Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas
The dataset features radon-222 (222Rn), a radioactive tracer naturally present and frequently employed to assess submarine groundwater discharge (SGD). This collection is part of a study aimed at refining SGD estimations in shallow estuaries through the prediction of 222Rn variations using accessibl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923007369 |
| _version_ | 1797430054453510144 |
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| author | William W. Wolfe Dorina Murgulet Bimal Gyawali Blair Sterba-Boatwright |
| author_facet | William W. Wolfe Dorina Murgulet Bimal Gyawali Blair Sterba-Boatwright |
| author_sort | William W. Wolfe |
| collection | DOAJ |
| description | The dataset features radon-222 (222Rn), a radioactive tracer naturally present and frequently employed to assess submarine groundwater discharge (SGD). This collection is part of a study aimed at refining SGD estimations in shallow estuaries through the prediction of 222Rn variations using accessible hydroclimatic parameters [1]. The dataset includes measurements of 222Rn in water gathered recurringly from Aug. 2019 to June 2021 at half-hour intervals, at a monitoring station near the shore in Corpus Christi Bay, TX, USA (n = 10,660). Additionally, the data set encompasses continuous, accessible hydroclimatic parameters (e.g., wind speed and direction, atmospheric pressure, water temperature, tide height, creek and river discharge rate, n = 35,088). These parameters were integrated into two machine learning models - Random forest (RF) and Deep Neural Network (DNN) – aiming to interpret the variations in 222Rn and forecast during the data gap. A generalized additive model (GAM) was utilized, focusing on interpreting the variability in 222Rn inventory, particularly influenced by windspeed and direction. The tools and data presented herein afford prospects to 1) forecast 222Rn inventories in areas with significant data voids using only publicly accessible hydroclimatic parameters, and 2) refine SGD estimations affected by wind, thereby offering valuable insights for the planning of field expeditions and the development of management strategies for coastal water and solute budgets. |
| first_indexed | 2024-03-09T09:22:07Z |
| format | Article |
| id | doaj.art-650a9e5e44564f9c909d7e10930c6302 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-09T09:22:07Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-650a9e5e44564f9c909d7e10930c63022023-12-02T06:59:56ZengElsevierData in Brief2352-34092023-12-0151109651Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, TexasWilliam W. Wolfe0Dorina Murgulet1Bimal Gyawali2Blair Sterba-Boatwright3Center for Water Supply Studies, Texas A&M University-Corpus Christi, 78412, United StatesCenter for Water Supply Studies, Texas A&M University-Corpus Christi, 78412, United States; Corresponding author.Center for Water Supply Studies, Texas A&M University-Corpus Christi, 78412, United States; Department of Earth and Atmospheric Science, University of Houston, 77204, United StatesDepartment of Mathematics and Statistics, Texas A&M University-Corpus Christi, 78412, United StatesThe dataset features radon-222 (222Rn), a radioactive tracer naturally present and frequently employed to assess submarine groundwater discharge (SGD). This collection is part of a study aimed at refining SGD estimations in shallow estuaries through the prediction of 222Rn variations using accessible hydroclimatic parameters [1]. The dataset includes measurements of 222Rn in water gathered recurringly from Aug. 2019 to June 2021 at half-hour intervals, at a monitoring station near the shore in Corpus Christi Bay, TX, USA (n = 10,660). Additionally, the data set encompasses continuous, accessible hydroclimatic parameters (e.g., wind speed and direction, atmospheric pressure, water temperature, tide height, creek and river discharge rate, n = 35,088). These parameters were integrated into two machine learning models - Random forest (RF) and Deep Neural Network (DNN) – aiming to interpret the variations in 222Rn and forecast during the data gap. A generalized additive model (GAM) was utilized, focusing on interpreting the variability in 222Rn inventory, particularly influenced by windspeed and direction. The tools and data presented herein afford prospects to 1) forecast 222Rn inventories in areas with significant data voids using only publicly accessible hydroclimatic parameters, and 2) refine SGD estimations affected by wind, thereby offering valuable insights for the planning of field expeditions and the development of management strategies for coastal water and solute budgets.http://www.sciencedirect.com/science/article/pii/S2352340923007369Radioactive tracersCoastal groundwaterSubmarine groundwater discharge (SGD)Random forestMachine learning |
| spellingShingle | William W. Wolfe Dorina Murgulet Bimal Gyawali Blair Sterba-Boatwright Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas Data in Brief Radioactive tracers Coastal groundwater Submarine groundwater discharge (SGD) Random forest Machine learning |
| title | Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas |
| title_full | Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas |
| title_fullStr | Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas |
| title_full_unstemmed | Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas |
| title_short | Long-term radon-222 (222Rn) and hydroclimatic dataset for a coastal estuary, Corpus Christi Bay, Texas |
| title_sort | long term radon 222 222rn and hydroclimatic dataset for a coastal estuary corpus christi bay texas |
| topic | Radioactive tracers Coastal groundwater Submarine groundwater discharge (SGD) Random forest Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923007369 |
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