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Stavi, I., Eldad, S., Xu, C., Xu, Z., Gusarov, Y., Haiman, M., et al. (2024). Ancient agricultural terrace walls control floods and regulate the distribution of Asphodelus ramosus geophytes in the Israeli arid Negev. Catena, 234, 107588.
Abstract: 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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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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Ola, I., Drebenstedt, C., Burgess, R. M., Mensah, M., Hoth, N., Okoroafor, P., et al. (2024). Assessing petroleum contamination in parts of the Niger Delta based on a sub-catchment delineated field assessment. Environmental Monitoring and Assessment, 196(6), 585.
Abstract: The Niger Delta in Nigeria is a complex and heavily contaminated area with over 150,000 interconnected contaminated sites. This intricate issue is compounded by the region’s strong hydrological processes and high-energy environment, necessitating a science-based approach for effective contamination assessment and management. This study introduces the concept of sub-catchment contamination assessment and management, providing an overarching perspective rather than addressing each site individually. A description of the sub-catchment delineation process using the digital elevation model data from an impacted area within the Delta is provided. Additionally, the contamination status from the delineated sub-catchment is reported. Sediment, surface water and groundwater samples from the sub-catchment were analyzed for total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs), respectively. Surface sediment TPH concentrations ranged from 129 to 20,600 mg/kg, with subsurface (2-m depth) concentrations from 15.5 to 729 mg/kg. PAHs in surface and subsurface sediment reached 9.55 mg/kg and 0.46 mg/kg, respectively. Surface water exhibited TPH concentrations from 10 to 620 mg/L, while PAHs ranged from below detection limits to 1 mg/L. Groundwater TPH concentrations spanned 3 to 473 mg/L, with total PAHs varying from below detection limits to 0.28 mg/L. These elevated TPH and PAH levels indicate extensive petroleum contamination in the investigated sediment and water environment. Along with severe impacts on large areas of mangroves and wetlands, comparison of TPH and PAH concentrations with sediment and water quality criteria found 54 to 100% of stations demonstrated exceedances, suggesting adverse biological effects on aquatic and sediment biota are likely occurring.
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Patel, D., Pamidimukkala, P., & Chakraborty, D. (2024). Groundwater quality evaluation of Narmada district, Gujarat using principal component analysis. Groundwater for Sustainable Development, 24, 101050.
Abstract: In the present study, the ground water quality parameters were monitored during pre- and post-monsoon seasons across Narmada district, Gujarat, India. Monitoring was done in 89 drinking water samples collected by grid sampling method from the study area. Uranium and fluoride were analyzed along with associated parameters such as pH, dissolved oxygen, Cl−, NO3−, F−, SO42−, total alkalinity, total dissolved solids and hardness. In 4% samples the fluoride content was found to be above WHO permissible limits of 1.5 mg/L (2.36 mg/L in Undaimandava, 1.55 mg/L in Shira, 3.04 mg/L in Fatehpur and 1.83 mg/L in Dholivav) during pre-monsoon season (PRM) and 4.74 mg/L, 2.41 mg/L, 2.34 mg/L and 3.99 mg/L respectively in Bantawadi, Shira, Undai Mandava and Fatepur villages during post-monsoon (POM). The uranium level was within WHO limits in both POM and PRM seasons. The quality of the water was evaluated by Principal Component and Pearson Correlation statistical analysis techniques. The PRM and POM correlation study indicated a strong correlation of TDS with EC, Chloride, total alkalinity and bicarbonate and U while moderately strong correlation of TDS with fluoride were observed indicating that chloride, total alkalinity, bicarbonate, U and fluoride contributed to TDS and EC. Principal component analysis was applied for 14 variables, from which 3 factors were extracted during PRM and POM seasons. The extracted components, contributed 84.391% and 83.315%, to variation during PRM and POM seasons respectively. The study indicated that the analyzed water samples in Narmada district were safe for drinking purpose. However, Tilakwada tehsil groundwater was observed to be unsustainable for drinking, without further water treatment, but was appropriate for agricultural purposes. The study will help the residents of the district to understand the present water quality status and will also help in future management to protect the ground water of Narmada district.
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Hubbard, B. E., Gallegos, T. J., Stengel, V., Hoefen, T. M., Kokaly, R. F., & Elliott, B. (2024). Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain. Journal of Geochemical Exploration, 257, 107370.
Abstract: VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey’s (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA. Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data. The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities.
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