Saltwater intrusion into coastal aquifers in the contiguous United States — A systematic review of investigation approaches and monitoring networks

Panthi, Jeeban; Pradhanang, Soni M.; Nolte, Annika; Boving, Thomas B.

doi:10.1016/j.scitotenv.2022.155641

Cited by 41 publications

(7 citation statements)

References 123 publications

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“…Sea level rise and saltwater intrusion are two hallmark conditions present at the coastal interface [75]. Beyond sea surface temperature, ocean color and sea surface height are variables intimately linked to the coastal environment [76,77,78]. We hypothesize that global datasets considering these parameters have the same challenges when conformation to a gridded structure and neural network process are prescribed.…”

Section: Discussionmentioning

confidence: 99%

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

et al. 2023

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The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet’s dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United States drainage basins of varying acreage from smaller to very large (Bear, Colorado, Connecticut, and Mississippi). F2F combines model and ground observations of water-cycle fluxes in the form of surface runoff, subsurface baseflow, and gauged streamflow. We use these time series datasets to simulate, visualize, and analyze the watershed basin response to the varying climates and magnitudes of hydroclimatic fluxes in each river basin. Experiments modulating the time lag between remotely sensed and ground-truth measurements are performed to assess the metrological limits of forecasting with this platform. The resultant mean Nash–Sutcliffe and Kling–Gupta efficiency values are both greater than 90%. Our results show that a hydrological machine learning platform such as F2F can become a powerful resource to simulate and forecast hydroclimatic extremes and the resulting watershed responses and natural hazards in a changing global climate.

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Section: Discussionmentioning

confidence: 99%

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

et al. 2023

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“…Combining traditional and artificial recharge techniques is one way to overcome the disadvantages of both. Although many studies investigate saltwater intrusion in coastal aquifers, only a limited number study the control methods of saltwater intrusion [27,28].…”

Section: Artificial Groundwater Rechargementioning

confidence: 99%

Developing Functional Recharge Systems to Control Saltwater Intrusion via Integrating Physical, Numerical, and Decision-Making Models for Coastal Aquifer Sustainability

Miky,

Issa,

Mahmod

2023

JMSE

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Controlling the hydraulic heads along a coastal aquifer may help to effectively manage saltwater intrusion, improve the conventional barrier’s countermeasure, and ensure the coastal aquifer’s long-term viability. This study proposed a framework that utilizes a decision-making model (DMM) by incorporating the results of two other models (physical and numerical) to determine proper countermeasure components. The physical model is developed to analyze the behavior of saltwater intrusion in unconfined coastal aquifers by conducting two experiments: one for the base case, and one for the traditional vertical barrier. MODFLOW is used to create a numerical model for the same aquifer, and experimental data are used to calibrate and validate it. Three countermeasure combinations, including vertical barrier, surface, and subsurface recharges, are numerically investigated using three model case categories. Category (a) model cases investigate the hydraulic head’s variation along the aquifer to determine the best recharge location. Under categories (b) and (c), the effects of surface and subsurface recharges are studied separately or in conjunction with a vertical barrier. As a pre-set of the DMM, evaluation and classification ratios are created from the physical and numerical models, respectively. The evaluation ratios are used to characterize the model case results, while the classification ratios are used to classify each model case as best or worst. An analytical hierarchy process (AHP) as a DMM is built using the hydraulic head, salt line, repulsion, wedge area, and recharge as selection criteria to select the overall best model case. According to the results, the optimum recharging location is in the length ratio (LR) from 0.45 to 0.55. Furthermore, the DMM supports case3b (vertical barrier + surface recharge) as the best model case to use, with a support percentage of 48%, implying that this case has a good numerical model classification with a maximum repulsion ratio (Rr) of 29.4%, and an acceptable wedge area ratio (WAR) of 1.25. The proposed framework could be used in various case studies under different conditions to assist decision-makers in evaluating and controlling saltwater intrusion in coastal aquifers.

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“…Sin embargo, el seguimiento por sí solo no puede salvar estos ecosistemas en riesgo; es necesario mejorar la comprensión y la capacidad de modelar los umbrales o los puntos de inflexión asociados a la pérdida de ecosistemas frente a su supervivencia para apoyar la planificación proactiva de la gestión de los recursos y las comunidades costeras 58,137 . Se necesita información sobre cuándo y dónde puede producirse la intrusión de agua salada y sus impactos 69,230,231 .…”

Section: Principales Incertidumbres Y Brechas En La Investigaciónunclassified

Cap. 9. Efectos costeros. En: La Quinta Evaluación Nacional del Clima

2023

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Saltwater intrusion into coastal aquifers in the contiguous United States — A systematic review of investigation approaches and monitoring networks

Cited by 41 publications

References 123 publications

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

Developing Functional Recharge Systems to Control Saltwater Intrusion via Integrating Physical, Numerical, and Decision-Making Models for Coastal Aquifer Sustainability

Cap. 9. Efectos costeros. En: La Quinta Evaluación Nacional del Clima

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