Conceptual models in hydrology are widely used, allow for easy interpretation and require little data. Machine learning models in hydrology often outperform conceptual models but lack the ease of interpretability, require large amounts of data and and do not obey physical laws. Hybrid approaches aiming to combine the advantages of both approaches are becoming more popular. A Neural Ordinary Differential Equations approach is introduced to combine a differential equation-based conceptual model with a neural network. Additionally, conceptual models and LSTM models are used as benchmarks. The models are tested using the LamaH-CE dataset as well as the E-OBS dataset. In many cases the hybrid models outperform the conceptual model. However, to further improve the performance of hybrid models more research is needed to make the models more computationally efficient and optimized training strategies are required to explore the full potential of the approach.