| 1. |
- Adyasari, D., et al.
(författare)
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Radon-222 as a groundwater discharge tracer to surface waters
- 2023
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Ingår i: Earth-Science Reviews. - : Elsevier BV. - 0012-8252. ; 238
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Tidskriftsartikel (refereegranskat)abstract
- The naturally occurring isotope radon-222 (222Rn) is widely employed as a tracer for groundwater discharge to lakes, lagoons, rivers, estuaries, and coastal oceans. However, owing to the highly diverse hydrogeological settings, limitations, and assumptions when applying the 222Rn mass balance, there is a clear need to create a uniform approach that will constrain the uncertainties in the reported groundwater fluxes. This review paper provides an overview of the 222Rn measurement techniques and discusses 222Rn mass balances and their application to various hydrological environments. We address the primary uncertainties faced when applying 222Rn mass balances including, (1) atmospheric evasion, (2) groundwater endmember, (3) offshore mixing loss, (4) steady-state assumptions, and (5) upscaling groundwater discharge from 222Rn measurements, and methods that can be applied to minimize these uncertainties. Finally, we provide guidelines and open-source scripts (i.e., R codes and FINIFLUX) that should assist future studies using 222Rn to quantify groundwater discharge to surface waters.
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| 2. |
- McKenzie, Tristan, et al.
(författare)
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Traditional and novel time-series approaches reveal submarine groundwater discharge dynamics under baseline and extreme event conditions
- 2021
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater is a vital resource for humans and groundwater dependent ecosystems. Coastal aquifers and submarine groundwater discharge (SGD), both influenced by terrestrial and marine forces, are increasingly affected by climate variations and sea-level rise. Despite this, coastal groundwater resources and discharge are frequently poorly constrained, limiting our understanding of aquifer responses to external forces. We apply traditional and novel time-series approaches using an SGD dataset of previously unpublished resolution and duration, to analyze the dependencies between precipitation, groundwater level, and SGD at a model site (Kiholo Bay, HawaiModified Letter Turned Commai). Our objectives include (1) determining the relative contribution of SGD drivers over tidal and seasonal periods, (2) establishing temporal relationships and thresholds of processes influencing SGD, and (3) evaluating the impacts of anomalous events, such as tropical storms, on SGD. This analysis reveals, for example, that precipitation is only a dominant influence during wet periods, and otherwise tides and waves dictate the dynamics of SGD. It also provides time lags between intense storm events and higher SGD rates, as well as thresholds for precipitation, wave height and tides affecting SGD. Overall, we demonstrate an approach for modeling a hydrological system while elucidating coastal aquifer and SGD response in unprecedented detail.
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| 3. |
- McKenzie, Tristan, et al.
(författare)
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Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters
- 2023
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Ingår i: Water Resources Research. - 0043-1397 .- 1944-7973. ; 59:3
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Tidskriftsartikel (refereegranskat)abstract
- Submarine groundwater discharge (SGD) is an important driver of coastal biogeochemical budgets worldwide. Radon (222Rn) has been widely used as a natural geochemical tracer to quantify SGD, but field measurements are time consuming and costly. Here, we use deep learning to predict coastal seawater radon in SGD-impacted regions. We hypothesize that deep learning could resolve radon trends and enable preliminary insights with limited field observations of groundwater tracers. Two deep learning models were trained on global coastal seawater radon observations (n = 39,238) with widely available inputs (e.g., salinity, temperature, water depth). The first model used a one-dimensional convolutional neural network (1D-CNN-RNN) framework for site-specific gap filling and producing short-term future predictions. A second model applied a fully connected neural network (FCNN) framework to predict radon across geographically and hydrologically diverse settings. Both models can predict observed radon concentrations with r2 > 0.76. Specifically, the FCNN model offers a compelling development because synthetic radon tracer data sets can be obtained using only basic water quality and meteorological parameters. This opens opportunities to attain radon data from regions with large data gaps, such as the Global South and other remote locations, allowing for insights that can be used to predict SGD and plan field experiments. Overall, we demonstrate how field-based measurements combined with big-data approaches such as deep learning can be utilized to assess radon and potentially SGD beyond local scales.
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| 4. |
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| 5. |
- Taniguchi, M., et al.
(författare)
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Submarine Groundwater Discharge: Updates on Its Measurement Techniques, Geophysical Drivers, Magnitudes, and Effects
- 2019
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media SA. - 2296-665X. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- The number of studies concerning Submarine Groundwater Discharge (SGD) grew quickly as we entered the twenty-first century. Many hydrological and oceanographic processes that drive and influence SGD were identified and characterized during this period. These processes included tidal effects on SGD, water and solute fluxes, biogeochemical transformations through the subterranean estuary, and material transport via SGD from land to sea. Here we compile and summarize the significant progress in SGD assessment methodologies, considering both the terrestrial and marine driving forces, and local as well as global evaluations of groundwater discharge with an emphasis on investigations published over the past decade. Our treatment presents the state-of-the-art progress of SGD studies from geophysical, geochemical, bio-ecological, economic, and cultural perspectives. We identify and summarize remaining research questions, make recommendations for future research directions, and discuss potential future challenges, including impacts of climate change on SGD and improved estimates of the global magnitude of SGD.
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| 6. |
- Wilson, Stephanie J., et al.
(författare)
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Global subterranean estuaries modify groundwater nutrient loading to the ocean
- 2024
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Ingår i: Limnology And Oceanography Letters. - 2378-2242.
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Tidskriftsartikel (refereegranskat)abstract
- Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater-derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr−1 for DIN, DON, and DIP, respectively. Despite large natural variability, total SGD fluxes likely exceed global riverine nutrient export. Fresh SGD is a small source of new nutrients, but saline SGD is an important source of mostly recycled nutrients. Nutrients exported via SGD via subterranean estuaries are critical to coastal biogeochemistry and a significant nutrient source to the oceans.
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