Abstract: In Southern Africa elevated nitrate concentrations are observed in mostly uninhabited semi-arid areas. In the Kalahari of Botswana groundwater locally exhibits concentrations up to 600mg/l. It is assumed, that nitrate found in the groundwater originates mainly from nitrogen input and transformations in the soils. Our investigations in the Kalahari between Serowe and Orapa show that cattle raising is an important source for enhanced nitrate concentrations in the soils (Arenosols). But also in termite mounds very high nitrate stocks were found, and under natural vegetation (acacia trees and shrubs) nitrate concentrations were mostly unexpectedly high. This nitrate enrichment in the soils poses a serious threat to the groundwater quality. However, calculated soil water age distributions in the unsaturated zone clearly show that today’s nitrate pollution of the groundwater below the investigation area could originate from natural sources, but cannot be caused by the current land use for cattle raising.

Abstract: Qanat is a type of drain that extract water from aquifers by gravity. Significant amount of fresh water used in Middle East and other parts of the world are supplied by qanats. Despite their importance, discharge variation of these type of wells received almost no attention. The aim of this research is to obtain a Laplace domain solution of discharge variation of a qanat installed in an anisotropic unconfined aquifer subjected to arbitrary areal recharge and nearby pumping well(s) discharge. A new semi-analytical solution of drawdown is obtained first to implement the effects of arbitrary areal recharge and nearby pumping well(s) using the principle of superposition. Then, the discharge variation solution of the qanat is obtained from the drawdown solution. To establish a constant-head boundary condition at the qanat periphery, the qanat is discretized into several segments. The results of this study are presented in dimensionless discharge-dimensionless time curves. The effects of hydraulic as well as geometric parameters on the discharge variation of the qanat due to arbitrary areal recharge, falling of water table from its initial position and discharge of nearby wells are explored. We also investigate the influences of distance and screen depth and location of the nearby well on the discharge variation of the qanat. The results of this study can be utilized for multiple purposes: 1) to predict discharge of qanat in response to rainfall and nearby pumping well(s); 2) to estimate the aquifer parameters using hydrograph of the qanat; 3) to determine optimal location and pumping pattern of the nearby wells to minimize their influences on the discharge of the qanat; 4) to calculate water budget of aquifers drained by a qanat. The equation presented in this work can also be used to estimate discharge of a horizontal drain installed in cropland subjected to arbitrary irrigation pattern.

Abstract: In this study, the application of the Well Reversal Technique (WRT) and the impact of reversal time on the efficiency of uranium mining via In-Situ Leaching (ISL) were investigated. A prevalent issue in ISL mineral extraction is the formation of stagnant zones caused by limited access of the lixiviant, which leads to increased operating expenditures. The WRT, which involves altering the function of some wells from injection to production or vice versa, is a potential solution to this problem. The efficiency of WRT is heavily dependent on the well pattern and reversal time. Two commonly used well patterns in ISL are the 9-spot (row arrangement) and 7-spot (hexagonal arrangement). The objective of this study was to determine the optimal reversal time for a 9-spot well pattern through mathematical modeling of hydrodynamic and physico-chemical processes and subsequent economic assessment. A mathematical model of uranium extraction processes was developed using the principles of mass conservation, Darcy’s, and mass action laws. The results obtained for a 9-spot well pattern without reversal, with two reversal options, and a 7-spot scheme were analyzed comparatively. The 7-spot scheme without reversal was found to be the most effective of the options examined. The application of WRT on a 9-spot well pattern allows to enhance production efficiency to a level comparable to that of a 7-spot well pattern. Additionally, the effect of reversal time on recovery was studied based on two well reversal options. The results from calculation revealed that the optimal scenario was when the well reversal is conducted immediately after the time point at which the average concentration of the pregnant solution in the production wells reaches its peak value. The overall efficiency of WRT application was determined through economic calculations of capital (CAPEX) and operating (OPEX) expenditures. Based on economic calculations, it was determined that the utilization of WRT results in a 3–18% increase in mineral production efficiency for a 9-point scheme, depending on the chosen reversal method.

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.