Sap Flow and Evapotranspiration Models.

 

In the orchards of Yarra Valley, a team of scientists from Griffith University and Implexx sought to bridge the gap between sophisticated models and real-world observations of water use by trees. They focused on combining sap flow measurements with the E2.88 model, which simplifies evapotranspiration predictions by normalizing for leaf area, and tested it against 20 variations using apple and pear trees over a full growing season.

Published in the international journal Agricultural and Forest Meteorology, the results were striking. While the models performed well mid-season, their accuracy faltered during early and late growth stages due to the interplay of phenology (seasonal biological changes) and canopy conductance (how well leaves exchange water and gases). For apple trees, the Penman-Monteith ASCE-EWRI model stood out, while for pears, the Valiantzas model shone brightest.

 

Further Details.

This study unfolds as a meticulous exploration of the interactions between biological processes and atmospheric demands, centring on the challenge of accurately predicting transpiration—the water plants release into the atmosphere. Conducted in the orchards of Yarra Valley, Victoria, Australia, researchers tested 20 evapotranspiration (ETo) models, including the E2.88 model, which is rooted in a widely recognized standard (the Penman-Monteith FAO56 model). Their objective was to determine the reliability of these models in predicting transpiration under real-world conditions.

 

The Foundation: Why Study ETo Models?

Evapotranspiration is a critical process in the hydrological cycle, influencing agriculture, ecology, and urban planning. The E2.88 model, first proposed by Pereira et al. (2006), assumes a direct relationship between reference evapotranspiration (ETo) and actual transpiration, normalized by the leaf area index (LAI) of a reference crop. While theoretically sound, its real-world application is influenced by dynamic factors like canopy conductance and phenology—parameters that evolve with the life cycle of plants.

 

The Study’s Scope and Methods.

The researchers monitored Granny Smith apple and Beurre Bosc pear trees over a full growing season (October 2020 to June 2021). Using advanced sap flow sensors (Implexx Sap Flow Sensors), they measured actual sap flow and transpiration. They also tracked canopy conductance, leaf area index, and meteorological data.

The study divided the season into three phenological stages:

1. Early Season: Leaf area expands.
2. Mid-Season: Leaf area stabilizes at its peak.
3. Late Season: Leaf area declines due to senescence.

 

Key Findings and Insights.

1. Model Accuracy Varies by Season:
   • In the mid-season, when leaf area was stable, models like the Penman-Monteith ASCE-EWRI and Valiantzas performed exceptionally, predicting transpiration within a 2–7% margin of error.
   • During the early and late seasons, when biological processes like leaf development and senescence were prominent, model accuracy declined. Transpiration predictions were less reliable due to a "decoupling" effect, where canopy conductance was less synchronized with atmospheric evaporative demand.
2. Phenology’s Role:
   • Early and late in the season, apple trees exhibited higher canopy conductance, allowing more water vapor to escape into the atmosphere regardless of external demand.
   • In pears, the late-season canopy conductance dropped, reflecting tighter coupling with atmospheric conditions.
3. The Importance of Humidity:
   • Models incorporating relative humidity (RH) as a parameter consistently outperformed those that didn’t. RH’s strong correlation with sap flow and transpiration made it a crucial factor for predictive accuracy.

 

Implications for Science and Practice.

The findings emphasize that while mid-season transpiration can be effectively modelled with existing methods, capturing early and late-season dynamics requires integrating more biological parameters into models. This is especially critical for agricultural water management, where precise predictions can optimize irrigation strategies, improve water conservation, and enhance crop yield.

Additionally, the study highlighted the potential for using simplified models (e.g., the Valiantzas equation) that require fewer meteorological inputs, making them more accessible for applications in resource-limited settings.

 

A Path Forward.

Future research could delve deeper into the physiological and morphological drivers of canopy conductance and explore alternative parameters, such as stomatal behaviour or xylem conductivity. These insights could lead to refined models that better reflect the complexities of plant-water interactions across different species and environmental contexts.

This study not only advances the understanding of transpiration modelling but also underscores the intricate dance between biology and the atmosphere—a relationship central to sustaining ecosystems and agricultural systems alike.

 

References.

• Forster et al. 2022. Phenology and canopy conductance limit the accuracy of 20 evapotranspiration models in predicting transpiration.
• Pereira et al. 2006. Penman–Monteith reference evapotranspiration adapted to estimate irrigated tree transpiration.