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Detailed ocean currents in the southeastern tropical Indian Ocean adjacent to southern Sumatran and Javan coasts have not been fully explained because of limited observations. In this study, zonal current characteristics in the region have been studied using simulation results of a 1/8∘ global hybrid coordinate ocean model from 1950 to 2013. The simulated zonal currents across three meridional sections were then investigated using an empirical orthogonal function (EOF), where the first three modes account for 75 %-98 % of the total variance. The first temporal mode of EOF is then investigated using ensemble empirical mode decomposition (EEMD) to distinguish the signals. This study has revealed distinctive features of currents in the South Java Current (SJC) region, the Indonesian Throughflow (ITF)-South Equatorial Current (SEC) region, and the transition zone between these regions. The vertical structures of zonal currents in southern Java and offshore Sumatra are characterized by a one-layer flow. Conversely, a two-layer flow is observed in the nearshore and transition regions of Sumatra. Current variation in the SJC region has peak energies that are sequentially dominated by semiannual, intraseasonal, and annual timescales. Meanwhile, the transition zone is characterized by semiannual and intraseasonal periods with pronounced interannual variations. In contrast, interannual variability associated with El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modulates the prominent intraseasonal variability of current in the ITF-SEC region. ENSO has the strongest influence at the outflow ITF, while the IOD's strongest influence is in southwestern Sumatra, with the ENSO (IOD) leading the current by 4 months (1 month). Moreover, the contributions (largest to smallest) of each EEMD mode at the nearshore of Java and offshore Sumatra are intraseasonal, semiannual, annual, interannual, and long-term fluctuations. The contribution of long-term

The role of COVID-19 pandemic lockdown in improving air quality was reported extensively for land regions globally. However, limited studies have explored these over oceanic areas close to high anthropogenic activities and emissions. The Bay of Bengal (BoB) basin is one such region adjacent to the highly populated South Asian region. We find that Aerosol Optical Depth (AOD) over the BoB declined by as much as 0.1 or 30% during the peak lockdown of April 2020 compared to long-term climatology during 2003-2019. Simultaneously, the sea surface temperature (SST) rose by 0.5-1.5°C over the central and north-western parts of the BoB with an average increase of 0.83°C. We show that up to 30% of this observed warming is attributable to reduced atmospheric aerosols. The study highlights the importance of anthropogenic emissions reduction due to COVID lockdown on short-term changes to SST over ocean basins with implications to regional weather.

Chlorophyll-a can be used as a proxy for phytoplankton and thus is an essential water quality parameter. The presence of phytoplankton in the ocean causes selective absorption of light by chlorophyll-a pigment resulting in change of the ocean color that can be identified by ocean color remote sensing. The accuracy of chlorophyll-a concentration (Chl-a) estimated from remote sensing sensors depends on the bio-optical algorithm used for the retrieval in specific regional waters. In this work, it is attempted to estimate Chl-a from two currently active satellite sensors with relatively good spatial resolutions considering ocean applications. Suitability of two standard bio-optical Ocean Color (OC) Chlorophyll algorithms, OC-2 (2-band) and OC-3 (3-band) in estimating Chl-a for turbid waters of the northern coastal Bay of Bengal is assessed. Validation with in-situ data showed that OC-2 algorithm gives an estimate of Chl-a with a better correlation of 0.795 and least bias of 0.35 mg/m3. Further, inter-comparison of Chl-a retrieved from the two sensors, Landsat-8 OLI and Sentinel-2 MSI was also carried out. The variability of Chl-a during winter, pre-monsoon, and post-monsoon seasons over the study region were inter-compared. It is observed that during pre-monsoon and post-monsoon seasons, Chl-a from MSI is over estimated compared to OLI. This work is a preliminary step toward estimation of Chl-a in the coastal oceans utilizing available better spatially resolved sensors.

COrona VIrus Disease (COVID) 2019 pandemic forced most countries to go into complete lockdown and India went on complete lockdown from 24th March 2020 to 8th June 2020. To understand the possible implications of lockdown, we analyze the long-term distribution of Net Primary Productivity (NPP) in the North Indian Ocean (NIO) and the factors that influence NPP directly and indirectly, for the period 2003-2019 and 2020 separately. There exists a seasonal cycle in the relationship between Aerosol Optical Depth (AOD), Chlorophyll-a (Chl-a) and NPP in agreement with the seasonal transport of aerosols and dust into these oceanic regions. In Arabian Sea (AS), the highest Chl-a (0.58 mg/m3), NPP (696.57 mg/C/m2/day) and AOD (0.39) are observed in June, July, August, and September (JJAS). Similarly, maximum Chl-a (0.48 mg/m3) and NPP (486.39 mg/C/m2/day) are found in JJAS and AOD (0.27) in March, April, and May (MAM) in Bay of Bengal. The interannual variability of Chl-a and NPP with wind speed and Sea Surface Temperature (SST) is also examined, where the former has a positive and the latter has a negative feedback to NPP. The interannual variability of NPP reveals a decreasing trend in NPP, which is interlinked with the increasing trend in SST and AOD. The analysis of wind, SST, Chl-a, and AOD for the pre-lockdown, lockdown, and post lockdown periods of 2020 is employed to understand the impact of COVID-19 lockdown on NPP. The assessment shows the reduction in AOD, decreased wind speeds, increased SST and reduced NPP during the lockdown period as compared to the pre-lockdown, post-lockdown and climatology. This analysis is expected to help to understand the impact of aerosols on the ocean biogeochemistry, nutrient cycles in the ocean biogeochemical models, and to study the effects of climate change on ocean ecosystems.

A planned artificial reef (AR) deployment program as part of a fisheries enhancement might be a useful tool for managers to supplement traditional ways to utilize available space and augment local productivity. Several AR deployment initiatives have been carried out globally, but they are rarely subjected to a rigorous site selection process. We created a site selection procedure in this study that includes systematic stages including exclusion mapping, underwater visual transect, benthic composition, seawater quality, and comparative visual mapping. This research focused on restoring the fishing grounds for artisanal fishermen by deploying AR along the southeast coast of India. The results of each stage in the procedure enabled us to choose suitable locations at a target depth with low wave action, no slope, and a good substrate capable of supporting an AR. Analysis of variance (ANOVA-one way) showed significant (p < 0.05) spatial variation for depth, slope, seawater current, salinity, chlorophyll-a, benthic density, and diversity. The geographical information system (GIS) based model output showed space allocation for AR deployment. The GIS methodology for site selection was developed to be easily adaptable to the demands of diverse artificial reef programs. The integrated strategy has proven to be a successful regulatory intervention for AR deployment practices in order to facilitate coastal restoration and management.

In the Maldives, an MIT team is conducting experiments to combat sea-level rise by redirecting natural sand movement.

The productivity and sustainability of coastal, marine, and estuarine ecosystems are heavily reliant on the quality of coastal waters (JHA et al., 2013; JHA et al., 2015; DHEENAN et al., 2016). These locations are thought to have greater biodiversity than open ocean zones (GRAY, 1997). Among the biological components of soft bottom sediments, macrobenthic organisms are essential for ecosystem functioning (Pandey et al., 2022). They play a vital role in secondary production and nutrient exchange between the pelagic and benthic realms and therefore constitute essential elements of estuarine and coastal habitats (SNELGROVE, 1998) and are a reliable indicator for assessing the biotic integrity of the coastal ecosystem (RYU et al., 2011; Pandey et al., 2021). Due to their sedentary lifestyle, long life cycle, and differential response against the perturbation, they also serve as useful bio-indicator (GESTEIRA-GOMEZet al., 2003) for health monitoring and assessment of coastal ecosystems (GANESH et al., 2014) and evaluating the success of conservation efforts (WINBERGT et al., 2007). Identification of factors responsible for spatiotemporal patterns in macrofaunal assemblages is the primary goal amongst many marine benthic ecological studies (BOLAM et al., 2008; DUTERTRE et al., 2013; PANDEY and GANESH , 2019b). However, such pattern in the benthic community is often hard to predict due to the complex interplay between environmental parameters and biological interactions (ELLIS et al., 2006). The relationship between natural environmental factors and macrobenthos is reported to help delineate faunal distribution patterns, characterising benthic habitats, establish baseline knowledge, and enabling the detection of spatial and temporal variations (BOLAM et al., 2008; SHUMCHENIA and King, 2010; Dutertre et al., 2013). Numerous studies (Van Hoey et al., 2004; Hily et al., 2008; DUTERTRE et al., 2013; Pandey and Ganesh, 2019b) have identified sediment properties as a significan