Exploring Modelling Assumptions and their Impact on Extreme Discharges for the Meuse Catchment

Floods are the most frequent natural disaster and due to climate change the frequency and intensity of these events are increasing. Therefore, it is becoming increasingly important to obtain accurate estimations of extreme discharges. Statistical modelling is widely used to estimate extreme discharges by fitting observed extreme discharges to an...

Operational Streamflow Drought Forecasting for the Rhine River at Lobith Using the LSTM Deep Learning Approach

Under future warmer climates, drought events are projected to occur more frequently with increasing impacts in many regions and river basins. This study focuses on exploring the potential of the LSTM deep learning (DL) approach for operational streamflow drought forecasting for the Rhine River at Lobith with a lead time (LT) of up to 46 days. ...

Assessing the applicability of Transformer-based architectures as rainfall-runoff models

Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...