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Joseph, J., Külls, C., Arend, M., Schaub, M., Hagedorn, F., Gessler, A., et al. (2019). Application of a laser-based spectrometer for continuous in situ measurements of stable isotopes of soil CO2 in calcareous and acidic soils. Soil, 5(1), 49–62.
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Lartigue, J. E., Charrasse, B., Reile, B., & Descostes, M. (2020). Aqueous inorganic uranium speciation in European stream waters from the FOREGS dataset using geochemical modelling and determination of a U bioavailability baseline. Chemosphere, 251, 126302.
Abstract: The concentration of the bioavailable uranium fraction (Ubio) at the European scale was deduced by geochemical modelling considering several definitions found in the literature and the FOREGS European stream waters geochemical atlas dataset to produce a Ubio baseline. A sensitivity analysis was performed using three thermodynamic databases. We also investigated the link between total dissolved uranium (Uaq) concentrations, speciation and global stream water chemistry on the one hand, and the lithology and ages of the surrounding rocks on the other. The more U-enriched the stream sediments or rock type contexts are, which tends to be the case with rocks containing silicates (4.1 mg/kg), the less U-concentrated the stream waters are (0.15 μg/L). Sedimentary rocks lead to slightly higher Uaq concentrations (0.34 μg/L) even if the concentration in sediment (Used) is relatively low (1.6 mg/kg). This trend is reversed for Ubio, with higher concentrations in a crystalline context. The mean estimated Ubio value ranges from 1.5.10−3 to 65.3 ng/L and can fluctuate by 3 orders of magnitude depending on the considered definition as opposed to by 2 orders of magnitude accountable to differences between thermodynamic databases. The classification of the water in relation to the two surrounding rock lithologies makes it possible to reduce the mean variability for the Ubio concentrations. Irrespective of the definition of Ubio considered, in 59% of cases the Ubio fraction represents less than 1% of Uaq. Several threshold values relating to Ubio were proposed, assuming knowledge only of the aqueous concentrations of the major elements and Uaq.
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Strandmann, P. A. E. P. von, Reynolds, B. C., Porcelli, D., James, R. H., Calsteren, P. van, Baskaran, M., et al. (2006). Assessing continental weathering rates and actinide transport in the Great Artesian Basin. Geochimica et Cosmochimica Acta, 70(18, Supplement), 497.
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Jing, M., Kumar, R., Attinger, S., Li, Q., Lu, C., & Heße, F. (2021). Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking. Advances in Water Resources, 149, 103849.
Abstract: Travel time distributions (TTDs) provide an effective way to describe the transport and mixing processes of water parcels in a subsurface hydrological system. A major challenge in characterizing catchment TTD is quantifying the travel times in deep groundwater and its contribution to the streamflow TTD. Here, we develop and test a novel modeling framework for an integrated assessment of catchment scale TTDs through explicit representation of 3D-groundwater dynamics. The proposed framework is based on the linkage between a flux tracking scheme with the surface hydrologic model (mHM) for the soil-water compartment and a particle tracking scheme with the 3D-groundwater model OpenGeoSys (OGS) for the groundwater compartment. This linkage provides us with the ability to simulate the spatial and temporal dynamics of TTDs in these different hydrological compartments from grid scale to regional scale. We apply this framework in the Nägelstedt catchment in central Germany. Simulation results reveal that both shape and scale of grid-scale groundwater TTDs are spatially heterogeneous, which are strongly dependent on the topography and aquifer structure. The component-wise analysis of catchment TTD shows a time-dependent sensitivity of transport processes in soil zone and groundwater to driving meteorological forcing. Catchment TTD exhibits a power-law shape and fractal behavior. The predictive uncertainty in catchment mean travel time is dominated by the uncertainty in the deep groundwater rather than that in the soil zone. Catchment mean travel time is severely biased by a marginal error in groundwater characterization. Accordingly, we recommend to use multiple summary statistics to minimize the predictive uncertainty introduced by the tailing behavior of catchment TTD.
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Mabrouk, M., Han, H., Fan, C., Abdrabo, K. I., Shen, G., Saber, M., et al. (2023). Assessing the effectiveness of nature-based solutions-strengthened urban planning mechanisms in forming flood-resilient cities. Journal of Environmental Management, 344, 118260.
Abstract: Cities have experienced rapid urbanization-induced harsh climatic events, especially flooding, inevitably resulting in negative and irreversible consequences for urban resilience and endangering residents’ lives. Numerous studies have analyzed the effects of anthropogenic practices (land use changes and urbanization) on flood forecasting. However, non-structural mitigation’s effectiveness, like Nature-Based Solutions (NBS), has yet to receive adequate attention, particularly in the Middle East and North Africa (MENA) region, which have become increasingly significant and indispensable for operationalizing cities efficiently. Therefore, our study investigated the predictive influence of incorporating one of the most common NBS strategies called low-impact development tools (LID) (such as rain gardens, bio-retention cells, green roofs, infiltration trenches, permeable pavement, and vegetative swale) during the urban planning of Alexandria, Egypt, which experiences the harshest rainfall annually and includes various urban patterns. City characteristics-dependent 14 LID scenarios were simulated with recurrence intervals ranging from 2 to 100 years using the LID Treatment Train Tool (LID TTT), depending on calibrated data from 2015 to 2020, by the Nash-Sutcliffe efficiency index and deterministic coefficient, and root-mean-square error with values of 0.97, 0.91, and 0.31, respectively. Our findings confirmed the significant effectiveness of combined LID tools on total flood runoff volume reduction by 73.7%, revealing that different urban patterns can be used in flood-prone cities, provided LID tools are considered in city planning besides grey infrastructure to achieve optimal mitigation. These results, which combined multiple disciplines and were not explicitly mentioned in similar studies in developing countries, may assist municipalities’ policymakers in planning flood-resistant, sustainable cities.
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Külls, C., Salameh, E., & Udluft, P. (2000). Assessing water supplies for irrigation-availability of natural resources and sustainability indices. In Tropentag Hoffenheim.
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Uddin, M. G., Diganta, M. T. M., Sajib, A. M., Hasan, M. A., Moniruzzaman, M., Rahman, A., et al. (2023). Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches. Heliyon, 9(9), 19668.
Abstract: Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3−), chloride (Cl−), phosphate (PO43−), sulfate (SO42−), nitrite (NO2−), nitrate (NO3−), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley’s ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the “poor” and “marginal” categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP.
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Kumar, V., Setia, R., Pandita, S., Singh, S., & Mitran, T. (2022). Assessment of U and As in groundwater of India: A meta-analysis. Chemosphere, 303, 135199.
Abstract: More than 2.5 billion people depend upon groundwater worldwide for drinking, and giving quality water has become one of the great apprehensions of human culture. The contamination of Uranium (U) and Arsenic (As) in the groundwater of India is gaining global attention. The current review provides state-of-the-art groundwater contamination with U and As in different zones of India based on geology and soil texture. The average concentration of U in different zones of India was in the order: West Zone (41.07 μg/L) \textgreater North Zone (37.7 μg/L) \textgreater South Zone (13.5 μg/L)\textgreater Central Zone (7.4 μg/L) \textgreater East Zone (5.7 μg/L) \textgreaterSoutheast Zone (2.4 μg/L). The average concentration of As in groundwater of India is in the order: South Zone (369.7 μg/L)\textgreaterCentral Zone (260.4 μg/L)\textgreaterNorth Zone (67.7 μg/L)\textgreaterEast Zone (60.3 μg/L)\textgreaterNorth-east zone (9.78 μg/L)\textgreaterWest zone (4.14 μg/L). The highest concentration of U and As were found in quaternary sediments, but U in clay skeletal and As in loamy skeletal. Results of health risk assessment showed that the average health quotient of U in groundwater for children and adults was less than unity. In contrast, it was greater than unity for As posing a harmful impact on human health. This review provides the baseline data regarding the U and As contamination status in groundwater of India, and appropriate, effective control measures need to be taken to control this problem.
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Singh, A., Patel, S., Bhadani, V., Kumar, V., & Gaurav, K. (2024). AutoML-GWL: Automated machine learning model for the prediction of groundwater level. Engineering Applications of Artificial Intelligence, 127, 107405.
Abstract: 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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Pavelic, P., Srisuk, K., Saraphirom, P., Nadee, S., Pholkern, K., Chusanathas, S., et al. (2012). Balancing-out floods and droughts: Opportunities to utilize floodwater harvesting and groundwater storage for agricultural development in Thailand. Journal of Hydrology, 470-471, 55–64.
Abstract: Summary Thailand’s naturally high seasonal endowment of water resources brings with it the regularly experienced problems associated with floods during the wet season and droughts during the dry season. Downstream-focused engineering solutions that address flooding are vital, but do not necessarily capture the potential for basin-scale improvements to water security, food production and livelihood enhancement. Managed aquifer recharge, typically applied to annual harvesting of wet season flows in dry climates, can also be applied to capture, store and recover episodic extreme flood events in humid environments. In the Chao Phraya River Basin it is estimated that surplus flows recorded downstream above a critical threshold could be harvested and recharged within the shallow alluvial aquifers in a distributed manner upstream of flood prone areas without significantly impacting existing large-medium storages or the Gulf and deltaic ecosystems. Capturing peak flows approximately 1year in four by dedicating around 200km2 of land to groundwater recharge would reduce the magnitude of flooding and socio-economic impacts and generate around USD 250M/year in export earnings for smallholder rainfed farmers through dry season cash cropping without unduly compromising the demands of existing water users. It is proposed that farmers in upstream riparian zones be co-opted as flood harvesters and thus contribute to improved floodwater management through simple water management technologies that enable agricultural lands to be put to higher productive use. Local-scale site suitability and technical performance assessments along with revised governance structures would be required. It is expected that such an approach would also be applicable to other coastal-discharging basins in Thailand and potentially throughout the Asia region.
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