Investigating groundwater vulnerability variation under future abstraction scenarios to estimate optimal pumping reduction rates

Siarkos, Ilias; Sevastas, Stefanos; Mallios, Zisis; Theodossiou, Nicolaos; Ifadis, I. M.

doi:10.1016/j.jhydrol.2021.126297

Cited by 13 publications

(3 citation statements)

References 58 publications

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“…In the study area, the residents rely on groundwater for subsistence and agricultural needs [ 31 ]. However, due to excessive groundwater extraction and agricultural pollution, groundwater quality is under severe pressure [ 32 ]. The state and changing trend of groundwater quality would be revealed in advance so that protection measures will be taken.…”

Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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“…Regarding anthropogenic projection scenarios, many studies develop scenarios in which the groundwater abstraction rates increase (Chang et al, 2020;Ansari et al, 2021;Aslam et al, 2022), in line with rising populations. Only a few studies projected the abstraction to decrease in the future (Mustafa et al, 2019;Siarkos et al, 2021), and when they do, it is estimated not as the likely scenario but as a recommended policy to achieve sustainable abstraction rates. Nevertheless, The future of the groundwater regime in the Bandung groundwater basin becomes uncertain with the latest geospatial planning of Indonesia.…”

Section: Introductionmentioning

confidence: 99%

The impact of future climate projections and anthropogenic activities on basin-scale groundwater availability

Rusli,

Bense,

Mustafa

et al. 2024

Preprint

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Abstract. Groundwater is under the pressure of changing climate and increasing anthropogenic demand. In this study, we project the effect of these two processes on the projected future groundwater status. Climate projections of Representative Concentration Pathway (RCP) 4.5 and RCP8.5 from the Coupled Model Intercomparison Project Phase 6 (CMIP6) drive a one-way coupled fully distributed hydrological and groundwater model. In addition, three plausible groundwater abstraction scenarios with diverging predictions from increasing, constant, to decreasing volumes and spatial distribution are used. Groundwater status projections are assessed for the short-term (2030), mid-term (2050), and long-term (2100) periods. We use the Bandung groundwater basin as our study case, located 120 km from the current capital city of Indonesia, Jakarta, which is currently under a relocation plan. It is selected as the future anthropogenic uncertainties in the basin, related to the projected groundwater abstraction, is in agreement with our developed scenarios. Results show that changes in the projected climate input, including intensifying rainfall and rising temperature, do not propagate notable changes in groundwater recharge. Under the current unsustainable groundwater abstraction rate, the confined piezometric heads are projected to drop up to a maximum of 7.14 m, 15.25 m, and 29.51 m in 2030, 2050, and 2100, respectively. When groundwater abstraction expands in proportion to the present population growth, the impact is worsened almost two-fold. In contrast, if the groundwater abstraction decreases because of the relocated capital city, the groundwater storage starts to show replenishment potential. As a whole, projected groundwater status changes are dominated by anthropogenic activity, and less so by changes in climatic forcing. The results of this study are expected to demonstrate and inform responsible parties in operational water management on the issue of the impact of projected climate forcing and anthropogenic activity on future groundwater status.

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“…Researchers implied Bayesian Model Averaging to examine the uncertainties of using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability of Lake Urmia, Iran, and concluded that the higher uncertainties occur in areas with higher vulnerability (Gharekhani et al, 2022). A research work proposed a new method to estimate the optimal pumping reduction rate in contaminated aquifers by considering spatio-temporal variation of groundwater vulnerability as well as focusing on the relationship between vulnerability change and pumping reduction (Siarkos et al, 2021). Researchers implied the DRASTIC model to predict groundwater vulnerability using hydro-geochemical data and GIS (Rahman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Comparison of the DRASTIC + L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods

2023

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One of the most important and perilous groundwater contaminants is nitrate, which is considered a potential environmental risk in Iran and an international issue globally. The study area is the Karaj uncon ned aquifer, Central Iran, subjected to quantity and quality hazards due to unbalanced urban, industrial, and agricultural developments. Evaluation of the vulnerability condition of this aquifer against nitrate as the Contamination index is essential since the most amount of consumption water in the area is provided from groundwater. The study aims to detect vulnerable zones of the Karaj aquifer using DRASTIC + L and Modi ed VEBHAT vulnerability models. The weighting of the parameters has been performed employing the recent Multiple-Criteria Decision-Making (MCDM) methods, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Best Worst Method (BWM). The results demonstrated that the most vulnerability had occurred from the east to the south of the aquifer (high to super-critical), including urban and residential areas, regarding both the vulnerability models' plots. The plots of the two methods are similar and did not exhibit considerable differences. The Modi ed VEBHAT has more certainty than the DRASTIC + L and has optimized and improved the DRASTIC + L model because the most vulnerable zone in the output plots of the Modi ed VEBHAT model is in the east of the aquifer, which has good agreement with the nitrate contaminant mass with the highest concentration.

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Investigating groundwater vulnerability variation under future abstraction scenarios to estimate optimal pumping reduction rates

Cited by 13 publications

References 58 publications

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

The impact of future climate projections and anthropogenic activities on basin-scale groundwater availability

Comparison of the DRASTIC + L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods

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