigenetic deposits (e.g. hydrocarbon) are hosted by the impact structure and result from post-impact processes. In the progenetic and syngenetic deposits, the shock-wave induced fracturing and melting aid the formation of deposits, whereas in the epigenetic deposits, the highly fractured lithostratigraphic units of higher porosity and permeability, like the central elevated area (CEA) or the rim, act as traps. In this review, we provide a holistic view of the mineral and energy resources associated with impact craters, and use some of the remote sensing techniques to identify the mineral deposits as supplemented by a schematic model of the types of deposits formed during cratering process

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), Quasi-Persistent 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

Satellite-based assessment of the August 2018 flood in parts of Kerala, India

From 1 June to 29 August 2018, Kerala, a state in southwestern India, recorded 36% excess rainfall than normal levels, leading to widespread floods and landslides events and resulting in 445 deaths. In this study, satellite-based data were used to map the flood inundation in the districts of Thrissur, Ernakulam, Alappuzha, Idukki and Kottayam. Specifically, flood delineation was enabled with Sentinel-1A radar data of 21 August 2018 and was compared with an average pre-flood, water-cover map based on Modified Normalized Difference Water Index (MNDWI) that was developed using a January and February 2018 Sentinel-2A dataset. A 90% increase in water cover was observed during the August 2018 flood event. Low lying areas in the coastal plains of Kuttanad and the Kole lands of Thrissur, had marked a rise of up to 5 and 10 m of water, respectively, during this deluge. These estimates are conservative as that the flood waters had started receding prior to the August 21 Sentinel-1A imagery

The Tertiary sequence of Varkala coastal cliffs, southwestern India: An ideal site for Global Geopark

Varkala, along the southwestern coast of Peninsular India, has a unique place in Indian geology and geomorphology due to the presence of coastal lateritic cliffs, which exposes the entire Mio-Pliocene sequence of Warkalli Formation, and is declared as the type area. Stratigraphically, this formation exposes carbonaceous clay with lenses of lignite and sticks of marcasite, followed by variegated clays and sandstone. The presence of variegated lithounits endows beauty to these cliffs. Varkala cliffs, edging the Arabian Sea, run for a length of 7.5 km. These cliffs, together with confined beaches, made Varkala a popular tourist destination. Several geodiversity spots within the Varkala Cliff geoheritage site make Varkala geologically unique, just like the vestiges of the last separation of Indian subcontinent from the Mascarene Plateau; showcasing lateritization and distribution of beach placers, and jarosite, formed as a diagenetic replacement mineral from marcasite and considered as a Martian analog, are distinctiveness of the cliff. Additionally, Varkala is an internationally acclaimed beach tourist destination. Furthermore, there are several geoheritage sites as well as socio-cultural-historical sites in the hinterland of Varkala Cliff geoheritage site, which are within the proposed Varkala Global Geopark jurisdiction. Thus, this area fulfills all the criteria to be a Global Geopark. The socio-economic-environmental analysis showcases the changes that have occurred in these 3-end members. When the economic sphere was unaffected, the social scenario was slightly affected (25%) whereas the environmental aspect then drastically deteriorated by 75%. But, the SWOT analysis still elects Varkala as a potential Global Geopark. The concept of geopark contributes to at least one of the 17 goals in Agenda 2030 Sustainable Development Goals (SDG) of the United Nations (UN). Consequently, this work also aims at propagating, not only the need for converting the geologically prominent areas to a geopark, but also attaining SDG, whatever is possible through geoparks

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at line uri \u3ehttps://doi.org/10.17026/dans-x6c-y7x2\u3e (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

Using Radar Remote Sensing from Space to Monitor Dams

Monitoring dams is critical for identifying vulnerabilities affecting its performance and managing the risk of failure. Traditionally, dam monitoring is performed by a combination of visual surveillance and instrumentation. American Society of Civil Engineers (ASCE) has been giving a D grade for the nation’s 91,000-plus dams since the beginning of the grading system (1998) to date. The traditional approach can be subjective and laborious, considering the monitoring needs in the United States. This study presents the Interferometric Synthetic Aperture Radar (InSAR) application for monitoring deformations at dam sites to understand its vulnerability. The study evaluates the use of InSAR at three (two in India and one in the United States) dam sites to monitor its safety. The Sentinel-1 and ENVISAT Advanced Synthetic Aperture Radar (ASAR) images were processed using Persistent Scatterers Interferometry (PSI). Results indicate that, combined with traditional methods, remote sensing using radar offers an opportunity for long-term dam monitoring and the development of a deformation-based early warning system