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EGU 2020: Ecosystem scale estimates of transpiration: an overview of methods for eddy covariance data.
See the presentation here.
Here we present an overview of methods for partitioning evapotranspiration (ET) from eddy covariance data. We focus on methods that are designed to use the core energy and carbon fluxes, as well as meteorological data, and do not require supplemental measurements or campaigns. A comparison of three such methods for estimating transpiration (T) showed high correlations between them (R2 of daily T between 0.80 and 0.87) and higher correlations to daily stand T estimates from sap flow data (R2 between 0.58 and 0.66) compared to the tower ET (R2 = 0.49). However, the three methods show significant differences in magnitude, with T/ET values ranging from 45% to 77%. Despite the differences in magnitude, the methods show plausible patterns with respect to LAI, seasonal cycles, WUE, and VPD; moreover, they represent an improvement compared to using ET as a proxy for T even when filtering for days after rain. Finally, we outline practical aspects of applying the methods, such as how to apply the methods and code availability.
Code and examples of how to estimate transpiration from eddy covariance data.
We are working toward a manuscript on evapotranspiration partitioning of eddy covariance data. The paper will utilize three partitioning methods:
‣ Perez-Priego et al (2018). Partitioning Eddy Covariance Water Flux Components Using Physiological and Micrometeorological Approaches. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1029/2018JG004637
‣ Nelson et al (2018). Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1029/2018JG004727
‣ Zhou et al (2016). Partitioning evapotranspiration based on the concept of underlying water use efficiency: ET PARTITIONING. Water Resources Research, 52(2), 1160–1175. https://doi.org/10.1002/2015WR017766
To run a tutorial for the TEA (Nelson et al 2018), uWUE (Zhou et al 2016), and Perez-Priego methods, check ou the links below. Each can be run within a browser without any installation!
If you try out the tutorial, please feel free to give me some feedback via email ⬆⬆⬆
My work at BGI
Current work is focused on three main areas:
- Using data driven methods to partition water fluxes into component parts e.g. soil evaporation and plant transpiration.
- Finding signatures of ecosystem plant status from sub-daily patterns in water, carbon and energy fluxes.
- Examining the underlying physical and biochemical processes controlling latent heat fluxes.
Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm
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.
Comparison of mean seasonal cycle of T/ET (5‐day aggregation) results from model simulations (JSBACH, CASTANEA, and MuSCIA) and TEA algorithm partitioning of original EC data (ET, GPP, and meteorological variables). Modeled years were 2001–2006 for FI‐Hyy and FR‐Hes, and 1998–2003 for DE‐Tha. Seasonal cycles are an average of 5 days. TEA = Transpiration Estimation Algorithm; EC = eddy covariance; ET = evapotranspiration; GPP = gross primary productivity. FI‐Hyy = Hyytiälä, Finland; FR‐Hes = Hesse beech forest in France; DE‐Tha = Anchor Station Tharandt, Germany.
See the full paper here.
- Jung, Martin, Christopher Schwalm, Mirco Migliavacca, Sophia Walther, Gustau Camps-Valls, Sujan Koirala, Peter Anthoni, Simon Besnard, Paul Bodesheim, Nuno Carvalhais, Frederic Chevallier, Fabian Gans, Daniel S. Groll, Vanessa Haverd, Kazuhito Ichii, Atul K. Jain, Junzhi Liu, Danica Lombardozzi, Julia E. M. S. Nabel, Jacob A. Nelson, Martijn Pallandt, Dario Papale, Wouter Peters, Julia Pongratz, Christian Rödenbeck, Stephen Sitch, Gianluca Tramontana, Ulrich Weber, Markus Reichstein, Philipp Koehler, Michael O'Sullivan, and Anthony Walker. n.d. “Scaling Carbon Fluxes from Eddy Covariance Sites to Globe: Synthesis and Evaluation of the FLUXCOM Approach.” Biogeosciences Discussions, https://doi.org/10.5194/bg-2019-368
- Stoy, Paul C., Tarek S. El-Madany, Joshua B. Fisher, Pierre Gentine, Tobias Gerken, Stephen P. Good, Anne Klosterhalfen, Shuguang Liu, Diego G. Miralles, Oscar Perez-Priego, Angela J. Rigden, Todd H. Skaggs, Georg Wohlfahrt, Ray G. Anderson, A. Miriam J. Coenders-Gerrits, Martin Jung, Wouter H. Maes, Ivan Mammarella, Matthias Mauder, Mirco Migliavacca, Jacob A. Nelson, Rafael Poyatos, Markus Reichstein, Russell L. Scott, and Sebastian Wolf. 2019. “Reviews and Syntheses: Turning the Challenges of Partitioning Ecosystem Evaporation and Transpiration into Opportunities.” Biogeosciences 16, no. 9 (October 1, 2019): 3747-3775. https://doi.org/10.5194/bg-16-3747-2019
- Nelson, Jacob A., Nuno Carvalhais, Matthias Cuntz, Nicolas Delpierre, Jurgen Knauer, Jérome Ogée, Mirco Migliavacca, Markus Reichstein, and Martin Jung. "Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm." Journal of Geophysical Research: Biogeosciences, December 21, 2018. https://doi.org/10.1029/2018JG004727
- Nelson, Jacob A., Nuno Carvalhais, Mirco Migliavacca, Markus Reichstein, and Martin Jung. "Water-Stress-Induced Breakdown of Carbon Water Relations: Indicators from Diurnal FLUXNET Patterns." Biogeosciences 15, no. 8 (April 20, 2018): 2433-2447. https://doi.org/10.5194/bg-15-2433-2018
- Nelson, Jacob A., and Bruce Bugbee. 2015. “Analysis of Environmental Effects on Leaf Temperature under Sunlight, High Pressure Sodium and Light Emitting Diodes.” PloS One 10 (10): e0138930. https://doi.org/10.1371/journal.pone.0138930
- Nelson, Jacob A., and Bruce Bugbee. 2014. “Economic Analysis of Greenhouse Lighting: Light Emitting Diodes vs. High Intensity Discharge Fixtures.” PloS One 9 (6): e99010. https://doi.org/10.1371/journal.pone.0099010
Data and Code
Conferences and Workshops
- Examining Transpiration from Ecosystem to Global Scales, 5-7 September 2018 at the Max Planck Institute for Biogeochemistry in Jena, Germany. http://www.bgc.mpg.de/~jnelson/transpiration2018/
- Nelson, JA, N Carvalhais, M Cuntz, N Delpierre, J Knauer, M Migliavacca, J Ogee, M Reichstein, and M Jung. 2017. “Data Driven Estimation of Transpiration from Net Water Fluxes: The TEA Algorithm.” In AGU Fall Meeting Abstracts.
- Nelson, Jacob, Martin Jung, Nuno Carvalhais, Mirco Migliavacca, and Markus Reichstein. 2016. “Understanding Ecosystems’ Sub-Daily Water and Carbon Flux Changes during Dry-down Events.” In EGU General Assembly Conference Abstracts, 18:17549.