I am happy to announce our new DFG project „Characterisation and modelling of multi-compartment karst systems by integrated interpretation of spring signals – iKarst“ in cooperation between the TU Dresden, Prof. Liedl and Dr. Reimann and University of Goettingen (Prof. Sauter, Dr. Kordilla). The project is scheduled to run for three years and includes one Ph.D. student and one Post-Doc.
Karst systems are heterogeneous hydrological systems with pronounced anisotropy. The characterization of such systems is extremely complex, since many processes have not yet been sufficiently understood. The significance of classical field and terrain investigations in karst systems can be decisively increased by suitable model concepts. Due to the complexity of karst systems, numerical simulation methods are suitable for describing the flow and transport behaviour of water and dissolved substances. Numerous numerical tools can describe the individual karst compartments (surface zone, vadose zone, phreatic zone), but there is a lack of integrated hydrological models with physical-based approaches that can describe the relevant physical processes and their interaction.
Our preliminary work shows that the source signals in karst regions are characterized by a multitude of interacting processes (generation of input signals, exchange between karst conduits and solid rock matrix or conduit storage processes). Furthermore, ambiguous model solutions (e.g. resulting from the model structure, the parameters used, etc.) limit the application of highly parameterized models for complex karst systems. This enables a significantly improved understanding of processes with regard to groundwater flow and transport of dissolved substances.
The forward modeling of idealized scenarios is used to investigate the systematic behavior of different karst types. For example, input signals into the conduit system are identified and quantified based on multiple source signals (flow, heat and dissolved substances). At the same time, the relevance of the respective processes and parameters is examined by means of sensitivity analysis. Relevant model properties and parameters as well as resulting model uncertainties are then specifically examined and identified with the aid of inverse methods (e.g. automatic parameterization).
For real catchment areas, the necessary model complexity is determined with suitable criteria depending on available information and data. The model complexity can be adjusted step by step up to highly parameterized models in order to be able to make statements specifically and taking into account the available computing capacities. Thus, future requirements with regard to the exploration and characterization of karst areas can be identified. Water management in karst systems is also part of our new project.