Modelling for Science, for a better future - some recent outcomes
Rupture of the Indian slab in the 2011 Mw 6.9 Sikkim Himalaya earthquake and its tectonic implications
by T. C. Sunilkumar, Anil Earnest, Silpa K and Ronia Andrews
Unlike the other Himalayan plate boundary segments, the eastern Nepal to Bhutan Himalayan region is not known to have generated prominent shallow thrust faulting earthquakes, typical of the ongoing convergence. This region has unusual strike‐slip earthquake occurrences over the depth ranges of 40‐120 km, possibly indicating intraslab deformation. Here, we present for the first time a slip distribution model for the largest ever recorded intraslab strike‐slip earthquake in this region, the Mw 6.9 Sikkim event that occurred on 18th September 2011. Relying on kinematic source process modeling, our results indicate a NE‐SW trending, steeply dipping sinistral source zone within the underthrusting Indian slab. The rupture propagated radially, with a low rupture velocity of 1.7 km/s, breaking a large asperity of 20×20 km2 with a maximum slippage of 1.6 m. The rupture nucleated at a depth of 45 km and reached upper mantle depths. The computed co‐seismic stress drop value is 13.6 MPa. We suggest that most of the aftershocks occurred on the conjugate plane, possibly due to stress triggering. Stress inversion of focal mechanisms indicates a transpressive stress regime throughout the crust and pure strike‐slip regime in the upper mantle. We observed a unimodal distribution of earthquakes beneath the Higher Himalaya. This indicates a strong, brittle Indian slab and unravels a scenario of an eventual break‐up of the lithosphere; the key trigger might be variation in the convergence rates along the Himalayan arc.
Source: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018JB015931 and doi: 10.1029/2018jb015931
Digital revolution and Big Data: a new revolution in agriculture
by S. Himesh, E.V.S. Prakasa Rao, K.C. Gouda, K.V. Ramesh, V. Rakesh, G.N. Mohapatra, B. Kantha Rao, S.K. Sahoo and P. Ajilesh
This review considers the role of Big Data (BD), the digital revolution, the application of Internet of Things (IoTs) and sensor technologies in the agriculture sector. The introduction is focussed on the ongoing research efforts on BD within agriculture sector, basic features of BD and latest development in BD analytics tools. In subsequent sections, the importance of BD applications in the agriculture sector and examples of their success stories in increasing farm productivity, current scenario on BD and digital agriculture, the future prospects of BD and bottlenecks in its implementation in agriculture sector are discussed. Agriculture sector is undergoing a new revolution and transformation, driven by IoT, sensor technologies, BD and cloud computing. This digital revolution in agriculture is very promising and will enable the agriculture sector to move to the next level of farm productivity and profitability. This transformation process looks irreversible and poised to revolutionize not only agriculture but the entire farm-to-food sector.
Monsoon season local control on precipitation over warm tropical oceans
by K Rajendran, Sulochana Gadgil and Sajani Surendran
Understanding local SST control on precipitation during monsoon is important for deducing climate change due to global warming, particularly for warm oceans. Studies of the relationship of the precipitation over tropical oceans with local sea surface temperature (SST), on the monthly scale, have shown that the propensity for precipitation is high for SST above a threshold of 27.5∘C/28∘C. However, for warm oceans with SST above the threshold such as the Bay of Bengal and South China Sea, for each SST, there is a large variation of precipitation and the SST-precipitation relationship is weak. On daily scale mean precipitation increases slightly with SST when precipitation lags SST by a few days, but the relationship between them is rather weak. But, when SST is above the threshold, for daily, pentad, 10-day and monthly time scales, and with or without time lag, the curve depicting the variation of mean precipitation with SST explains only a small fraction of precipitation variance and hence cannot be considered to be representative of the SST–precipitation relationship, or used to deduce the impact of SST on precipitation. On the other hand, the local control on precipitation is predominantly atmospheric dynamics with the relationship of variation of precipitation to low-level convergence on all timescales, being strong. This suggests that for warm oceans, the limiting resource for precipitation/convection is not SST but dynamics.
Citation
Rajendran, K., Gadgil, S. & Surendran, S. Meteorology and Atmospheric Physics (2018). https://doi.org/10.1007/s00703-018-0649-7, 1-15pp
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