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Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Author

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  • Bisrat Ayalew Yifru

    (Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon-si 24341, Gangwon-do, Republic of Korea)

  • Kyoung Jae Lim

    (Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Gangwon-do, Republic of Korea)

  • Seoro Lee

    (Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon-si 24341, Gangwon-do, Republic of Korea)

Abstract

Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, and equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be a persistent challenge due to complex surface and subsurface watershed processes. Therefore, in addition to the fundamental framework, numerous techniques have been used to enhance prediction accuracy and physical consistency. This work provides a well-organized review of more than two decades of efforts to enhance SFP in a physically consistent way using process modeling and flow domain knowledge. This review covers hydrograph analysis, baseflow separation, and process-based modeling (PBM) approaches. This paper provides an in-depth analysis of each technique and a discussion of their applications. Additionally, the existing techniques are categorized, revealing research gaps and promising avenues for future research. Overall, this review paper offers valuable insights into the current state of enhanced SFP within a physically consistent, domain knowledge-informed data-driven modeling framework.

Suggested Citation

  • Bisrat Ayalew Yifru & Kyoung Jae Lim & Seoro Lee, 2024. "Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review," Sustainability, MDPI, vol. 16(4), pages 1-27, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1376-:d:1334527
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    References listed on IDEAS

    as
    1. Juliane Mai & James R. Craig & Bryan A. Tolson & Richard Arsenault, 2022. "The sensitivity of simulated streamflow to individual hydrologic processes across North America," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Ming Zhong & Hongrui Zhang & Tao Jiang & Jun Guo & Jinxin Zhu & Dagang Wang & Xiaohong Chen, 2023. "A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4841-4859, September.
    3. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.
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