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

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

This study demonstrates partitioning E_ET observations into E_T and E_E [soil evaporation (E_Es) and interception evaporation (E_Ei)] through an ‘analytical solution’ of g_C, T_C 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 (L_ai) information in conjunction with meteorological, sensible heat flux (H) and E_ET observations from EC tower into the SEB equations for retrieving canopy and soil temperatures (T_S, T_C), g_C, and E_T.

E_T estimates from TRANSPIRE were tested and evaluated with a remote sensing based E_T estimate from an analytical model (STIC1.2), where lumped E_ET 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 E_T/E_ET ratio in a very similar way. While E_T accounted for 60±10% of the annual E_ET in the tropical savanna, E_T in the arid mulga contributed 75±12% of the annual E_ET. Seasonal variation of E_T was higher in the arid, semi-arid ecosystems (50 - 90%), as compared to the humid tropical ecosystem (10 - 50%). The TRANSPIRE model reasonably captured E_T variations along with soil moisture and precipitation dynamics in both sparse and homogeneous vegetation and showed the potential of partitioning E_ET observations for cross-site comparison with a variety of models.