A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems

Eichelmann, Elke; Mantoani, Mauricio C.; Chamberlain, Samuel D.; Hemes, Kyle S.; Oikawa, Patricia Y.; Szutu, Daphne; Valach, Alex; Verfaillie, Joseph; Baldocchi, Dennis D.

doi:10.1111/gcb.15974

Title: A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems

Journal Article · Thu Nov 04 00:00:00 EDT 2021 · Global Change Biology

DOI:https://doi.org/10.1111/gcb.15974· OSTI ID:1982911

^[1]; Mantoani, Mauricio C. ^[1];

^[2]; Hemes, Kyle S. ^[2]; Oikawa, Patricia Y. ^[3]; Szutu, Daphne ^[2]; Valach, Alex ^[2]; Verfaillie, Joseph ^[2];

^[2]

University College Dublin (Ireland)
Univ. of California, Berkeley, CA (United States)
California State Univ., East Bay, Hayward, CA (United States)

Abstract Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation ( E ) and transpiration ( T ) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local‐to‐regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E . Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento‐San Joaquin Delta, California, USA, as well as leaf‐level T data for validation. The four wetlands vary in their vegetation make‐up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R ² across all sites. The order of variable inclusion (and importance) was: VPD > gap‐filled sensible heat flux ( H _gf) > air temperature ( T _air ) > friction velocity ( u _∗ ) > other variables. The model using VPD, H _gf, T _air , and u _∗ showed the best performance during validation with independent data and had a mean testing R ² value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T /ET partitioning results which were more consistent with CO ₂ exchange data especially for more heterogeneous sites with large E contributions. Our method improves the understanding of T /ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T /ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). AmeriFlux

Sponsoring Organization:: USDOE Office of Science (SC); USDA

Grant/Contract Number:: AC02-05CH11231; 2011-67003-30371; 7079856)

OSTI ID:: 1982911

Alternate ID(s):: OSTI ID: 1830275

Journal Information:: Global Change Biology, Vol. 28, Issue 3; ISSN 1354-1013

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Title: A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems

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