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Leeuwen, Z. R. van, Klaar, M. J., Smith, M. W., & Brown, L. E. (2024). Quantifying the natural flood management potential of leaky dams in upland catchments, Part II: Leaky dam impacts on flood peak magnitude. Journal of Hydrology, 628, 130449.
Abstract: Leaky dams are an increasingly popular natural flood management measure, yet their impacts on flood peak magnitude have not yet been empirically quantified for a range of event types and magnitudes, even at the stream scale. In this study, the novel application of a transfer function noise modelling approach to empirical Before-After-Control-Impact stage data from an upland catchment allowed leaky dam effectiveness in reducing flood peak magnitude to be quantified. Flood peak stage and discharge magnitude changes were assessed from empirical data for 50 single and multi-peaked high flow events with return periods ranging from less than one year to six years. Overall, event peak magnitude was significantly reduced following the installation of eight leaky dams on the impact stream. Effectiveness was highly variable, but on average, flood peak magnitude was reduced by 10% for events with a return period up to one year. Some of the variability was explained by the size of the event and whether it was a single or multi-peaked event. This finding emphasises the need to manage expectations by considering both a range of event magnitudes and types when designing or assessing leaky dam natural flood management schemes.
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Vogel, J. C., Talma, A. S., & Heaton, T. H. E. (1981). Gaseous nitrogen as evidence for denitrification in groundwater. Journal of Hydrology, 50, 191–200.
Abstract: By investigating the nitrate, oxygen, nitrogen and argon concentrations and 15N14N ratios in artesian groundwater with radiocarbon ages ranging up to 27,000 yr. a process of very slow denitrification in a confined aquifer is demonstrated. The calculated nitrogenisotope fractionation factor associated with this reaction is comparable to that reported for bacterial cultures in vitro and in vivo.
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Wang, B., Luo, Y., Liu, J. -hui, Li, X., Zheng, Z. -hong, Chen, Q. -qian, et al. (2022). Ion migration in in-situ leaching (ISL) of uranium: Field trial and reactive transport modelling. Journal of Hydrology, 615, 128634.
Abstract: Acid in-situ leaching (ISL) can be used as a mining technique for in situ uranium recover from underground. Acids and oxidants as lixiviants were continuously injected into a sandstone-type uranium deposit in Bayan-Uul (China). It was conducted to facilitate the dissolution of uranium minerals to generate uranyl ions, which could then be extracted for the recovery of uranium resources by the pumping cycle. A reactive transport model based on PHAST was developed to investigate the dynamic reactive migration process of uranium. The simulated results well reproduce the fluid dynamic evolution in the injecting and pumping units, as well as the dynamic release of uranium. The simulated leaching area indicates that the uranium ore leaching area was much larger than the acidification area. In addition, the pollution plume of uranium and acid water was larger than that of the leaching area, which can be used as a reference for uranium mining schemes. Furthermore, the parameter sensitivity analysis indicates the volume fraction of uranium ore and the reaction rate were the main factors affecting uranium leaching efficiency. Without considering the blockage of pores by precipitation, the Fe2+ in the reinjection fluid had a significant negative influence on uranium leaching.
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Wang, B., Luo, Y., Qian, J. -zhong, Liu, J. -hui, Li, X., Zhang, Y. -hong, et al. (2023). Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium. Journal of Hydrology, 626, 130234.
Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.
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Xu, W. D., Burns, M. J., Cherqui, F., Duchesne, S., Pelletier, G., & Fletcher, T. D. (2022). Real-time controlled rainwater harvesting systems can improve the performance of stormwater networks. Journal of Hydrology, 614, 128503.
Abstract: Real-Time Control (RTC) technology is increasingly applied in Rainwater Harvesting (RWH) systems to optimise their performance related to water supply and flood mitigation. However, most studies to date have focussed on testing the benefits at an individual site scale, leaving the potential benefits for downstream stormwater networks largely untested. In this study, we developed a methodology to predict how at-source RTC RWH systems influence the behaviour of a stormwater network. Simulation was enabled by coupling the drainage model in SWMM with an RTC RWH model coded using the R software. We modelled two different RTC strategies across a range of system settings (e.g. storage size for RWH and proportion of storage to which RTC is applied) under two different climate scenarios—current and future climates. The simulations showed that RTC reduced flooding volume and peak flow of the stormwater network, leading to a potential mitigation of urban flooding risks, while also providing a decentralised supplementary water supply. Implementing RTC in more of RWH storages yielded greater benefits than simply increasing storage capacity, in both current and future climates. More importantly, the RTC systems are capable of more precisely managing the resultant flow regime in reducing the erosion and restoring the pre-development conditions in sensitive receiving waters. Our study suggests that RTC RWH storages distributed throughout a catchment can substantially improve the performance of existing drainage systems, potentially avoiding or deferring expensive network upgrades. Investments in real-time control technology would appear to be more promising than investments in detention volume alone.
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