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.

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.

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.

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.

Physically Based, Hydrologic Model Results Based on Three Precipitation Products

The main objective of the study is to examine the accuracy of and differences among simulated streamflows driven by rainfall estimates from a network of 22 rain gauges spread over a 2,170 km2 watershed, NEXRAD Stage III radar data, and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite data. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physically based, distributed parameter, grid-structured, hydrologic model, was used to simulate the June-2002 flooding event in the Upper Guadalupe River watershed in south central Texas. There were significant differences between the rainfall fields estimated by the three types of measurement technologies. These differences resulted in even larger differences in the simulated hydrologic response of the watershed. In general, simulations driven by radar rainfall yielded better results than those driven by satellite or rain-gauge estimates. This study also presents an overview of effects of land cover changes on runoff and stream discharge. The results demonstrate that, for major rainfall events similar to the 2002 event, the effect of urbanization on the watershed in the past two decades would not have made any significant effect on the hydrologic response. The effect of urbanization on the hydrologic response increases as the size of the rainfall event decreases.

Synthesis, microstructure and thermal expansion studies on Ca0 center dot 5+x/2Sr0 center dot 5+x/2Zr4P6-2x Si-2x O-24 system prepared by co-precipitation method

We report on the synthesis, microstructure and thermal expansion studies on Ca0 center dot 5 + x/2Sr0 center dot 5 + x/2Zr4P6 -aEuro parts per thousand 2x Si-2x O-24 (x = 0 center dot 00 to 1 center dot 00) system which belongs to NZP family of low thermal expansion ceramics. The ceramics synthesized by co-precipitation method at lower calcination and the sintering temperatures were in pure NZP phase up to x = 0 center dot 37. For x a parts per thousand yen 0 center dot 5, in addition to NZP phase, ZrSiO4 and Ca2P2O7 form as secondary phases after sintering. The bulk thermal expansion behaviour of the members of this system was studied from 30 to 850 A degrees C. The thermal expansion coefficient increases from a negative value to a positive value with the silicon substitution in place of phosphorous and a near zero thermal expansion was observed at x = 0 center dot 75. The amount of hysteresis between heating and cooling curves increases progressively from x = 0 center dot 00 to 0 center dot 37 and then decreases for x > 0 center dot 37. The results were analysed on the basis of formation of the silicon based glassy phase and increase in thermal expansion anisotropy with silicon substitution.

Regionalization of precipitation in India-a review

Regionalization of precipitation refers to delineation of rain gauges in an area into homogeneous groups (clusters or regions). Various regionalization procedures are employed by researchers in hydrometeorology for addressing a wide spectrum of problems. This paper provides an overview of underlying concepts as well as advantages and limitations of procedures that have been developed over the past six decades. Emphasis is given to studies that have been carried out in India. Following this, gaps where more research needs to be focussed are highlighted, and challenges for regionalization in a climate change scenario are discussed.

Role of the cloud adjustment time scale in simulation of the interannual variability of Indian summer monsoon

The simulation of precipitation in a general circulation model relying on relaxed mass flux cumulus parameterization scheme is sensitive to cloud adjustment time scale (CATS). In this study, the frequency of the dominant intra-seasonal mode and interannual variability of Indian summer monsoon rainfall (ISMR) simulated by an atmospheric general circulation model is shown to be sensitive to the CATS. It has been shown that a longer CATS of about 5 h simulates the spatial distribution of the ISMR better. El Nio Southern Oscillation-ISMR relationship is also sensitive to CATS. The equatorial Indian Ocean rainfall and ISMR coupling is sensitive to CATS. Our study suggests that a careful choice of CATS is necessary for adequate simulation of spatial pattern as well as interannual variation of Indian summer monsoon precipitation.

Effect of pH on structural and magnetic properties of nanocrystalline Y3Fe5O12 by aqueous co-precipitation method

The Y3Fe5O12 (YIG) nanopowders were synthesised at different pH using co-precipitation method. The effect of pH on the phase formation of YIG is characterised using XRD, TEM, FTIR and TG/DTA. From the Scherer formula, the particle sizes of the powders were found to be 13, 19 and 28 nm for pH=10, 11 and 12 respectively. It is found that as the pH of the solution increase the particle size is also increases. It is also clear from the TG/DTA curves that as the pH is increasing the weight losses were found to be small. The nanopowders were sintered at 600, 700, 800 and 900 degrees C for 5 h using conventional sintering method. The phase formation is completed at 800 degrees C/5 h which is correlated with TG/DTA. The average grain size of the samples is found to be similar to 161 nm. The high values of M-s=23 emu g(-1) and H-c=22 Oe were recorded for the sample sintered at 900 degrees C.

Role of pH controlled DNA secondary structures in the reversible dispersion/precipitation and separation of metallic and semiconducting single-walled carbon nanotubes

Single-stranded DNA (ss-DNA) oligomers (dA(20), d(C(3)TA(2))(3)C-3] or dT(20)) are able to disperse single-walled carbon nanotubes (SWNTs) in water at pH 7 through non-covalent wrapping on the nanotube surface. At lower pH, an alteration of the DNA secondary structure leads to precipitation of the SWNTs from the dispersion. The structural change of dA(20) takes place from the single-stranded to the A-motif form at pH 3.5 while in case of d(C(3)TA(2))(3)C-3] the change occurs from the single-stranded to the i-motif form at pH 5. Due to this structural change, the DNA is no longer able to bind the nanotube and hence the SWNT precipitates from its well-dispersed state. However, this could be reversed on restoring the pH to 7, where the DNA again relaxes in the single-stranded form. In this way the dispersion and precipitation process could be repeated over and over again. Variable temperature UV-Vis-NIR and CD spectroscopy studies showed that the DNA-SWNT complexes were thermally stable even at similar to 90 degrees C at pH 7. Broadband NIR laser (1064 nm) irradiation also demonstrated the stability of the DNA-SWNT complex against local heating introduced through excitation of the carbon nanotubes. Electrophoretic mobility shift assay confirmed the formation of a stable DNA-SWNT complex at pH 7 and also the generation of DNA secondary structures (A/i-motif) upon acidification. The interactions of ss-DNA with SWNTs cause debundling of the nanotubes from its assembly. Selective affinity of the semiconducting SWNTs towards DNA than the metallic ones enables separation of the two as evident from spectroscopic as well as electrical conductivity studies.

Laboratory Studies on Stabilization of an Expansive Soil by Lime Precipitation Technique

Lime stabilization prevails to be the most widely adopted in situ stabilization method for controlling the swell-shrink potentials of expansive soils despite construction difficulties and its ineffectiveness in certain conditions. In addition to the in situ stabilization methods presently practiced, it is theoretically possible to facilitate in situ precipitation of lime in soil by successive permeation of calcium chloride (CaCl2 ) and sodium hydroxide (NaOH) solutions into the expansive soil. In this laboratory investigation, an attempt is made to study the precipitation of lime in soil by successive mixing of CaCl2 and NaOH solutions with the expansive soil in two different sequences.Experimental results indicated that in situ precipitation of lime in soil by sequential mixing of CaCl2 and NaOH solutions with expansive soil developed strong lime-modification and soil-lime pozzolanic reactions. The lime-modification reactions together with the poorly de- veloped cementation products controlled the swelling potential, reduced the plasticity index, and increased the unconfined compressive strength of the expansive clay cured for 24 h. Comparatively, both lime-modification reactions and well-developed crystalline cementation products (formed by lime-soil pozzolanic reactions) contributed to the marked increase in the unconfined compressive strength of the ex-pansive soil that was cured for 7–21 days. Results also show that the sequential mixing of expansive soil with CaCl2 solution followed by NaOH solution is more effective than mixing expansive soil with NaOH solution followed by CaCl2 solution. DOI: 10.1061/(ASCE)MT .1943-5533.0000483. © 2012 American Society of Civil Engineers.

Comparison of structural and luminescence properties of Dy2O3 nanopowders synthesized by co-precipitation and green combustion routes

Dysprosium oxide (Dy2O3) nanopowders were prepared by co-precipitation (CP) and eco-friendly green combustion (GC) routes. SEM micrographs prepared by CP route show smooth rods with various lengths and diameters while, GC route show porous, agglomerated particles. The results were further confirmed by TEM. Thermoluminescence (TL) responses of the nanopowder prepared by both the routes were studied using gamma-rays. A well resolved glow peak at 353 degrees C along with less intense peak at 183 degrees C was observed in GC route while, in CP a single glow peak at 364 degrees C was observed. The kinetic parameters were estimated using Chen's glow peak route. Photoluminescence (PL) of Dy2O3 shows peaks at 481, 577,666 and 756 nm which were attributed to Dy3+ transitions of F-4(9/2)-H-6(15/2), H-6(11/2), H-6(11/2) and H-6(9/2), respectively. Color co-ordinate values were located in the white region as a result the product may be useful for the fabrication of WLED'S. (C) 2014 Elsevier Ltd. All rights reserved.

Influence of source concentration on structural and optical properties of SnO2 nanoparticles prepared by chemical precipitation method

In present work, a systematic study has been carried out to understand the influence of source concentration on structural and optical properties of the SnO2 nanoparticles. SnO2 nanoparticles have been prepared by using chemical precipitation method at room temperature with aqueous ammonia as a stabilizing agent. X-ray diffraction analysis reveals that SnO2 nanoparticles exhibit tetragonal structure and the particle size is in range of 4.9-7.6 nm. High resolution transmission electron microscopic image shows that all the particles are nearly spherical in nature and particle size lies in range of 4.6-7 nm. Compositional analysis indicates the presence of Sn and O in samples. Blue shift has been observed in optical absorption spectra due to quantum confinement and the bandgap is in range of 4-4.16 eV. The origin of photoluminescence in SnO2 is found to be due to recombination of electrons in singly occupied oxygen vacancies with photo-excited holes in valance band.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.