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The rapid decline of groundwater levels (GWL) due to pervasive groundwater abstraction in the densely populated (~1 billion) Indus-Ganges-Brahmaputra-Meghna (IGBM) transboundary river basins of South Asia, necessitates a robust framework of prediction and understanding. While few localized studies exist, three-dimensional regional-scale characterization of GWL prediction is yet to be implemented. Here, 'support vector machine', a machine-learning-based method, is applied to data from the Gravity Recovery and Climate Experiment (GRACE) and data on land-surface-model-based groundwater storage and meteorological variables, to predict the GWL anomaly (GWLA) in the IGBM. The study has three main objectives, (1) to understand the spatial (observation well locations) and subsurface (shallow vs. deep observation wells) variability in prediction results for in-situ GWLA data for a large number of observation wells (n = 4,791); (2) to determine its relationship with groundwater abstraction, and; (3) to outline the advantages and limitations of using GRACE data for predicting GWLAs. The findings, based on individual observation well results, suggest significant prediction efficiency (median statistics: r > 0.71, NSE > 0.70; p < 0.05) in most of the IGBM; however, the study identifies hotspots, mostly in the agriculture-intensive regions, having relatively poor model performance. Further analysis of the subsurface depth-wise prediction statistics reveals that the significant dominance of pumping in the deeper depths of the aquifer is linked to the relatively poor model performance for the deep observation wells (screen depth > 35 m) compared with the shallow observation wells (screen depth < 35 m), thus, highlighting the limitation of GRACE in representing spatial and depth-dependent local-scale pumping. [ABSTRACT FROM AUTHOR]

The present study is undertaken in the eastern coast of India, along the coastal tract of Bay of Bengal (BoB), to delineate the submarine groundwater discharge (SGD)-borne nutrient flux at temporal scale and their impact to coastal ecology and biogeochemical processes. Solutes chemistry, seepage meter study, stable-isotopic signature, and geophysical techniques were used to identify the surface water–groundwater interaction zone, SGD rate and nutrient flux. The estimated rate of major annual discharge of nutrient fluxes were 240 and 224 mM m−2 day−1 for NO3− and Fetot. The variation of solute and nutrient fluxes was depending on the load of terrestrial water masses, which is triggered by the local monsoonal meteoric recharge. The ecohydrological response to this solute flux results in spatio-temporal patterns of N and P-sensitive algal blooms in the intertidal zones. Most algae were identified as dinoflagellates and some haptophytes, with greenish and brownish hue that provides a distinct look to the coastal landscape. The algal blooms were found to be substantially influenced by the seasonal-nutrients flux and discharge location. Our study is expected to increase the understanding of a rarely reported eco-hydrological response to terrestrial–marine water interactions and their implications in the tropical ocean adjoining the Indian Subcontinent. [ABSTRACT FROM AUTHOR]