Application of neo-deterministic seismic hazard assessment to India
by Imtiyaz A.Parvez
Earthquakes constitute one of the most feared natural disasters that occur with no warning and can result in great destruction along with loss of lives, particularly the seismically earthquake-prone countries like India. One way to mitigate the destructive impact of such earthquakes is to conduct a seismic hazard and risk assessment and take remedial measures. This chapter aims at demonstrating significant contributions to the application of neo-deterministic seismic hazard assessment (NDSHA) in India and neighboring region. NDSHA has been built on a rigorous theoretical basis and exploits the currently available computational resources that permit to compute realistic synthetic seismograms. Parvez et al. (2003) published the first-ever neo-deterministic seismic hazard map of India by computing synthetic seismograms with input data set consisting of structural models, seismogenic zones, focal mechanisms, and earthquake catalogs. Later, using updated input data and advanced computational tools, Parvez et al. (2017) produced a revised neo-deterministic seismic hazard map of India. High-resolution NDSHA has also been applied at the regional and local scale in India, particularly in Gujarat state, Kolkata, and the capital city Delhi. This chapter also discusses how peak ground acceleration estimates alone are not sufficient for the adequate design of buildings and infrastructures, since displacements play a critical role and the dynamical analysis of the structure response requires scenario-based complete time series of ground motion. The applications discussed in this chapter can be used as a preventive definition of the seismic hazard without waiting for the great earthquakes and also help those earthquake and civil engineers to avail realistically estimated ground motion database for the safe design of buildings and other infrastructure in the country.
Sinking Slab Stress and Seismo-Tectonics of the Indo-Burmese Arc: A Reappraisal
by Anil Earnest, T. C. Sunilkumar and K. Silpa
Differing views on the north-eastern Indian plate subduction and its seismic potential place the subduction process as relict, ceased, creeping, or fully locked, challenging the seismic hazard uncertainty estimates of Indo-Burmese Arc (IBA) at either extreme. To clear some of these contentions, we re-examine the state of the IBA stress field using available earthquake faulting mechanism solutions. Our stress inversion results underline the tectonic complexity and emphasize that the stress of this subduction is primarily driven by the northward oblique motion of the Indian Plate. Models highlight the presence of the least compressive stresses as the margin-normal component, all along the shallow sinking slab. Furthermore, solutions link the shallow strike-slip earthquakes with the lateral slab shear due to oblique slip-partitioning. Spatial variations in the Indian slab stress field indicate higher margin parallel compression at the Naga versus the Chin and Rakhine-Arakan segments, implying resistance to the northward motion of India, illuminating the buttressing effects on the IBA. Models also depict active shortening across the Kabaw Fault. Inversion solutions bring out a higher compressional environment toward the south, along the Sagaing Fault. Finally, based on the overall stress field variations, we speculate that the Rakhine-Bangladesh Megathrust could be a low-stress forearc, as across the arc interacting stress fields are weak, capable of generating megathrust ruptures. Being weak, the stress replenishment times could be longer than the mechanically strong subduction zones. Due to the spatial stress field variations and buttressing effects, the interface may exhibit a variable rupture mode.
On the Semidiurnal Variation in Surface Rainfall Rate over the Tropics in a Global Cloud-Resolving Model Simulation and Satellite Observations
by Toshiro INOUE, Kavirajan RAJENDRAN, Masaki SATOH, Hiroaki MIURA
Do seasonal forecasts of Indian summer monsoon rainfall show better skill with February initial conditions?
by K Rajendran, Sajani Surendran, Stella Jes Varghese and A Chakraborty
Prediction for Indian summer monsoon rainfall (ISMR) is generated by integrating model from initial conditions (ICs) of weather at some time prior to season. We examine the factors responsible for the widely reported highest ISMR forecast skill for February ICs in climate forecast system (CFSv2) model. Skill for February ICs is highest only based on correlation between observed and predicted year-to-year variation of ISMR, whereas other skill scores indicate highest skill for late-April/early-May ICs having shorter yet useful forecast lead-time. Higher correlation for February ICs arises from correct forecasting of 1983 ISMR excess, which is however due to wrong forecast of La Niña and correlation drops to lower value than that for late-April/early-May ICs if 1983 is excluded. Forecast skill for sea surface temperature variation over equatorial central Pacific (ENSO) in Boreal summer is lowest for February ICs indicating role of dynamical drift induced by long forecast lead-time. Model deficiencies such as oversensitivity of ISMR to ENSO and unrealistic ENSO influence on variation of convection over equatorial Indian Ocean (EQUINOO) lead to errors in ISMR forecasting. In CFSv2, ISMR is mostly decided by ENSO whereas in observation it is influenced by ENSO and EQUINOO independently.
Source: https://www.currentscience.ac.in/Volumes/120/12/1863.pdf
Assessment of air pollution status during COVID-19 lockdown (March–May 2020) over Bangalore City in India
by K. C. Gouda, Priya Singh, Nikhilasuma P, Mahendra Benke, Reshama Kumari, Geeta Agnihotri, Kiran M Hungund, Chandrika M, Kantha Rao B, Ramesh V & Himesh S
The coronavirus disease 2019 (COVID-19), which became a global pandemic by March 2020, forced almost all countries over the world to impose the lockdown as a measure of social distancing to control the spread of infection. India also strictly implemented a countrywide lockdown, starting from 24 March to 12 May 2020. This measure resulted in the reduction of the sources of air pollution in general: industrial, commercial, and vehicular pollution in particular, with visible improvement in ambient air quality. In this study, the impact of COVID-19 lockdown on the ambient concentration of air pollutants over the city of Bangalore (India) is assessed using Continuous Ambient Air Quality Measurement (CAAQM) data from 10 monitoring stations spread across the city. The data was obtained from Central Pollution Control Board (CPCB) and Karnataka State Pollution Control Board (KSPCB). The analysis of the relative changes in the ambient concentration of six major air pollutants (NO, NO2, NOX, PM2.5, O3, and SO2) has been carried out for two periods: March–May 2020 (COVID-19 lockdown) and the corresponding period of 2019 during when there was no lockdown. The analysis revealed significant reduction in the concentration of ambient air pollutants at both daily and monthly intervals. This can be attributed to the reduction in sources of emission; vehicular traffic, industrial, and other activities. The average reduction in the concentration of NO, NO2, NOX, PM2.5, and O3 between 01 March and 12 May 2020 was found to be 63%, 48%, 48%, 18%, and 23% respectively when compared to the same period in 2019. Similarly, the comparative analysis of pollutant concentrations between pre-lockdown (01–23 March 2020) and lockdown (24 March–12 May 2020) periods has shown a huge reduction in the ambient concentration of air pollutants, 47.3% (NO), 49% (NO2), 49% (NOX), 10% (SO2), 37.7% (PM2.5), and 15.6% (O3), resulting in improved air quality over Bangalore during the COVID-19 lockdown period. It is shown that the strict lockdown resulted in a significant reduction in the pollution levels. Such lockdowns may be useful as emergency intervention strategies to control air pollution in megacities when ambient air quality deteriorates dangerously.
- Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
- Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
- Prediction of COVID‐19 cases using the weather integrated deep learning approach for India
- Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model
Page 18 of 27
