 The paper proposes a novel data-driven approach using long short-term memory, LSTM, network to model rainfall runoff in catchments with snow influence. The LSTM's ability to learn long-term dependencies is essential for modeling storage effects. The authors use 241 catchments from the camel's dataset and compare their results to the Sacramento soil moisture accounting model, SACSMA, coupled with snow, 17 snow routine. They also demonstrate the potential of LSTM as a regional hydrological model that predicts discharge for multiple catchments. Additionally, they show how process understanding learned at the regional scale can be transferred to individual catchments to improve model performance. The results indicate that the LSTM outperforms SACSMA plus snow, 17, highlighting its potential for hydrological modeling applications. This article was authored by F. Krotzert, F. Krotzert, D. Klotz, and others. We are article.tv, links in the description below.