Indian Coastline Research Articles

Trend Analysis of Extreme Wind and Wave Height at Key Port Locations Along the Indian Coastline

Trend Analysis of Extreme Wind and Wave Height at Key Port Locations Along the Indian Coastline

Impact Assessment of Ports and Effluent Discharge on Macrobenthic Communities in Indian Coastal Ecosystems: A Comprehensive Review

ABSTRACTThe burgeoning industrialization and urbanization along the Indian coastline have led to an increased prevalence of port infrastructure and effluent discharge, raising concerns about their potential impact on the delicate macrobenthic communities in coastal ecosystems. This comprehensive review aims to evaluate and synthesize existing literature on the ecological repercussions of port activities and effluent discharge on macrobenthic organisms in Indian coastal regions. The study encompasses a thorough analysis of relevant research articles, government reports, and scientific databases, focusing on diverse ecosystems such as estuaries, mangroves, and open coastlines. The review discusses the various stressors associated with port operations, including dredging, habitat alteration, and chemical contamination from effluent discharge. Furthermore, it explores the response mechanisms of macrobenthic communities, encompassing changes in species composition, abundance, and diversity. Special attention is given to the potential long‐term impacts on ecosystem functioning and services. The synthesis of available data enables the identification of knowledge gaps and the formulation of recommendations for future research and management strategies. Understanding the cumulative effects of port activities on macrobenthic communities is crucial for developing sustainable practices and policies to mitigate adverse impacts while fostering the resilience of coastal ecosystems in the face of anthropogenic pressures. This review contributes to the growing body of knowledge on the ecological consequences of human activities in Indian coastal areas, providing valuable insights for researchers, policymakers, and environmental managers alike.

Metagenomic analysis of pathogenic bacteria and virulence factor genes in coastal sediments from highly urbanized cities of India

A metagenomic approach was employed to investigate the diversity and distribution of Virulence Factors Genes (VFGs) and Pathogenic Bacteria (PB) in sediment samples collected from highly urbanized cities along the Indian coastline. Among the study locations, Mumbai, Veraval and Paradeep showed a higher abundance of PB, with Vibrio and Pseudomonas as dominant at the genus level, and Escherichia coli and Pseudomonas aeruginosa at the species level. In total, 295 VFGs were detected across all sediment samples, of which 40 VFGs showed a similarity of ≥90 % with the Virulence Database (VFDB) and were focused in this study. Among the virulent proteins, twitching motility protein and flagellar P-ring were found to be prevalent and significantly associated with Vibrio spp., and Pseudomonas spp., indicating potential bacterial pathogenicity. This investigation serves as the basis for future studies and provides insights into the comprehensive taxonomic profiles of PB, VFGs and their associated PB in the coastal sediments of India.

Diversity of Foraminifera from the Indian Coast

Foraminifera, members of the class Granuloreticulosa, are marine protozoans distributed across both pelagic and benthic zones of the ocean. After the death of these microscopic organisms, their shells are washed along the intertidal sandy shores along with sand granules. This organism's shell is called a test which is composed of calcium carbonate, silica, or agglutinated materials that characterize the genera of Foraminifera. In the present study, the sand samples were collected along the different sites of the Indian coastline viz. Juhu Beach (Mumbai), Alibaug Beach (Raigad), Palolem Beach (Goa), Kanyakumari Beach (Tamil Nadu), and Puri Beach (Odisha). In this study, samples from Juhu Beach showed the highest foraminiferan diversity (24%) followed by Alibaug (21%) > Kanyakumari = Puri (19%) > Goa (17%). Across all selected study sites, the prevalence of specimens from the Globothalamea class was notable, comprising 39% of the total foraminiferans. This was followed by Monothalamae (26%), Fusulinata (13%), Tubothalamea (11%), and Nodosariata (11%). 13 extinct genera were also documented in the study.

Satellite‐Derived Bathymetry in Dynamic Coastal Geomorphological Environments Through Machine Learning Algorithms

AbstractIn the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation.

Stypopodium Kütz. - a new generic record for India from the Bay of Bengal

The study highlights the first-ever documentation of the genus Stypopodium along the Indian coastline, specifically in Andhra Pradesh. Previously reported in various Indian Ocean regions but not in India, this discovery fills a significant gap in understanding its distribution. The species Stypopodium zonale is thoroughly examined, including its physical characteristics, microscopic features, habitats, distribution, and taxonomic notes, complemented by accompanying photo plates. Additionally, the proposal of a lectotype for heterotypic synonyms, namely Fucus zonalis, Zonaria lobata, and Zonaria fuliginosa is presented.

Delineation of the general framework and quantification of heavy minerals distribution at Durgarajupatnam coast, Nellore, East Coast of India

The present study involves quantifying the presence, concentration, and distribution patterns of heavy minerals in 24 beach samples from the Govindampalli-Durgarajupatnam (GP-DP) coast, Nellore, East coast of India. It explores fraction-wise distribution (+60, +120, +230) in four micro-environments (dune, backshore, berm, and foreshore). Notably, this study is the first of its kind in the investigation area. Using optical properties, minerals such as opaques, sillimanite, garnet, zircon, amphiboles, pyroxenes, tourmaline, etc., were identified. Total Heavy Minerals (THM) percentage in the area was determined as 19.03 %. Analysis reveals higher ratios of heavy minerals in the +230 fraction and foreshore environment, indicating the dominance of littoral currents and waves. Economically, a modest amount of heavy minerals (average 19.03 %) is present along the coast. The main objective is to highlight the availability and concentration of heavy minerals in different micro-environments. This objective aligns with the broader context of coastal sediment investigations and contributes valuable information for the development and utilization of placer mineral deposits along the Indian coastline. The study's implications extend to scientific understanding, resource assessment, environmental considerations, industrial applications, economic feasibility, strategic planning, policymaking, and contributes to global discussions on coastal sediment investigations and placer mineral deposits utilization.

Investigatory analysis of the natural hazards on the Indian coastline

Predictive analysis, comparative analysis, and image processing can provide vital insights into understanding natural phenomena. Water bodies surround India on three sides, so natural disasters (cyclones, floods, and other related hazards) and rising water levels due to meteorological fluctuations are common occurrences. The coastal states of India, due to their diverse nature, are constantly exposed to various risks. The study focuses on the changes and disasters in the Indian Ocean surrounding the Indian shorelines. A systematic approach has been employed to examine the fluctuations in meteorological factors of nine Indian coastal states for the period of 2001–2021. The fluctuations were computed for four meteorological seasons Summer (March–May), Monsoon (June–September), Post-monsoon (October–November), and Winter (December–February). These fluctuations are studied, and trends are put forward to examine their effects on natural disasters. The results of the study focus on the correlations between the factors and disasters and their respective predictions.

Heavy metals concentration in zooplankton (copepods) in the western Bay of Bengal.

Along the coastline, urbanisation and industrialization pose significant challenges to marine habitats and biodiversity. Most wastewater that reaches the marine environment contains toxic metals, which, as they are non-biodegradable, accumulate in the biota and enter the marine food chain. This study presents the concentration of nine heavy metals (Fe, Zn, Cu, Co, Cr, Mn, Ni, Pb, and Cd) in zooplankton (copepods) during the Spring Intermonsoon/Pre-Monsoon (April 2019) in the western Bay of Bengal (BoB). Higher Fe concentrations were found in copepods both nearshore (42,352µg/g) and offshore (9835µg/g). However, the relative amounts of heavy metals in copepods from 16 locations (eight nearshore and eight offshore) varied in the order of Fe > Zn > Cu > Co > Cr > Mn > Ni > Pb > Cd. The copepods in the nearshore locations had higher concentrations of heavy metals than those offshore. Compared to earlier data on heavy metals, particularly those that are physiologically non-essential and toxic, the current study demonstrates higher amounts of these metals in copepods in the inshore BoB [Ni (avg. 58.7 ± 5.5µg/g), Pb (avg. 25.6 ± 4.7µg/g), and Cd (avg. 16.4 ± 0.9µg/g)]. Considering the strong East India Coastal Currents in the western BoB, trace metal-concentrated copepods could be transported too far along the Indian coastline from the current study area.

Species distribution models to predict the potential niche shift and priority conservation areas for mangroves (Rhizophora apiculata, R. mucronata) in response to climate and sea level fluctuations along coastal India

Mangroves are more than just a tree as they are home to thousands of species, carbon absorbers and a natural coastal fortress against floods and storm surges. Hence, conservation planning and decision making for this valuable ecosystem must involve identification of priority conservation areas (PCAs) at species-specific dimensions. In the present study, we adopted an ensemble modelling approach for the distribution of two mangrove species (Rhizophora apiculata, Rhizophora mucronata) using high-resolution environmental and edaphic datasets, to identify the PCAs for future conservation. We also identified the key environmental variables shaping their distribution and precisely estimated the core distributional shift along the Indian coastline under changing climate scenario. The findings revealed that about 5844 km2 and 7846 km2 areas were identified as extremely suitable areas, which were distributed along Maharashtra coast and Kerala coast for R. mucronata and R. apiculata, respectively, in the current climate scenario. The distribution of R. apiculata was found to be mostly shaped by mean diurnal range and annual mean temperature, whereas the distribution of R. mucronata was primarily shaped by annual mean temperature and altitude. The highest range expansion of mangrove species occurred during middle Holocene due to high precipitation and sea-level rise and this finding is further supported by fossil pollen evidence. The suitable habitat range for R. apiculata is predicted to increase along Kerala coast under RCP2.6 scenario by 6.90% and 6.93% and under RCP8.5 scenario by 9.33% and 9.90% in the year 2050 and 2070, respectively, whereas the range for R. mucronata is getting reduced in the future climate scenario. Overall, our predictions reveal a steady migration of conducive mangrove habitat towards land or higher elevations due to relative sea level rise in future. These results would aid in planning a long-term species-specific conservation and management strategy for mangrove ecosystem along coastal India.

Evidences of localized coastal warming near major urban centres along the Indian coastline: past and future trends.

Large-scale urbanization near the coasts is reported to directly impact physical and biogeochemical characteristics of near shore waters, through hydro-meteorological forcing, developing abnormalities such as coastal warming. This study attempts to understand the impact-magnitude of urban expansion on coastal sea surface temperature (SST) rise in the vicinity of six major cities along the Indian coastline. Different parameters such as air temperature (AT), relative humidity (RH), wind speed (WS), precipitation (P), land surface temperature (LST) and aerosol optical depth (AOD) representing the climate over the cities were analysed and AT was found to have highest correlation with increasing coastal SST values, specifically, along the western coast (R2 > 0.93). Autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were employed to analyse past (1980-2019) and forecast future (2020-2029) SST trends off all urban coasts. ANN provided comparatively better prediction accuracy with RMSE values ranging from 0.40 to 0.76K compared to the seasonal ARIMA model (RMSE: 0.60-1K). Predictionaccuracy further improved by coupling ANN with discrete wavelet transformation (DWT) which could reduce the data noise (RMSE: 0.37-0.63K). The entire study period (1980-2029) revealed significant and consistent increase in SST values (0.5-1K) along the western coastal cities which varied considerably along the east coast (from north to south), indicating the influence of tropical cyclones combined with increased river influx. Such unnatural interferences in the dynamic land-atmosphere-ocean circulation not only render the coastal ecosystems vulnerable to degradation but also potentially develop a feedback effect which impacts the general climatology of the region.

Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing

Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

Systematic account of the genus Avrainvillea Decne. and a novelty from the Indian coastline

The genus Avrainvillea is the siphonous green algae usually found in tropical and subtropical regions. It is represented by 38 taxa worldwide with a high degree of morphological plasticity. In India, it is a less-known genus that encompasses four taxa with insufficient data. Hence, the current attempt is to account for the genus Avrainvillea in India, revealing the existence of six species collected from various maritime states of India. Among them, Avrainvillea visakhapatnamensis, a new and fascinating species reported from the coastline of Andhra Pradesh. The species novelty is distinguished by aggregated or clustered patches, compressed or flattened stipe, tortuous medullary siphon, elongated cordate plugs, central ovate plugs, and amygdaloid sporangium with other known species under this genus. Likewise, A. ridleyi reported for the first time on the Indian coast, and lectotypification is also designated. Further, the particulars furnished on the type details, descriptions, phenology, distribution, and exsiccata of the genus Avrainvillea in India.

Ensemble modeling approach to predict the past and future climate suitability for two mangrove species along the coastal wetlands of peninsular India

Mangroves support numerous ecosystem services and help in reducing coastal ecological risks, yet they are declining rapidly due to climate change, sea level fluctuations and human activities. It is important to understand their responses to climate and sea level changes and identify conservation target areas at spatio-temporal scales, specifically in regions of rich mangrove biodiversity. In this study, we predicted the potential impact of past (Middle Holocene, ∼6000 years), current and future (2050s, 2070s; RCP 2.6 and RCP 8.5) climate change scenarios on the two dominant species in the coastal mangrove forest wetlands of India, i.e., Rhizophora mucronata and Avicennia officinalis through an ensemble species distribution modeling approach. The ensemble modeling has been carried out by integrating eight single algorithm methods. Based on the receiver operating characteristics of area under the curve (AUC) and true skill statistics (TSS) values the ensemble modeling has yielded the highest predictive performance for SVM for both the species and lowest by CART for R. mucronata and BIOCLIM for A. officinalis. The internal evaluation metrics of the resulting Species distribution models (SDMs) tested its robustness with AUC-0.97 and TSS-0.89 for A. officinalis and AUC-0.99 and TSS-0.90 for R. mucronata. Precipitation of Wettest Month (Bio 13) and Mean Temperature of Warmest Quarter (Bio 10) was the most important variable (54–67%) for the distribution of A. officinalis and Precipitation Seasonality (Bio 15) and Precipitation of Warmest Quarter (Bio 18) for R. mucronata. High precipitation and sea-level highstand during middle Holocene led to the maximum range expansion of suitable habitat for the mangrove species which is also validated in the present study by the fossil pollen datasets. Total mangrove habitat in current and future climatic scenarios decreased in 2.6 and 8.5 Representative Concentration Pathways (RCPs) for 2050 and 2070 which indicates the vulnerability of the species to climate change impacts. Mangrove species are projected to shift their ranges more towards land in future experiencing a decrease in the amount of suitable coastal area available to them throughout the Indian coastline. The plausible cause for this range shift may be due to higher precipitation that is usually associated with longer period of soil inundation and because of the rise in sea level. Our findings will assist in formulating species-specific restoration plans for these keystone species in context of climate change in the Indian Subcontinent.

Connectivity networks and delineation of disconnected coastal provinces along the Indian coastline using large‐scale Lagrangian transport simulations

AbstractOcean circulation is the engine of dispersal and population connectivity in marine ecosystems, knowledge of which is essential for conservation planning. Understanding connectivity patterns at a large scale can help define the spatial extent of metapopulations. In this study, we built connectivity networks from Lagrangian transport simulations of neutrally buoyant particles released along the Indian coastline, a significant region in the Indian Ocean. We assessed the variation in connectivity networks across release periods for the major drivers of oceanography including the two monsoonal seasons, El Niño–Southern Oscillation (ENSO) years and for the entire range of region‐specific pelagic larval durations (PLDs) for marine invertebrates. We detected well‐connected communities, mapped frequent connectivity breaks, and assessed the functional role of coastal areas within the connectivity network using node metrics. Network characteristics did not differ based on the ENSO year, but varied with season and PLD. Connectance for the Indian coastline was relatively low, ranging from 0.5% to 3.4%, and increased significantly for PLD larger than 20 d. The number of cohesive coastal communities decreased gradually from 60 (PLD < 4 d) to 30 (PLD > 20 d) with increasing PLD. Despite variation in the location of connectivity breaks with the time of particle release within a monsoonal season, four disconnected provinces were consistently identified across the entire PLD range, which partially overlapped with observed genetic and biogeographic breaks reported along the Indian coastline. Our results support the adoption of a coordinated management framework within each of the four provinces delineated in the present study.

Marine endophytes from the Indian coasts: The untapped sources of sustainable anticancer drug discovery

For centuries, the Indian outlook on medicines has had foundations in the exploration of her rich biodiversity. Today, natural product (NP) research delivers an ample chemical space for drug discovery for almost every pathological condition. However, endophytes have emerged as a pandora's box of therapeutic secondary metabolites to resolve ecological, sustainability, and cost concerns. Marine sources of endophytes have garnered attention in the pharmaceutical sector, exemplified by increased FDA-approved marine drugs. With chemotherapy being the most common treatment for cancer patients, the need for novel drugs is steeping high. Endophytes from marine sources present a diverse natural productome for anticancer leads, especially for resistant cancers. For the first time, this review highlights the sustainable biotechnological potential of marine endophytes from the 3000 km long Indian coastline, a biogeographic zone. We also discuss the new cell death mechanisms and targets for marine anticancer drugs, emphasizing endophytes isolated from the Indian coasts. The advances in the NP yield enhancement strategies and possible chemical modifications on the product to reduce the complexity and increase the specificity for higher therapeutic value, followed by the challenges and prospects, are also reviewed.

Spatio-temporal analysis identifies marine mammal stranding hotspots along the Indian coastline

Marine mammal strandings provide vital information on species’ life histories, population health and status of marine ecosystems. Opportunistic reporting of strandings also serve as a powerful low-cost tool for monitoring these elusive mammals. We collated data over ~ 270 years available through various open access databases, reports and publications. Annual strandings along the Indian coast (mean = 11.25 ± SE 9.1) increased in the last two years of the study (2015–2017, mean = 27.66 ± SE 8.5 strandings /year). We found that stranding events spike during June—September along the west coast and during December–January along the east coast. We identified several sections of the coastline, such as Mumbai (0.38 strandings/km), Kozhikode (0.28 strandings/km), Tuticorin (0.4 strandings/km), Rameswaram (1.82 strandings/km), Chennai (0.32 strandings/km) and Bhubaneshwar (0.26 strandings/km) with a higher number of stranded animals reported. Emerging Hotspot Analysis located new and consecutive hotspots along the north-west coast, and sporadic hotspots along the south-east coast. We recommend establishing regional stranding response centres at the identified hotspots coordinated by a National Stranding Centre with adequately trained personnel and central funding support. Regular stranding response training programs for field veterinarians, and frontline personnel of State Forest Departments near stranding hotspots would provide an improved understanding of marine mammal health and threats in Indian waters. Further, the suggested National Stranding Centre needs to maintain a ‘National Stranding Database’ for long-term marine mammal conservation planning in India.

Hydrodynamic Performance Assessment of a Geo-Synthetic Seawall

In the recent years, various geo-synthetic components find an extensive application in civil and coastal engineering practice. Commonly, geo-synthetics have a wide application for secondary or tertiary purposes, such as filtration, separation, barrier, reinforcement, whereas, they can be potentially exploited for various other applications in the coastal engineering practice. A sustainable seawall cross-section comprising different geo-synthetic products was proposed to be erected along the Pallana Beach ([Formula: see text] N and [Formula: see text] E), located in Alleppey district of Kerala along the south-west Indian coastline. Since the subsoil at the location is poorly graded, it was decided to replace the conventional materials like rock boulders and concrete armour units with geo-synthetic products. A comprehensive physical model study was conducted to assess the hydrodynamic performance (i.e. reflection, run-up, and pressure distribution) of the geo-synthetic seawall cross-section for a wide range of random wave characteristics and two water depth conditions. The relative overtopping rates for the seawall at varying water levels are computed conservatively from the guidelines prescribed by the EurOtop Manual and XGB Overtopping model.

Characteristics of individual surface waves measured by moored buoys in the coastal waters of the eastern Arabian Sea

In this study, the characteristics of the individual surface waves in the eastern Arabian Sea over one year are examined based on the measured time-series data using Waverider buoys at four locations covering ∼500 km of the western Indian coastline. At all locations, the number of waves is high during the non-monsoon compared to the monsoon period (June–September). Number of waves in a year is higher in the northern location (∼6,417,657) than in the southern locations (∼5,497,168) since comparatively more short-period waves are observed in the north. Maximum wave height varied from 7.0 to 7.6 m, and the steepness of the largest waves varies from 0.056 to 0.082 at different locations and is much smaller than the wave breaking limit. Number of short-period waves observed is higher than the long-period waves at all locations except in the monsoon, when a significant number of intermediate-period waves are present. The mean wave steepness of the 30-min record is ∼22% higher than the conventionally reported significant wave steepness based on mean wave period. Hilbert–Huang transform method is used to detect the freak wave and analyze the abnormal wave group structure. It is observed that freak wave occurrence is high during the monsoon.

Growth enhancement of agricultural crops using seaweed liquid fertilizer

Extensive use of chemical fertilizer is leading to infertility of soil and various hazardous effect on the ecosystem. Using biofertilizer reduces these harmful effects. Many are commercially available, being a poor match to chemical fertilizer they are not used on a large scale. Near coastal regions seaweeds are used as biofertilizers. Recent studies show seaweeds have multiple growth regulators, macro and micronutrients, polysaccharides which are necessary for plant growth. The study is carried out to check the bioactivity of Seaweed liquid fertilizer (SLF) as a growth stimulant. Sargassum cinereum, Ulva rigida and Ahnfeltia plicata found on the Indian coastline were used. SLFs were prepared from each of three species in varying concentration (0.02%, 0.04%, 0.06%, 0.08% and 0.1% v/v) with water and Urea as control. The test plant selected was Vigna radiata. Agronomic characters like germination rate, shoot length, flowering and fruiting period of test plants were studied. Phyto-hormones from SLF were detected by thin-layer chromatography. Elemental analysis of micronutrients in SLF was carried by ICP-AES. Along with Chlorophyll and Protein estimation of grown plants were checked. SLF shelf life was studied using chemical and biological preservatives. The effect of 0.06% concentration of all SLFs showed enhancement in growth and phytochemicals in plants. Plants treated with SLF showed flowering earlier than control plants. Thus, using SLF bio-fertilizer can become an alternative method to reduce the use of harmful chemical fertilizers.