by S Vishal Gupta, Imtiyaz A Parvez, Ankit, Prosanta K Khan & Rakesh Chandra
The Srinagar region of Kashmir Valley in North West Himalayas, covers more than 2 million inhabitants and is exposed to high seismic risk. In order to gain insight on potential site effects and subsurface structure of the region, we carried out an extensive high-resolution microtremor ambient noise survey at 429 locations. The acquired dataset was processed using the Horizontal to Vertical Spectral Ratio (HVSR) technique to map the resonance frequency, the thickness of sedimentary cover, and to identify areas prone to seismic amplification. We provide a spatial classification of the obtained HVSR curves in four types: (1) clear peak H/V curves relating the strong impedance contrast in the subsurface; (2) multiple peaks (or Broad) H/V curves corresponding to sloping internal stratification of sediments; (3) two peaks H/V curves related to two different impedance contrast existing in the subsurface; (4) flat H/V curves around and over hard rock outcroppings. The HVSR curves show the peaks in the range of 0.22 Hz to 9.96 Hz indicating heterogeneous and complex sedimentary cover in the region. Inversion of the HVSR curves gives the shear waves velocity distribution which highlights two distinct reflective surfaces in most of the areas. In addition, we also used the estimated fundamental frequency of various types of houses/buildings located in Srinagar city to assess the possibility of resonance in case of occurrence of any earthquake. This study adds a value to the region in earthquake engineering, seismic hazard and risk evaluation purpose for Srinagar and its suburbs.
Source: https://www.tandfonline.com/doi/abs/10.1080/13632469.2020.1816232?journalCode=ueqe20
by Stella Jes Varghese, Sajani Surendran, B Ajithkumar, Kavirajan Rajendran & Akio Kitoh
The impact of climate change on agricultural yield is one amongst the major concerns the world is witnessing. Our study focusses on rice yield prediction for an agricultural research station in Kerala with the help of climate change scenario input from the Meteorological Research Institute (MRI) Global Climate Model (GCM) projection under Representative Concentration Pathway 8.5 (RCP8.5). We have used Cropping System Model (CSM) Crop Estimation through Resource and Environment Synthesis (CERES) Rice within Decision Support System for Agrotechnology Transfer (DSSAT) package for predicting the yield. Our study has the novelty of using very high-resolution climate data from a model which is highly skilful in capturing the present-day climate features and climatic trends over India (in particular, over the Western Ghats), as input for simulating the future crop yield. From this study, we find that the rice yield decreases due to rise in temperature and reduction in rainfall, thereby reducing the crops maturity time in the future. Based on our results, the adaptation measures suggested to achieve better yield under future warming conditions are: (i) to opt for alternative rice varieties which have tolerance to high temperatures and consume less water, and (ii) shifting of planting date to the most appropriate window.
by K Silpa and Anil Earnest
Here we study the evolution of stress and its rotations in the Sumatra-Andaman Subduction Zone (SASZ) after the 2004 Mw 9.2 megathrust earthquake, using moment tensor stress inversions. Models indicate returning of North Andaman stress regime to the inter-seismic oblique-compression, after a short stint of extension. Likewise, Little Andamans experience extension indicating a trench-ward guidance of extensional stresses. Similarly, Nicobar regime is presently normal-oblique, from early post-seismic reverse-oblique, and North Sumatra experiences pure-reverse regime. The influence of the Wharton Basin stress field and 2012 earthquakes on the SASZ fore-arc deformation is also probed. A near-complete co-seismic stress-relief is observed at Nicobar, followed by North Sumatra and Little Andaman. Co-seismic and post-seismic model comparisons indicate strong correlation with zones of co-seismic stress relief and regions of rapid post-seismic reloading. The North Andaman shows a northward compressive shear than the margin-normal component of subduction, as evident from prominent oblique stress regime with lower co-seismic stress relief and lack of post-seismic back rotations. At Andaman Spreading Ridge (ASR), transition from oblique to extensional stress indicates lateral shear to back-arc spreading enhancement.
by Kantha Rao Bhimala, V. Rakesh, K. Raghavendra Prasad & G. N. Mohapatra
Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were analyzed to study the vegetation dynamics over different meteorological subdivisions in India for the period 2000–2016. Soil moisture (SM), rainfall (RF), and land use land cover (LULC) data were analyzed to identify the climatic and anthropogenic drivers that cause vegetation changes at the subdivision scale. Principal component analysis and MK (Mann-Kendall) test showed significant greening trend over semi-arid regions of Northwest India (NWI) and South India (SI) while slight browning trend seen over some of the subdivisions in Indo-Gangetic (IG) plains and Western Ghats (WG). It is found that the NDVI has superior correlation with soil moisture compared with rainfall and the croplands (CL) found to have significant increasing trend over the NWI and SI. Increasing trend in soil moisture over the NWI and SI may have contributed to increase in CL area and the greening trend. Over IG plains, the NDVI showed moderate correlation with SM and RF, and the greening trend (browning trend) in some regions can be attributed to increase in natural vegetation mosaic (decrease of CL). The NDVI has shown browning trend over the core monsoon regions of Madhya Pradesh (an increase of barren lands over west MP and decrease of CL over east MP) and Western Ghats (significant decrease of CL over Konkan and Goa). This study revealed that the soil moisture and LULC changes are the major driving factors for the vegetation changes over majority of the subdivisions in India.
by Sanchit Minocha and Imtiyaz A. Parvez
Abstract
The Gorkha Earthquake that occurred on 25th April 2015 was a long anticipated, low angle thrust-faulting shallow event in Central Nepal that devastated the mountainous southern rim of the High Himalayan range. The earthquake was felt throughout central and eastern Nepal, much of the Ganges River plain in northern India, and northwestern Bangladesh, as well as in the southern parts of the Plateau of Tibet and western Bhutan. Two large aftershocks, with magnitudes 6.6 and 6.7, occurred in the region within one day of the main event, and several dozen smaller aftershocks occurred in the region during the succeeding days. In this study, we have analyzed the 350 aftershocks of the 2015 Gorkha Earthquake of Mw 7.8 to understand the spatial and temporal distribution of b-value and the fractal correlation dimension. The b-value is found to be 0.833 ± 0.035 from the Gutenberg-Richter relation by the least squares method and 0.95 ± 0.05 by the maximum likelihood method, indicating high stress bearing source zone. The spatial and temporal correlation dimension is estimated to be 1.07 ± 0.028 and 0.395 ± 0.0027 respectively. Spatial correlation dimension suggests a heterogeneous distribution of earthquake epicenters over a linear structure in space, while the temporal correlation dimension suggests clustering of aftershock activity in the time domain. The spatial variation of the b-value reveals that the b-value is high in the vicinity of the mainshock which is due to the sudden release of stress energy in the form of seismic waves. The spatial distribution of correlation dimension further confirms a linear source in the source zone as it varies from 0.8-1.0 in most of the region. We have also studied the temporal variation of b-value and correlation dimension that shows positive correlation for about first 15 days, then a negative correlation for next 45 days and after that, a positive correlation. The positive correlation suggests that the probability of large magnitude earthquakes decreases in response to increased fragmentation of the fault zone. The negative correlation means that there is a considerable probability of occurrences of large magnitude earthquakes, indicating stress release along the faults of a larger surface area [1]. The correlation coefficient between b-value and the correlation dimension is estimated to be 0.26, which shows that there is no significant relation between them.
source: https://www.scirp.org/journal/paperinformation.aspx?paperid=102420
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