Faculty Publications

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Deterministic seismic hazard and landslide hazard zonation of Arunachal Pradesh
(Springer, 2022) Anand, G.; Rahangdale, A.; Mantri, S.S.; Singh, S.; Kolathayar, S.
This paper presents a seismically induced landslide hazard assessment for the state of Arunachal Pradesh, India, based on GIS techniques. A comprehensive earthquake catalog was prepared with data from various sources like USGS, ISC, etc., within a rectangular enclosure having a distance of 500 km in four cardinal directions from the Arunachal Pradesh state boundary. The catalog was homogenized in a unified moment magnitude scale. The earthquake data were collected for a period ranging from the 1500s to the year 2020. The earthquakes having a magnitude ≥4 are considered for this study as they are mainly responsible for inducing enough horizontal movement along the slopes for landslides. Considering the linear source model, the deterministic seismic hazard analysis was performed to estimate peak horizontal acceleration (PHA) at the bedrock level. The log-likelihood method was employed to decide the most efficient and reliable ground motion prediction equation (GMPE) for the Arunachal Pradesh region. Then peak ground acceleration (PGA) values generated at the surface due to the shaking of bedrock were calculated using a non-linear site amplification (considering the soil nature as B-type NHERP classification). The PGA values were considered to induce driving force on slopes, thus causing a landslide. The topographical slope map of Arunachal Pradesh was developed from CARTOSAT Digital Elevation Model Data (30m resolution). The study region was divided into 50 × 50 m2 grids. The seismically induced landslide hazard assessment was performed using Newmark’s methodology using PGA values and slope angles at the center of each grid. The critical factor of safety necessary to counter the landslide for corresponding PGA values was determined, and its spatial variation in the state is presented as contour maps. For any grid point in the study region, if the in-situ (available) static factor of safety is higher than the static factor of safety necessary to counter the landslide as predicted in the current study, that slope is regarded to be safe. © 2022, Indian Academy of Sciences.
Analyzing landslide susceptibility, health vulnerability and risk using multi-criteria decision-making analysis in Arunachal Pradesh, India
(Springer Science and Business Media Deutschland GmbH, 2023) Rehman, S.; Azhoni, A.
Landslides being a widespread disaster are associated with susceptibility, vulnerability and risk. The physical factors inducing landslides are relatively well-known. However, how landslide susceptibility will be exacerbated by climate change, impede the attainment of the sustainable development goals and increase health vulnerability is relatively less explored. We present an integrated assessment of landslide susceptibility, health vulnerability and overall risk to understand these interconnected dimensions using Arunachal Pradesh, India, as a case study, which is susceptible to landslides due to its topography and climate conditions. Landslide susceptibility was examined using twenty landslide conditioning parameters through the fuzzy analytical hierarchy process (FAHP). The susceptibility map was validated using the area under the ROC curve (AUC). National Family Health Survey (NFHS 4) data were used to analyze the health vulnerability, while the overall risk was computed through the integration of susceptibility and vulnerability. Landslide susceptibility analysis indicated that nearly 22% area of the state is characterized by moderate susceptibility followed by high (17%) and very high susceptibility (13%). High elevation, slope, rainfall, SPI, drainage density and complex geology were identified as the causative factors of landslides. In the case of health vulnerability, East Kameng and Lohit districts were found to be very highly vulnerable, while Papum Pare, Changlang and Tirap districts experience high health vulnerability due to high degree of exposure and sensitivity. Overall risk analysis revealed over 16.8% area of the state is under moderate risk followed by high (9.8%) and very high (4.2%) risk. Linking this analysis with the climate change projections and SDG goals attainment revealed that Papum Pare, Upper Subansiri, Tirap and West Kameng require priority for lessening susceptibility, vulnerability and risk for achieving sustainable development. A strong correlation (99%) between HVI and risk further demonstrates the need for lessening health vulnerability and risk in the study area. Furthermore, our study contributes additional insights into landslide susceptibility by considering heal vulnerability and risk which may help in planning sustainable development strategies in a changing climate. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.
Livelihood vulnerability assessment and climate change perception analysis in Arunachal Pradesh, India
(Springer Science and Business Media Deutschland GmbH, 2023) Rehman, S.; Azhoni, A.; Chabbi, P.H.
Climate change induced frequent disasters pose severe threats to agro-based rural livelihoods. Perceptions of risks play a critical role in planning and averting disasters. Lack of analytical documentation concerning how vulnerable communities perceive climate risks is a barrier to addressing and averting disasters and maladaptation. Applying a mixed approach, this study examines the perception of households concerning climate change and analyses the impacts of climate change on livelihood in Arunachal Pradesh, the largest northeastern state of India, with severe climate related challenges. Conceptual livelihood vulnerability index (LVI) framework of Intergovernmental Panel on Climate Change is adopted to analyse the climate change induced vulnerability on livelihood. A total 450 households from 18 villages located in the districts of Arunachal Pradesh were surveyed during October, 2021 for retrieving the ground complexities in the region. Decrease in yields, frequent landslides and floods, livestock losses and unpredictable weather condition were perceived by the sampled households. The LVI analysis indicated that households are vulnerable in Arzoo, Perum, Pekong and Amliang villages requiring priority for lessening livelihood vulnerability and increasing coping capacity of the communities. Correlation analysis indicated that climate variability, natural disaster, health, food and social components attributed to livelihood vulnerability in the study area. Alternate livelihood, enhancing preparedness to disasters, inclusion of women in workforce, sustainable livelihood practices and government assistance are some of the suggestions made to enhance the adaptation of local communities in a sustainable way. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

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