Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm. Issue 12 (21st December 2018)

• Record Type: Journal Article
• Authors: Nelson, Jacob A.; Carvalhais, Nuno; Cuntz, Matthias; Delpierre, Nicolas; Knauer, Jürgen; Ogée, Jérome; Migliavacca, Mirco; Reichstein, Markus; Jung, Martin
• Is Part Of: Journal of geophysical research. Volume 123:Issue 12(2018)
• Page Start: 3617
• Page End: 3632
• Publication Date: 2018-12-21
• Subjects: transpiration -- eddy covariance -- water use efficiency -- ecohydrology -- ecophysiology -- machine learning; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2018JG004727
• Languages: English
• ISSNs: 2169-8953

Abstract

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 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. Plain Language Summary: While it is widely known that plants need water to survive, exactly how much water plants in an ecosystem use is hard to quantify. However, many places have been measuring how much total water leaves an ecosystem, both the water plants use directly and the water that simply evaporates from the soil or the surfaces of leaves, using eddy covariance towers. These eddy covariance towers also measure the coming and going of carbon, such as the total amount of carbon taken up by photosynthesis. Here we present the idea that by using the signals from both photosynthesis and total water losses together, we can capture the water signal related to plants, namely, transpiration, using an algorithm called Transpiration Estimation Algorithm (TEA). To verify that TEA is working the way we expect, we test it out using artificial ecosystem simulations where transpiration and photosynthesis come from mathematical models. By thoroughly testing TEA, we have a better idea of how it will work in a real world situation, hopefully opening the door for a better understanding on how much water ecosystems are using and how it might affect our changing planet. Key Points: TEA is a method for extracting water use efficiency (WUE) dynamics from flux data with minimal assumptions Validation shows that TEA is able to derive patterns of WUE and transpiration from three different models Method is applicable to eddy covariance data sets, opening the door to wide‐scale transpiration estimates