by Sapna Ghavri, Rajeev Kumar Yadav and Sridevi Jade
The Kachchh paleo-rift basin of western India which recently experienced Mw 7.6, 26th January 2001 Bhuj earthquake, is one of the most active and vulnerable intra-cratonic zones in the world categorized as seismic zone V, i.e. having potential to produce earthquakes of intensity IX or higher. We analysed the geodetic and seismic principal strain rates to reveal the seismic hazard potential of the Kachchh paleo-rift basin. The geodetic strain rates are calculated from the reported surface motion in the region using a modified least-square inversion scheme that utilizes distance and spatial coverage dependent weight factor on the uncertainty in the plate motion to get a reliable estimate of principal strain rates and their orientations at regular grid nodes. An optimal weighting factor yields a mean rate of compression of ~ − 16 ± 6 nano strain/yr towards north-northeast in the region. The seismic strain rates are estimated based on fault plane solutions of the significant past earthquakes (Mw ≥ 2.5) of the region using the Kostrov formulations. The seismic strain rate and stress inversion estimate indicate that the region released elastic strain at an average rate of ~ − 85 nano strain/yr towards north-south in the past 350 years. A composite analysis of the geodetic and seismic strain rates indicates 900–3000 years of recurrence interval of major (Mw 7.8–8) earthquakes in the region. Our analysis indicates a high rate of strain build-up on the reactivated major faults in the region that have the potential to generate devastating (Mw ≥6) earthquakes.
Low-frequency changes in the tropical Indian Ocean surface temperature have previously been investigated in the context of the Indian Ocean basin-wide (IOBM) and dipole (IOD) modes. The IOBM and IOD are the leading eigenmodes estimated from a traditional anomaly of SST. This approach ignores the possibility of multiple seasonal cycles (SCs) having different geographic patterns and interannually modulating amplitudes. The analyses presented here are anchored on the four sets of multivariate seasonal cycles independently extracted from the monthly observations of sea surface temperature (SST), surface wind, and surface pressure variations. We show that the secular warming, encapsulated by the monotonic variations of the first SC of SST (SST–SC1), differs from the previous linear trend patterns and has the most significant variance in the Indian Ocean Warm Pool (IOWP). Hence, these warming tendencies quantify the monotonic expansion rates of IOWP. The most significant interannual responses of Indian Ocean SST to remote forces (such as El Niño and La Niña) are also captured by SST–SC1. Unlike the traditional IOBM but similar to SST–SC1’s secular warming, these remotely forced interannual signals also have considerable variances in IOWP. The interannual variations in SST’s third seasonal cycle (i.e., SST–SC3) inherit SST–SC3’s dipole pattern but diverge from classical IOD in many aspects and are predominantly controlled by local processes. However, they are insufficient to account for the total interannual signals on their own. The collective interannual variations of four seasonal cycles—with significant variances off Africa’s eastern shores—demonstrate basin-wide unipolar patterns. Hence, SST interannual signals in the north-western Indian Ocean and the constantly growing warming in the IOWP influence climate and weather over countries surrounding the Indian Ocean. Thus, this study offers a simple way to separate three types of climate signals: secular, internal, and remotely induced climate fluctuations.
by Ramees R. Mir, Imtiyaz A. Parvez, Gabi Laske & Vinod K. Gaur
This paper presents estimated misorientation angles of broadband seismic sensors of the Kashmir-Zanskar network and their effects on anisotropy determinations and great-circle-path deviations. The misorientations were calculated from the difference between backazimuths of Rayleigh waves and those of the great-circle-arcs connecting the source and receiver. Waveforms of global Rayleigh waves extracted from the records of 13 broadband seismographs in the Kashmir-Zanskar region of Northwestern Himalaya, and 3 others around the region, were used to evaluate misorientation errors in each of these sensor installations. Three of the 16 were found to have orientation errors between ± 5 and 10° with respect to the geographic north, 4 between 10 and 16° and the remainder with <5°. These misalignments had resulted in leakage of a substantial amount of energy in the transverse component receiver functions which, after correction, led to sharper amplitudes and polarities. Indeed, the SKS-derived azimuths of the fast component were found to be quite sensitive to instrument misalignment, suffering ~ 16° shift from a ~ 15.5° error in orientation. A notable observation revealed by misalignment corrections was the substantial, up to 20°, off-great-circle arc deviations even along shorter path arrivals from regional events, offering a qualitative ordination of the region’s heterogeneities. The paper also presents probability distribution functions of the estimated power spectral density of ambient noise at each station compared with global high and low-noise models and near-source earthquake models. The results provide a first-order assessment of small earthquake detection capability of this network, down to M1.0, also confirmed by some of the smallest events located.
Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011–2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011–2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.
Heat waves are increasing in frequency and exhibit high spatial variability in their distribution over India. There are limited studies focused on thermal indices over India due to the nonavailability of high-resolution (HR) climate data. Here we develop dynamically downscaled HR (4 × 4 km) daily climate information for the months of April to June during 2001–2016 using a regional climate model called Weather Research and Forecasting (WRF) Model, which are validated with station observations. The thermal comfort, heat stress, and its spatiotemporal variability and change over India are quantified in terms of indices like excessive heat factor (EHF), the heat index (HI), humidex, apparent temperature (AT), and wet bulb globe temperature (WBGT). The results show that there is an increasing trend in annual heat waves coverage (22,240 km2/year), annual frequency (0.07 days/year), and average intensity (0.04 °C/year) during 2001–2016. The spatial distribution of indices exhibits high spatial and temporal variability. The days with the severe threshold of indices are significantly increasing over north India at the rate of EHF (15.9%), HI (14.9%), humidex (15.9%), AT (13.4%), and WBGT (13.8%). The heat waves’ most vulnerable hotspots are on the parts of Rajasthan, Uttar Pradesh, Madhya Pradesh, and the coastal regions of Andhra Pradesh and Odisha. During heat waves, prolonged exposure under the sun will lead to adverse health impacts, and it is mostly observed over severe heat wave zone. These findings stress the need for developing suitable mitigation strategies for a sustainable ecosystem with minimum impact.
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To develop cutting edge data science products as a horizontal across the CSIR Themes and position as an Institute of Excellence in Bigdata and Artificial Intelligence.
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