Probabilistic streamflow prediction in ungauged natural catchments with Temporal Fusion Transformers

Accurate prediction of streamflow is essential for sound water resources management but remains a complex task due to the dynamic nature of hydrological processes, imprecision in meteorological data, and measurement challenges. Recent advancements in deep learning have demonstrated the potential of data-driven models to extract and identify complex temporal dependencies in large hydro-meteorological datasets (e.g. [1-2]).

This work evaluates the ability of Temporal Fusion Transformers (TFTs) to predict daily streamflow across catchments in Mainland Portugal, using meteorological input data derived from ERA5-Land (reanalysis dataset) and geomorphological descriptors. TFTs are a relatively novel deep-learning architecture that is being explored in hydrology (e.g., [3-4]). It incorporates the well-tested Long Short-Term Memory (LSTM) architecture with transformers, potentially offering possibilities of improved performance and partial explainability of predictions.

The methodology for the application to ungauged catchments relies on straightforward cross-validation with holdout samples. Although all considered catchments are monitored by gauging stations, streamflow observations at the various locations are selectively hidden from the model during calibration and validation, allowing a full controlled emulation of ungauged conditions on the test subsets.

Model performance is benchmarked against calibrated Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological models. Results show that TFTs achieve comparable predictive skill in ungauged settings when compared to locally calibrated HBV counterparts, while providing probabilistic predictions with limited explainability.

The capability for specialization is also investigated. Indeed, it is shown that retraining a general-purpose “ungauged” TFT on a previously unknown time series, even with only a limited number of observations, can lead to substantial improvements in predictive skill.

The proposed framework offers a practical and scalable solution for streamflow estimation in data-scarce and ungauged catchments. By relying on globally available data and static catchment characteristics, the approach can be transferred to regions with limited measurement networks, reducing dependence on long-term observations. The probabilistic outputs further enhance decision-making by explicitly quantifying predictive uncertainty, a critical factor for risk-informed planning, supporting operational water resources management and early warning systems.

[1] Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.

[2] Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall–runoff predictions of extreme events, Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, 2022. 

[3] Koya, S. R., Roy, T.: Temporal Fusion Transformers for streamflow prediction: Value of combining attention with recurrence. J. Hydrol., 637, 131301. https://doi.org/10.1016/j.jhydrol.2024.131301, 2024.

[4] He, M., Jiang, S., Ren, L., Cui, H., Qin, T., Du, S., Zhu, Y., Fang, X., Xu, C.: Streamflow prediction in ungauged catchments through use of catchment classification and deep learning. J. Hydrol., 639, 131638. https://doi.org/10.1016/j.jhydrol.2024.131638, 2024.