Extended range forecasting of stream water temperature with deep learning models

Abstract. Stream water temperatures influence water quality with effects on aquatic biodiversity, drinking water provision, electricity production, agriculture, and recreation. Therefore, stakeholders would benefit from an operational forecasting service that would support timely action. Deep learning models are well-suited to provide probabilistic forecasts at individual stations of a monitoring network. Here we train and evaluate several state-of-the-art models using 10 years of data from 54 stations across Switzerland. Static catchment features, time of the year, meteorological observations from the past 64 days, and their ensemble forecasts for the following 32 days are included as predictors in the models to estimate daily maximum water temperature over the next 32 days. Results show that the Temporal Fusion Transformer (TFT) model performs best with a Continuous Rank Probability Score (CRPS) of 0.70 °C averaged over all lead times, stations, and 90 forecasts distributed over 1 year. The TFT is followed by the Recurrent Neural Network Encoder – Decoder with a CRPS of 0.74 °C, and the Neural Hierarchical Interpolation for Time Series with a CRPS of 0.75 °C. These deep learning models outperform other simpler models trained at each station: Random Forest (CRPS = 0.80 °C), Multi-layer Perceptron neural network (CRPS = 0.81 °C), and Autoregressive linear model (CRPS = 0.96 °C). The average CRPS of the TFT degrades from 0.38 °C at lead time of 1 day to 0.90 °C at lead time of 32 days, largely driven by the uncertainty of the meteorological ensemble forecasts. In addition, TFT water temperature predictions at new and ungauged stations outperform those from the other models. When analyzing the importance of model inputs, we find a dominant role of observed water temperature and future air temperature, while including precipitation and time of the year further improve predictive skill. Operational probabilistic forecasts of daily maximum water temperature are generated twice per week with our TFT model and are publicly available at https://www.drought.ch/de/impakt-vorhersagen-malefix/wassertemperatur-prognosen/. Overall, this study provides insights on the extended range predictability of stream water temperature, and on the applicability of deep learning models in hydrology.