Transient Investigation of Saltwater Upconing in Laboratory-Scale Coastal Aquifer

The objective of this research was to examine the response of seawater intrusion and retreat to freshwater abstraction from a well in laboratory-scale coastal aquifer under transient conditions. This has been done experimentally and numerically through qualitative and quantitative analysis. The laboratory experiments were completed in a two-dimensional laboratory tank for two beads sizes, namely 1090 µm and 780 µm. The SEAWAT code was used for the numerical simulations. The experimental results showed that the vulnerability of the pumping well to salinization was higher for the low permeability aquifer, whereby the saltwater upconing process was observed at an abstraction rate 40 % smaller in the lower permeability aquifer compared to the high permeability aquifer. In the lower permeability scenario, the inland penetration of the saline plume was up to 41% larger than in the higher permeability scenario, for an equivalent pumping rate increment. In addition, the process of decay (after the abstraction had ceased) of the wedge was slower in the lower permeability aquifer, which suggests a slower retreat of the wedge. The qualitative comparison of the shape of the saline plume and the quantitative comparison of the transient toe length data between experimental and numerical results showed excellent agreement. The flow velocity field analysis revealed that the local reduction of the magnitude of the flow velocity along the upper part of the interface was a major factor contributing to the saltwater upconing mechanism

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

Data Availability Statement: Data sharing is not applicable to this article.This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with respect to the flow monitoring station. Long short-term memory (LSTM)-based models were used to examine the contribution of rainfall data on streamflow-forecasting performance by investigating five scenarios whereby RF data from different weather stations were incorporated depending on their geographical positions. Specifically, the All-RF scenario included all rainfall data collected at 11 stations; Upstream-RF (Up-RF) and Downstream-RF (Down-RF) included only the rainfall data measured upstream and downstream of the streamflow-measuring station; Pearson-RF (P-RF) only included the rainfall data exhibiting the highest level of correlation with the streamflow data, and the Flow-only (FO) scenario included streamflow data. The evaluation metrics used to quantitively assess the performance of the models included the RMSE, MAE, and the coefficient of determination, R2. Both ML models performed best in the FO scenario, which shows that the diversity of input features (hydrological and meteorological data) did not improve the predictive accuracy regardless of the positions of the weather stations. The results show that the P-RF scenarios yielded better prediction accuracy compared to all the other scenarios including rainfall data, which suggests that only rainfall data upstream of the flow monitoring station tend to make a positive contribution to the model’s forecasting performance. The findings evidence the suitability of simple monolayer LSTM-based networks with only streamflow data as input features for high-performance and budget-wise river flow forecast applications while minimizing data processing time.UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 101083481]. This research is part of the Horizon Europe WE-ACT project

A new physical barrier system for seawater intrusion control

The construction of subsurface physical barriers is one of various methods used to control seawater intrusion (SWI) in coastal aquifers. This study proposes the mixed physical barrier (MPB) as a new barrier system for seawater intrusion control, which combines an impermeable cutoff wall and a semi-permeable subsurface dam. The effect of the traditionally-used physical barriers on transient saltwater wedge dynamics was first explored for various hydraulic gradients, and the workability of the MPB was thereafter thoroughly analysed. A newly developed automated image analysis based on light-concentration conversion was used in the experiments, which were completed in a porous media tank. The numerical code SEAWAT was used to assess the consistency of the experimental data and examine the sensitivity of the performance of the barriers to various key parameters. The results show that the MPB induced a visible lifting of the dense saline flux upward towards the outlet by the light freshwater. This saltwater lifting mechanism, observed for the first time, induced significant reduction to the saline water intrusion length. The use of the MPB yielded up to 62% and 42% more reduction of the saltwater intrusion length than the semi-permeable dam and the cutoff wall, respectively. The performance achieved by the MPB with a wall depth of 40% of the aquifer thickness was greater than that of a single cutoff wall with a penetration depth of 90% of the aquifer thickness (about 13% extra reduction). This means that the MPB could produce better seawater intrusion reduction than the traditionally used barriers at even lower cost

Transient Investigation of the Critical Abstraction Rates in Coastal Aquifers: Numerical and Experimental Study

This research investigated the transient saltwater upconing in response to pumping from a well in a laboratory-scale coastal aquifer. Laboratory experiments were completed in a 2D flow tank for a homogeneous aquifer where the time evolution of the saltwater wedge was analysed during the upconing and the receding phase. The SEAWAT code was used for validation purposes and to thereafter examine the sensitivity of the critical pumping rate and the critical time (the time needed for the saltwater to reach the well) to the well design and hydrogeological parameters. Results showed that the critical pumping rate and the critical time were more sensitive to the variations of the well location than the well depth. The critical time increased with increasing the location and depth ratios following a relatively linear equation. For all the configurations tested, the lowest critical pumping rate was found for the lower hydraulic conductivity, which reflects the vulnerability of low permeability aquifers to salinization of pumping wells. In addition, higher saltwater densities led to smaller critical pumping rate and shorter critical time. The influence of the saltwater density on the critical time was more significant for wells located farther away from the initial position of the interface. Moreover, increasing the dispersivity induced negligible effects on the critical pumping rate, but reduced the critical time for a fixed pumping rate

Assessing the protective effect of cutoff walls on groundwater pumping against saltwater upconing in coastal aquifers

Data availability: No data was used for the research described in the article.Copyright © 2022 The Authors. Subsurface physical barriers are amongst the most effective methods to mitigate seawater intrusion in coastal aquifers. The main objective of this study was to examine the impact of cutoff walls on saltwater upconing using laboratory and numerical modelling experiments. Physical experiments were first completed to reproduce the saltwater upconing process in a laboratory-scale coastal aquifer model incorporating an impermeable cutoff wall. Numerical modelling was used for validation purposes and to perform additional simulations to explore the protective effect of cutoff walls against saltwater upconing. The results suggest that the cutoff wall did not substantially delay the saltwater upconing mechanism in the investigated configurations. Laboratory and numerical observations showed the existence of some residual saline water, which remained on the upper part of the aquifer on the seaward side of the wall following the retreat of the saltwater. The protective effect of cutoff walls was noticeably sensitive to the design parameters. Specifically, cutoff walls installed close to the pumping well enabled the implementation of higher pumping rates, therefore a more optimal use of the freshwater, especially for deeper wells. The results highlighted that the penetration depth of the cutoff walls may not necessarily need to exceed the depth of the pumping well to ensure effectiveness, which is of great importance from construction and economic perspectives

Applications of machine learning to water resources management: A review of present status and future opportunities

Data availability: No data was used for the research described in the article.The corrected proof will be replaced by version of record in due course.Copyright © 2024 The Authors. Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence.EPSRC project reference 2339403 to S. Sayed and A. Ahmed

The impact of future hydrology stresses and climate change on submarine groundwater discharge in arid regions: A case study of the Nile Delta aquifer, Egypt

Data availability: No data was used for the research described in the article.Code availability: Upon request.Supplementary material is available online at https://www.sciencedirect.com/science/article/pii/S2214581823000824?via%3Dihub#sec0150 .Copyright © 2023 The Authors. Study region Climate change is expected to severely impact Egypt's Nile Delta Aquifer (NDA). Despite its large freshwater reservoir, estimated at 400 Billion Cubic metres (BCM), climate-change-induced drivers (drought and sea-level rise) coupled with increasing groundwater over-abstraction will cause a gradual reduction of the available freshwater volume. This study used the numerical model SEAWAT to study the impact of the main hydrogeological and anthropogenic factors on the response of the submarine groundwater discharge (SGD) (i.e., the net fresh seaward groundwater flux) and seawater intrusion (SWI) in the NDA. Study focus Five scenarios were examined, including (i) a probable Sea-level rise (SLR), (ii) expected reduction in Nile hydrograph and its branches, (iii) freshwater overpumping, (iv) the combination of reduction in Nile hydrograph and overpumping, and (v) the combination of these scenarios in the years 2030, 2050 and 2070. New hydrological insights for the region The results show that the increasing saltwater head due to SLR coupled with a reduction in Nile flow and overpumping ultimately results in the landward shifting of the saltwater within the aquifer. In addition, the resulting salinity increase in the aquifer caused a significant increase in the deterioration of a large quantity of freshwater volume with a subsequent reduction of the SGD. Also, the salt mass variation (SMV) in scenario 5 increased to 7.09%, 10.69%, and 12.99%, while the groundwater discharges variation (SGDV) to the sea declined by 21.90%, 42.38%, and 61.95% in the years 2030, 2050 and 2070, respectively. Moreover, the coastal aquifers required the management of the SGD to keep the balance between the freshwater and saltwater interface. This study is useful for the future planning and water resources management in coastal regions for integrated management of SGD, SWI, and aquifer freshwater storage. Also, the applications of smart measurements of SGD and groundwater salinity are required for coastal aquifers management.This study did not receive any funding

Sub-national challenges to Europe’s constitutional structure

Supervisor: Professor Giorgio Monti, European University InstituteAward date: 26 November 2012This LL.M. thesis investigates how European Union law reacts when sub-national actors behave as an autonomous level of public authority. It will look at how supranational law has traditionally dealt with sub-national actors and then examine whether Article 4(2) TEU, introduced into the European legal order by the Treaty of Lisbon, constitutes a new interpretative device having the potential to change the ways in which European Union law looks at these entities. My analysis will show firstly, that the sub-national scale has become increasingly important, both at domestic and European scales, during the past decades, something which is now also reflected by primary law since the enactment of Article 4(2) TEU. It will be seen secondly that it remains unclear whether this provision stands for the direct recognition of local and regional self-government or whether the latter is recognized only indirectly as forming part of the Member State’s national identity. Thirdly, it will be seen that the case law preceding the insertion of Article 4(2) TEU into the framework of supranational law is rather heterogeneous. This draws a picture of incoherence as in some areas of the law the Court creates space for sub-national autonomies while in others it does not. Whether this will change under the influence of Article 4(2) TEU largely depends on how this provision will be interpreted in future times. I will illustrate that the Court could opt either for a bold or a more cautious approach in this respect. While the first option would allow to gently create some space for sub-national actors within EU law without conflicting with firmly established principles of the supranational legal order, the second approach could remedy the incoherence that currently characterises this area