Assessment of foliage clumping effects on evapotranspiration estimates in forested ecosystems Academic Article uri icon

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abstract

  • In forested ecosystems, the aggregation of leaves into different spatial structures at the canopy, branch and shoot scales leads to a non-random spatial distribution of foliar elements. However, effect of foliar aggregation or clumping on the estimation of terrestrial evapotranspiration (ET) is not yet well understood. To evaluate the effect of foliar clumping, the process-based Boreal Ecosystem Productivity Simulator (BEPS) is used to simulate ET at eight flux tower sites in North American forests. BEPS separates a canopy into sunlit and shaded leaf groups in the calculation of canopy-level ET and clumping affects this separation. Three cases are simulated with BEPS, and the modeled ET values are compared with flux tower measurements: Case I serves as a baseline, in which LAI and clumping index at the sites are considered. In this case, BEPS can explain 43–75% the variance of measured ET at these sites. For Case II, the LAI is considered but clumping is ignored; resulting in an overestimation of annual ET at all sites by ∼5% at the most clumped sites. In Case III, when effective LAI is used (i.e. clumping is not considered), the site-averaged mean annual ET is underestimated by 11.5%, with the largest underestimation found at the site with most clumping (CA-DF49; 19.1%). In both Case II and Case III, the more clumped a canopy is, the larger bias is found in the ET estimation (p<0.001). The estimated biases are robust to the errors in key driving variables and model parameters. When LAI is derived from optical measurements on the ground and from satellite platforms without considering the effect of clumping, it can cause substantial underestimation of ET.These results demonstrate the need for considering foliage clumping in process-based ET modeling, with potential biases having large implications for carbon cycle modeling, water budget calculations and understanding and predicting ecosystem responses to future climatic scenarios.

publication date

  • January 2016