Upper Ocean Four-Dimensional Variational Data Assimilation in the Arabian Sea and Bay of Bengal

A regional ocean circulation model with four-dimensional variational data assimilation scheme is configured to study the ocean state of the Indian Ocean region (65°E–95°E; 5°N–20°N) covering the Arabian Sea (AS) and Bay of Bengal (BoB). The state estimation setup uses 10 km horizontal resolution and 5 m vertical resolution in the upper ocean. The in-situ temperature and salinity, satellite-derived observations of sea surface height, and blended (in-situ and satellite-derived) observations of sea surface temperature alongwith their associated uncertainties are used for data assimilation with the regionally configured ocean model. The ocean state estimation is carried out for 61 days (1 June to 31 July 2013). The assimilated fields are closer to observations compared to other global state estimates. The mixed layer depth (MLD) of the region shows deepening during the period of assimilation with AS showing higher MLD compared to the BoB. An empirical forecast equation is derived for the prediction of MLD using the air–sea forcing variables as predictors. The surface and sub-surface (50 m) heat and salt budget tendencies of the region are also investigated. It is found that at the sub-surface, only the advection and diffusion temperature and salt tendencies are important.     

Upper ocean high resolution regional modeling of the Arabian Sea and Bay of Bengal

In this paper, effort is made to demonstrate the quality of high-resolution regional ocean circulation model in realistically simulating the circulation and variability properties of the northern Indian Ocean(10°S–25°N,45°–100°E) covering the Arabian Sea(AS) and Bay of Bengal(BoB). The model run using the open boundary conditions is carried out at 10 km horizontal resolution and highest vertical resolution of 2 m in the upper ocean.The surface and sub-surface structure of hydrographic variables(temperature and salinity) and currents is compared against the observations during 1998–2014(17 years). In particular, the seasonal variability of the sea surface temperature, sea surface salinity, and surface currents over the model domain is studied. The highresolution model's ability in correct estimation of the spatio-temporal mixed layer depth(MLD) variability of the AS and BoB is also shown. The lowest MLD values are observed during spring(March-April-May) and highest during winter(December-January-February) seasons. The maximum MLD in the AS(BoB) during December to February reaches 150 m (67 m). On the other hand, the minimum MLD in these regions during March-April-May becomes as low as 11–12 m. The influence of wind stress, net heat flux and freshwater flux on the seasonal variability of the MLD is discussed. The physical processes controlling the seasonal cycle of sea surface temperature are investigated by carrying out mixed layer heat budget analysis. It is found that air-sea fluxes play a dominant role in the seasonal evolution of sea surface temperature of the northern Indian Ocean and the contribution of horizontal advection, vertical entrainment and diffusion processes is small. The upper ocean zonal and meridional volume transport across different sections in the AS and BoB is also computed. The seasonal variability of the transports is studied in the context of monsoonal currents.     

Way forward to achieve sustainable and cost-effective biofuel production from microalgae: a review

Driven by policies aimed at enhancing energy security and mitigating greenhouse gas emissions, the production and use of biofuels have significantly increased in recent years. Microalgae owing to its multiple advantages which include high lipid content, sustainable biomass production, effective land and water utilization are the most potential biofuel feedstock that can provide drop-in fossil fuel replacements without stimulating competition for agricultural resources and are considered to be more environmentally benign than the first- and second-generation biofuel feedstocks. However, there are many existing technical and scientific impediments that are yet to be resolved. Keeping this in view, the present review provides a concise account of the microalgal species known to accumulate high levels of lipid and describes the main factors that should be taken into consideration while selecting suitable algal strains for mass cultivation. The underlining advantages and limitations of raceway pond and photobioreactor cultivation systems are also examined. The recent advances in genetic engineering of microalgae to improve biomass and lipid productivity are then highlighted, which include the ongoing debate over the biosafety issues pertinent to the use of genetically modified algae. Furthermore, a wide range of high-value products that can be co-produced from microalgae have been discussed. The review concludes with a comprehensive summary of the major techno-economic constraints to commercialization of algal-derived biofuels along with promising methods for overcoming these challenges in order to produce cost-competitive and environmentally sustainable biofuel.     

Nature and source of the ore-forming fluids associated with orogenic gold deposits in the Dharwar Craton

Neoarchean orogenic gold deposits, associated with the greenstone-granite milieus in the Dharwar Craton include(1) the famous Kolar mine and the world class Hutti deposit;(2) small mines at HiraBuddini, Uti, Ajjanahalli, and Guddadarangavanahalli;(3) prospects at Jonnagiri; and(4) old mining camps in the Gadag and Ramagiri-Penakacherla belts. The existing diametric views on the source of ore fluid for formation of these deposits include fluids exsolved from granitic melts and extracted by metamorphic devolatilization of the greenstone sequences. Lode gold mineralization occurs in structurally controlled higher order splays in variety of host rocks such as mafic/felsic greenstones, banded iron formations, volcaniclastic rocks and granitoids. Estimated metamorphic conditions of the greenstones vary from lower greenschist facies to mid-amphibolite facies and mineralizations in all the camps are associated with distinct hydrothermal alterations. Fluid inclusion microthermometric and Raman spectroscopic studies document low salinity aqueous-gaseous(H_2O + CO_2 ± CH_4 + NaCl) ore fluids,which precipitated gold and altered the host rocks in a narrow P-T window of 0.7-2.5 kbar and 215-320℃. While the calculated fluid O-and C-isotopic values are ambiguous, S-isotopic compositions of pyrite-precipitating fluid show distinct craton-scale uniformity in terms of its reduced nature and a suggested crustal sulfur source.Available ages on greenstone metamorphism, granitoid plutonism and mineralization in the Hutti Belt are tantamount, making a geochronology-based resolution of the existing debate on the metamorphic vs.magmatic fluid source impossible. In contrast, tourmaline geochemistry suggests involvement of single fluid in formation of gold mineralization, primarily derived by metamorphic devolatilization of mafic greenstones and interlayered sedimentary rocks, with minor magmatic contributions. Similarly, compositions of scheelite, pyrite and arsenopyrite point toward operation of fault-valves that caused pressure fluctuation-induced fluid phase separation, which acted as the dominant process of gold precipitation,apart from fluid-rock sulfidation reactions. Therefore, results from geochemistry of hydrothermal minerals and those from fluid inclusion microthermometry corroborate in constraining source of ore fluid,nature of gold transport(by Au-bisulfide complex) and mechanism of gold ore formation in the Dharwar Craton.     

Hybrid Gaussian-cubic radial basis functions for scattered data interpolation

Scattered data interpolation schemes using kriging and radial basis functions (RBFs) have the advantage of being meshless and dimensional independent; however, for the datasets having insufficient observations, RBFs have the advantage over geostatistical methods as the latter requires variogram study and statistical expertise. Moreover, RBFs can be used for scattered data interpolation with very good convergence, which makes them desirable for shape function interpolation in meshless methods for numerical solution of partial differential equations. For interpolation of large datasets, however, RBFs in their usual form, lead to solving an ill-conditioned system of equations, for which, a small error in the data can cause a significantly large error in the interpolated solution. In order to reduce this limitation, we propose a hybrid kernel by using the conventional Gaussian and a shape parameter independent cubic kernel. Global particle swarm optimization method has been used to analyze the optimal values of the shape parameter as well as the weight coefficients controlling the Gaussian and the cubic part in the hybridization. Through a series of numerical tests, we demonstrate that such hybridization stabilizes the interpolation scheme by yielding a far superior implementation compared to those obtained by using only the Gaussian or cubic kernels. The proposed kernel maintains the accuracy and stability at small shape parameter as well as relatively large degrees of freedom, which exhibit its potential for scattered data interpolation and intrigues its application in global as well as local meshless methods for numerical solution of PDEs.     

Hydroclimatological Perspective of the Kerala Flood of 2018

Flood is among the deadliest disasters in India, and the frequency of floods and extreme precipitation events is projected to increase under the warming climate. The frequency of floods in India varies geographically as some regions are more prone to floods than the others. The Kerala flood of 2018 caused enormous economic damage, affected millions of people, and resulted in the death of more than 400 people. Here we provide a hydroclimatological perspective on the Kerala flood of 2018. Using the observations and model simulations from the Variable Infiltration Capacity (VIC) model, we show that the 2018 extreme precipitation and runoff conditions that caused flooding were unprecedented in the record of the past 66 years (1951–2017). Our results show that mean monsoon precipitation has significantly declined while air temperature has significantly increased during 1951–2017 in Kerala. The drying and warming trends during the monsoon season resulted in a declined total runoff in large part of the state in the last 66 years. Apart from the mean hydroclimatic conditions, extreme precipitation, and extreme total runoff have also declined from 1951 to 2017. However, 1 and 2-day extreme precipitation and extreme runoff conditions in August 2018 exceeded substantially from the long-term 95^th percentiles recorded during 1951–2017. Since there is no increase in mean and extreme precipitation in Kerala over the last six decades, the extreme event during August 2018 is likely to be driven by anomalous atmospheric conditions due to climate variability rather anthropogenic climate warming. The severity of the Kerala flood of 2018 and the damage caused might be affected by several factors including land use/land cover change, antecedent hydrologic conditions, reservoir storage and operations, encroachment of flood plains, and other natural factors. The impacts of key drivers (anthropogenic and natural) on flood severity need to be established to improve our understanding of floods and associated damage.