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Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm
Author(s) -
Nelson Jacob A.,
Carvalhais Nuno,
Cuntz Matthias,
Delpierre Nicolas,
Knauer Jürgen,
Ogée Jérome,
Migliavacca Mirco,
Reichstein Markus,
Jung Martin
Publication year - 2018
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1029/2018jg004727
Subject(s) - transpiration , evapotranspiration , water use efficiency , primary production , water cycle , eddy covariance , water use , algorithm , mathematics , atmospheric sciences , environmental science , ecosystem , ecology , chemistry , physics , photosynthesis , biology , biochemistry
Plant transpiration ( T ), biologically controlled movement of water from soil to atmosphere, currently lacks sufficient estimates in space and time to characterize global ecohydrology. Here we describe the Transpiration Estimation Algorithm (TEA), which uses both the signals of gross primary productivity and evapotranspiration (ET) to estimate temporal patterns of water use efficiency (WUE, i.e., the ratio between gross primary productivity and T ) from which T is calculated. The method first isolates periods when T is most likely to dominate ET. Then, a Random Forest Regressor is trained on WUE within the filtered periods and can thus estimate WUE and T at every time step. Performance of the method is validated using terrestrial biosphere model output as synthetic flux data sets, that is, flux data where WUE dynamics are encoded in the model structure and T is known. TEA reproduced temporal patterns of T with modeling efficiencies above 0.8 for all three models: JSBACH, MuSICA, and CASTANEA. Algorithm output is robust to data set noise but shows some sensitivity to sites and model structures with relatively constant evaporation levels, overestimating values of T while still capturing temporal patterns. The ability to capture between‐site variability in the fraction of T to total ET varied by model, with root‐mean‐square error values between algorithm predicted and modeled T / E T ranging from 3% to 15% depending on the model. TEA provides a widely applicable method for estimating WUE while requiring minimal data and/or knowledge on physiology which can complement and inform the current understanding of underlying processes.