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The study introduces CH-RUN, a deep-learning-based model for reconstructing daily runoff across Switzerland from 1962 to 2023, utilizing temperature, precipitation, and static catchment properties as inputs. The model, which employs long short-term memory (LSTM) and convolutional architectures, demonstrates superior performance over traditional hydrological models in capturing runoff patterns and trends. This data-driven approach facilitates efficient large-scale simulations and provides valuable insights f

Author

sanchitkukreja5

Language

EN