Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Abstract

Evaporation (E) and transpiration (T ) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T , which makes it difficult to validate satellite data and land surface models. Here, we review current progress in partitioning E and T and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For example, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T can exceed 95 % of ET from certain ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T , yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon-water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.

Transpiration from subarctic deciduous woodlands: Environmental controls and contribution to ecosystem evapotranspiration

“…Our results also show that both increased woodland cover and increased woodland density under climate change conditions (Rundqvist et al, 2011) will result in larger controls of the water fluxes by the canopies of deciduous trees as opposed to the understorey vegetation. However, our upscaling exercise also shows that adequately accounting for understorey components (and transpiration vs. evaporation processes; Stoy et al, 2019) may be necessary to constrain future hydrological changes in these areas. The highly variable and patchy nature of subarctic vegetation may require flux upscaling approaches considering spatial variation not only of land cover (Hartley et al, 2015), but also of LAI (Stoy, Williams, et al, 2013).…”

Plant Water Uptake Thresholds Inferred From Satellite Soil Moisture

“…Our model does not partition plant transpiration and soil water evaporation losses from the surface soil, and this is a challenge in most ecohydrological studies (Stoy et al, ). We can assume Ψ 0 , Ψ * , and E max / E p generally reflect plant water uptake behavior if the shape of the piecewise function for soil water losses (Figure S1) is determined by vegetation.…”

Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

“…As precipitation is the key climatic factor controlling ET in arid and semiarid regions (Fig. 6), discrepancies between different forcing precipitation (Sun et al, 2018) may be the main source of large uncertainty there. In comparison, the uniform forcing data reduced the inter-model range in ET es-timates from the TRENDY LSMs.…”