On distribution of CMEs speed in solar cycle 23

We have analyzed the data for more than 12900 coronal mass ejections (CMEs) which were obtained by SOHO/LASCO during the period of 1996-2007. The online CME catalogue contains all major CMEs detected by LASCO C2 and C3 coronagraphs. Basically we determine the CME speeds from the linear and quadratic fits to the height-time measurements. It is found that linear (constant speed) fit is preferable for 90% of the CMEs. The distribution of speeds of CMEs in solar cycle 23 is presented along with those obtained by others. As expected, the speeds decrease in the decay phase of the cycle 23. There is an unusual drop in speed in the year 2001 and an abnormal increase in speed in the year 2003 due to the high concentration of CMEs, X-class soft X-ray flares, solar energetic particle (SEP) events and interplanetary shocks observed during October-November period called Halloween events.     

The role of fluids in the formation of talc deposits of Rema area,Kumaun Lesser Himalaya

Talc deposits of Rema area in the Kumaun Inner Lesser Himalaya are hosted within high magnesium carbonates of the Proterozoic Deoban Formation. These deposits occur as irregular patches or pockets mainly within magnesite bodies, along with impurities of magnesite, dolomite and clinochlore. Textures represent different phases of reactions between magnesite and silica to produce talc. Petrography, XRD and geochemistry reveal that the talc has primarily developed at the expense of magnesite and silica, leaving dolomite largely un-reacted. Early fluid inclusions in magnesite and dolomite associated with talc are filled with H₂O+NaCl+KCl ± MgCl₂ ± CaCl₂ fluids, which represent basin fluid system during diagenesis of carbonates. Their varied degree of re-equilibration was although not pervasive but points to increased burial, and hence requires careful interpretation. H₂O-CO₂ fluid with XCO₂ between 0.06 and 0.12 was equilibrated with talc formation. The reaction dolomite+quartz → talc was not extensive because T-X_CO2 was not favourable, and talc was developed principally after magnesite+quartz.     

Oblateness effect of Saturn on periodic orbits in the Saturn-Titan restricted three-body problem

We explore the effect of oblateness of Saturn (more massive primary) on the periodic orbits and the regions of quasi-periodic motion around both the primaries in the Saturn-Titan system in the framework of planar circular restricted three-body problem. First order interior and exterior mean motion resonances are located. The effect of oblateness is studied on the location, nature and size of periodic and quasi-periodic orbits, using the numerical technique of Poincare surface of sections. Some of the periodic orbits change to quasi-periodic orbits due to the effect of oblateness and vice-versa. The stability of the orbits around Saturn, Titan and both varies with the inclusion of oblateness. The centers of the periodic orbits around Titan move towards Saturn, whereas those around Saturn move towards Titan. For the orbit around Titan at C=2.9992, x=0.959494, the apocenter becomes pericenter. By incorporating oblateness effect, the orbit around Titan at C=2.99345, x=0.924938 is captured by Saturn, remains in various trajectories around Saturn, and as time progresses it spirals away around both the primaries.     

Velocity Distribution and the Moments of Turbulent Flow over a Sand-Gravel Mixture Bed

Water Resources - The current study performed the laboratory flume to examine the velocity distribution and the velocity moments of turbulent flow over a sand-gravel mixture bed. The 3D...     

X-ray emission from solar flares

Solar X-ray Spectrometer (SOXS), the first space-borne solar astronomy experiment of India was designed to improve our current understanding of X-ray emission from the Sun in general and solar flares in particular. SOXS mission is composed of two solid state detectors, viz., Si and CZT semiconductors capable of observing the full disk Sun in X-ray energy range of 4–56 keV. The X-ray spectra of solar flares obtained by the Si detector in the 4–25 keV range show evidence of Fe and Fe/Ni line emission and multi-thermal plasma. The evolution of the break energy point that separates the thermal and non-thermal processes reveals increase with increasing flare plasma temperature. Small scale flare activities observed by both the detectors are found to be suitable to heat the active region corona; however their location appears to be in the transition region.     

Rainstorm-generated sediment yield model based on soil moisture proxies (SMP)

This study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC-I-AMC-III) by coupling the improved initial abstraction (I_a-λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness-of-fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE-observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process.     

High-Resolution Quasi-3D Electric Resistivity Tomography for Deciphering Groundwater Potential Zones in Lateritic Terrain

Natural Resources Research - Groundwater is under constant threat of exploitation with increasing demands. Therefore, there is a need for more advanced methods for exploring potential groundwater...     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Simulating rainfall time-series: how to account for statistical variability at multiple scales?

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.     

Estimation of carbon stock for greenhouse gas emissions from hydropower reservoirs

The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO₂eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO₂eq./kWh and 167.70 Gg C year^?1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions.