Pacific Walker Circulation variability in coupled and uncoupled climate models

There is still considerable uncertainty concerning twentieth century trends in the Pacific Walker Circulation (PWC). In this paper, observational datasets, coupled (CMIP5) and uncoupled (AGCM) model simulations, and additional numerical sensitivity experiments are analyzed to investigate twentieth century changes in the PWC and their physical mechanisms. The PWC weakens over the century in the CMIP5 simulations, but strengthens in the AGCM simulations and also in the observational twentieth century reanalysis (20CR) dataset. It is argued that the weakening in the CMIP5 simulations is not a consequence of a reduced global convective mass flux expected from simple considerations of the global hydrological response to global warming, but is rather due to a weakening of the zonal equatorial Pacific sea surface temperature (SST) gradient. Further clarification is provided by additional uncoupled atmospheric general circulation model simulations in which the ENSO-unrelated and ENSO-related portions of the observed SST changes are prescribed as lower boundary conditions. Both sets of SST forcing fields have a global warming trend, and both sets of simulations produce a weakening of the global convective mass flux. However, consistent with the strong role of the zonal SST gradient, the PWC strengthens in the simulations with the ENSO-unrelated SST forcing, which has a strengthening zonal SST gradient, despite the weakening of the global convective mass flux. Overall, our results suggest that the PWC strengthened during twentieth century global warming, but also that this strengthening was partly masked by a weakening trend associated with ENSO-related PWC variability.     

Sensitivity of global warming to the pattern of tropical ocean warming

The current generations of climate models are in substantial disagreement as to the projected patterns of sea surface temperatures (SSTs) in the Tropics over the next several decades. We show that the spatial patterns of tropical ocean temperature trends have a strong influence on global mean temperature and precipitation and on global mean radiative forcing. We identify the SST patterns with the greatest influence on the global mean climate and find very different, and often opposing, sensitivities to SST changes in the tropical Indian and West Pacific Oceans. Our work stresses the need to reduce climate model biases in these sensitive regions, as they not only affect the regional climates of the nearby densely populated continents, but also have a disproportionately large effect on the global climate.

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Development of Intermediate Microfabric in Kaolin Clay and Its Consolidation Behaviour

The effect of microfabric on the mechanical behaviour of clays has been explored previously based on the response of dispersed and flocculated microfabrics only. However, the natural clays often have the geometric arrangement of particles between these two extreme cases which can be termed as intermediate microfabric. This paper explores the formation of intermediate microfabric of kaolin clay and its impact on soil’s consolidation behaviour by performing self-weight consolidation, slurry consolidation and 1-D consolidation tests. The effect of calgon content (dispersing agent) on geometric arrangement of the particles has been evaluated through cluster size distribution by performing double hydrometer tests. Then these clay slurries have been used to perform the AFM (Atomic Force Microscopy) test to obtain the variation in average angle of particle orientation with respect to the calgon content present in the slurry. AFM technique provides 3D image of the clay sample and 2D image with Z-information with the potential of measuring intermediate microfabric of clayey soil quantitatively including dispersed and flocculated microfabrics. Other traditional techniques such as SEM, TEM & XRD are limited to only qualitative analysis of soil’s microfabric, thus, having no capability to measure intermediate microfabric of clay. A methodology of preparing bulk specimens of clay with intermediate microfabric has been developed using slurry consolidation technique; and then these specimens have been consolidated under 1-D loading to evaluate the effect of intermediate microfabric on compressibility and permeability of clay. In this study, all the experiments reports that the dispersed type geometric arrangement increases with the increase in calgon content in soil up to 2 % and then the reverse behaviour is observed at 3 %; which may depend on the required amount of sodium cations to neutralize the negatively charged faces of the clay platelets present in the slurry.     

Modified Dubois Model for Estimating Soil Moisture with Dual Polarized SAR Data

This paper discusses a new methodology to estimate soil moisture in agriculture region using SAR data with the use of HH and HV polarization. In this study the semi empirical model derived by Dubois et al. (IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926, 1995) was modified to work using σ _HH instead of two like polarization equations σ_HH, σ_VV so that soil moisture can be obtained for the larger area frequently. The field derived roughness correlated with the cross polarization ratio (HV/HH) to replace the one unknown parameter ‘s’ in the Dubois model and hence the dielectric constant was derived by inverting the Dubois model equation (HH). The Topp et al. (Water Resources Research, 16(3), 574–582, 1980) model was used to retrieve soil moisture using the dielectric constant. The mid incidence angle was used to overcome the incident angle effect and it worked successfully to the larger extent. The result is realistic overall, especially where surface has less variation in the roughness and vegetation since the penetration capability of C-band is limited when plant grows hence model valid in the initial period of cultivation. The derived model is having good scope for soil moisture monitoring with the present availability of Indian RISAT data.     

A hypo-plastic approach for evaluating railway ballast degradation

Repetitive or cyclic rail loading deteriorates the engineering properties of the railway ballast by particle crushing and rearrangement. Most of the classical elasto-plastic models are unable to predict such ballast degradation despite successfully predicting the overall load–deformation behavior during cyclic densification. In this context, the present study delivers a novel hypo-plastic modeling approach coupled with breakage mechanics theory to bridge the gap of the conventional models. The hypo-plastic approach enables to predict the nonlinear load–deformation response of ballast-type granular materials for both monotonic and cyclic loading conditions, while circumventing the requirement of notional yield condition to predict the inelastic behavior. Breakage mechanics theory, on the other hand, establishes the links between particle comminution and the macroscopic deformation. The novelty of the proposed approach is threefold. Firstly, unlike the conventional hypo-plastic approaches, the development of the proposed model is within the continuum thermodynamics framework. Secondly, the model requires less number of physically identifiable parameters as compared to that of earlier models employed for assessing the particle breakage under cyclic loading. Third and finally, the numerical implementation of the model as a user-defined material is simple for solving boundary value problems. Under the compressive deformation regime, the model prediction of the ballast degradation along with the cyclic densification response agrees reasonably well with the experimental results found in the literature.     

Development of Information Evaluation System for Smart City Planning Using Geoinformatics Techniques

Urbanization is a continuous process, which transforms the traditional setup into the modern era. Uneven population growth over the next 3 decades is expected in cities. In this context, the new technology-based solutions are desirable, which can provide more efficient and robust approaches for urban infrastructure planning. Keeping these visions, the smart city concept is evolving in the developing countries like India, which appears a new paradigm of systematic intelligent urban infrastructure planning in the city. This research work comprises of grids-based, eight different land use and infrastructure-related information evaluation systems namely urban settlements information evaluation system, water resource information evaluation system, urban green space information evaluation system, intelligent transportation information evaluation system, basic infrastructure information evaluation system, power distribution information evaluation system, telecommunication mast information evaluation system and city surveillance information evaluation system for the development of smart city priority zones in the Bhopal city, capital of Madhya Pradesh, India. The LISS IV and Cartosat 1 sensor digital Image data along with ancillary data were used. The GPS survey was carried out to confirm ground realities, and digital intelligent map was prepared for various utilities after integration of thematic layers. The geostatistical analysis and weights-based methods were used for the creation of information evaluation system for city planning. The investigation of results have shown that various parameters related to smart city planning can be analyzed in a more efficient manner by dividing the area into an appropriate size grid. The spatial integration of information evaluation systems were carried out and areas were divided into low-, medium- and high-priority zones. These prominent categories such as high-priority zones covered the 14% of the study area, similarly medium-priority zones covered the 34% of the study area and low-priority zones covered the 52% of the study area. This research work investigations are very useful for the formulation of new strategic plans, in order to achieve better land use and infrastructure features utilization for the smart city planning.     

Groundwater Quality Assessment in a Hyper-arid Region of Rajasthan,India

Groundwater is an important source of livelihood in regions where rainfall is scanty, surface water sources are absent, and all domestic and agricultural needs are fulfilled with groundwater. This study deals with groundwater quality assessment in a hyper-arid region using multivariate statistical analysis. A total of 43 samples were collected and analyzed using principal component analysis and hierarchical cluster analysis to model the relationship and interdependence among the various physicochemical variables contributing to groundwater quality in the study area. The results of the statistical techniques showed that the variables are in strong correlation with each other. Cluster analysis proved to be a good tool to ascertain the spatial similarity between the contributing variables. The methodology adopted in the present study has been found to be effective and can be utilized to establish strong water quality monitoring network in similar areas.

     

Categories are in flux,but their computational representations are fixed: That's a problem

As research advances, our conceptual understanding also changes. Computational approaches do little to recognize the evolution that occurs at the conceptual level during the research process. This can result in misunderstanding between knowledge producers and consumers and so inhibit the reusability of outcomes. In this article, we describe how changes at the conceptual level can be represented, along with related changes to data and methods, and how appropriate connections between these various artefacts can be maintained. To demonstrate these ideas, we show how categories used in remote sensing and land cover analysis change over time and how these changes are linked to various research activities. We present a new system (called AdvoCate) that augments typical GIS and remote sensing functionality with a conceptual model of categories that can undergo change, and that also captures the cause of conceptual change and its extent. We argue that concepts and categories should be represented explicitly and richly within GIS, because without this, we have a poor idea of what our modeled entities really mean, and by implication how they should be used appropriately. We demonstrate the usefulness of this deeper representation using examples of category evolution from a land cover mapping exercise.