A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

On the role of cloud adjustment time scale in simulating precipitation with Relaxed Arakawa-Schubert convection scheme

The precipitation by Relaxed Arakawa-Schubert cumulus parameterization in a General Circulation Model (GCM) is sensitive to the choice of relaxation parameter or specified cloud adjustment time scale. In the present study, we examine sensitivity of simulated precipitation to the choice of cloud adjustment time scale (tau(adj)) over different parts of the tropics using National Center for Environmental Prediction (NCEP) Seasonal Forecast Model (SFM) during June-September. The results show that a single specified value of tau(adj) performs best only over a particular region and different values are preferred over different parts of the world. To find a relation between tau(adj) and cloud depth (convective activity) we choose six regions over the tropics. Based on the observed relation between outgoing long-wave radiation and tau(adj), we propose a linear cloud-type dependent relaxation parameter to be used in the model. The simulations over most parts of the tropics show improved results due to this newly formulated cloud-type dependent relaxation parameter.

Effect of Grain Size on Structural and Magnetic Properties of CuFe2O4 Nanograins Synthesized by Chemical Co-Precipitation

CuFe2O4 nanograins have been prepared by the chemical co-precipitation technique and calcined in the temperature range of 200-1200 degrees C for 3 h. A wide range of grain sizes has been observed in this sintering temperature range, which has been determined to be 4 to 56 nm. Formation of ferrite has also been confirmed by FTIR measurement through the presence of wide band near 600 and 430 cm(-1) for the samples in the as-dried condition. Systematic variation of wave number has been observed with the variation of the calcination temperature. B-H loops exhibit transition from superparamagnetic to ferrimagnetic state above the calcination temperature of 900 degrees C. Coercivity of the samples at lower calcination temperature of 900 degrees C reduces significantly and tends towards zero coercivity, which is suggestive of superparamagnetic transition for the samples sintered below this temperature. Frequency spectrum of the real and imaginary part of complex initial permeability have been measured for the samples calcined at different temperature, which shows wide range of frequency stability. Curie temperature, T-c has been measured from temperature dependence initial permeability at a fixed frequency of 100 kHz. Although there is small variation of T-c with sintering temperature, the reduction of permeability with temperature drastically reduce for lower sintering temperature, which is in conformity with the change of B-H loops with the variation of sintering temperatures.

Defect-Free Hyperbranched Polydithioacetal via Melt Polymerization

Degree of branching (DB) describes the level of structural perfection of a hyperbranched polymer when compared to its defect-free analogue, namely the dendrimer. The strategy most commonly used to achieve high DB values, specifically while using AB(2) type self-condensations, is to design an AB2 monomer wherein the reaction of the first B-group leads to an enhancement of the reactivity of the second one. In the present study, we show that an AB2 monomer carrying a dimethylacetal unit and a thiol group undergoes a rapid self-condensation in the melt under acid-catalysis to yield a hyperbranched polydithioacetal with no linear defects. NMR studies using model systems reveal that the intermediate monothioacetal is relatively unstable under the polymerization conditions and transforms rapidly to the dithioacetal; because this second step occurs irreversibly during polymer formation, it leads to a defect-free hyperbranched polydithioacetal. TGA studies of the polymerization process provided some valuable insights into the kinetics of polymerization. An additional virtue of this approach is that the numerous terminal dimethylacetal groups are very labile and can be quantitatively transformed by treatment with a variety of functional thiols; the terminal dimethylacetals were, thus, reacted with various thiols, such as dodecanethiol, benzyl mercaptan, ethylmercaptopropionate, and so on, to demonstrate the versatility of these systems as sulfur-rich hyperscaffolds to anchor different kinds of functionality on their periphery.

Synthesis of BiFeO by carbonate precipitation

Magnetoelectric multiferroic BiFeO3 (BFO) was synthesized by a simple carbonate precipitation technique of metal nitrate solutions. X-ray powder diffraction and thermo-gravimetric analysis (TGA) revealed that the precipitate consists of an intimate mixture of crystalline bismuth carbonate and an amorphous hydroxide of iron. The precipitate yielded BiFeO3 at an optimal calcination temperature of similar to 560A degrees C. Energy dispersive X-ray (EDX) analysis showed 1:1 ratio between Bi and Fe in the oxide. X-ray photoelectron spectroscopy (XPS) studies confirmed that Fe to be in +3 oxidation states both in the precipitated powder and BiFeO3. The synthesized BFO exhibits a very weak ferromagnetic correlation at room temperature and the degree of which increases slightly on cooling down to 10 K suggesting alteration in the long range spatial modulation of the spins arrangement as compared to the bulk BiFeO3.

In Situ Electrochemical Polymerization at Air-Water Interface: Surface-Pressure-Induced, Graphene-Oxide-Assisted Preferential Orientation of Polyaniline

In situ electrochemical polymerization of aniline in a Langmuir trough under applied surface pressure assists in the preferential orientation of polyaniline (PANI) in planar polaronic structure. Exfoliated graphene oxide (EGO) spread on water surface is used to bring anilinium cations present in the subphase to air-water interface through electrostatic interactions. Subsequent electrochemical polymerization of aniline under applied surface pressure in the Schaefer mode results in EGO/PANT composite with PANT in planar polaronic form. The orientation of PANI is confirmed by electrochemical and Raman spectroscopic studies. This technique opens up possibilities of 2-D polymerization at the air-water interface. Electrochemical sensing of hydrogen peroxide is used to differentiate the activity of planar and coiled forms of PANI toward electrocatalytic reactions.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Ex Situ X-ray Diffraction, X-ray Absorption Near Edge Structure, Electron Spin Resonance, and Transmission Electron Microscopy Study of the Hydrothermal Crystallization of Vanadium Oxide Nanotubes: An Insight into the Mechanism of Formation

The nucleation and growth of vanadium oxide nanotubes (VOx-NT) have been followed by a combination of numerous ex situ techniques. long the hydrothermal process. Intermediate solid phases extracted at different reaction times have been characterized by powder X-ray diffraction, scanning and transmission electron microscopy, electron spin resonance, and V-K edge :X-ray absorption near-edge structure spectroscopy. The supernatant vanadate solutions extracted during the hydrothermal treatment have been studied by liquid V-51 NMR and flame. spectroscopy. For short durations of the hydrothermal synthesis, the initial V2O5-surfactant intercalate. is progressively transformed into VOx-NT whose crystallization starts to be detected after a hydrothermal treatment of 24 h. Upon heating from 24 h to 7 days, VOx-NT are obtained in larger amount and with an improved crystallinity. The detection of soluble amines and cyclic metavanadate V4O12](4-) in the supernatant solution along the hydrothermal process suggests that VOx-NT result from a dissolution precipitation mechanism. Metavanadate species V4O12](4-) could behave as molecular precursors in the polymerization reactions leading to VOx-NT.

Thermal degradation kinetics of thermoresponsive poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) copolymers prepared via RAFT polymerization

Reversible addition-fragmentation chain transfer polymerization at 70 A degrees C in N,N-dimethylformamide was used to prepare poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) copolymers in various compositions to afford well-defined polymers with pre-determined molecular weight, narrow molecular weight distribution, and precise chain end structure. The copolymer compositions were determined by H-1 NMR spectroscopy. The reactivity ratios of N-isopropylacrylamide (NIPAM) and N,N-dimethylacrylamide (DMA) were calculated as r (NIPAM) = 0.838 and r (DMA) = 1.105, respectively, by the extended Kelen-Tudos method at high conversions. The lower critical solution temperature of PNIPAM can be altered by changing the DMA content in the copolymer chain. Differential scanning calorimetry and thermogravimetric analysis at different heating rates were carried out on these copolymers to understand the nature of thermal degradation and to determine its kinetics. Different kinetic models were applied to estimate various parameters like the activation energy, the order, and the frequency factor. These studies are important to understand the solid state polymer degradation of N-alkyl substituted polymers, which show great potential in the preparation of miscible polymer blends due to their ability to interact through hydrogen bonding.