Abbas A. auteur aut IRD Baek S. auteur aut IRD Kim M. auteur aut IRD Ligaray M. auteur aut IRD Ribolzi Olivier auteur aut IRD Silvera Norbert auteur aut IRD Min J. H. auteur aut IRD Boithias Laurie auteur aut IRD Cho K. H. auteur aut IRD text journalArticle eng text/pdf born digital access Recent intensification in climate change have resulted in the rise of hydrological extreme events. This demands modeling of hydrological processes at high temporal resolution to better understand flow patterns in catchments. To model surface and sub-surface flows in a catchment we utilized a physically based model called Hydrological Simulated Program-FORTRAN and two deep learning-based models. One deep learning model consisted of only one long short-term memory (simple LSTM), whereas the other model simulated processes in each hydrological response unit (HRU) by defining one separate LSTM for each HRU (HRU-based LSTM). The models use environmental time-series data and two-dimensional spatial data to predict surface and sub-surface flows at 6-minute time step simultaneously. We tested our models in a tropical humid headwater catchment in northern Lao PDR and compared their performances. Our results showed that the simple LSTM model outperformed the other models on surface runoff prediction with the lowest MSE (7.4e - 5 m(3 )s(-1)), whereas HRU-based LSTM model better predicted patterns and slopes in sub-surface flow in comparison with the other models by having the smallest MSE value (3.2e - 4 m(3 )s(-1)). This study demonstrated the performance of a deep learning model when simulating hydrological cycle with high temporal resolution. specialized Deep learning model Long short-term memory (LSTM) Sub-surface flow Surface runoff Hydrological Simulated Program-FORTRAN LAOS 062 020 122 590 125370 [14 p.] 2020 0022-1694 https://www.documentation.ird.fr/hor/fdi:010080544 10.1016/j.jhydrol.2020.125370 0022-1694 [F B010080544] https://www.documentation.ird.fr/hor/fdi:010080544 https://www.documentation.ird.fr/intranet/publi/2021/01/010080544.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2021-02-08 2025-02-24 fdi:010080544 fre