Characteristics of aerosol optical depth and Ångström parameters over Mohal in the Kullu Valley of Northwest Himalayan Region,India

The measurements using a ground based multi wavelength radiometer (MWR) at Mohal (31°54′N, 77°07′E, 1154 m AMSL) in the Kullu valley of Northwestern Himalayan region show that the spectral aerosol optical depth (AOD) and turbidity coefficient, β, are high in summer, moderate in monsoon season, low in winter and lowest in autumn, while wavelength exponent, α, has an opposite trend. Average annual value of AOD at 500 nm is 0.24±0.01, 0.43±0.02, and 0.28±0.02; that of β is 0.14±0.01, 0.22±0.02, and 0.17±0.03; and that of α is 1.06±0.09, 1.16±0.10, and 0.86±0.13, respectively, for clear, hazy and partially clear sky days. The considerably greater value of β on hazy days indicates more coarse particles in mountain haze. The fractional asymmetry factor (AF) is more negative in summer and autumn months. The AOD and β have significantly positive correlation with temperature and wind speed, suggesting high AODs and turbidity on hot and windy days.     

Suspended sediment transport during tropical‐cyclone floods in Fiji

Flow records, rising‐stage sediment samplers, and a sand suspension model are used to examine suspended sediment concentrations during major floods caused by tropical cyclones TC Joni and TC Kina in the Rewa River, Fiji. The highest concentrations of total suspended solids were measured during the early stages of TC Kina. The suspension model predicts higher sand concentrations for TC Kina compared with TC Joni because of the larger slope and higher shear stresses during Kina. Extremely high wash load concentrations early in TC Kina are at least partly due to remobilization of fine sediment deposited during the earlier TC Joni flood. Samples from the TC Kina had volumetric concentrations larger than 5%, indicating hyperconcentrated streamflows. Mass‐density shear stresses in the hyperconcentrated flows are up 1·6 times larger than clear‐water shear stresses, but they occur early during low stages of the flood and probably do not result in severe bed erosion. Copyright © 2002 John Wiley & Sons, Ltd.     

Magnetostatic stiff-fluid model of cylindrical symmetry in general relativity

We study the static stiff-fluid model for perfect fluid distributions in the presence of incident magnetic field. The magnetic field is surrounded by static stiff fluid of infinite electric conductivity and it is due to the electric current flowing along theZ-axis. The various physical and geometrical properties together with the state of model in absence of magnetic field are also discussed.     

An LRS Bianchi Type I Bulk Viscous Fluid String Cosmological Model in General Relativity

An LRS Bianchi type I string dust cosmological model with and without bulk viscosity following a method used by Letelier and Stachel, is investigated. To get a determine solution, it is assumed that σ∝θ whereσ is shear and θ is scalar of expansion and which leads to A =αBⁿ were n is a constant. The physical and geometrical aspects of the model are also discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Some inhomogeneous cosmological models of plane symmetry with electromagnetic field

Some inhomogeneous cosmological models of plane symmetry in presence of electromagnetic field have been obtained. Various physical and geometrical properties of the models with some special cases are also discussed.     

Inhomogeneous viscous fluid cosmological model with electromagnetic field in general relativity

The object of this paper is to investigate the behaviour of electromagnetic field in inhomogeneous cosmological models obtained for viscous fluid distributions. The various particular cases when both the electromagnetic and viscosity are absent, are also discussed.     

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.