Abstract: The origin of groundwater recharge and subsequent flow paths are often difficult to establish in fractured, multi-lithological, and highly compartmentalized aquifers such as the Troodos Fractured Aquifer (TFA). As the conjunctive use of stable isotopes and hydrogeochemical data provides additional information, we established a monitoring network for stable isotopes in precipitation in Cyprus. The local meteoric water line, altitude effect and seasonal variation of stable isotopes in precipitation are derived from monitoring data. Stable isotopes and hydrogeochemical data are combined to model water-rock interactions and groundwater evolution along a complete ophiolite sequence. As a result a generic hydrogeologic description for the observed water types is developed. Isotope hydrology was applied in conjunction with hydrogeochemical modelling in Kargiotis Watershed, a major north-south transect of the TFA. PHREEQC was used for hydrogeochemical modelling to establish generic descriptions for observed water types. Mean precipitation-weighted values from 16 monitoring stations were used to calculate the Local Meteoric Water Line (LMWL), which was found to be equal to δ2H = (6.58 ± 0.13)*δ18O + (12.64 ± 0.91). A general decrease of 1.22‰ for δ2H and 0.20‰ for δ18O in precipitation was calculated per 100 m altitude. A generic groundwater evolution path was established: 1. Na/MgClHCO3, 2. MgHCO3, 3. Ca/MgHCO3, 4. Ca/MgNaHCO3, 4a. MgNa/CaHCO3/Cl, 5. NaMg/CaHCO3/Cl, 6. NaHCO3, 7. Na/MgHCO3SO4, 8. NaSO4Cl/HCO3. Hydrogeologic descriptions, consisting of groundwater origin, flow path and possible active water-rock processes, have been realised for the observed water types. The first two water types occur in serpentine and ultramafic-gabbro springs. Type 3 waters represent early stages of recharge and/or short flow paths, in gabbro whereas types 4 and 5 are typical for further percolating waters in gabbro and diabase. Water types 6 and 7 occur both in diabase and in the basal group and represent the regional flow. Water type 8 is the end member of regional, upwelling groundwater in the basal group. The presented descriptions and methods have practical applications in groundwater exploration, characterization, and protection. The methodology can be applied in other complex aquifer systems.

Abstract: Im vorliegenden Beitrag werden beispielhaft unterschiedliche Ansätze des Risikomanagements und das Verständnis von Nachhaltigkeit in der Wasserwirtschaft dargelegt. Die Darstellung richtet sich insbesondere an Leser aus anderen Fachdisziplinen, wie das Rechts- und Finanzwesen, den Fahrzeug- und Maschinenbau oder auch die sozialen Berufe. Die Zusammenhänge werden überblicksartig mit einzelnen konkreten Beispielen dargestellt mit dem Fokus auf die grundsätzlichen Denk- und Vorgehensweisen.

Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.

Abstract: Analyses of satellite data mainly over the world’s ocean surfaces have shown that during 1986–2006 global average values of atmospheric water vapor, precipitation and evaporation have increased at a relative rate of 0.0013a−1; this is roughly in accordance with the Clausius-Clapeyron equation for the average temperature trend during this period, and amounts to 0.065K−1 at the average temperature of T=14∘C. Application of this concept over the world’s land surfaces yields an average global evaporation trend during the past half century of around 0.4 to 0.5 mma−2; this confirms the values obtained in previous studies with totally different methods.