Groundwater flow and transport in deep crystalline rock is an important area of research. This is partly due to its relevance for constructing a long term repository for storing radioactive spent nuclear fuel in deep bedrock. Understanding the behavior of flow and transport processes in deep crystalline rock is crucial in developing a sustainable solution to this problem. This study aims to increase the understanding of how channel network models (CNM) can be applied to represent groundwater flow and solute transport in sparsely fractured crystalline rock under site specific conditions. A main objective was to determine how to incorporate structural and hydrogeological site characterization data in the construction of the CNMs. In addition to this, the associated key parameters of the CNMs were investigated to gain further understanding of model site application. To that end, a scripting approach with the python scripting library Pychan3d was used to create alternative channel network representations of a field site. A conceptual discrete fracture network (DFN) model was constructed using field site data obtained from a structural model of the fractures present at the site of the Tracer Retention Understanding Experiments (TRUE) - Block Scale at the Äspö Hard Rock Laboratory (HRL). This conceptual model was used as a base for constructing two different alternatives, denoted respectively as sparse and dense, of a CNM. The sparse CNM consisted of a limited amount of channels for each fracture, while the dense CNM acted as a DFN proxy, taking the full extent of the fracture areas into account and creating a dense, large network of flow channels for each fracture. In order to verify the performance of the generated CNMs, a reproduction of tracer tests performed at the same specific field site was attempted using a particle tracking technique. In addition to this, long term predictions of solute transport without the interference of the pumps used during the tracer tests were done in order to estimate transport time distributions. Pychan3d and the scripting approach was successfully used to create CNMs respecting specific conditions from the TRUE-Block Scale site. The sparse CNM was found to give very adequate flow and transport responses in most cases and to be relatively easier to calibrate than its dense counterpart. The long term transport predictions at the site according to the models seem to follow a channelized pattern, with only a few select paths for transport. The difficulties encountered in matching the dense CNM with the tracer tests most likely stem from difficulties in flow calibration, as well as certain key parameters being assigned too generically.