Surface and sub-surface flow estimation at high temporal resolution using deep neural networks

Ather Abbas, Sangsoo Baek, Minjeong Kim, Mayzonee Ligaray, Olivier Ribolzi, Norbert Silvera, Joong Hyuk Min, Laurie Boithias*, Kyung Hwa Cho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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 m3 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 m3 s−1). This study demonstrated the performance of a deep learning model when simulating hydrological cycle with high temporal resolution.

Original languageEnglish (US)
Article number125370
JournalJournal of Hydrology
Volume590
DOIs
StatePublished - Nov 2020

Bibliographical note

Funding Information:
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through “The Chemical Accident Prevention Technology Development Project” funded by Korea Ministry of Environment (MOE) (No. 2016001970001). The authors sincerely thank the Lao Department of Agricultural Land Management (DALaM) for its support, including granting the permission for field access, and the M-TROPICS Critical Zone Observatory (https://mtropics.obs-mip.fr/), which belongs to the French Research Infrastructure OZCAR (http://www.ozcar-ri.org/), for data access.

Funding Information:
This work was supported by Korea Environment Industry & Technology Institute ( KEITI ) through “The Chemical Accident Prevention Technology Development Project” funded by Korea Ministry of Environment ( MOE ) (No. 2016001970001 ). The authors sincerely thank the Lao Department of Agricultural Land Management (DALaM) for its support, including granting the permission for field access, and the M-TROPICS Critical Zone Observatory (https://mtropics.obs-mip.fr/), which belongs to the French Research Infrastructure OZCAR (http://www.ozcar-ri.org/), for data access.

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • Deep learning model
  • Hydrological Simulated Program-FORTRAN
  • Long short-term memory (LSTM)
  • Sub-surface flow
  • Surface runoff

ASJC Scopus subject areas

  • Water Science and Technology

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