Radon-222 as a groundwater discharge tracer to surface waters

Adyasari, Dini; Dimova, Natasha; Dulai, Henrietta; Gilfedder, Benjamin; Cartwright, Ian; McKenzie, Trista; Füleky, Péter

doi:10.1016/j.earscirev.2023.104321

Cited by 40 publications

(10 citation statements)

References 169 publications

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“…A full radon mass balance (e.g., Adyasari et al., 2023; Burnett & Dulaiova, 2003; Dulaiova et al., 2010) is still required to derive SGD estimates. For direct SGD prediction, we would require additional site‐specific information, such as coastal morphology, groundwater radon concentrations, and SGD plume area and thickness (e.g., Adyasari et al., 2023; Burnett et al., 2007; Rodellas et al., 2021) because coastal seawater radon activities do not directly translate to SGD rates. The additional information required to estimate SGD is not available for most sites, so it is not possible to go as far as predicting SGD.…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation in the FCNN model is that 85% of radon activities used in the model are under 500 Bq/m 3 . While this range is representative of previous coastal studies (e.g., Adyasari et al, 2023;Taniguchi et al, 2019), model results may have lower accuracy for new locations with high radon concentrations (>500 Bq/m 3 ) because the model learned from a limited number of data points. Additionally, the data used to train the model may be biased toward areas with likely SGD is implicated, reflecting a predisposition toward conducting temporal radon field studies where SGD is occurring.…”

Section: Simplified Prediction Of Coastal Seawater Radon Activitiesmentioning

confidence: 96%

“…Currently, radon is the only SGD tracer that has been measured using automated techniques to produce large temporal data sets. While radon‐based SGD estimates have well‐defined shortcomings and uncertainties (e.g., Adyasari et al., 2023; Taniguchi et al., 2019), the applicability of radon has been demonstrated in a wide variety of coastal settings. Longer‐term SGD measurements are vital for protecting water resources and coastal water quality under climate change (e.g., sea level rise, drought, increasing storm event frequency, and severity), land use changes, and coastal aquifer overpumping.…”

Section: Introductionmentioning

confidence: 99%

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Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters

McKenzie

Dulai

Lee

et al. 2023

Water Resources Research

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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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Section: Discussionmentioning

confidence: 99%

Section: Simplified Prediction Of Coastal Seawater Radon Activitiesmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters

McKenzie

Dulai

Lee

et al. 2023

Water Resources Research

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“…222 Rn was used as a qualitative tracer for benthic exchange in this study. Porewater observations, including estimates of 222 Rn diffusion from sediments, bay water residence times, and radium ( 226 Ra) observations would be needed to build a full radon mass balance (Adyasari et al., 2023) and quantify related porewater benthic CH 4 fluxes. Unfortunately, these additional 226 Ra data are not available because sampling large volumes of porewater is often required for 222 Rn analysis, which was not possible in the organic rich matte (Lee & Kim, 2006).…”

Section: Methodsmentioning

confidence: 99%

Methane Emissions in Seagrass Meadows as a Small Offset to Carbon Sequestration

et al. 2023

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Seagrass meadows are effective carbon sinks recognized for their potential role in climate change mitigation (Fourqurean et al., 2012;Lovelock & Duarte, 2019;Mcleod et al., 2011). Seagrass meadows sequester carbon dioxide (CO 2 ) through photosynthesis (Van Dam et al., 2021) and trap allochthonous particles within their canopy (Gacia et al., 2002). Part of this carbon is then stored as biomass and as organic carbon in sediments for centuries and even millennia (Serrano et al., 2021(Serrano et al., , 2016. Seagrass meadows account for 10%-18% of the total carbon burial (27 44 Tg C yr −1 ) in the ocean, even though they cover only 0.1% of the global ocean area (Kennedy et al., 2010). In addition, about 5% of the particulate organic carbon and dissolved organic carbon produced within seagrass habitats is exported beyond the meadows and stored in the deep ocean (Duarte & Krause-Jensen, 2017). Seagrass meadows are considered an important blue carbon ecosystem that should be protected and restored to mitigate anthropogenic CO 2 emissions.

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“…The natural radon ( 222 Rn) activity in the stream water was used in addition to the salt tracer investigations to provide insight into both the spatial distribution and the quantity of groundwater discharge into the Schäfertal stream along the stream section. Radon is an excellent tracer for investigating groundwater-surface water interactions (Adyasari et al, 2023). Longitudinal stream radon measurements allow the (1) localization of groundwater discharge zones and (2) calculation of radon mass balances within defined sections of the stream and subsequently groundwater discharge into the stream.…”

Section: Groundwater Discharge Investigated By Radon ( 222 Rn) Measur...mentioning

confidence: 99%

Seasonal variation and release of soluble reactive phosphorus in an agricultural upland headwater in central Germany

Rode

Tittel

Reinstorf

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Soluble reactive phosphorus (SRP) concentrations in agricultural headwaters can display pronounced seasonal variability at low flow, often with the highest concentrations occurring in summer. These SRP concentrations often exceed eutrophication levels, but their main sources, spatial distribution, and temporal dynamics are often unknown. The purpose of this study is therefore to differentiate between potential SRP losses and releases from soil drainage, anoxic riparian wetlands, and stream sediments in an agricultural headwater catchment. To identify the dominant SRP sources, we carried out three longitudinal stream sampling campaigns for SRP concentrations and fluxes. We used salt dilution tests and natural 222Rn to determine water fluxes in different sections of the stream, and we sampled for SRP, Fe, and 14C dissolved organic carbon (DOC) to examine possible redox-mediated mobilization from riparian wetlands and stream sediments. The results indicate that a single short section in the upper headwater reach was responsible for most of the SRP fluxes to the stream. Analysis of samples taken under summer low-flow conditions revealed that the stream water SRP concentrations, the fraction of SRP within total dissolved P (TDP), and DOC radiocarbon ages matched those in the groundwater entering the gaining section. Pore water from the stream sediment showed evidence of reductive mobilization of SRP, but the exchange fluxes were probably too small to contribute substantially to SRP stream concentrations. We also found no evidence that shallow flow paths from riparian wetlands contributed to the observed SRP loads in the stream. Combined, the results of this campaign and previous monitoring suggest that groundwater is the main long-term contributor of SRP at low flow, and agricultural phosphorus is largely buffered in the soil zone. We argue that the seasonal variation of SRP concentrations was mainly caused by variations in the proportion of groundwater present in the streamflow, which was highest during summer low-flow periods. Accurate knowledge of the various input pathways is important for choosing effective management measures in a given catchment, as it is also possible that observations of seasonal SRP dilution patterns stem from increased mobilization in riparian zones or from point sources.

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Radon-222 as a groundwater discharge tracer to surface waters

Cited by 40 publications

References 169 publications

Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters

Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters

Methane Emissions in Seagrass Meadows as a Small Offset to Carbon Sequestration

Seasonal variation and release of soluble reactive phosphorus in an agricultural upland headwater in central Germany

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