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.     

Hydrological simulation of a conservation bench terrace system in a subhumid climate

Abstract

A finite element model to simulate runoff and soil erosion from agricultural lands has been developed. The sequential solutions of the governing differential equations were found: Richards' equation with a sink term for infiltration and soil water dynamics under cropped conditions; St Venant equation with kinematic wave approximation for overland and channel flow; and sediment continuity equation, for soil erosion. The model developed earlier has been improved to simulate erosion/deposition in impoundments and predicted and observed soil loss values were in reasonably good agreement when the model was tested for a conservation bench terrace (CBT) system. The finite element model was extensively applied to study the hydrological behaviour of a CBT system vis-à-vis the conventional system of sloping borders. The model estimates runoff and soil loss reasonably well, under varying conditions of rainfall and at different crop growth stages. The probable reasons for discrepancies between observation and simulation are reported and discussed. Sensitivity analysis was carried out to study the effect of various hydrological, soil and topographical parameters, such as ratio of contributing to receiving areas, weir length, depth of impoundment, slope of contributing area, etc. on the flow behaviour in a CBT system.     

A study on precursors leading to geomagnetic storms using artificial neural network

Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.