EGU2020-5193, updated on 17 Aug 2023
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Disentangling ecosystem transpiration from evapotranspiration observations employing simplified vegetation-substrate energy balance model

Kaniska Mallick, Dennis Baldocchi, Andrew Jarvis, Ivonne Trebs, Mauro Sulis, and Joseph Berry
Kaniska Mallick et al.
  • Luxembourg Institute of Science and Technology, Remote Sensing and Natural Resources Modeling, Luxembourg (

Evapotranspiration (EET) observed by eddy covariance (EC) towers is composed of physical evaporation (EE) from wet surfaces and biological transpiration (ET), that involves soil moisture uptake by roots and water vapor transfer regulated through the canopy-stomatal conductance (gC) during photosynthesis. ET plays a dominant role in the global water cycle and represents 80% of the total terrestrial EET. Understanding the magnitude and variability of ET are critical to assess the ecophysiological responses of vegetation to drought. While separating ET signals from lumped EET observations and/or simulating ET by terrestrial systems models is insufficiently constrained owing to the large uncertainties in disentangling gC from the aggregated canopy-substrate conductance (gcS), evaluating ecosystem ET derived through partitioning EET observations (or model simulation) is also challenging due to the absence of any ecosystem-scale measurements of this biotic flux and gC. To date, the main methods for partitioning EC-EET observations are largely based on regressing EET with gross photosynthesis (Pg) and atmospheric vapor pressure deficit (DA) observations. However, such methods ignore the essential feedback of the surface energy balance (SEB) and canopy temperature (TC) on gC and ET.

This study demonstrates partitioning EET observations into ET and EE [soil evaporation (EEs) and interception evaporation (EEi)] through an ‘analytical solution’ of gC, TC and canopy vapor pressures by employing a Shuttleworth-Gurney vegetation-substrate energy balance model with minimal complexity. The model is called TRANSPIRE (Top-down partitioning evapotRANSPIRation modEl), which ingests remote sensing land surface temperature (LST) and leaf area index (Lai) information in conjunction with meteorological, sensible heat flux (H) and EET observations from EC tower into the SEB equations for retrieving canopy and soil temperatures (TS, TC), gC, and ET.

ET estimates from TRANSPIRE were tested and evaluated with a remote sensing based ET estimate from an analytical model (STIC1.2), where lumped EET was partitioned by employing a moisture availability constraints across an aridity gradient in the North Australian Tropical Transect (NATT) by using time-series of 8-day MODIS Terra LST and LAI products in conjunction with EC measurements from 2011 to 2018. Both methods captured the seasonal pattern of ET/EET ratio in a very similar way. While ET accounted for 60±10% of the annual EET in the tropical savanna, ET in the arid mulga contributed 75±12% of the annual EET. Seasonal variation of ET was higher in the arid, semi-arid ecosystems (50 - 90%), as compared to the humid tropical ecosystem (10 - 50%). The TRANSPIRE model reasonably captured ET variations along with soil moisture and precipitation dynamics in both sparse and homogeneous vegetation and showed the potential of partitioning EET observations for cross-site comparison with a variety of models.

How to cite: Mallick, K., Baldocchi, D., Jarvis, A., Trebs, I., Sulis, M., and Berry, J.: Disentangling ecosystem transpiration from evapotranspiration observations employing simplified vegetation-substrate energy balance model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5193,, 2020.