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Abstract:
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 (GPP) and evapotranspiration (ET) to estimate temporal patterns of water use efficiency (WUE, i.e. the ratio between GPP 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 datasets, i.e. 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 3 models: JSBACH, MuSICA, and CASTANEA. Algorithm output is robust to dataset noise, but shows some sensitivity to sites and model structures with relatively constant evaporation levels, overestimating values of T while still capturing temporal patterns. Ability to capture between site variability in the fraction of T to total ET varied by model, with RMSE values between algorithm predicted and modeled T/ET ranging from 3 to 15 % depending on 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.
