Peak Hydrological Event Simulation with Deep Learning Algorithm

Most floods are caused by heavy rainfall events, including the disaster in the Simbach catchment in 2016. For the Simbach catchment, a study was already carried out using the conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model to simulate the extreme event of 2016. While the calibration model performance is classified as very good, the overall validation is classified as unsatisfactory. Recent studies showed that data-driven models outperform benchmark rainfall-runoff models. A widely used data-driven model is the Long-Short-Term-Memory algorithm (LSTM). The main advantage of this algorithm is the ability to learn short-term as well as long term dependencies.The objective of this work is to determine if a data-driven model outperforms the conceptual model. For this purpose, in a first step a LSTM model is setup and its results are compared with the results of the HBV model. It is assumed that the LSTM model outperforms the HBV model in training and validation but is not able to simulate the extreme event, because the extrapolation capabilities of Neuronal Networks are poor if they operate outside of their training range. In a second step, it is studied if the model performance can be improved by providing more features to the model. Therefore, different feature combinations are provided to the model. Furthermore, it is assumed that providing more data to the model will improve its performance. Therefore, in a third step more events are used for training and validation.It was concluded that the LSTM model is able to simulate the rainfall-runoff process. A satisfactory overall model performance can be achieved using only precipitation as input data and a small training dataset of four events. But, as the HBV model, the LSTM model is not able to simulate the extreme event, because no extreme event is present within the training dataset. However, the LSTM model outperforms the HBV model, because the LSTM generalizes better. Furthermore, the model performance of the LSTM model using six events can be improved by providing additionally the soil moisture class as input data. Whereas providing more features to the model results in worse model performance. Providing more events to the model does not significantly improve its performance. However, the model improved especially for the event in June 2015. If the model is trained with more events having higher magnitude than the 2015 event, the event in 2015 is no longer classified as an out-of-sample event, resulting in better model performance. Providing the model more events and more input features does not significantly improve the model performance. The results show the potential and limitations using the LSTM model in modeling extreme events.