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River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models

Author

Listed:
  • Kiyoumars Roushangar

    (University of Tabriz)

  • Sadegh Abdelzad

    (University of Tabriz)

Abstract

River flow or runoff is an important water flux that can pose great threats to water security because of changes in its timing, magnitude, and seasonality. In this study, runoff variations in two different climate conditions, including the semi-arid and humid climates, were assessed using an integrated method based on the LARS-WG and Long Short-term Memory (LSTM) models. Runoff variations were simulated for the base period of 2001–2020 and it was forecasted for the future period of 2021–2040. Climatic parameters were evaluated using the LARS-WG statistical model for the selected stations under the RCPs scenarios. Then, outputs obtained under the RCPs scenarios, including minimum temperature, maximum temperature, precipitation, and sunshine hours, were considered as the input of the LSTM model. Results showed that the amount of mean runoff in spring for semi-arid areas and in spring and winter for humid areas will decrease in the next twenty years compared to the base period. For other seasons in both climates, the amount of runoff will increase. The maximum rainfall increase under the RCP8.5 scenario was 66.66 mm for the humid region and 30.06 mm for the semi-arid region compared to the base period. Also, the maximum temperature increase for the semi-arid region was 1.18 °C under the RCP8.5 scenario, and for the humid region was 1.07 °C under the RCP2.6 scenario. Results showed that the LSTM method can successfully model the future river flow values and river flow related to the previous day has a significant impact on the modeling process.

Suggested Citation

  • Kiyoumars Roushangar & Sadegh Abdelzad, 2023. "River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3813-3831, August.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:10:d:10.1007_s11269-023-03527-8
    DOI: 10.1007/s11269-023-03527-8
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    References listed on IDEAS

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    1. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    2. Esmaeil Silakhori & Mohammad Reza Dahmardeh Ghaleno & Sarita Gajbhiye Meshram & Ehsan Alvandi, 2022. "To assess the impacts of climate change on runoff in Golestan Province, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 281-300, May.
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