Pallikaranai, India
Pallikaranai, India
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Durga Rao G.,ICMAM PD | Kanuri V.V.,ICMAM PD | Kumaraswami M.,ICMAM PD | Ezhilarasan P.,ICMAM PD | And 6 more authors.
Chemistry and Ecology | Year: 2017

Dissolved nutrients, Chl-a and primary productivity were measured from seven transects along the coastal waters of the southeastern Arabian Sea during northeast monsoon. Ten major estuaries were chosen to study the influence of estuarine discharge on the nutrient dynamics in the coastal waters. The mean water discharge of the estuaries in the north (64.8 ± 18 × 105 m3 d−1) was found to be higher than those in the south (30.6 ± 21.4 × 105 m3 d−1), whereas the nutrient concentrations were found to be higher in the estuaries of the south. The results from the offshore waters were discussed in accordance with the depth contour classification, that is, shelf (depth ≤ 30 m) and slope waters (depth ≥ 30 m). Our results suggest that the estuarine discharge plays a major role in the nutrient distribution in near shore shelf waters, whereas in shelf and slope waters, it was mainly controlled by in situ biological processes. The inorganic form of N to P ratios were found to be higher than Redfield ratio in slope waters when compared with shelf waters, suggesting that PO4 3− (<0.15 µmol L−1) is a limiting nutrient for primary production. The multivariate statistical analysis revealed that the nutrient dynamics in the coastal waters was controlled by both biological and physical processes. © 2017 Informa UK Limited, trading as Taylor & Francis Group

Rao V.R.,ICMAM PD | Reddy N.T.,ICMAM PD | Sriganesh J.,University of Madras | Murthy M.V.R.,ICMAM PD | Murty T.S.,University of Ottawa
Marine Geodesy | Year: 2011

The Andhra coast iswell known for cyclones and less known for tsunamis. The December 26, 2004, Sumatran Indian Ocean tsunami, created considerable damages along the south Andhra coast, especially along the Krishnapatnam, Kavali, and Ongole coasts. Of these, Krsihnapatnam and Kavali were the most affected and have experienced runup levels of 1.9 m and inundation distances of 200-1350 m. In places of straight coast, the tsunami run-up was limited to berm level (200-400 m from the shoreline) whereas in places occupied by creeks, inlets, and river mouths the run-up extended far inland up to 600-1350 m. The TUNAMI-N2 model simulations on propagation times, run-up, and inundation distances along Krishnapatnam, Kavali, and Ongole coasts are well agreed with the observed features of tsunami. The travel times of Sumatra 2004 tsunami to reach the shore along the three coasts are 175, 180, and 190 minutes, respectively, which are reproduced well in model simulations. Evaluation tsunami hazard levels along the three coastal sites are discussed in terms of run-up as most of the villages, public places, and shrimp culture ponds are located within 1-1.5 km of the shoreline. © Taylor & Francis Group, LLC.

Panda U.S.,ICMAM PD | Mahanty M.M.,NIOT | Ranga Rao V.,ICMAM PD | Patra S.,ICMAM PD | Mishra P.,ICMAM PD
Procedia Engineering | Year: 2015

Sustainable restoration of endangered coastal ecosystems is today of great environmental interest and scientific value. Among the coastal ecosystems, the lagoon shows a wide range of geographical and ecological variation. The Chilika Lagoon a Ramsar Site of international importance and the largest brackish water lagoon in Indian sub-continent is one of the finest repositories of aquatic biodiversity and a source of fishery, sustaining the livelihood and nutritional need of about 0.2 million local fisherfolk. Sedimentation, from riverine discharge and disintegration of macrophytes, choking of the outer channel, shifting of the inlet mouth, decline in water area and increase in vegetated area, and the opening of new inlets are the dominant processes influencing the cotemporary phase of lagoon transformation in the Chilika. Circulation, fate and transport of nutrients are the critical component in determining the lagoon ecosystem. The present study discusses a water quality model coupled with a hydrodynamic and advection-dispersion model to describe the important physical, chemical and biological processes. The hydrodynamic and advection-dispersion model simulates the flow forcing, transport, mixing and dispersion of water quality concentration at different spatio-temporal scales. Bed resistance coefficient, the eddy viscosity coefficient (Smagorinsky formulation) and the wind friction coefficient in hydrodynamic model, heat exchange coefficients and dispersion coefficients in advection/dispersion (AD) model are the major calibration factors. The water quality model includes physical processes (reaeration, settling), biochemical processes (adsorption, transformation) and biological processes (organic degradation, primary production). Different rate constants have been calibrated with several sensitivity analyses to obtain an optimized validation. Skill test analysis of temperature (r2=0.77), salinity (r2=0.68), DO (r2=0.52) and nutrients proves the potential of the model to simulate water quality at different spatiotemporal scales. The model can act as an excellent tool to simulate water quality and prediction of futuristic scenarios. © 2015 The Authors. Published by Elsevier Ltd.

Usha T.,ICMAM PD | Murthy M.V.R.,ICMAM PD | Reddy N.T.,ICMAM PD | Mishra P.,ICMAM PD
Natural Hazards | Year: 2012

Natural disasters can neither be predicted nor prevented. Urban areas with a high population density coupled with the construction of man-made structures are subjected to greater levels of risk to life and property in the event of natural hazards. One of the major and densely populated urban areas in the east coast of India is the city of Chennai (Madras), which was severely affected by the 2004 Tsunami, and mitigation efforts were severely dampened due to the non-availability of data on the vulnerability on the Chennai coast to tsunami hazard. Chennai is prone to coastal hazards and hence has hazard maps on its earth-quake prone areas, cyclone prone areas and flood prone areas but no information on areas vulnerable to tsunamis. Hence, mapping has to be done of the areas where the tsunami of December 2004 had directly hit and flooded the coastal areas in Chennai in order to develop tsunami vulnerability map for coastal Chennai. The objective of this study is to develop a GIS-based tsunami vulnerability map for Chennai by using a numerical model of tsunami propagation together with documented observations and field measurements of the evidence left behind by the tsunami in December 2004. World-renowned and the second-longest tourist beach in the world "Marina" present in this region witnessed maximum death toll due to its flat topography, resulting in an inundation of about 300 m landward with high flow velocity of the order of 2 m/s. © 2011 Springer Science+Business Media B.V.

Murthy M.V.R.,ICMAM PD | Usha T.,ICMAM PD | Pari Y.,ICMAM PD | Reddy N.T.,ICMAM PD
Marine Geodesy | Year: 2011

The 2004 Sumatra tsunami left a deep and dark footprint on coastal Cuddalore in southeast India, whichwas one of theworst affected districts in the mainland. Assessment of natural hazards typically relies on analysis of past occurrences of similar disaster events. Assessment of tsunami hazard to the Indian coast poses a scientific challenge because of the paucity of both historical events and data. However, construction of tsunami hazard maps is the key step in tsunami risk assessment and forms the basis for evacuation and future land use planning along coastal areas. To this end, a set of inundation scenarios was built based on realistic tectonic sources that can generate tsunamis in the Indian Ocean. From historical records, three earthquake sources have been identified and a hypothetical worst case scenario was generated. Numerical models were constructed to predict the extent of inundation and run-up in each case, using a finite difference code on nested grids derived from the high resolution elevation and bathymetry datasets collected for the study area. The model was validated using field data collected immediately after the 2004 tsunami and was then used to generate the other inundation scenarios. Tsunami hazard maps for coastal Cuddalore were prepared by overlaying the numerical model outputs along with details on land use, elevation, cadastral land parcels, infrastructure, high tide line, and coastal regulation zones. © Taylor & Francis Group, LLC.

Manokaran S.,Annamalai University | Mishra P.,ICMAM PD | Ajmal Khan S.,Annamalai University | Lyla P.S.,Annamalai University | And 2 more authors.
Indian Journal of Geo-Marine Sciences | Year: 2014

Present study consists analyses of surface sediment samples collected from the continental shelf region at depths of 30 m, 50 m, 75 m, 100 m, 150 m and 200 m. Six types of sediment textures were found (coarse sand, medium sand, fine sand, coarse silt, medium silt and fine slit). Particle diameter decreased with increasing depth. The phi mean ranged between 0.466φ (coarse sand - Tammenapatanam 75 m) and 7.8306φ (fine silt -Singarayakonda 75 m). Flow from Krishna river was found to influence the particle diameter of sediment in Singarayakonda where the sediment was silty from 50 m depth onwards whereas in the other transects where the river flow was comparatively low, the sediment was silty only at 200 m depth. Silty sediments were found very well sorted. Negative skweness values observed in the middle depths indicated the transition zones. Sediments with leptokurtic distribution indicate deposits with a high degree of textural maturity and reworking. The first percentile and median of size distribution pattern reflects suspension and rolling mode of transportational history, indicating the complexity in the hydrodynamic processes operating in the study area.

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