Abstract: The calculation of a groundwater recharge temperature based on the dissolved concentrations of Ne, Ar, Kr and Xe requires a correction for noble gas supersaturation due to excess air entrainment. This entrainment is commonly attributed to the recharge process or to air contamination at the wellhead during sample collection. With the exception of some local studies, most work has concentrated on interpretation of the recharge temperature or quantification of the radiogenic content for palaeoclimatic and dating purposes. The magnitude and source of the excess air is not directly relevant to these studies and so is often ignored. In this work, excess air Ne and other data have been calculated from new and published noble gas data sets for several groundwater systems. For younger groundwaters which have been recharged under one broad climatic regime, the amount of air entrainment increases according to lithology in the order granites, sandstones and limestones respectively. A negative correlation between precipitation and excess air entrainment is identified in at least one aquifer, and some of the mechanisms which may influence the entrainment process are discussed.

Abstract: The city of Alcudia de Crespins, in the centre of the Valencia province (east of Spain), has an exceptional water distribution system that in the past served fresh water to many houses in the town. This system is formed by more than one km of tunnels and underground cisterns, and dates probably in the late medieval times, while it has been in use and suffering modifications until 1955. This paper presents the structure and characteristics of such exceptional system, and explains the functioning parameters of the infrastructure.

Abstract: This study refines the hydrogeological conceptual model of an area with three interconnected aquifers, namely the Crystalline Aquifer System (CAS – igneous and metamorphic rocks), which is in contact with the Tubarão Aquifer System (TAS – sedimentary rocks) and the Diabase Aquifer System (DAS – diabase rocks). The detailed investigation involved geophysical logging and hydraulic and hydrodynamic characterization with straddle packers in a local tubular well, in which groundwater presents high uranium concentrations. Hydrogeochemical and isotope (δ2H, δ18O, 3H, δ13C, 14C) analysis in this well and in other three neighboring wells, with lower U concentrations, showed that ancient and modern waters (3H from <0.8 to 1.12 TU, 14C from 69.43 to 78.72 pMC) mix within the aquifer. During groundwater pumping, vertical fractures in the diabase aquifer possibly induce water mixing and recharge of the deeper levels of the aquifers from shallow layers. The high [U] are related to ancient waters from a confined aquifer hosted in CAS that reaches the wells through hydraulically active fractures located deeper than 159 m depth. Groundwater apparent ages do not increase systematically with depth, revealing a complex circulation model for CAS. The results obtained from the other wells, which are all located on drainage lineaments, reveal that one extracts modern water from DAS and TAS, another one extracts modern and ancient water from DAS and CAS, and the third extracts only ancient water from CAS, confirming the complexity of the local hydrogeology. Regarding regional groundwater management, the study revealed the need to characterize the sources of groundwater in each well, in order to protect modern waters from anthropogenic contamination and to protect ancient groundwater from overexploitation, as CAS hosts groundwaters recharged thousands of years ago or more.

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.

Abstract: LED flourimeter has been used for microanalysis of uranium concentration in groundwater samples collected from six districts of South West (SW), West (W) and North East (NE) Punjab, India. Average value of uranium content in water samples of SW Punjab is observed to be higher than WHO, USEPA recommended safe limit of 30µgl−1 as well as AERB proposed limit of 60µgl−1. Whereas, for W and NE region of Punjab, average level of uranium concentration was within AERB recommended limit of 60µgl−1. Average value observed in SW Punjab is around 3–4 times the value observed in W Punjab, whereas its value is more than 17 times the average value observed in NE region of Punjab. Statistical analysis of carcinogenic as well as non carcinogenic risks due to uranium have been evaluated for each studied district.