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Stavi, I.; Eldad, S.; Xu, C.; Xu, Z.; Gusarov, Y.; Haiman, M.; Argaman, E. |
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Ancient agricultural terrace walls control floods and regulate the distribution of Asphodelus ramosus geophytes in the Israeli arid Negev |
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Year |
2024 |
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Catena |
Abbreviated Journal |
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234 |
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107588 |
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Geo-archaeology, Hydrological connectivity, Hydrological modelling, Runoff harvesting, Soil and water conservation, Watershed management |
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Ancient stone terrace walls aimed at harvesting water runoff and facilitating crop production are widespread across the drylands of the Middle East and beyond. In addition to retaining the scarce water resource, the terrace walls also conserve soil and thicken its profile along ephemeral stream channels (wadis) by decreasing fluvial connectivity and mitigating erosional processes. In this study, we created hydrological models for three wadis with ancient stone terrace walls in the arid northern Negev of Israel, where the predominant geophyte species is Asphodelus ramosus L. A two-dimensional (2D) rain-on-grid (RoG) approach with a resolution of 2 m was used to simulate the rain events with return periods of 10, 20, 50, and 99 % (10-y, 5-y, 2-y, and yearly, respectively) based on the Intensity-Duration-Frequency rain curves for the region. To evaluate the effect of stone terrace walls on fluvial hydrology and geomorphology, the ground level was artificially elevated by 20 cm at the wall locations in a digital terrain model (DTM), using the built-in HEC-RAS 2D terrain modification tool. Our results showed that the terraced wadis have a high capacity to mitigate runoff loss, but a lesser capacity to delay the peak flow. Yet, for all rainstorm return periods, peak flow mitigation was positively related to the number of terrace walls along the stream channel. Field surveys in two of the studied wadis demonstrated that the A. ramosus clones were found in proximity to the stone terrace walls, presumably due to the greater soil–water content there. The results thus suggest that the terrace walls provide improved habitat conditions for these geophytes, supporting their growth and regulating their distribution along the wadi beds. |
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0341-8162 |
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THL @ christoph.kuells @ Stavi2024107588 |
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229 |
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Soh, Q.Y.; O’Dwyer, E.; Acha, S.; Shah, N. |
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Title |
Robust optimisation of combined rainwater harvesting and flood mitigation systems |
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Journal Article |
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Year |
2023 |
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Water Research |
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245 |
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120532 |
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Rainwater harvesting, Flood mitigation, Robust stochastic optimisation, Sustainable environmental engineering, Decision tool, Urban residential estates |
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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. |
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0043-1354 |
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THL @ christoph.kuells @ Soh2023120532 |
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243 |
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Smedley, P.L.; Kinniburgh, D.G. |
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Title |
Uranium in natural waters and the environment: Distribution, speciation and impact |
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Journal Article |
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2023 |
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Applied Geochemistry |
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148 |
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105534 |
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Drinking water, Mine water, NORM, Radionuclide, Redox, U isotopes, Uranium, Uranyl |
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The concentrations of U in natural waters are usually low, being typically less than 4 μg/L in river water, around 3.3 μg/L in open seawater, and usually less than 5 μg/L in groundwater. Higher concentrations can occur in both surface water and groundwater and the range spans some six orders of magnitude, with extremes in the mg/L range. However, such extremes in surface water are rare and linked to localized mineralization or evaporation in alkaline lakes. High concentrations in groundwater, substantially above the WHO provisional guideline value for U in drinking water of 30 μg/L, are associated most strongly with (i) granitic and felsic volcanic aquifers, (ii) continental sandstone aquifers especially in alluvial plains and (iii) areas of U mineralization. High-U groundwater provinces are more common in arid and semi-arid terrains where evaporation is an additional factor involved in concentrating U and other solutes. Examples of granitic and felsic volcanic terrains with documented high U concentrations include several parts of peninsular India, eastern USA, Canada, South Korea, southern Finland, Norway, Switzerland and Burundi. Examples of continental sandstone aquifers include the alluvial plains of the Indo-Gangetic Basin of India and Pakistan, the Central Valley, High Plains, Carson Desert, Española Basin and Edwards-Trinity aquifers of the USA, Datong Basin, China, parts of Iraq and the loess of the Chaco-Pampean Plain, Argentina. Many of these plains host eroded deposits of granitic and felsic volcanic precursors which likely act as primary sources of U. Numerous examples exist of groundwater impacted by U mineralization, often accompanied by mining, including locations in USA, Australia, Brazil, Canada, Portugal, China, Egypt and Germany. These may host high to extreme concentrations of U but are typically of localized extent. The overarching mechanisms of U mobilization in water are now well-established and depend broadly on redox conditions, pH and solute chemistry, which are shaped by the geological conditions outlined above. Uranium is recognized to be mobile in its oxic, U(VI) state, at neutral to alkaline pH (7–9) and is aided by the formation of stable U–CO3(±Ca, Mg) complexes. In such oxic and alkaline conditions, U commonly covaries with other similarly controlled anions and oxyanions such as F, As, V and Mo. Uranium is also mobile at acidic pH (2–4), principally as the uranyl cation UO22+. Mobility in U mineralized areas may therefore occur in neutral to alkaline conditions or in conditions with acid drainage, depending on the local occurrence and capacity for pH buffering by carbonate minerals. In groundwater, mobilization has also been observed in mildly (Mn-) reducing conditions. Uranium is immobile in more strongly (Fe-, SO4-) reducing conditions as it is reduced to U(IV) and is either precipitated as a crystalline or ‘non-crystalline’ form of UO2 or is sorbed to mineral surfaces. A more detailed understanding of U chemistry in the natural environment is challenging because of the large number of complexes formed, the strong binding to oxides and humic substances and their interactions, including ternary oxide-humic-U interactions. Improved quantification of these interactions will require updating of the commonly-used speciation software and databases to include the most recent developments in surface complexation models. Also, given their important role in maintaining low U concentrations in many natural waters, the nature and solubility of the amorphous or non-crystalline forms of UO2 that result from microbial reduction of U(VI) need improved quantification. Even where high-U groundwater exists, percentage exceedances of the WHO guideline value are variable and often small. More rigorous testing programmes to establish usable sources are therefore warranted in such vulnerable aquifers. As drinking-water regulation for U is a relatively recent introduction in many countries (e.g. the European Union), testing is not yet routine or established and data are still relatively limited. Acquisition of more data will establish whether analogous aquifers elsewhere in the world have similar patterns of aqueous U distribution. In the high-U groundwater regions that have been recognized so far, the general absence of evidence for clinical health symptoms is a positive finding and tempers the scale of public health concern, though it also highlights a need for continued investigation. |
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0883-2927 |
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THL @ christoph.kuells @ smedley_uranium_2023 |
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118 |
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Smedley, P.L.; Bearcock, J.M.; Ward, R.S.; Crewdson, E.; Bowes, M.J.; Darling, W.G.; Smith, A.C. |
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Monitoring of methane in groundwater from the Vale of Pickering, UK: Temporal variability and source discrimination |
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2023 |
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Chemical Geology |
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636 |
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121640 |
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Aquifer, Biogenic, Ethane, Hydrocarbons, Methane, Shale gas |
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Groundwater abstracted from aquifers in the Vale of Pickering, North Yorkshire, UK and monitored over the period 2015–2022, shows evidence of variable but commonly high concentrations of dissolved CH4. Sampled groundwater from the Jurassic organic-rich Kimmeridge Clay Formation (boreholes up to 180 m depth) has concentrations up to 57 mg/L, and concentrations up to 59 mg/L are found in groundwater from underlying confined Corallian Group limestone (borehole depths 50–227 m). The high concentrations are mainly from boreholes in the central parts of the vale. Small concentrations of ethane (C2H6, up to 800 μg/L) have been found in the Kimmeridge Clay and confined Corallian groundwaters, and of propane (C3H8, up to 160 μg/L) in deeper boreholes (110–180 m) from these formations. The concentrations are typically higher in groundwater from the deeper boreholes and vary with hydrostatic pressure, reflecting the pressure control on CH4 solubility. The occurrences contrast with groundwater from shallow Quaternary superficial deposits which have low CH4 concentrations (up to 0.39 mg/L), and with the unconfined and semi-confined sections of the Corallian aquifer (up to 0.7 mg/L) around the margins of the vale. Groundwater from the Quaternary, Kimmeridge Clay formations and to a small extent the confined Corallian aquifer, supports local private-water supplies, that from the peripheral unconfined sections of Corallian also supports public supply for towns and villages across the region. Dissolved methane/ethane (C1/C2) ratios and stable-isotopic compositions (δ13C-CH4, δ2H-CH4 and δ13C-CO2) suggest that the high-CH4 groundwater from both the Kimmeridge Clay and confined Corallian formations derives overwhelmingly from biogenic reactions, the methanogenesis pathway by CO2 reduction. A small minority of groundwater samples shows a more enriched δ13C-CH4 composition (−50 to −44 ‰) which has been interpreted as due to anaerobic or aerobic methylotrophic oxidation in situ or post-sampling oxidation, rather than derivation by a thermogenic route. Few of the existing groundwater sites are proximal to abandoned or disused conventional hydrocarbon wells that exist in the region, and little evidence has been found for an influence on groundwater dissolved gases from these sites. The Vale of Pickering has also been under recent consideration for development of an unconventional hydrocarbon (shale-gas) resource. In this context, the monitoring of dissolved gases has been an important step in establishing the high-CH4 baseline of groundwaters from Jurassic deposits in the region and in apportioning their sources and mechanisms of genesis. |
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0009-2541 |
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THL @ christoph.kuells @ smedley_monitoring_2023 |
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172 |
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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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2024 |
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Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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