Runoff Forecasting in Unmeasured Catchments and Rapid Flash Flood Prediction Based on Deep Learning.

Runoff forecasting is a long-standing challenge in hydrology, particularly in unmeasured catchments and rapid flash flood prediction. For unmeasured catchment forecasting, we introduce the encoder-decoder-based dual-layer long short-term memory (ED-DLSTM) model[1]. This model fuses static spatial granularity attributes with temporal dynamic variables to achieve streamflow forecasting at a global scale. ED-DLSTM reaches an average Nash efficiency coefficient (NSE) of 0.75 across more than 2000 catchments from historical datasets in the United States, Canada, Central Europe, and the United Kingdom. Additionally, ED-DLSTM is applied to 150 fully ungauged catchments in Chile, achieving a high NSE of 0.65. The interpretability of the transfer capacities of ED-DLSTM is effectively tracked through the cell state induced by adding a spatial attribute encoding module, which can spontaneously form hydrological regionalization effects after performing spatial coding for different catchments.Moreover, rapid flood prediction with daily resolution is challenged to capture changes in runoff over short periods. To address this, we also propose a benchmark evaluation for runoff and flood forecasting based on deep learning (RF-Bench) at an hourly scale. We introduce the Mamba model to hydrology for the first time. The benchmark also includes Dlinear, LSTM, Transformer, and its improved versions (Informer, Autoformer, Patch Transformer). Results indicate that the Patch Transformer exhibits optimal predictive capability across multiple lead times, while the traditional LSTM model demonstrates stable performance, and the Mamba model strikes a good balance between performance and stability. We reveal the attention patterns of Transformer models in hydrological modeling, finding that attention is time-sensitive and that the attention scores for dynamic variables are higher than those for static attributes.Our work [2,3] provides the hydrological community with an open-source, scalable platform, contributing to the advancement of deep learning in the field of hydrology. [1] Zhang, B., Ouyang, C., Cui, P., Xu, Q., Wang, D., Zhang, F., Li, Z., Fan, L., Lovati, M., Liu, Y., Zhang, Q., 2024. Deep learning for cross-region streamflow and flood forecasting at a global scale. The Innovation 5, 100617. https://doi.org/10.1016/j.xinn.2024.100617[2] Zhang, B., Ouyang, C., Wang, D., Wang, F., Xu, Q., 2023. A PANN-Based Grid Downscaling Technology and Its Application in Landslide and Flood Modeling. Remote Sensing 15, 5075. https://doi.org/10.3390/rs15205075[3] Xu, Q., Shi, Y., Bamber, J.L., Ouyang, C., Zhu, X.X., 2024. Large-scale flood modeling and forecasting with FloodCast. Water Research 264, 122162. https://doi.org/10.1016/j.watres.2024.122162

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.