EGU2020-6216
https://doi.org/10.5194/egusphere-egu2020-6216
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Application of deep recurrent neural networks for modeling surface and sub-surface flow at high temporal resolution

Ather Abbas1, Sangsoo Baek1, Minjeong Kim1, Mayzonee Ligaray1, Olivier Ribolzi2, Norbert Silvera3, Joong-Hyuk Min4, Laurie Boithias2, and Kyung Hwa Cho1
Ather Abbas et al.
  • 1Ulsan National Institute of Science and Technology, Urban and Environmental Engineering, Ulsan, Korea, Republic of
  • 2Geosciences Environnement Toulouse, Université de Toulouse, CNRS, IRD, UPS, 31400 Toulouse, France
  • 3iEES-Paris, UMR 7618 (IRD, CNRS, UPMC), Centre IRD d’Ile de France – 32, avenue Henri Varagnat, 93143 Bondy cedex, France
  • 4Water Quality Assessment Research Division, National Institute of Environmental Research, Environmental Research Complex, Hwangyeong-ro 42, Seo-gu, Incheon 22689, Korea

Recent increase in climate change has resulted in rise of hydrologic extreme events, which demands better understanding of flow patterns in catchment. Modeling surface and sub-surface flow at high temporal resolution helps to understand catchment dynamics. In this study, we simulated surface and sub-surface flow in a Laotian catchment at 6-minute resolution. We used one physically based model called Hydrological Simulated Program-FORTRAN (HSPF) and developed two deep learning-based models. One deep learning model consisted of only one long short-term memory (LSTM), whereas the other model simulated processes in each hydrologic response unit (HRU) by defining one separate LSTM for each HRU. The models consider environmental data as well as changing landuse in catchment and predict surface and sub-surface flows. Our results show that simple LSTM model outperformed other models for surface runoff prediction, whereas the HRU-based LSTM model better predicted patterns and slopes in sub-surface flow in comparison with other models.

How to cite: Abbas, A., Baek, S., Kim, M., Ligaray, M., Ribolzi, O., Silvera, N., Min, J.-H., Boithias, L., and Cho, K. H.: Application of deep recurrent neural networks for modeling surface and sub-surface flow at high temporal resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6216, https://doi.org/10.5194/egusphere-egu2020-6216, 2020

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