Using tide for rainfall runoff simulation with feature projection and reversible instance normalization.

Abstract

From LSTMs (Long Short-Term Memory) to Transformers, various networks have been used for runoff forecasting, though many complex structures may be unnecessary. This study introduces RR-TiDE, a simple model based on the Time Series Dense Encoder. RR-TiDE employs fully Multilayer Perceptron architecture for modeling and is specifically designed with understanding of hydrological processes. To manage non-stationarity in hydrological data, RR-TiDE incorporates Reversible Instance Normalization. The model was trained using the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset and evaluated on two tasks: (1) multi-basin runoff simulation; (2) prediction in data-sparse basins. In the first task, RR-TiDE outperformed both Transformer and LSTM-based models across all metrics for 7-day runoff predictions, which indicates that RR-TiDE is highly suitable for rainfall-runoff simulation. In the second task, it achieved a median NSE of 0.82 in 1-day runoff forecasting in 51 watersheds. This suggests that RR-TiDE possesses robust generalization capability, enabling spatial extrapolation. Comparisons were made between models with and without the feature projection layer and RevIN to further understand their individual contributions. Results indicate that the feature projection layer can effectively enhance the performance of RR-TiDE. Although RevIN provided limited overall improvements, it helped stabilize loss fluctuations during training, aiding model convergence.