Seasonal-to-diurnal scale isotopic signatures of tidally-influenced submarine groundwater discharge to the Bay of Bengal: Control of hydrological cycle on tropical oceans

Debnath, Palash; Das, Kousik; Mukherjee, Abhijit; Ghosh, N. C.; Rao, S. V. N.; Kumar, Sudhir; Krishan, Gopal; Joshi, Gopal

doi:10.1016/j.jhydrol.2019.01.077

Cited by 19 publications

(2 citation statements)

References 47 publications

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“…Groundwater is rich in these isotopes relative to seawater (Burnett et al, 2006). In addition, their different half-lives allow tracing processes that occur at different time scales and provide information on groundwater fluxes (Michael et al, 2011;Kiro et al, 2013;Rodellas et al, 2017;Tamborski et al, 2017;Debnath et al, 2019;Yi et al, 2019, Diego-Feliu et al, 2021. Given that 222 Rn is an inert gas and thus not influenced by the physicochemistry of coastal groundwater (e.g.…”

Section: Introductionmentioning

confidence: 99%

A multidisciplinary approach to characterizing coastal alluvial aquifers to improve understanding of seawater intrusion and submarine groundwater discharge

Martinez-Perez

Luquot

Carrera

et al. 2022

Journal of Hydrology

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Section: Introductionmentioning

confidence: 99%

A multidisciplinary approach to characterizing coastal alluvial aquifers to improve understanding of seawater intrusion and submarine groundwater discharge

Martinez-Perez

Luquot

Carrera

et al. 2022

Journal of Hydrology

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“…Groundwater, as an active component of the global hydrological cycle, is extensively distributed within pore spaces and fractures of crustal lithospheres (Aeschbach‐Hertig & Gleeson, 2012; J. Han et al., 2017; H. Dai et al., 2019; De Graaf et al., 2019; Debnath et al., 2019; Gleeson et al., 2020; Ferreira et al., 2021). An accurate representation of groundwater distribution within subsurface environment is therefore crucial for addressing the problems that emerge at geoscience‐, energy‐, and environmental‐related contents, including land subsidence (Miller & Shirzaei, 2015; H. Dai, Ye, et al., 2017; Ha et al., 2020; Zhu et al., 2020), contaminant transports (Z. Dai et al., 2009; Ghanbarian et al., 2015; Cao et al., 2021; X. Zhang, Ma, Yin, et al., 2021), nuclear waste disposal (Viswanathan et al., 1998; Juhlin & Stephens, 2006; Wolfsberg et al., 2017; X. Zhang, Ma, Dai, et al., 2021), water inrush in tunnel and mine excavations (Dong et al., 2020; Ma et al., 2017; Qin et al., 2019), methane migration (Barth‐Naftilan et al., 2018; M. R. Soltanian et al., 2018; Pape et al., 2020; Ershadnia, Wallace, Hosseini, et al., 2021; Stanish et al., 2021; Wen et al., 2021), geological carbon storage (Castelletto et al., 2013; Z. Dai et al., 2014; Ershadnia, Wallace, Hajirezaie, et al., 2021; Fu et al., 2017; Njiekak et al., 2013), and groundwater and surface water interactions (Brunner et al., 2017; Jia et al., 2021; Wallace & Soltanian, 2021a, 2021b; Xin et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

Chen

Dai

Dong

et al. 2022

Water Resources Research

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Groundwater monitoring networks are direct sources of information for revealing subsurface system dynamic processes. However, designing such networks is difficult due to uncertainties in the spatial heterogeneity of aquifer parameters such as permeability (k). This study combines deep learning and information theory with an optimization framework to address network design problems in heterogeneous aquifer systems. The framework first employs a generative adversarial network to parameterize heterogeneous k distribution using a low-dimensional latent representation. Then, surrogate models are developed based on the deep neural networks to perform uncertainty quantification of pressure heads and solute concentrations at locations of pre-designed candidate monitoring stations. The monitoring stations are then ranked using the greedy search algorithm based on the maximum information minimum redundancy (MIMR) criterion. In order to depict the importance of each candidate monitoring location, the hotspot maps of the selection probability (P s ) are derived from MIMR repetition results. Comprehensive monitoring networks derived from the hotspot maps are then conducted as the final monitoring stations to improve monitoring information compared to MIMR results. Additionally, nine entropy quantization strategies are compared to evaluate their effects on monitoring network optimization results. Results indicate that caution should be taken when selecting entropy quantization strategies to achieve the accuracy required for model calibration and to improve the efficiency of monitoring optimization. Considering high-dimensional uncertainties associated with aquifer parameters, the developed framework can provide important insights for monitoring network designs in various earth observational projects. Plain Language SummaryGroundwater is widely distributed throughout the crust and lithosphere playing a critical role in geological evolution processes and environmental problems. This study presents an integrated framework for designing groundwater monitoring networks in heterogeneous aquifer systems. The proposed model takes advantage of the strength of deep-learning algorithms and deals with high-dimensional and highly non-linear problems of entropy-based methods for designing the monitoring networks. The framework can efficiently provide monitor locations for complex aquifers. The monitoring data collected by the designed network can significantly reduce uncertainties associated with high-dimensional parameters and contain the least amount of redundant information. Further, the proposed method is significant in practice as credible groundwater forecasting requires model calibrations that use monitoring data.

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Unravelling the signatures of submarine groundwater discharge and seawater intrusion along the coastal plains of Odisha, India: a multi-proxy approach

Nayak,

Nandimandalam

2024

Environ Geochem Health

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Seasonal-to-diurnal scale isotopic signatures of tidally-influenced submarine groundwater discharge to the Bay of Bengal: Control of hydrological cycle on tropical oceans

Cited by 19 publications

References 47 publications

A multidisciplinary approach to characterizing coastal alluvial aquifers to improve understanding of seawater intrusion and submarine groundwater discharge

A multidisciplinary approach to characterizing coastal alluvial aquifers to improve understanding of seawater intrusion and submarine groundwater discharge

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

Unravelling the signatures of submarine groundwater discharge and seawater intrusion along the coastal plains of Odisha, India: a multi-proxy approach

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