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\begin{document}

\title{Global runoff partitioning based on Budyko-constrained machine learning}



\author[1]{Shujie Cheng}%
\author[2]{Petra Hulsman}%
\author[2]{Akash Koppa}%
\author[3]{Hylke E. Beck}%
\author[1]{Lei Cheng}%
\author[2]{Diego G. Miralles}%
\affil[1]{Wuhan University}%
\affil[2]{Ghent University}%
\affil[3]{King Abdullah University of Science and Technology}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\begin{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\selectlanguage{ngerman}° 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.%
\end{abstract}\selectlanguage{ngerman}%



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\textbf{Hosted file}

\verb`963516_0_art_file_10989324_rqqkfh.docx`  available at \url{https://authorea.com/users/620083/articles/644294-global-runoff-partitioning-based-on-budyko-constrained-machine-learning}\selectlanguage{english}
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