Problem:

This project deals with the modeling of river flows, with a focus on streams in Switzerland. Current state-of-the-art spatially distributed hydrological models are too computationally expensive to be run over entire Switzerland at a fine spatial resolution and often lead to mediocre river flow estimates at ungauged locations (out-of-sample predictions). We wish to develop various probabilistic models which are computationally scalable, propagate uncertainty adequately, and yield satisfactory flow predictions at ungauged locations.

Impact:

The successful development of such models, and their application to the entire network of river streams in Switzerland, will have important implications for water resources management. Furthermore, if run in an online fashion, these models would greatly improve drought and flood monitoring, with possible deployment beyond Switzerland.

Proposed approach:

We are currently developing two models in parallel. The first one is a recurrent neural network with long short-term memory (LSTM) cells, adapted for multiple gauging stations which are connected by the river network seen as a directed graph. The second model builds on the first principles by combining additive models (with group ridge-penalized splines) for every gauging station and propagating the predicted discharge through the network (routing). We plan on exploring a third approach, primarily for uncertainty quantification, based on Gaussian processes defined on river networks.