Karst aquifers have been an important research topic for hydrologists for years. Due to their high storage capacity, karst aquifers are an important source of water for the environment. On the other hand, it is safety-critical because of its role in floods. Mugla Karst Aquifer (SW, Turkey) is the only major water-bearing formation in the close environs of Mugla city. Flooding in the wet season occurs every year in the recharge plains. The aquifer discharges by the seaside springs in the Akyaka district which is the main touristic point of interest in the area. Non-porous irregular internal structures make the karsts more difficult to study. Therefore, many different methodologies have been developed over the years. In this study, unit hydrograph analysis, correlation and spectral analyses were applied on the rainfall and spring water-level time series data. Although advanced karst formations can be seen on the surface like the sinkholes, it has been revealed that the interior structure is not highly karstified. 100–130 days of regulation time was found. This shows that the Mugla Karst has quite inertial behavior. Yet, the storage of the aquifer system is quite high, and the late infiltration effect caused by alluvium plains was detected. This characterization of the hydrodynamic properties of the Mugla karst system represents an important step to consider the rational exploitation of its water resources in the near future.

The Strategic Water Storage & Recovery (SWSR) Project in Liwa, Abu Dhabi is a leading and unique hydrogeology project in the world because of both its financial and scientific dimensions. The objective of the project is to store desalinized water in the local Liwa aquifer, to be able to supply water to Abu Dhabi in case of emergency. A total of 315 recovery wells have been drilled in pursuance of the scope of the SWSR project. Out of the total 315 wells, 25 wells met construction problems and were removed from the study. The remaining 290 wells have been analyzed using step drawdown tests (SDTs) and the model of Rorabaugh. This provided a large and unique database regarding the parameters of this model: linear aquifer-loss coefficient (B), non-linear well-loss coefficient (C) and the exponent p. Analysis of this exceptional data set revealed noteworthy and novel findings: (1) the range of the exponent p values is found to be very extensive, varying from 0.35 to 6.01. For comparison, the highest values of p given in the literature very seldom exceed 4; (2) p behaves like a lognormal variable; (3) parameters C and p are closely correlated. A semi-logarithmic diagram displays a linear relation of p vs. log C, with a determination coefficient R² = 0.83; (4) A graphical and tabulated procedure, termed General Well Efficiency Criteria, is proposed to assess well efficiency. Given the very wide range of p values implied in this procedure, it becomes possible thus to assess the efficiency of any well analyzed with an SDT. This study finally raises questions about Jacob’s model validity, which assumes that p is constant and equal to 2.

In this study, Çubuk 2, Kesikköprü and Asartepe Dam Lake, were investigated. A total of 60 sediment samples were collected and the concentrations of 10 elements (Cu, Pb, Zn, Ni, Mn, Fe, Co, Cr, Al, As) were determined. The obtained findings were compared to the limit values for the sediment quality guidelines (SQGs) and were investigated using sediment assessment methods. The results indicate that the values for SQGs revealed that the concentrations of Ni and As should be emphasised. Toxic unit (TU) values showed that As and Ni together constitute 69.95–80.09% of the total toxic effect alone for all lakes. In addition, the Contamination factor (Cf) and Enrichment factor (EF) detected the concentration of As for Kesikköprü Dam Lake as being at a medium and higher accumulation levels, although the Geoaccumulation index (Igeo) detected that As leads to contamination below the medium level. When the total element accumulation was evaluated, no risk was detected for the lakes. Cluster analysis (CA) revealed quite different elemental profiles for 3 lakes. The strongest correlation between the elements was observed for Cu-Ni (r = 0.942) in Çubuk 2, whereas Çubuk 2 was the lake with the highest seasonal elemental variation among the lakes investigated.

The direct estimation of soil hydraulic conductivity (Ks) requires expensive laboratory measurement to present adequately soil properties in an area of interest. Moreover, the estimation process is labor and time-intensive due to the difficulties of collecting the soil samples from the field. Hence, innovative methods, such as machine learning techniques, can be an alternative to estimate Ks. This might facilitate agricultural water and nutrient management which has an impact on food and water security. In this spirit, the study presents neural-network-based models (artificial neural network (ANN), deep learning (DL)), tree-based (decision tree (DT), and random forest (RF)) to estimate Ks using eight combinations of soil data under calcareous alluvial soils in a semi-arid region. The combinations consisted of soil data such as clay, silt, sand, porosity, effective porosity, field capacity, permanent wilting point, bulk density, and organic carbon contents. The results compared with the well-established model showed that all the models had satisfactory results for the estimation of Ks, where ANN7 with soil inputs of sand, silt, clay, permanent wilting point, field capacity, and bulk density values showed the best performance with mean absolute error (MAE) of 2.401 mm h⁻¹, root means square error (RMSE) of 3.096 mm h⁻¹, coefficient of determination (R²) of 0.940, and correlation coefficient (CC) of 0.970. Therefore, the ANN could be suggested among the neural-network-based models. Otherwise, RF could also be used for the estimation of Ks among the tree-based models.