Stochastic simulation and prediction of turbidity dynamics in karst systems. Case study: Mokra karst spring (SE Serbia)

Characterization of a karst system includes the analysis of two components – quantitative and qualitative one. Forecasting of future values of groundwater parameters can be very useful in defining the amounts of water needed for a reliable water supply. Stochastic simulation and forecasting were carried out for time series of precipitation and Mokra karst spring turbidity recorded in 2015. Simulation models within groundwater management would have a function in the early warning system which will enable timely response of groundwater source management.

Vulnerability Assessment as a Basis for Sanitary Zone Delineation of Karst Groundwater Sources—Blederija Spring Case Study

The application of groundwater vulnerability methods has great importance for the sanitary protection zones delineation of karstic sources. Source vulnerability assessment of karst groundwater has mainly relied on the European approach (European Cooperation in Science and Technology—COST action 620), which includes analysis of the K factor, which refers to water flow through the saturated zone of the karst system. In the paper, two approaches to groundwater vulnerability assessment have been applied, COP + K and TDM (Time-Dependent Model) methods, to produce the most suitable source vulnerability map that can be transformed into sanitary protection zones maps. Both methods were tested on the case example of Blederija karst spring in Eastern Serbia. This spring represents a classical karst spring with allogenic and autogenic recharge. Dual aquifer recharge points out the necessity for the inclusion of the vulnerability assessment method created especially for the assessment of karst groundwater. Obtained vulnerability maps show similar results, particularly in the spring and the ponor areas, and these zones are most important for future protection. The COP + K method brings out three vulnerability classes that can be directly transformed into three sanitary protection zones. Contrary to the previous one, the TDM method uses water travel time as a vulnerability degree. The results show that the final map can be easily used to define sanitary zones considering different national legislation.

Karst spring discharge modeling based on deep learning using spatially distributed input data

Despite many existing approaches, modeling karst water resources remains challenging and often requires solid system knowledge. Artificial Neural Network approaches offer a convenient solution by establishing a simple input-output relationship on their own. However, in this context, temporal and especially spatial data availability is often an important constraint, as usually no or few climate stations within a karst spring catchment are available. Hence spatial coverage is often unsatisfying and can introduce severe uncertainties. To avoid these problems, we use 2D-Convolutional Neural Networks (CNN) to directly process gridded meteorological data followed by a 1D-CNN to perform karst spring discharge simulation. We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorologic-hydrological characteristics and hydrodynamic system properties. We compare our 2D-models both to existing modeling studies in these regions and to 1D-models, which use climate station data, as it is common practice. Our results show that our models are excellently suited to model karst spring discharge and rival the simulation results of existing approaches in the respective areas. The 2D-models learn relevant parts of the input data and by performing a spatial input sensitivity analysis we can further show their potential for karst catchment localization and delineation.

Origin and Factors Effecting Dissolved Organic Matter in Karst Spring, Southwest China

The dissolved biomarker composition of water samples was detected and quantified through gas chromatography-mass spectrometry to investigate the source of organic matter and its influencing factors in a subtropical monsoon karst spring. All water samples were collected in Chongqing, southwest China, in May, June, and July 2015. An analysis of the carbon number distribution characteristics and molecular characteristic parameters indicated that the dissolved organic matter in water samples mainly came from microorganism. The average biomarker contents in the samples were controlled by multi-factor coupling and showed an increasing trend in May, June, and July; this trend was consistent with temperature and inverse with rainfall. Human activities, such as urbanization and land use type, affected the fatty acid composition of the spring water. In forested land, the main fatty acids in water were saturated normal fatty acids with carbon numbers of 14,16, and 18, respectively. The C16:1ω9 (c) fatty acid, denoting the origin of algae, was found in high abundance in the water samples of the Mudu River Basin, which contains agricultural land far from urban areas. C18:1 ω9 (t), a biomarker of Gram-negative bacteria, was detected in large quantities in the cultivated land of the Laolongdong watershed.