Modeling groundwater and surface water interaction: An overview of current status and future challenges

Ntona, Maria Margarita; Busico, Gianluigi; Mastrocicco, Micòl; Kazakis, Nerantzis

doi:10.1016/j.scitotenv.2022.157355

Cited by 78 publications

(23 citation statements)

References 136 publications

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“…In many studies of conjunctive use management, such hydrological interactions are reflected in the modeling (Danskin and Gorelick 1985;Reichard and Bredehoeft 1984). By bringing in richer data-for example on permeability of surface water obtained from measurements of parameters such as precipitation, temperature, and relative humidity (Ntona et al 2022)-it can be expected that our model will enable more reliable simulations.…”

Section: Discussionmentioning

confidence: 99%

Conjunctive surface water and groundwater management in a multiple user environment

Zhang,

Sato

2024

Environ Econ Policy Stud

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The conjunctive use of surface water and groundwater is practiced worldwide as a measure to address water supply uncertainty. This paper focuses on a scenario in which an aquifer is open to multiple users and examines the impact of such an open-access environment on the stabilization function of conjunctive management. We construct a non-cooperative stochastic dynamic game model where multiple users utilize groundwater intake from a common aquifer as a complement to fluctuating surface water. We also propose a simpler baseline applicable to dynamic environments to compute the benefits of the stabilization function when users are unable to adjust groundwater intake to surface water uncertainties. We then apply the model to the real-world case of the water supply environment of the Cao’e River in China. Simulation results show that open access leads to diminishing stabilization values as higher pumping costs due to declining stocks lead to a weakening of users’ incentive to utilize groundwater flexibly. Furthermore, the stabilization function itself is destabilized as the number of users increases. This is because greater groundwater intake and a decline in stocks amplify variations in pumping costs caused by different patterns of surface-water fluctuation.

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Section: Discussionmentioning

confidence: 99%

Conjunctive surface water and groundwater management in a multiple user environment

Zhang,

Sato

2024

Environ Econ Policy Stud

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“…Although the contribution of groundwater to the hydrological regime alteration assessment has been highly acknowledged, especially in southern European countries, few methods manage to include the groundwater component in the hydrological regime assessment. An insight regarding the multifactor and interconnected process between surfacegroundwater interactions can be provided using new advancements, such as isotope analysis and element speciation, statistical analysis and modelling, thermal approach and geophysical techniques such as electrical resistivity tomography and airborne electromagnetic surveys [169,170].…”

Section: Discussionmentioning

confidence: 99%

Hydrological Regime Alteration Assessment in the Context of WFD 2000/60: A European and Global Review

Mentzafou,

Katsafados,

Papadopoulos

et al. 2023

Sustainability

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Although the impact of hydrology on the ecological status of surface water bodies has been highly recognised, the hydrological regime alteration assessment has proven to be a challenging task. In this context, an extensive structured review analysis was used as a research method to investigate the strength and limitations of the hydrological regime alteration assessment methods as adopted by each member of the European Environment Agency and the cooperating countries, according to the Water Framework Directive 2000/60, as well as to propose future directions. The review was also widened to include the methods currently used worldwide in the hydrological alteration studies and the supporting software tools developed. The implementation of a common methodology on a European scale is not applicable, since a single approach would not be able to cope with the regional needs and conditions. The main limitation in almost all the methods developed by European countries and worldwide is the need for a flow time series of high temporal resolution, so as to also capture the systems’ extreme high and low flows. Automatic monitoring systems for rivers can provide a solution. Additionally, hydrological modelling may provide the necessary data for the definition of the reference conditions. Nevertheless, the main limitations of the methodologies reviewed and the challenge for future development are the incorporation of the groundwater contribution to the hydrological regime and the development of quantitative relationships between flow alteration and ecological response.

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“…For instance, the acquisition of groundwater level data may be constrained by the density of monitoring networks and monitoring technology. Additionally, the complex interactions between groundwater dynamics and surface water require accurate simulation through advanced models [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Zhang,

Zhou,

Deng

et al. 2024

Water

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Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

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Modeling groundwater and surface water interaction: An overview of current status and future challenges

Cited by 78 publications

References 136 publications

Conjunctive surface water and groundwater management in a multiple user environment

Conjunctive surface water and groundwater management in a multiple user environment

Hydrological Regime Alteration Assessment in the Context of WFD 2000/60: A European and Global Review

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

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