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Global runoff partitioning based on Budyko-constrained machine learning
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  • Shujie Cheng,
  • Petra Hulsman,
  • Akash Koppa,
  • Hylke E. Beck,
  • Lei Cheng,
  • Diego G. Miralles
Shujie Cheng
Wuhan University
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Petra Hulsman
Ghent University
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Akash Koppa
Ghent University
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Hylke E. Beck
King Abdullah University of Science and Technology
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Lei Cheng
Wuhan University

Corresponding Author:[email protected]

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Diego G. Miralles
Ghent University

Corresponding Author:[email protected]

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Abstract

Understanding the partitioning of runoff into baseflow and quickflow is crucial for informed decision-making in water resources management, guiding the implementation of flood mitigation strategies, and supporting the development of drought resilience measures. Methods that combine the physically-based Budyko framework with machine learning (ML) have shown promise in estimating global runoff. However, such ‘hybrid’ approaches have not been used for baseflow estimation. Here, we develop a Budyko-constrained ML approach for baseflow estimation by incorporating the Budyko-based baseflow coefficient (BFC) curve as a physical constraint. We estimate the parameters of the original Budyko curve and the newly developed BFC curve based on 13 climatic and physiographic characteristics using boosted regression trees (BRT). BRT models are trained and tested in 1226 catchments worldwide and subsequently applied to the entire global land surface at a 0.25° grid scale. The catchment-trained models exhibit strong performance during the testing phase, with R2 values of 0.96 and 0.88 for runoff and baseflow, respectively. Results reveal that, on average, 30.3% (spatial standard deviation std=26.5%) of the continental precipitation is partitioned into runoff, of which 20.6% (std=22.1%) is baseflow and 9.7% (std=10.3%) is quickflow. Among the 13 climatic and physiographic characteristics, topography and soil-related characteristics generally emerge as the most important drivers, although significant regional variability is observed. Comparisons with previous datasets suggest that global runoff partitioning is still highly uncertain and warrants further research.
24 May 2023Submitted to ESS Open Archive
25 May 2023Published in ESS Open Archive