It is shown that a regionally trained probabilistic deep learning model improves operational streamflow forecasts 5 for the Rhine basin and highlights that deep learning can complement established operational systems without replacing them, offering a practical pathway to integrate data-driven methods into operational practice.
The results demonstrate that deep learning models, particularly when enriched with spatially interpolated inputs and land cover variability, significantly enhance forecasting accuracy and provide valuable projections for future water availability but also informs sustainable water management strategies in climate-sensi...
Ayse Ozer, A. Shahbazi, Halit Apaydın· Water resources management· 0 citations
Analysis of riverine flood forecasting models revealed that PatchTST outperformed the other models during moderate‐flow regimes while falling behind during extreme flooding events, and sensitivity analysis results indicated that PatchTST was slightly more sensitive to the selected training data features.
Krishna Panthi, Mostafa Saberian, Vidya S. Samadi· JAWRA Journal of the America...· 0 citations
ABSTRACT This study develops and compares two data-driven models for short-term water level forecasting based on a MLP and a LSTM network. Both models were trained for a six-hour forecasting horizon using hourly water level and rainfall data from seven stations in the Guaíba Lake watershed, southern Brazil, from Novemb...
G. R. T. de Lima, L. Silva, L. B. L. Santos et al.· RBRH· 0 citations
Evapotranspiration (ET) forecasts from the U.S. National Water Model (NWM) carry systematic biases that are most apparent in water-stressed, intensively managed regions. This study develops a machine learning (ML) post-processing framework that bias-corrects 1–7 day accumulated ET (ACCET) forecasts from the NWM medium-...
Abin Raj Chapagain, I. Maghami, Daniel P. Ames et al.· Water· 0 citations
Accurate streamflow prediction is critical for effective water resource management, and recent advances in deep learning and multi‐basin data sets have significantly improved model skill. Global deep‐learning models, trained on thousands of catchments have demonstrated strong generalization across diverse climates an...
Brett Snider, Ehsan Roshani· Water Resources Research· 0 citations
This study evaluates the potential and limitations of open/model-derived hydroclimatic data for multi-horizon river discharge forecasting in Babahoyo, Ecuador. A quantitative, applied, retrospective longitudinal design used daily data for 2017–2026. The discharge target is the GloFAS v4 seamless product—reanalysis unti...
Rolando Licapa-Redolfo, A. Haro-Sarango, Persi Vera-Zelada et al.· Atmosphere· 0 citations
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