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Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions


Natel de Moura, Carolina; Seibert, Jan; Detzel, Daniel Henrique Marco (2022). Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions. Hydrology Research, 53(5):657-667.

Abstract

Better understanding the predictive capabilities of hydrological models under contrasting climate conditions will enable more robust decision-making. Here, we tested the ability of the long short-term memory (LSTM) for daily discharge prediction under changing conditions using six snow-influenced catchments in Switzerland. We benchmarked the LSTM using the Hydrologiska Byråns Vattenbalansavdelning (HBV) bucket-type model with two parameterizations. We compared the model performance under changing conditions against constant conditions and tested the impact of the time-series size used in calibration on the model performance. When calibrated, the LSTM resulted in a much better fit than the HBV. However, in validation, the performance of the LSTM dropped considerably, and the fit was as good or poorer than the HBV performance in validation. Using longer time series in calibration improved the robustness of the LSTM, whereas HBV needed fewer data to ensure a robust parameterization. When using the maximum number of years in calibration, the LSTM was considered robust to simulate discharges in a drier period than the one used in calibration. Overall, the HBV was found to be less sensitive for applications under contrasted climates than the data-driven model. However, other LSTM modeling setups might be able to improve the transferability between different conditions.

Abstract

Better understanding the predictive capabilities of hydrological models under contrasting climate conditions will enable more robust decision-making. Here, we tested the ability of the long short-term memory (LSTM) for daily discharge prediction under changing conditions using six snow-influenced catchments in Switzerland. We benchmarked the LSTM using the Hydrologiska Byråns Vattenbalansavdelning (HBV) bucket-type model with two parameterizations. We compared the model performance under changing conditions against constant conditions and tested the impact of the time-series size used in calibration on the model performance. When calibrated, the LSTM resulted in a much better fit than the HBV. However, in validation, the performance of the LSTM dropped considerably, and the fit was as good or poorer than the HBV performance in validation. Using longer time series in calibration improved the robustness of the LSTM, whereas HBV needed fewer data to ensure a robust parameterization. When using the maximum number of years in calibration, the LSTM was considered robust to simulate discharges in a drier period than the one used in calibration. Overall, the HBV was found to be less sensitive for applications under contrasted climates than the data-driven model. However, other LSTM modeling setups might be able to improve the transferability between different conditions.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Water Science and Technology
Uncontrolled Keywords:Water Science and Technology
Language:English
Date:1 May 2022
Deposited On:07 Dec 2022 11:06
Last Modified:27 Jun 2024 01:42
Publisher:IWA Publishing
ISSN:1998-9563
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.2166/nh.2022.044
Project Information:
  • : FunderCAPES
  • : Grant ID
  • : Project Title
  • : FunderESKAS – Swiss Government Excellence Scholarships for Foreign Scholars
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)