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\begin{document}

\title{A harmonized global gridded transpiration product based on collocation
analysis}



\author[1]{Changming Li}%
\author[1]{Juntai Han}%
\author[1]{Ziwei Liu}%
\author[1]{Zhuoyi Tu}%
\author[1]{Hanbo Yang}%
\affil[1]{Tsinghua University}%


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  \date{June 10, 2024}


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\begin{abstract}
Transpiration (T) is pivotal in the global water cycle, responding to
soil moisture, atmospheric stress, climate changes, and human impacts.
Therefore, establishing a reliable global transpiration dataset is
essential. Different global transpiration products exhibit significant
differences, necessitating the evaluation of errors. Collocation
analysis methods have been proven effective for assessing the errors in
these products, which can subsequently be used for multisource fusion.
However, previous results did not consider error cross-correlation,
rendering the results less reliable. In this study, we employ
collocation analysis, taking error cross-correlation into account, to
effectively analyze the errors in multiple transpiration products and
merge them to obtain a more reliable dataset. The results demonstrate
its superior reliability. The outcome of this research is a long-term
daily global transpiration dataset at 0.1\selectlanguage{ngerman}° resolution from 2000 to 2020.
Using the transpiration after partitioning at FLUXNET sites as a
reference, we compare the performance of the merged product with input
datasets. The merged dataset performs well across various vegetation
types and is validated against in-situ observations. Incorporating
non-zero ECC considerations represents a significant theoretical and
proven enhancement over previous methodologies that neglected such
conditions, highlighting its reliability in enhancing our understanding
of transpiration dynamics in a changing world.%
\end{abstract}\selectlanguage{ngerman}%



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