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

Bisrat Ayalew Yifru, Kyoung Jae Lim and Seoro Lee ()
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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

Sustainability, 2024, vol. 16, issue 4, 1-27

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.

Keywords: baseflow; data-driven modeling; streamflow prediction; physically consistent; process-based modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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