Evaluation of significant sources influencing the variation of physico-chemical parameters in Port Blair Bay,South Andaman,India by using multivariate statistics

Port Blair is the capital city of Andaman & Nicobar Islands, the union territory of India. More than 50% of the population of these islands lives around Port Blair Bay. Therefore the anthropogenic effects in the bay water were studied for monitoring purpose from seven stations. Physico-chemical parameters of seawater were analyzed in samples collected once in every 3 months for 2 years from seven sampling stations located in Port Blair Bay, South Andaman Island to evaluate the spatial and tidal variation. Cluster analysis and factor analysis were applied to the experimental data in an attempt to understand the sources of variation of physico-chemical parameters. In cluster analysis, the stations Junglighat Bay and Phoenix Bay having high anthropogenic influence formed a separate group. The factors obtained from factor analysis indicated that the parameters responsible for physico-chemical variations are mainly related to land run-off, sewage outfall and tidal flow.     

On Simulation and Analysis of Variable‐Rate Pumping Tests

Analytical solutions for constant‐rate pumping tests are widely used to infer aquifer properties. In this note, we implement a methodology that approximates the time‐varying pumping record as a series of segments with linearly varying pumping rates. We validate our approach using an analytical solution for a sinusoidally varying pumping test. We also apply our methodology to analyze synthetic test data and compare the results with those from a commonly used method where rate variations are represented by a series of constant‐rate steps.     

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.     

A site-specific study on the measurement of sorption coefficients for radionuclides

A study on the potential of geological media from the vicinity of the mining site in Tummalapalle region of Andhra Pradesh in India for retardation of radionuclide migration in groundwater was conducted. The studies included the measurement of sorption coefficients for six radionuclides, uranium, thorium, lead, bismuth, radium and polonium, between two groundwater simulants and two site-specific samples of geological media. Initial parametric studies involving chemical composition, pH, calcium carbonate and organic carbon contents of both geological media and ground water simulants were carried out. Significant differences in sorption coefficients were observed as a result of varying pH, calcium carbonate and presence of trace quantities of organic contents in simulant solutions. For example, uranium has a hundred fold lower distribution coefficient in the case of simulant solution having higher carbonate content. Similarly, in the case of the geological media having higher calcium carbonate and organic carbon contents, higher distribution coefficients were obtained for all radionuclides. Among the six radionuclides studied thorium showed the largest and radium the smallest distribution coefficient values for the soil samples assessed. The site-specific sorption coefficients will be used for contaminant transport study.     

Gneiss-charnockite relation around Ponmudi,southern Kerala: evidences and implications of prograde charnockite formation in southern India

Isochemical conversion of garnet-biotite bearing paragneiss to charnockite in the Precambrian Khondalite belt of southern Kerala is described from Ponmudi area. Petrographic evidences indicate the formation of hypersthene by the breakdown of biotite in the presence of quartz following the reaction: Biotite + quartz → hypersthene + K-feldspar + vapour. The estimated pressure — temperature conditions of metamorphism are around 5–7 kbars and 750° ± 40°C. Presence of CO₂-rich, mixed CO₂-H₂O and H₂O-rich inclusions were noticed in gneiss as well as in charnockites. Charnockites contain abundant CO₂-rich inclusions.     

Chlorophyll modulation of mixed layer thermodynamics in a mixed-layer isopycnal General Circulation Model — An example from Arabian Sea and equatorial Pacific

Western tropical Indian Ocean, Arabian Sea, and the equatorial Pacific are known as regions of intense bio-chemical-physical interactions: the Arabian Sea has the largest phytoplankton bloom with seasonal signal, while the equatorial Pacific bloom is perennial with quasi-permanent upwelling. Here, we studied three dimensional ocean thermodynamics comparing recent ocean observation with ocean general circulation model (OPYC) experiment combined with remotely sensed chlorophyll pigment concentrations from the Coastal Zone Color Scanner (CZCS). Using solar radiation parameterization representing observations that a higher abundance of chlorophyll increases absorption of solar irradiance and heating rate in the upper ocean, we showed that the mixed layer thickness decreases more than they would be under clear water conditions. These changes in the model mixed layer were consistent with Joint Global Ocean Flux Study (JGOFS) observations during the 1994-1995 Arabian Sea experiment and epi-fluorescence microscopy (EFM) on samples collected during Equatorial Pacific Ocean Climate Study (EPOCS) in November, 1988. In the Arabian Sea, as the chlorophyll concentrations peak in October (3 mg/m³) after the summer plankton bloom induced by coastal upwelling, the chlorophyll induced biological heating enhanced the sea surface temperature (SST) by as much as 0.6‡C and sub-layer temperature decreases and sub-layer thickness increases. In the equatorial Pacific, modest concentrations of chlorophyll less than 0.3 mg/m³ is enough to introduce a meridional differential heating, which results in reducing the equatorial mixed layer thickness to more than 20 m. The anomalous meridional tilting of the mixed layer bottom enhances off equatorial westward geostrophic currents. Consequently, the equatorial undercurrent transports more water from west to east. We proposed that these numerical model experiments with use of satellite andin situ ocean observations are consistent under three dimensional ocean circulation theory combined with solar radiation transfer process.     

A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data

The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions. The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging Spectroradiometer Indian Pines hyperspectral benchmark data set and presented.     

Experimental superensemble forecasts of tropical cyclones over the Bay of Bengal

This study entails the implementation of an experimental real time forecast capability for tropical cyclones over the Bay of Bengal basin of North Indian Ocean. This work is being built on the experience gained from a number of recent studies using the concept of superensemble developed at the Florida State University (FSU). Real time hurricane forecasts are one of the major components of superensemble modeling at FSU. The superensemble approach of training followed by real time forecasts produces the best forecasts for tracks and intensity (up to 5 days) of Atlantic hurricanes and Pacific typhoons. Improvements in track forecasts of about 25–35% compared to current operational forecast models has been noted over the Atlantic Ocean basin. The intensity forecasts for hurricanes are only marginally better than the best models. In this paper, we address tropical cyclone forecasts over the Bay of Bengal for the years 1996–2000. The main result from this study is that the position and intensity errors for tropical cyclone forecasts over the Bay of Bengal from the multimodel superensemble are generally less than those of all of the participating models during 1- to 3-day forecasts. Some of the major tropical cyclones, such as the November 1996 Andhra Pradesh cyclone and October 1999 Orissa super cyclone were well handled by this superensemble approach. A conclusion from this study is that the proposed approach may be a viable way to construct improved forecasts of Bay of Bengal tropical cyclone positions and intensity.     

Effect of Lime Stabilized Soil Cushion on Strength Behaviour of Expansive Soil

An experimental investigation was undertaken to study the effects of lime-stabilized soil-cushion on the strength behavior of expansive soil. In the present investigation, a series of laboratory tests (Unconfined compression tests and CBR tests) were conducted on both expansive soil alone and expansive soil cushioned with lime-stabilized non-expansive cohesive soil. Lime contents of 2, 4, 6, 8 and 10% by dry weight of cohesive non-swelling soil was used in the stabilized soil cushion. Both expansive soil and lime stabilized soil cushion were compacted to Standard Proctor’s optimum condition with thickness ratio 2:1. Tests on cushioned expansive soils were conducted at different curing and soaking periods i.e., 7, 14, 28 and 56 days. The test results revealed that maximum increase in strength was achieved after 14 days of curing or soaking period with 8% of lime content.     

GaRiRO: Gradient and residual integrated rank ordering of stations in rainfall monitoring network

This paper presents a new method that integrates gradient and residual values for rank ordering of stations in a monitoring network (GaRiRO). The innovation is derived from the fact that the parameter (dependent variable) gauged through the monitoring network is modelled using independent variables that influence its measured quantity. And the dependent variable exhibit non-stationary spatial gradient with respect to the independent variables, particularly in complex terrain. GaRiRO technique was developed to prioritize the rain gauge stations for optimizing the existing network and selection of the best locations for relocation or installation of gauges. Although initially aimed to assist hydrologists with a ranking scheme for rain gauge stations, it can be applied to any environmental, meteorological or hydro-meteorological monitoring network. The new procedure is based on deriving gradient and residual value at each station by modeling the spatial relationship of dependent-independent variable using geographically weighted regression (GWR) technique. For the prospective stations with no record, the gradient value is estimated using GWR model and the residual value is derived from the residual map generated by applying kriging technique on the residual derived at all gauged locations. The method combines the decision factor with analytical strength of GIS for prioritizing the stations which results in limited number of trials for installation or relocation of gauges to yield optimized network configuration.