Rainfall variability and indices of extreme rainfall-analysis and perception study for two stations over Central Himalaya,India

The analysis of rainfall pattern and indices of extreme rainfall events is performed for two meteorological stations located in the Central Himalayan Region which is highly vulnerable to rain-induced hazards. The records of these rain-induced disasters suggest that such events are generally observed in later part of monsoon season, when soil is saturated after monsoon rains. An attempt is made here to test trends of 19 different extreme rainfall indices that have been widely used in the literature, using daily rainfall data for two urban centres (Nainital and Almora) over the period 1992–2005. We have used statistical tools such as Sen’s method and Mann–Kendall test for detection of trend in annual rainfall, monsoon rainfall, number of rainy days and 1-day extreme rainfall. Principal component analysis gives the correlation between different extreme rainfall indices. Time series of principal components are representing the trends of extreme indices, their variation and interrelation between different indices. The perception study conducted in the same sites indicates that extreme rainfall events and change in rainfall amount and timing are well perceived by the local people.     

MHD free-convection and mass transfer flow of an elasto-viscous fluid

The effects of magnetic field and mass transfer on the flow of an elasto-viscous fluid past an infinite vertical plate, when the plate is moving with uniform velocityU, are discussed. The magnetic lines of force are assumed to be fixed relative to the plate. The Laplace transform method is used to obtain the expression for velocity. The effect of various parameters, occurring into the problem, is discussed with the help of tables.     

A model‐based data‐driven dictionary learning for seismic data representation

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.     

Effect of spatial resolution on regionalization of hydrological model parameters

Lack of availability of historical data series is one of the major hindrances in hydrological modelling. Regionalization of hydrological model parameters is one of the solutions to obtain the parameters for ungauged basins. Recently, lots of methodologies have been developed. They can be categorized as model calibration then fitting regression between model parameters and catchments characteristics, using some kind of transfer function. The aim of this study was to compare different regionalization methods as well as to look how the spatial resolution affects regionalization. In this study, a modified Lipschitz and monotony condition was used for regionalization. To identify the effect of the model resolution, the parameters of a distributed and semi‐distributed version of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model were regionalized. The study was conducted at the upper Neckar catchment of southwest Germany. It has been found that the combination of Lipschitz and monotony condition has performed reasonably. It has been seen that the distributed model structure has outperformed the semi‐distributed model structure. It shows under present data conditions that higher model resolution can describe processes of ungauged basins reasonably. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessment of Indian summer monsoon simulation by Community Atmosphere Model (CAM3)

Seasonal prediction of Indian Summer Monsoon (ISM) has been attempted for the current year 2011 using Community Atmosphere Model (CAM) developed at the National Centre for Atmospheric Research (NCAR). First, 30?years of model climatology starting from 1981 to 2010 has been generated to capture the variability of ISM over the Indian region using 30 seasonal simulations. The simulated model climatology has been validated with different sets of observed climatology, and it was observed that the simulated climatological rainfall is affected by model bias. Subsequently, a bias correction procedure using the Tropical Rainfall Measuring Mission (TRMM) 3B43 rainfall has been proposed. The bias-corrected rainfall climatology shows both spatial and temporal variability of ISM satisfactorily. Further, four sets of 10-member ensemble simulations of ISM 2009 and 2010 have been performed in hindcast mode using observed sea surface temperature (SST) and persistence of April SST anomaly, and it has been found that the bias-corrected model rainfall captures the seasonal variability of ISM reasonably well with some discrepancies in these two contrasting monsoon years. With this positive background, the seasonal prediction of ISM 2011 has been carried out in forecast mode with the assumption of persistence of May SST anomaly from June through September 2011. The model assessment shows an 11% deficiency in All-India Rainfall (AIR) of ISM 2011. In particular, the monthly accumulated rains are predicted to be 101% (17.6?cm), 86% (24.3?cm), 83% (21.0?cm) and 95% (15.5?cm) of normal AIR for the months of June, July, August and September, respectively.     

A Comprehensive Review on Microalgae-Based Biorefinery as Two-Way Source of Wastewater Treatment and Bioresource Recovery

In the past few years, microalgae have gained huge recognition from the scientific community due to their potential applications in the production of a broad array of bio-based products varying from biofuels to nanoparticles. Due to their elevated growth rate, high tolerance to various types of abiotic stresses, and complex metabolic capacity, microalgae can be used as promising tools for the attainment of a circular bioeconomy. Moreover, they can simply utilize nutrients from wastewater for biorefinery purposes, resulting in resource recovery coupled with wastewater treatment. However, due to their sub-optimal yields and high production costs, microalgae-based bio-products have not yet been commercialized. This review provides insights into the employment of microalgae as an efficient bioresource for the treatment of wastewater with simultaneous enactment as a biorefinery to produce biofuels, biochar, bioplastic, fertilizers, and other high-value bioproducts. Furthermore, the application of microalgal nanoparticles in wastewater treatment and prospects for genetic modification of microalgae for enhanced biorefinery capabilities have also been briefly highlighted.     

Parameterization of synoptic weather systems in the South Atlantic Bight for modeling applications

An event based, long-term, climatological analysis is presented that allows the creation of coastal ocean atmospheric forcing on the coastal ocean that preserves both frequency of occurrence and event time history. An algorithm is developed that identifies individual storm event (cold fronts, warm fronts, and tropical storms) from meteorological records. The algorithm has been applied to a location along the South Atlantic Bight, off South Carolina, an area prone to cyclogenesis occurrence and passages of atmospheric fronts. Comparison against daily weather maps confirms that the algorithm is efficient in identifying cold fronts and warm fronts, while the identification of tropical storms is less successful. The average state of the storm events and their variability are represented by the temporal evolution of atmospheric pressure, air temperature, wind velocity, and wave directional spectral energy. The use of uncorrected algorithm-detected events provides climatologies that show a little deviation from those derived using corrected events. The effectiveness of this analysis method is further verified by numerically simulating the wave conditions driven by the characteristic wind forcing and comparing the results with the wave climatology that corresponds to each storm type. A high level of consistency found in the comparison indicates that this analysis method can be used for accurately characterizing event-based oceanic processes and long-term storm-induced morphodynamic processes on wind-dominated coasts.     

Temporal Assessment of Growing Stock, Biomass and Carbon Stock of Indian Forests

The dynamics of terrestrial ecosystems depends on interactions between carbon, nutrient and hydrological cycles. Terrestrial ecosystems retain carbon in live biomass (aboveground and belowground), decomposing organic matter, and soil. Carbon is exchanged naturally between these systems and the atmosphere through photosynthesis, respiration, decomposition, and combustion. Human activities change carbon stock in these pools and exchanges between them and the atmosphere through land-use, land-use change, and forestry.In the present study we estimated the wood (stem) biomass, growing stock (GS) and carbon stock of Indian forests for 1984 and 1994. The forest area, wood biomass, GS, and carbon stock were 63.86 Mha, 4327.99 Mm³, 2398.19 Mt and 1085.06 Mt respectively in 1984 and with the reduction in forest area, 63.34 Mha, in 1994, wood biomass (2395.12 Mt) and carbon stock (1083.69 Mt) also reduced subsequently. The Conifers, of temperate region, stocked maximum carbon in their woods, 28.88 to 65.21 t C ha⁻¹, followed by Mangrove forests, 28.24 t C ha⁻¹, Dipterocarp forests, 28.00 t C ha⁻¹, and Shorea robusta forests, 24.07 t C ha⁻¹. Boswellia serrata, with 0.22 Mha forest area, stocked only 3.91 t C ha⁻¹. To have an idea of rate of carbon loss the negative changes (loss of forest area) in forest area occurred during 1984–1994 (10yrs) and 1991–1994 (4yrs) were also estimated. In India, land-use changes and fuelwood requirements are the main cause of negative change. Total 24.75 Mt C was lost during 1984–1994 and 21.35 Mt C during 1991–94 at a rate of 2.48 Mt C yr⁻¹ and 5.35 Mt C yr⁻¹ respectively. While in other parts of India negative change is due to multiple reasons like fuelwood, extraction of non-wood forest products (NWFPs), illicit felling etc., but in the northeastern region of the country shifting cultivation is the only reason for deforestation. Decrease in forest area due to shifting cultivation accounts for 23.0% of the total deforestation in India, with an annual loss of 0.93 Mt C yr⁻¹.