Abstract: Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.

Abstract: Combined large-scale rainwater harvesting (RWH) and flood mitigation systems are promising as a sustainable water management strategy in urban areas. These are multi-purpose infrastructure that not only provide a secondary, localised water resource, but can also reduce discharge and hence loads on any downstream wastewater networks if these are integrated into the wider water network. However, the performance of these systems is dependent on the specific design used for its local catchment which can vary significantly between different implementations. A multitude of design strategies exist, however there is no universally accepted standard framework. To tackle these issues, this paper presents a two-player optimisation framework which utilises a stochastic design optimisation model and a competing, high-intensity rainfall design model to optimise passively-operated RWH systems. A customisable tool set is provided, under which optimisation models specific to a given catchment can be built quickly. This reduces the barriers to implementing computationally complex sizing strategies and encouraging more resource-efficient systems to be built. The framework was applied to a densely populated high-rise residential estate, eliminating overflow events from historical rainfall. The optimised configuration resulted in a 32% increase in harvested water yield, but its ability to meet irrigation demands was limited by the operational levels of the treatment pump. Hence, with the inclusion of operational levels in the optimisation model, the framework can provide an efficient large-scale RWH system that is capable of simultaneously meeting water demands and reducing stresses within and beyond its local catchment.

Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.

Abstract: As an ancient underground hydraulic engineering facility, the Qanat system has been used to draw groundwater from arid regions. A qanat is a horizontal tunnel with a slight incline that draws groundwater from a higher location and delivers it to lower agricultural land. During long-term water delivery, the qanat tunnel has experienced different degrees of aging and collapse, which may result in the significant ground settlement and even disasters. This paper developed a two-dimensional laboratory system to investigate the influence of progressive failure on the stability of deeply buried qanat tunnels. The developed system is fully instrumented with a particle image velocimetry (PIV) system and earth pressure and displacement monitoring. A special cylindrical membrane tube is designed and connected to an advanced pressure–volume controller to simulate the step-wise failure process of the tunnel. Three model tests were conducted on a dry sand considering the buried qanat tunnels at three different depths. Experimental results clearly show the progressive evolution of soil arching effect in the dry sand associated with the progressive failure of the tunnels. The failure of the Qanat ground starts from the vault and develops upwards, which is closely related to the evolution of stress contour at three consecutive stages. Ground surface settlement and volume loss corresponding to three burial depths were compared. A deeply buried qanat tunnel has a small effect on surface settlement. Earth pressure evolution on the 2D plane shows the load redistribution when the qanat collapses. The maximum arch and the initial point of the limit state correspond to a volume loss of 12.5 % and 50 %, respectively. For the collapse of the deep buried qanat tunnel, ground earth pressure evolution can be divided into a stress-increasing region, stress-decreasing region, and no redistribution region. Furthermore, a multi trap-door model considering soil expansion is proposed to describe the progressive failure behavior and its effects.

Abstract: Acid in situ leaching (AISL) is a subsurface mining approach suitable for low-grade ores which does not generate tailings, and has been adopted widely in uranium mining. However, this technique causes an extremely high concentration of contaminants at post-mining sites and in the surroundings soon after the mining ceases. As a potential AISL remediation strategy, natural attenuation has not been studied in detail. To address this problem, groundwater collected from 26 wells located within, adjacent, upgradient, and downgradient of a post-mining site were chosen to analyze the fate of U(VI), SO42−, δ34S, and δ238U, to reveal the main mechanisms governing the migration and attenuation of the dominant contaminants and the spatio-temporal evolutions of contaminants in the confined aquifer of the post-mining site. The δ238U values vary from −0.07 ‰ to 0.09 ‰ in the post-mining site and from −1.43 ‰ to 0.03 ‰ around the post-mining site. The δ34S values were found to vary from 3.3 ‰ to 6.2 ‰ in the post-mining site and from 6.0 ‰ to 11.0 ‰ around the post-mining site. Detailed analysis suggests that there are large differences between the range of isotopic composition variation and the range of pollutants concentration distribution, and the estimated Rayleigh isotope fractionation factor is 0.9994–0.9997 for uranium and 1.0032–1.0061 for sulfur. The isotope ratio of uranium and sulfur can be used to deduce the migration history of the contaminants and the irreversibility of the natural attenuation process in the anoxic confined aquifer. Combining the isotopic fractionation data for U and S with the concentrations of uranium and sulfate improved the accuracy of understanding of reducing conditions along the flow path. The study also indicated that as long as the geological conditions are favorable for redox reactions, natural attenuation could be used as a cost-effective remediation scheme.