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Assessing the degradation of river channels is crucial in the Anthropocene era, particularly in the deltaic areas worldwide. The present study aims to investigate the degradation of the Mathabhanga-Churni River giving emphasis on the temporal change of channel morphology and hydraulic behaviour and comparing the current situation with the historical past based on planform morphology and meander geometry of 118 meander loops, and width, depth, cross-sectional area, and hydraulic behaviour (velocity, discharge, stream power) at 47 cross-sections. The absence of lateral dynamicity, the static character of channel sinuosity during the last century (1913–2018), and the constant meander morphology reflects the static meander geometry and degradation of the river. The reduction of average channel width, depth, and cross-sectional area supports the falling trend of channel forms. Findings on hydraulic behaviour indicate declining discharge (~50% reduction during 1915–2018) that induces the present-day downscaled velocity and lowered stream power. Neotectonics (eastward tilting of the Bengal delta) and anthropogenic stressors such as stream crossings, agriculture on river beds, brickfields, and urbanisation have a severe impact on the hydromorphology of the channel, turning the active river into a stagnant and polluted channel. [ABSTRACT FROM AUTHOR]

Coastal wetlands are of paramount importance as major reservoirs of blue carbon (C), playing a crucial role in providing nature-based solutions to mitigate climatic changes. This research aimed to analyse the dynamics of total blue C (TBC) and its components; viz. soil organic C (SOC), below ground C, and above ground C; as well how they are influenced by land use/ land cover (LULC) categories and wetland situations. Subsequently, study were identified as one restored wetland and another degraded wetland in the Medinipur Coastal Plain, India. The LULC categories were analyzed using Pleiades 1A and 1B satellite imagery, corresponding to the restored and degraded wetland, respectively. The quantification of SOC was based on point-specific sample data collected from both wetlands (nr=250; nd=84). Above ground biomass (AGB) was appraised employing allometric relationships involving field-measured dendrometric variables. Below ground biomass values were calculated using indirect allometric equations that take into account the AGB values. Integrating all the components, TBC stock of the restored and degraded wetlands were estimated at 246710.91 Mg and 7865.49 Mg, respectively. In the restored wetland, dense mangrove and open mangrove exhibited higher concentrations of blue C components, while other LULC categories demonstrated moderate to low densities. In the degraded wetland, the open mangrove category rec-high densities of C pools, whereas herbaceous vegetation, bare earth and sand, and waterbody exhibited lower concentrations. The results portrayed significant disparities (P<0.05) in blue C pools among different LULC categories in both wetlands. Furthermore, it was evident that wetland type and LULC category had notable (P<0.001) impacts on TBC dynamics, both individually and in combination. Overall, this research may aid in effective management of coastal wetlands as blue C sinks, emphasizing their significance as essential elements of climate mitigation strategies. [ABSTRACT FROM AUTHOR]

A new clevosaurid rhynchocephalian is described from the Upper Triassic Tiki Formation of the Rewa Gondwana Basin of India. The material is based on several partial craniomandibular elements containing acrodont and fully ankylosed tooth implantations and on the basis of multiple diagnostic features is assigned to a new taxon, Clevosaurus nicholasi. Phylogenetic analysis nests the Tiki rhynchocephalian within the clade Clevosauridae, where it is recovered as an early-diverging taxon basal to the other clevosaurs except for a clade comprising C. convallis + Sigmala sigmala. The autapomorphic characters of Clevosaurus nicholasi include a very robust dentary with an obliquely angled narrow lip of the secondary bone at the symphysis, anteriorly bifurcated Meckelian canal, a sub-dental shelf on the dentary, acrodont marginal anterior teeth, and absence of or smooth lateral and medial wear facets on the marginal dentary and maxillary teeth, respectively. The Late Triassic rhynchocephalian record of the Gondwana is relatively sparse in comparison with that of the Laurasian regions, and the new clevosaur represents the first Late Triassic record from India. Based on the paleobiogeographic distribution, a possible Gondwanan origin for Clevosauridae is hypothesized. [ABSTRACT FROM AUTHOR]

Potentiality of Synthetic Aperture Radar (SAR) based Radar Forest Degradation Index (RFDI) combined with field observations for monitoring spatio-temporal dynamics of intertidal mudflat vegetation was assessed in this study. Five vegetation zones were delineated in the Junput mudflat of eastern India with very high classification accuracy (Kappa coefficient ≥ 0.79). Fragmentation and coalescence patterns of different vegetation zones under two plantation initiatives were also analysed by different spatial metrics. Results reveal gradual degradation of tree dominated vegetation zones and growth of shrub dominated and herbaceous ones from 2007 to 2019. Plantation of exotic species like Eucalyptus globulus and Casuarina equisetifolia along the shoreline had been found to be less effective against storms and sea surges. Conversely, native mangrove plantations and associated herbs had demonstrated remarkable growth in the intertidal areas. Based on the findings, the study pointed out that a zone-wise cum site-specific plantation strategy is needed towards developing effective bio-shields. [ABSTRACT FROM AUTHOR]

A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the data. On removal of autocorrelation, the data could be used for performing operations on the time-series data, which otherwise could only be performed on any other regular regression data. The work uses forecasting techniques like the AutoRegessive Integrated Moving Average (ARIMA) and exponential smoothening, and then the time-series data is further worked on using Long Short Term Memory (LSTM). Later, regression techniques are used by manipulating the dataset. The work is benchmarked with several evaluation metrics on a test dataset, where XGBoost Regression technique outperformed the test. The uniqueness of this work is that it forecasts the daily rainfall for the year 2021 and 2022 in Vellore region. This work can be extended in the future to predict rainfall over a bigger region based on previously recorded time-series data, which can help the farmers and common people to plan accordingly and take precautionary measures. [ABSTRACT FROM AUTHOR]