EGUsphere EGUsphere EGUsphere EGUsphere Copernicus Publications Göttingen, Germany 10.5194/egusphere-2026-393 Enhancing hydrological hazard early warning: A 60-day streamflow forecasting framework integrating deep learning and process-based modeling Liu Zhijie 1 2 Yang Hanbo https://orcid.org/0000-0002-5925-0245 1 2 Yang Dawen https://orcid.org/0000-0002-2383-1881 1 2 Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China 12 02 2026 2026 1 21 Copyright: © 2026 Zhijie Liu et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-393/ The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-393/egusphere-2026-393.pdf

Reliable medium- and long-term streamflow forecasting is a cornerstone of hydrological hazard early warning and water resources management, yet achieving accurate predictions with sufficient lead time remains a formidable challenge. This study proposes a 60-day streamflow forecasting framework to strengthen early warning capabilities by systematically integrating a convolutional neural network (CNN) for bias correction of precipitation forecasts from the UK Met Office (UKMO) numerical weather prediction model, the Geomorphology-Based Eco-Hydrological Model (GBEHM) for streamflow simulation, and an autoregressive with exogenous input (ARX) model for statistical post-processing. Applying the proposed framework to the Upper Yangtze River Basin, results indicate that the CNN model reduces the areal-averaged precipitation root mean square error (RMSE) by around 35 % and elevates the temporal correlation coefficient (TCC) from 0.62 to 0.74 against raw UKMO forecasts across the 60-day horizon, with performance gains amplifying at longer lead times. Subsequently, when driving the GBEHM with corrected precipitation and applying ARX post-processing, the streamflow forecasts exhibit substantial enhancements with a reduction in RMSE of 36 %, a decrease in relative error (RE) from 48.2 % to 17.4 %, and an increase in Nash–Sutcliffe efficiency (NSE) from 0.33 to 0.72 compared to those driven by raw forecasts in terms of 60-day mean performance. Error decomposition identifies precipitation forecast errors which intensify with lead time as the dominant source of uncertainty for medium- and long-term streamflow forecasting, while confirming that hydrological model uncertainty remains a significant component, highlighting that the selection of a robust hydrological model is crucial for enhancing the reliability and predictive skill of the streamflow forecasts. By systematically leveraging the CNN to mitigate drifting meteorological biases, the GBEHM to capture physical catchment dynamics, and the ARX to minimize residual errors, the proposed framework extends the effective early warning horizon to 60 days with high volumetric accuracy and temporal consistency, providing vital decision support for flood and drought risk management and regional water security.