Hydrographical characteristics and oxygen isotopic signatures of water in a coastal environment (Mangalore) along the southeastern Arabian Sea

Coastal marine environments are important links between the continents and the open ocean. The coast off Mangalore forms part of the upwelling zone along the southeastern Arabian Sea. The temperature, salinity, density, dissolved oxygen and stable oxygen isotope ratio (delta O-18) of surface waters as well as those of bottom waters off coastal Mangalore were studied every month from October 2010 to May 2011. The coastal waters were stratified in October and November due to precipitation and runoff. The region was characterised by upwelled bottom waters in October, whereas the region exhibited a temperature inversion in November. The surface and bottom waters presented almost uniform properties from December until April. The coastal waters were observed to be most dense in January and May. Comparatively cold and poorly oxygenated bottom waters during the May sampling indicated the onset of upwelling along the region. delta O-18 of the coastal waters successfully documented the observed variations in the hydrographical characteristics of the Mangalore coast during the monthly sampling period. We also noted that the monthly variability in the properties of the coastal waters of Mangalore was related to the hydrographical characteristics of the adjacent open ocean inferred from satellite-derived surface winds, sea surface height anomaly data and sea surface temperatures.

Appraisal of groundwater resource in Holocene soil deposits by resistivity, hydrochemical and granulomerial studies in the Gulf of Mannar Coast from Southern India

This article is aimed to delineate groundwater sources in Holocene deposits area in the Gulf of Mannar Coast from Southern India. For this purpose 2-D electrical resistivity tomography (ERT), hydrochemical and granulomerical studies were carried out and integrated to identify hydrogeological structures and portable groundwater resource in shallow depths which in general appears in the coastal tracts. The 2-D ERT was used to determine the two-dimensional subsurface geological formations by multicore cable with Wenner array. Low resistivity of 1-5 Omega m for saline water appeared due to calcite at the depth of about 5 m below the ground level (bgl). Sea water intrusion was observed around the maximum resistivity as 5 Omega m at the 8 m depth, bgl in the calcite environs, but the calcareous sandstone layer shows around 15-64 Omega m at the 6 m depth, bgl. The hydrochemical variation of TDS, HCO3-, Cl-, Na+, K+, Ca2+, and Mg2+ concentrations was observed for the saline and sea water intrusion in the groundwater system. The granulometic analysis shows that the study area was under the sea between 5400 and 3000 year ago. The events of ice melting an unnatural ice-stone rain/hail among 5000-4000 years ago resulted in the inundation of sea over the area and deposits of late Holocene marine transgression formation up to Puthukottai quartzite region for a stretch of around 17 km.

Phytoplankton size structure in the southern Bay of Bengal modified by the Summer Monsoon Current and associated eddies: Implications on the vertical biogenic flux

The present study combines field and satellite observations to investigate how hydrographical transformations influence phytoplankton size structure in the southern Bay of Bengal during the peak Southwest Monsoon/Summer Monsoon (July-August). The intrusion of the Summer Monsoon Current (SMC) into the Bay of Bengal and associated changes in sea surface chemistry, traceable eastward up to 90 degrees E along 8 degrees N, seems to influence biology of the region significantly. Both in situ and satellite (MODIS) data revealed low surface chlorophyll except in the area influenced by the SMC During the study period, two well-developed cydonic eddies (north) and an anti-cyclonic eddy (south), closely linked to the main eastward flow of the SMC, were sampled. Considering the capping effect of the low-saline surface water that is characteristic of the Bay of Bengal, the impact of the cyclonic eddy, estimated in terms of enhanced nutrients and chlorophyll, was mostly restricted to the subsurface waters (below 20 m depth). Conversely, the anti-cyclonic eddy aided by the SMC was characterized by considerably higher nutrient concentration and chlorophyll in the upper water column (upper 60 m), which was contrary to the general characteristic of such eddies. Albeit smaller phytoplankton predominated the southern Bay of Bengal (60-95% of the total chlorophyll), the contribution of large phytoplankton was double in the regions influenced by the SMC and associated eddies. Multivariate analysis revealed the extent to which SMC-associated eddies spatially influence phytoplankton community structure. The study presents the first direct quantification of the size structure of phytoplankton from the southern Bay of Bengal and demonstrates that the SMC-associated hydrographical ramifications significantly increase the phytoplankton biomass contributed by larger phytoplankton and thereby influence the vertical opal and organic carbon flux in the region. (C) 2014 Elsevier B.V. All rights reserved.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.