Imaging subsurface geological complexity (2D/3D) beneath the Greater Srinagar region of the Kashmir basin, Northwest Himalaya
by S. Vishal Gupta, Imtiyaz A. Parvez and Prosanta K. Khan
A high-resolution microtremor measurement in Greater Srinagar city of the Kashmir valley has been analysed to image 2D and 3D subsurface geological complexities. This region is located in the highly seismogenic Himalayan belt and sits atop a deep sedimentary lake bed with a laterally varying thickness of soft sediments. Srinagar region is a major economic centre and the capital city of the Kashmir valley with 2 million inhabitants living at high seismic risk. To assess the subsurface complexity beneath the city, we present: (1) high-resolution subsurface shear wave velocity Vs structure using the horizontal-to-vertical spectral ratio inversion; (2) shear wave velocity for top 30 metres of soil column (VS30) map with National Earthquake Hazards Reduction Program site classification; (3) comparison of VS30 maps calculated from horizontal-to-vertical spectral ratio inversion and topographic slope methods; and (4) azimuthal behaviour of horizontal-to-vertical spectral ratio peaks, all of which unravel the subsurface spatial heterogeneity and suitability for the building of engineering structures in the study area. In addition, a new matlab code is applied to generate 3D subsurface Vs slices in the study region in different directions using its pre-generated 2D Vs profile data. The presented potentiality of microtremor horizontal-to-vertical spectral ratio technique in Srinagar region, which lies on the eastern edge of the basin with significant topographic irregularities, indicates an uneven distribution of local site effects (primary and secondary) in the case of strong ground motion. The comprehensive results can be promising in engineering analyses of local ground and structural responses in order to mitigate the impact of earthquake occurrence and seismic risk in the city and adjoining regions.
Source: https://doi.org/10.1002/nsg.12186
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.
Identification of malaria vulnerable zones in North East Region of India: Spatiotemporal assessment through geographical information system and self-organizing maps
by Rajasekhar Mopuri, Madhusudhan Rao Kadiri, Kantha Rao Bhimala and Srinivasa Rao Mutheneni
Homogeneity Adjustment of Surface Temperature Data and Study of the Climate Variability Over Coastal Odisha by a Climate Departure Index
by Bishnupada Sethi, Krushna Chandra Gouda, Sarat Chandra Sahu, Manas Kumar Mallick, Amrutanshu Panigrahi & Satish Kumar Samal
In the recent years, extreme temperature i.e. cold in winter and warm in summer has been observed in the coastal Odisha state in the east part of India. Hydro-meteorological disasters such as floods, droughts, heat waves and cold spells being experienced nowadays are unprecedented in living memory. In this work the climate variability over the coastal Odisha region is quantified considering the temperature parameter. A climate departure index (CDI) of three versions (simple, absolute and least-square) are calculated using monthly, annually and seasonally maximum, minimum as well as mean temperature form seven stations in the costal Odisha for the period 1969 to 2018 for assessing the degree of normal/unusual/extreme of a particular year in compared to the long-term average for the specific coast under consideration. Temperature time series data for the period 1969 to 2018 are homogenized by using the relative comparison between station observations and composite reference series by calculating CDI. Results indicates that CDI values of least-square on all cases are comparatively greater than simple and absolute CDIs. Least-square method is the appropriate method for determining the category of a year or a month or a season in terms of the temperature variation. Analysis based on mean temperature infers that only twenty extreme seasons are observed in monsoon season while three normal are observed during the month of October i.e., in post-monsoon season over the coastal Odisha. The relationship of climatic event including El Nino or La Nina feature of a particular year with respect to CDI is also investigated. The climatic variability is very high on some of the years based on analysis of index values of annual average of the temperatures (maximum, minimum and mean) over a region.
Source: https://doi.org/10.1007/s00024-024-03465-5
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