Rivers, channel networks, and reservoir cascades influence their behavior within the boundary conditions given by nature. The RTC-tools open-source toolbox is used to model these systems and to optimize them. In this Master thesis, the aim is to include pumped storage plants into the system. The basis of an early warning systems are observations and models. Observations give direct information about current conditions. How high are the water levels? Is there a large snow pack? However, observations do not give answers to questions related about the future, (will the water level reach flood level?) or about question related to system functioning: does the snow pack and high temperatures mean that there is a flood risk?
Fig. 1: Nant de Drance Pumped Storage plant (Marti Tunnel AG, 2020)
Figure 2: The basics of an early warning system: observation and forecast
For these questions we need modelling. This modelling consists of many aspects. For the FLOMRESPONS project, SINTEF and Deltares have set-up a base modelling system, however there is still much to be improved.
The topic of this MSc thesis is to identify where the current modelling can be improved, related to:
- Optimizing the hydrological model to the local conditions
- Pre-processing meteo forecasts for model input
- Post-processing ensemble streamflow forecasts
To get a general idea on pre- and postprocessing this paper is relevant for the Norwegian context: https://hess.copernicus.org/preprints/hess-2021-13/