Assessment of the impact of sea-level rise on seawater intrusion in sloping confined coastal aquifers

Shi, Wenlong; Lu, Chunhui; Werner, Adrian D.

doi:10.1016/j.jhydrol.2020.124872

Cited by 25 publications

(11 citation statements)

References 38 publications

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“…There has been an unprecedented rise in global sea levels, with devastating physical and socioeconomical effects on the coastal communities (Nicholls and Cazenave 2010;Nidhinarangkoon et al 2020;Ranjbar et al 2020;Schaefer et al 2020;Shi et al 2020;Stammer et al 2013). Since the start of the 20th century, the Global Mean Sea Level (GMSL) has risen by about 16-21cm, with more than 7 cm of this occurring since 1993 (Global Change Research Program 2019;Jevrejeva et al 2016;Yi et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Sea level rise (SLR) is a global environmental issue and a threat to coastal communities that are usually characterized with high population and infrastructure densification. These regions accommodate about 600 million people living in close vicinity of the sea and the figure is projected to double by 2060 (Nicholls and Cazenave 2010;Pycroft et al 2016;Shi et al 2020). Shoreline changes, rising frequency and intensity of flooding, erosion, change in coastal aquifers quality and groundwater are some of the physical impacts of SLR on coastal communities (A.…”

Section: Introductionmentioning

confidence: 99%

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Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

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This study aims to integrate a broad spectrum of ocean-atmospheric variables to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning techniques. 4 scenarios of different combinations of variables such as sea surface temperature, sea surface salinity, sea surface density, surface atmospheric pressure, wind speed, total cloud cover, precipitation and sea level data were used to train ARIMA, SVR and LSTM neural network models. Results show that atmospheric processes have more influence on prediction accuracy than ocean processes. Combining ocean and atmospheric variables improves the model prediction at all stations by 1-9% for both SVR and LSTM. The means of R accuracy of optimal performing LSTM, SVR and ARIMA models at all stations are 0.853, 0.748 and 0.710, respectively. Comparison of model performance shows that the LSTM model trained with ocean and atmospheric variables is optimal for predicting sea level variation at all stations except Pulua Langkawi where ARIMA model trained without ocean-atmospheric variables performed best due to the dominating tide influence. This suggests that performance and suitability of prediction models vary across regions and selecting an optimal prediction model depends on the dominant physical processes governing sea level variability in the area of investigation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

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“…So far, dozens of countries and regions have studied seawater intrusion, such as the United States, Netherlands, South Korea, Japan, China, and so on. In recent years, research on seawater intrusion has mainly focused on numerical simulation, the impact of climate change on seawater intrusion, and the prevention and control methods of seawater intrusion; for example, the authors of [19], using laboratory experiment and numerical simulation methods, studied the effect of aquifer recharge and flow barriers on seawater intrusion and found that recharging the toe of the saltwater wedge was more effective in preventing seawater intrusion; the authors of [20] studied the seawater intrusion of the Nile Delta aquifer under climate change conditions and found that the rise of sea level has a significant effect on seawater intrusion; the authors of [21] used an analytical solution to study the effect of aquifer slope on seawater intrusion and found that aquifers thin towards the sea are most susceptible to sea level rise and seawater intrusion.…”

Section: Introductionmentioning

confidence: 99%

Hydrochemical Characteristics and Irrigation Suitability Evaluation of Groundwater with Different Degrees of Seawater Intrusion

Wang

Liu

et al. 2020

Water

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Groundwater in coastal aquifers is often affected by seawater intrusion, resulting in water quality deterioration. Using groundwater influenced by seawater intrusion for irrigation can lead to crop failure, erosion of machinery and pipes, and adverse effects on farming. In this study, the results of water testing, methods of statistical analysis, ion ratios, a Piper diagram, and a variety of groundwater irrigation suitability models were used to analyze the chemical composition of groundwater and the influence of seawater intrusion. The result shows that the content of Na+, K+, Ca2+, Mg2+, Cl−, and SO42− in groundwater would increase due to seawater intrusion, and the increasing trend was consistent with the freshwater–seawater mixing line. With the deepening of seawater intrusion, the hydrochemical type gradually changes from Ca-HCO3·Cl to Na·Mg-Cl·SO4 and then to Na-Cl type, and the source of hydrochemical composition changes from “Rock Weathering Dominance” to “Evaporation Dominance”. When the Cl− concentration is greater than 7.1 meq/L, groundwater will corrode pipelines and instruments; when greater than 28.2 meq/L, excessively high salinity of groundwater will have adverse effects on planting; and when greater than 14.1 meq/L, the groundwater hardness is too high, which may make the groundwater unsuitable for cultivation.

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“…In this study, the factors affecting groundwater drawdown including extraction values, the transmissivity of aquifer formation, depth to the water table, topography (slope and elevation), location in the watershed, distance from water resources (groundwater recharge), annual precipitation, annual evaporation, and distance from industries and residential centers were used as the inputs of the modeling process (Awasthi et al 2005;Shiri et al 2013;Gong et al 2018;Guevara-Ochoa et al 2018, Pradhan et al 2018Abd-Elhamid et al 2020;Shi et al 2021). Examples of inputs are shown in Table (1).…”

Section: Factors Affecting Groundwater Drawdownmentioning

confidence: 99%

“…Distance from water resources (such as lakes and rivers), the location of groundwater withdrawal sites, and precipitation values affect the amount of groundwater recharge (Guevara-Ochoa et al 2018;Shi et al 2021). Numerous studies have been performed to simulate the uctuations of groundwater depth; however, estimation of annual drawdown values is challenging because of the complexity of the controlling factors and the needs for various inputs in the modeling process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of groundwater drawdown using artificial neural networks

Gholami

Sahour

2022

Environ Sci Pollut Res

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Groundwater drawdown is typically measured using pumping tests and eld experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on arti cial neural network (ANN)s and eld measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the in uence radius of each well (1000 m) were identi ed and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that, the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown (Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was tted to available input data to map the annual drawdown across the entire aquifer. The accuracy assessment of the nal map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.

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Assessment of the impact of sea-level rise on seawater intrusion in sloping confined coastal aquifers

Cited by 25 publications

References 38 publications

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Hydrochemical Characteristics and Irrigation Suitability Evaluation of Groundwater with Different Degrees of Seawater Intrusion

Prediction of groundwater drawdown using artificial neural networks

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