A note on stress rotations due to the 2004 Mw 9.2 Sumatra-Andaman megathrust earthquake
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.
Identification of vegetation responses to soil moisture, rainfall, and LULC over different meteorological subdivisions in India using remote sensing data
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.
Self-Organized Fractal Seismicity and b-Value of Aftershocks of the 2015 Gorkha Earthquake, Nepal
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
Detecting ionospheric disturbances using GPS without aliasing caused by non-uniform spatial sampling: Algorithm, validation and illustration
by K.Shimna and M. Sithartha Muthu Vijayan
Abstract
Ionospheric disturbance (ID) detected using GPS based Total Electron Content (TEC) measurements are widely used to study the morphology and dynamics of the ionosphere, and its impact on radio communication and satellite based navigation. The IDs normally derived as rate of change of TEC (ROT) between consecutive ionospheric pierce points at uniform time interval implicitly results in non-uniform spatial sampling along the GPS satellite tracks. The non-uniform spatial sampling introduces aliasing in ROT. These aliasing corrupt amplitude and Signal-to-Noise Ratio (SNR) of the detected IDs. In this study, we propose a Spatio-Periodic Leveling Algorithm (SPLA) to remove such aliasing. Efficiency of the proposed algorithm was tested by simulating the IDs along a satellite track and validated with GPS observations carried out during 2015 St. Patrick's day geomagnetic storm. Spatiotemporal, and SNR analyses of simulated and observed IDs reveal that the SPLA is (i) efficient in removing the aliases, (ii) increases the SNR on an average of 99.5% compared to ROT, (iii) removes the need of applying elevation cut-off, and (iv) expands the area of coverage up to 65%.
Source: https://www.sciencedirect.com/science/article/abs/pii/S1364682620302091?via%3Dihub
Atmospheric opacity using 220 GHz (1.36 mm) radiometer data and water vapor trends over Indian Astronomical Observatory (IAO), Hanle
by Shantikumar S.Ningombam, Sethulakshmy E.S, SrideviJade, T.S.Shrungeshwara, Chiranjeevi G.Vivek, Dorje Angchuk, T.P.Prabhu and Tashi Tshering Mahay
The present work reports atmospheric opacity at 220 GHz over Indian Astronomical Observatory (IAO)-Hanle during 2006 to 2018. The opacity data were derived from the radiometric measurement of sky-brightness temperature as a function of zenith angle for every 10 min during day and night. Since the operating frequency range is not completely opaque during rainfall or cloudy atmosphere, observations are performed even during rainfall or cloudy sky conditions. The minimum opacities observed at the site varied from 0.06-0.07 at 1st quartile and 0.08-0.09 at 2nd quartile during December-January. Opacity at Hanle is linearly correlated with high temporal resolution GPS (Global Positioning System) Precipitable Water Vapor (PWV) with a correlation coefficient of about 0.8. The observed median opacity at Hanle has increased by 44% during the best observing months (October–March) and 24% over the annual data (October-September) with reference to the opacity observed during 2000–2003. Such increasing opacity trends are apparently due to effects of the dynamics of the regional and global hydrological cycle. In order to assess the impacts of the regional and global hydrological cycle, PWV obtained from AErosol RObotic NETwork (AERONET), satellite and reanalysis data were studied at nine high-altitude astronomical observatories spread spatially across the globe such as Atacama desert in Chile, Mauna Loa Observatory in Hawaii, Hanle and Merak in India, Ali and Yangbajing Observatories in Tibet, LLAMA in Argentina, and Dome A and Dome C in the Antarctic Plateau during 2003-2018. On an average, PWV trend over Atacama desert is increasing by 0.01 to 0.03 mm per year and at IAO-Hanle, Merak and Ali by 0.01 to 0.05 mm per year, indicating non uniform dynamic hydrological cycles. Interestingly, there is no noticeable PWV trend for the sites located in the Antarctic Plateau. During the four best observing months, PWV observed at Hanle, Merak and Ali sites have values lower than the Mauna Loa observatory by 0.14 mm and 0.56 mm at 1st and 2nd quartiles, respectively.
Source: https://doi.org/10.1016/j.jastp.2020.105404
- Comparison of the Performance of Hybrid ETKF-3DVAR and 3DVAR Data Assimilation Systems on Short-Range Forecasts during Indian Summer Monsoon Season in a Limited-Area Model
- Dengue situation in India: Suitability and transmission potential model for present and projected climate change scenarios
- Ground motion modelling in northwestern Himalaya using stochastic finite-fault method
- Characteristics of pre-monsoon convective systems over south peninsular India and neighborhood using tropical rainfall measuring mission's precipitation radar
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