EGU26-2556, updated on 13 Mar 2026
https://doi.org/10.5194/egusphere-egu26-2556
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
A Novel Hydrological Signature-Informed Framework for Enhancing Extreme Streamflow Prediction Using Multi-Task Learning
1,- 1Key Laboratory of Mountain Hazards and Engineering Resilience (Chinese Academy of Sciences), Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China.
- 2Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
How to cite: wang, Z., li, C., and cui, P.: A Novel Hydrological Signature-Informed Framework for Enhancing Extreme Streamflow Prediction Using Multi-Task Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2556, https://doi.org/10.5194/egusphere-egu26-2556, 2026.