CSIR Fourth Paradigm Institute, Bangalore, India - Welcome to CSIR-4PI homepage! http://www.cmmacs.ernet.in/index.php/en/component/content/?view=featured Tue, 20 Mar 2018 20:51:30 +0530 Joomla! - Open Source Content Management en-gb Seismic hazard and risk assessment based on Unified Scaling Law for Earthquakes: thirteen principal urban agglomerations of India http://www.cmmacs.ernet.in/index.php/en/11-publications/478-seismic-hazard-and-risk-assessment-based-on-unified-scaling-law-for-earthquakes-thirteen-principal-urban-agglomerations-of-india http://www.cmmacs.ernet.in/index.php/en/11-publications/478-seismic-hazard-and-risk-assessment-based-on-unified-scaling-law-for-earthquakes-thirteen-principal-urban-agglomerations-of-india

by Imtiyaz A. Parvez, Anastasia Nekrasova, Vladimir Kossobokov

The deterministic seismic hazard map of India with spatially distributed peak ground acceleration was used to estimate seismic risk using two data sets of the Indian population—the model population data set and the data set based on India’s Census 2011. Four series of the earthquake risk maps of the region based on these two population density sets were cross-compared. The discrepancy of the population data and seismic risks estimation were illuminated for the thirteen principal urban agglomerations of India. The confirmed fractal nature of earthquakes and their distribution in space implies that traditional probabilistic estimations of seismic hazard and risks of cities and urban agglomerations are usually underestimated. The evident patterns of distributed seismic activity follow the Unified Scaling Law for Earthquakes, USLE, which generalizes Gutenberg–Richter recurrence relation. The results of the systematic global analysis imply that the occurrence of earthquakes in a region is characterized with USLE: log10N (M, L) = A + B × (5 − M) + C × log10L, where N(M, L)—expected annual number of earthquakes of magnitude M within an area of liner size L, A determines seismic static rate, B—balance between magnitude ranges, and C—fractal dimension of seismic loci. We apply the seismic hazard and risk assessment methodology developed recently based on USLE, pattern recognition of earthquake-prone geomorphic nodes, and neo-deterministic scenarios of destructive ground shaking. Objects of risk are individuals (1) as reported in the 2011 National Census data and (2) as predicted for 2010 by Gridded Population of the World (model GPWv3); vulnerability depends nonlinearly on population density. The resulting maps of seismic hazard and different risk estimates based on population density are cross-compared. To avoid misleading interpretations, we emphasize that risk estimates presented here for academic purposes only. In the matter of fact, they confirm that estimations addressing more realistic and practical kinds of seismic risks should involve experts in distribution of objects of risk of different vulnerability, i.e., specialists in earthquake engineering, social sciences, and economics.

Source: https://link.springer.com/article/10.1007%2Fs11069-018-3261-8

earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Wed, 14 Mar 2018 16:26:12 +0530
Monsoon rainfall over India in June and link with northwest tropical pacific http://www.cmmacs.ernet.in/index.php/en/11-publications/477-monsoon-rainfall-over-india-in-june-and-link-with-northwest-tropical-pacific http://www.cmmacs.ernet.in/index.php/en/11-publications/477-monsoon-rainfall-over-india-in-june-and-link-with-northwest-tropical-pacific

by Sajani Surendran, Sulochana Gadgil, Kavirajan Rajendran, Stella Jes Varghese and Akio Kitoh

Recent years have witnessed large interannual variation of all-India rainfall (AIR) in June, with intermittent large deficits and excesses. Variability of June AIR is found to have the strongest link with variation of rainfall over northwest tropical Pacific (NWTP), with AIR deficit (excess) associated with enhancement (suppression) of NWTP rainfall. This association is investigated using high-resolution Meteorological Research Institute model which shows high skill in simulating important features of Asian summer monsoon, its variability and the inverse relationship between NWTP rainfall and AIR. Analysis of the variation of NWTP rainfall shows that it is associated with a change in the latitudinal position of subtropical westerly jet over the region stretching from West of Tibetan Plateau (WTP) to NWTP and the phase of Rossby wave steered in it with centres over NWTP and WTP. In years with large rainfall excess/deficit, the strong link between AIR and NWTP rainfall exists through differences in Rossby wave phase steered in the jet. The positive phase of the WTP-NWTP pattern, with troughs over WTP and west of NWTP, tends to be associated with increased rainfall over NWTP and decreased AIR. This scenario is reversed in the opposite phase. Thus, the teleconnection between NWTP rainfall and AIR is a manifestation of the difference in the phase of Rossby wave between excess and deficit years, with centres over WTP and NWTP. This brings out the importance of prediction of phase of Rossby waves over WTP and NWTP in advance, for prediction of June rainfall over India.


source: https://link.springer.com/article/10.1007%2Fs00704-018-2440-6

earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Tue, 06 Mar 2018 21:57:47 +0530
Simulation of extreme temperature over Odisha during May 2015 http://www.cmmacs.ernet.in/index.php/en/11-publications/469-simulation-of-extreme-temperature-over-odisha-during-may-2015 http://www.cmmacs.ernet.in/index.php/en/11-publications/469-simulation-of-extreme-temperature-over-odisha-during-may-2015

by  K.C.Gouda, S.K.Sahoo, P.Samantray and S.Himesh

An extreme temperature event (heat wave) over the state of Odisha was unique as it lasted for about 2 weeks in the 3rd and 4th weeks of May 2015. There was a similar severe heat wave in western and central Odisha in the month of April 1998. The interesting feature of the recent episodic heat wave is that it prevailed in the late pre-monsoon season with wider spread in the state of Odisha. Around 12–15 cities experienced a daily maximum temperature of over 45 °C during the strong heat wave period, and 25th −27th May was declared as the red box zone. In this study, we first analysed the intense summer temperature of 2015 May using India Meteorological Department observations of daily maximum temperature. The observed heat wave phenomenon was then simulated using the Weather Research and Forecast Model (WRFV3) at 2-km horizontal resolution to assess its ability to forecast such a rare event. The observational analysis clearly indicated that this episodic event was unique both in terms of intensity, geographical spread and duration. An optimized configuration of the WRF model is proposed and implemented for the simulation of the episodic heat wave phenomenon (daily maximum temperature) over the state of Odisha. The time-ensemble simulation of the temperature is shown to be in close agreement with the station-scale observations.

Source: http://www.sciencedirect.com/science/article/pii/S2212094716300792

earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Wed, 11 Oct 2017 21:50:34 +0530
Comparative Study of Monsoon Rainfall Variability over India and the Odisha State http://www.cmmacs.ernet.in/index.php/en/11-publications/468-comparative-study-of-monsoon-rainfall-variability-over-india-and-the-odisha-state http://www.cmmacs.ernet.in/index.php/en/11-publications/468-comparative-study-of-monsoon-rainfall-variability-over-india-and-the-odisha-state

by K C Gouda, Sanjeeb Kumar Sahoo, Payoshni Samantray and Himesh Shivappa

Indian summer monsoon (ISM) plays an important role in the weather and climate system over India. The rainfall during monsoon season controls many sectors from agriculture, food, energy, and water, to the management of disasters. Being a coastal province on the eastern side of India, Odisha is one of the most important states affected by the monsoon rainfall and associated hydro-meteorological systems. The variability of monsoon rainfall is highly unpredictable at multiple scales both in space and time. In this study, the monsoon variability over the state of Odisha is studied using the daily gridded rainfall data from India Meteorological Department (IMD). A comparative analysis of the behaviour of monsoon rainfall at a larger scale (India), regional scale (Odisha), and sub-regional scale (zones of Odisha) is carried out in terms of the seasonal cycle of monsoon rainfall and its interannual variability. It is seen that there is no synchronization in the seasonal monsoon category (normal/excess/deficit) when analysed over large (India) and regional (Odisha) scales. The impact of El Niño, La Niña, and the Indian Ocean Dipole (IOD) on the monsoon rainfall at both scales (large scale and regional scale) is analysed and compared. The results show that the impact is much more for rainfall over India, but it has no such relation with the rainfall over Odisha. It is also observed that there is a positive (negative) relation of the IOD with the seasonal monsoon rainfall variability over Odisha (India). The correlation between the IAV of monsoon rainfall between the large scale and regional scale was found to be 0.46 with a phase synchronization of 63%. IAV on a sub-regional scale is also presented.
Source: http://www.mdpi.com/2225-1154/5/4/79#stats_id
earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Wed, 11 Oct 2017 21:49:07 +0530
Crustal Structure beneath the Kashmir Basin Adjoining the Western Himalayan Syntaxis http://www.cmmacs.ernet.in/index.php/en/11-publications/467-crustal-structure-beneath-the-kashmir-basin-adjoining-the-western-himalayan-syntaxis http://www.cmmacs.ernet.in/index.php/en/11-publications/467-crustal-structure-beneath-the-kashmir-basin-adjoining-the-western-himalayan-syntaxis

by Ramees R. Mir,  Imtiyaz A. Parvez,  Vinod K. Gaur,  Ashish,  Rakesh Chandra and  Shakil A. Romshoo

We present a crustal shear‐wave velocity model for the intermontane Kashmir valley derived from eight broadband seismic stations located on hard‐rock sites surrounding and within the valley. Receiver functions at these sites were calculated using the iterative time‐domain deconvolution method of Ligorria and Ammon (1999) jointly inverted with fundamental‐mode Rayleigh‐wave group velocity dispersion data to estimate the underlying shear‐wave velocity structure. The inverted Moho depths were further constrained within  ±2 km by forward modeling and conform to those independently estimated using  H‐κ(crustal thickness vs.  VP/VS) stacks. The Moho descends steeply (>15° NE) beneath the Pir Panjal range from a depth of  ∼40 km south of the Main Central thrust to  ∼58 km on the southwestern flank of the valley and undergoes an ∼4 km upwarp beneath the valley, thinning the overlying crust. Further northeast (NE) of the valley, the Moho dips gently ( ∼3°) beneath the Zanskar range. A persistent low‐velocity zone at a depth of  ∼12–16 km is interpreted as a sheared zone associated with the décollement separating Himalayan rocks from the Indian plate. Relocated seismicity from the International Seismological Centre (ISC, 2013) catalog (1964–2013), together with a small number of local earthquakes recorded by our network, is apparently confined south of the NE edge of the valley, suggesting that the transition of the Indian plate from locked décollement to aseismic creep lies near here. This result is consistent with the geodetic findings that a broad zone of partial seismic coupling exists beneath the Kashmir valley.

Source: https://pubs.geoscienceworld.org/bssa/article-abstract/107/5/2443/506668/crustal-structure-beneath-the-kashmir-basin?redirectedFrom=fulltext

earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Thu, 28 Sep 2017 17:28:26 +0530
Identifying the Transition Zone Between East and West Dharwar Craton by Seismic Imaging http://www.cmmacs.ernet.in/index.php/en/11-publications/465-identifying-the-transition-zone-between-east-and-west-dharwar-craton-by-seismic-imaging http://www.cmmacs.ernet.in/index.php/en/11-publications/465-identifying-the-transition-zone-between-east-and-west-dharwar-craton-by-seismic-imaging

by Ashish and Imtiyaz A. Parvez

The data from 12 temporary broadband seismic stations operated across east–west corridor in Dharwar region of Indian Peninsula along with ten other seismic stations operated by CSIR National Geophysical Research Institute (NGRI) in the region have been analysed that provide high-resolution image of southern Dharwar crust. Crust along the corridor is imaged by receiver function  H−kH−k  stacking, common conversion point stacking using data from 22 sites in combination with joint inversion modeling of receiver functions and Rayleigh wave group velocity dispersion curves. The velocity image reveals thinner crust (36–38 km) except one site (coinciding with Cuddapah basin on the surface) in East Dharwar Craton (EDC), while crust beneath the West Dharwar Craton (WDC) is thicker (46–50 km). This study also observed a transition zone between EDC and WDC starting west of Closepet granite to the east of Chitradurga Schist Belt (CSB), which shows diffused Moho with a thickness of 40–44 km. Chitradurga Schist Belt is identified as the contact between Mesoarchean (WDC) and Neoarchean (EDC) crustal blocks. The lowermost part of the crust ( Vs>4.0Vs>4.0 ) is thin (2–6 km) beneath EDC, intermediate (6–8 km) beneath transition zone and thicker (14–30 km) beneath WDC across the profile.

Source: https://link.springer.com/article/10.1007/s00024-017-1657-0

earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Mon, 18 Sep 2017 12:17:33 +0530
Neo-deterministic seismic hazard scenarios for India - a preventive tool for disaster mitigation http://www.cmmacs.ernet.in/index.php/en/11-publications/462-neo-deterministic-seismic-hazard-scenarios-for-india—a-preventive-tool-for-disaster-mitigation http://www.cmmacs.ernet.in/index.php/en/11-publications/462-neo-deterministic-seismic-hazard-scenarios-for-india—a-preventive-tool-for-disaster-mitigation

by Imtiyaz A. Parvez, Andrea Magrin, Franco Vaccari, Ashish, Ramees R. Mir, Antonella Peresan and Giuliano Francesco Panza

Current computational resources and physical knowledge of the seismic wave generation and propagation processes allow for reliable numerical and analytical models of waveform generation and propagation. From the simulation of ground motion, it is easy to extract the desired earthquake hazard parameters. Accordingly, a scenario-based approach to seismic hazard assessment has been developed, namely the neo-deterministic seismic hazard assessment (NDSHA), which allows for a wide range of possible seismic sources to be used in the definition of reliable scenarios by means of realistic waveforms modelling. Such reliable and comprehensive characterization of expected earthquake ground motion is essential to improve building codes, particularly for the protection of critical infrastructures and for land use planning. Parvez et al. (Geophys J Int 155:489–508, 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 catalogues. As described in Panza et al. (Adv Geophys 53:93–165, 2012), the NDSHA methodology evolved with respect to the original formulation used by Parvez et al. (Geophys J Int 155:489–508, 2003): the computer codes were improved to better fit the need of producing realistic ground shaking maps and ground shaking scenarios, at different scale levels, exploiting the most significant pertinent progresses in data acquisition and modelling. Accordingly, the present study supplies a revised NDSHA map for India. The seismic hazard, expressed in terms of maximum displacement (Dmax), maximum velocity (Vmax) and design ground acceleration (DGA), has been extracted from the synthetic signals and mapped on a regular grid over the studied territory.

Source: https://link.springer.com/article/10.1007/s10950-017-9682-0

earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Thu, 10 Aug 2017 10:50:26 +0530
Urban extreme rainfall events: categorical skill of WRF model simulations for localized and non-localized events http://www.cmmacs.ernet.in/index.php/en/11-publications/461-urban-extreme-rainfall-events-categorical-skill-of-wrf-model-simulations-for-localized-and-non-localized-events http://www.cmmacs.ernet.in/index.php/en/11-publications/461-urban-extreme-rainfall-events-categorical-skill-of-wrf-model-simulations-for-localized-and-non-localized-events

by G. N. Mohapatra, V. Rakesh and K. V. Ramesh

An objective method is used for determining the rainfall threshold for identifying extreme rainfall events (EREs) over the urban city, Bangalore, using observed rainfall data for a period of 35 years (1971–2005). Using this threshold, 52 EREs were identified during the period 2010–2014 using high-resolution rain-gauge observations. From these EREs, 15 localized and non-localized events were chosen based on spatial distribution to examine the forecast skill of the Weather Research and Forecasting (WRF) model. Apart from the conventional verification methods, a number of skill scores and indices were defined for a comprehensive evaluation of rainfall model skill. In general, the forecast underpredicted the magnitude of localized and non-localized EREs for the majority of cases; however, the model overpredicted light rainfall (≤10 mm day−1). The model showed a success rate of 59% in simulating light rainfall for localized EREs while 12% of events were missed and 29% were wrongly predicted. The success rate was significantly reduced at higher rainfall categories for localized and non-localized EREs, where the forecast missed the majority of rainfall events. The Reliability Index (RI) computed clearly showed that model skill is relatively higher for non-localized EREs compared to localized EREs. The average forecast reliability for non-localized and localized EREs were 74 and 51%, respectively. For localized EREs, model skill is relatively higher in rainfall location prediction (61%) compared to area (44%) and intensity (46%) prediction; while in the case of non-localized EREs, model skill is similar for location, intensity and area prediction. It is found that coupling an urban canopy model with WRF reduces the model errors particularly for lower rainfall thresholds.


Source: http://onlinelibrary.wiley.com/doi/10.1002/qj.3087/full


earnest@cmmacs.ernet.in (CSIR-4PI - Website Administrator) Featured Publications Fri, 04 Aug 2017 12:44:07 +0530