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.
by Jagat Dwipendra Ray, M Sithartha Muthu Vijayan and Ashok Kumar
Accurate geodetic crustal deformation estimates with realistic uncertainties are essential to constrain geophysical models. A selection of appropriate noise model in geodetic data processing based on the characteristics of the geodetic time series being studied is the key to achieving realistic uncertainties. In this study, we report noise characteristics of a 12-yr long global positioning system (GPS) geodetic time series (2002–2013) obtained from 22 continuous mode GPS stations situated in north-east India, Nepal and Bhutan Himalayas which are one of the most complex tectonic regimes influenced by the largest hydrological loading and impacted with a load of the largest inland glaciers. A comparison of the maximum log likelihood estimates of three different noise models – (i) white plus power law (WPL), (ii) white plus flicker law (WFL) and (iii) white plus random walk noise – adopted to process the GPS time series reveals that among the three models, ∼74% of the time series can be better described either by WPL or WFL model. The results further showed that the horizontals in Nepal Himalayas and verticals in north-east India are highly correlated with time. The impact analysis of noise models on velocity estimation shows that the conventional way of assuming time uncorrelated noise models (white noise) for constraining the crustal deformation of this region severely underestimates rate uncertainty up to 14 times. Such simplistic assumption, being adopted in many geodetic crustal deformation studies, will completely mislead the geophysical interpretations and has the potential danger of identifying any inter/intra-plate tectonic quiescence as active tectonic deformation. Furthermore, the analysis on the effect of the time span of observations on velocity uncertainties suggests 3 yr of continuous observations as a minimum requirement to estimate the horizontal velocities with realistic uncertainties for constraining the tectonics of this region.
Ray, J. D., M. S. M. Vijayan, and A. Kumar (2019), Noise characteristics of GPS time series and their influence on velocity uncertainties, J. Earth Syst. Sci., 128(6), 146, doi:10.1007/s12040-019-1179-5.
Abstract: Most of the studies of the observed variability of the Indian summer monsoon rainfall (ISMR), its prediction and of its impact have involved analysis of an index for ISMR derived by Parthasarathy et al. (1995) or the all-India rainfall during the summer monsoon, available from the India Meteorological Department (IMD) website. Both these indices are based on the average rainfall over the meteorological subdivisions of India. Rajeevan et al. (2006) first derived a gridded rainfall data set for the Indian region which was at a resolution of 1° and subsequently, Pai et al. (2014) have derived a finer resolution (0.25°) rainfall data set for the same region. At present, these data sets are widely used by modelers to generate the ‘observed’ ISMR for assessment of the skill of their models. However, in different studies, different regions are used for averaging the grid data to obtain the ‘observed’ ISMR. For proper assessment and comparison of the skill of the simulations/predictions by different models/versions, it is important that the same region be used for averaging the rainfall to obtain the observed ISMR in each case. Here, we suggest what we consider as the appropriate regions for averaging the rainfall in terms of the 1° and 0.25° to derive/represent ISMR, on the basis of the present understanding of the monsoonal regions and the Indian summer monsoon. We show that the interannual variation of the ISMR thus derived (by averaging rainfall over the regions identified in this study) from gridded data sets is largely consistent with the indices derived as the area weighted sub-divisional rainfall data used in the indices used earlier.
Citation: Sulochana Gadgil, K Rajendran and D S Pai (2019): A new rain-based index for the Indian summer monsoon rainfall, MAUSAM, 70 (3), 485-500
Seasonal forecasts of monsoon at regional scales are critical for many applications but are rarely attempted as even the skill at all-India scale is not yet adequate. However, the conventional approach of evaluation of forecast skill for all-India seasonal monsoon rainfall implicitly assumes that the model performance is more or less spatially homogeneous. It is possible, however, that over a climatically diverse region (with large latitudinal extent), the model skill is dependent on geographical location. In particular, over land-locked regions with large orography, like the Himalayan region, the intrinsic dynamics may play the dominant role in interannual variability; this would imply that even a GCM without interannual variability in lower boundary forcing through SST may produce appreciable skill. We explore this hypothesis based on simulations for the period 1980–2003 with multiple initial conditions with an atmospheric GCM already validated at all-India scale. Multi-scale validation of seasonal forecasts is carried out at regional (Uttarakhand) to station scale over Central Himalaya with multi-source observations. In accordance with our hypothesis and for realizable forecast skill with an atmospheric GCM, the simulations are conducted with climatological monthly SST. At regional (Uttarakhand) scale, the interannual variability in composite observation and ensemble simulation are correlated at 99% significant level, with phase synchronization of about 75%. At station scale, also the skill is found to be non-trivial, especially with respect to gridded observations. Our results thus provide an effective methodology for seasonal forecasting at regional scale over certain geographical locations.
by P Ajilesh, V Rakesh, Sanjeeb K Sahoo and S Himesh
In this study, 32 rainfall events spanning from 2012 to 2014 over the urban Indian city, Bangalore were simulated using the Weather Research and Forecast (WRF) model. Model simulations were carried out with a four‐nested domain initialized with Global Forecast System (GFS) data and the forecast was generated on an hourly basis. The forecasted rainfall at hobli‐level (Bangalore has 34 hobli divisions with an area of each hobli of the order of ~10 km2) was evaluated in terms of their intensity and pattern of spatial distribution by comparing with corresponding rain‐gauge observations. Also, the rainfall forecast skill of the model was evaluated statistically by computing Root Mean Square Error (RMSE), Bias, and Mean Absolute Error (MAE). Thermodynamic variables like Equivalent Potential Temperature, Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), K Index (KI), Lifted Index (LI), and Total Totals Index (TTI) were also derived from simulated model parameters for all the events and verified against corresponding observations. Results showed that the WRF model could simulate the rainfall events and associated thermodynamic features qualitatively; however, there were few hoblis where the relative errors in the forecast were more than 100%. The forecast errors were relatively lower for cases during the south‐west monsoon season compared to other seasons. It was found that the model underestimated thermodynamic indices like CAPE, dew point depression and the simulated LI were positive; these were indicative of model's limitation in simulating intense convection and a possible reason for underpredicted rainfall simulations.
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