LSTMs for Hydrological Modelling in Swiss Catchments

Simulation of the catchment rainfall-runoff transformation with physically based watershed models is a traditional way to predict streamflow and other hydrological variables at catchment scales. However, the calibration of such models requires large data inputs and computational power and contains many parameters which are often impossible to constrain or validate. An alternative approach is to use data-driven machine learning for streamflow prediction.

In the past few years, LSTM (long short-term memory) models and its variants have been explored in rainfall-runoff modelling. Typical applications use daily climate variables as inputs and model the rainfall-runoff transformation processes with different timescales of memory. This is especially useful as delays in runoff production by snow accumulation and melt, soil water storage, evapotranspiration, etc., can be included. In contrast to feed-forward ANNs (artificial neural networks), LSTMs are capable of maintaining the sequential temporal order of inputs, and compared to RNNs (recurrent neural networks), of learning the long-term dependencies. [1]

However, current work on LSTMs mostly focuses on the USA, the UK and Brazil, where CAMELS datasets are available [1, 2, 3]. Catchments at higher altitudes with snow-driven dynamics and sometimes glaciers are present in small number in these datasets (if at all). Systematic applications of LSTMs for streamflow prediction in climates where a significant part of the catchments are snow and ice dominated are missing. In this work, an FS-LSTM (fast slow-LSTM) previously applied in Brazil is adapted for Swiss catchments to fill this gap [3]. The FS-LSTM explored builds on the work of Hoedt et al. (2021) that imposed mass constraints on an LSTM, called MC-LSTM [4]. FS-LSTM adds a fast and slow part for streamflow, containing rainfall and soil moisture respectively. We will discuss benchmark results against an existing semi-distributed conceptual model widely used in Switzerland for streamflow simulation [5].

 

References:

[1]: Kratzert et al., Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks, 2018.

[2]: Lees et al., Hydrological concept formation inside long short-term memory (LSTM) networks, 2022.

[3]: Quinones et al., Fast-Slow Streamflow Model Using Mass-Conserving LSTM, 2021.

[4]: Hoedt et al., MC-LSTM: Mass-Conserving LSTM, 2021.

[5]: Viviroli et al., An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools, 2009.