COS 65-8 - Patterns and controls of water-use efficiency in an old-growth coniferous forest: analysis of fluxes and an ecosystem model

Tuesday, August 8, 2017: 4:00 PM
B113, Oregon Convention Center
Yueyang Jiang1, Christopher Still2, John B. Kim3, Bharat Rastogi2, Steve Voelker4 and Frederick C. Meinzer5, (1)Forest Ecosystems & Society, Oregon State University, Corvallis, OR, (2)Forest Ecosystems and Society, Oregon State University, Corvallis, OR, (3)Corvallis Forestry Sciences Laboratory, USDA Forest Service Pacific Northwest Research Station, Corvallis, OR, (4)Department of Plants, Soils and Climate, Utah State University, Logan, UT, (5)Pacific Northwest Research Station, USDA Forest Service, Corvallis, OR

Numerous studies have been conducted to estimate forest-scale water use efficiency (WUE=GPP/ET) through either stable carbon isotope analyses composition or eddy covariance measurements. However, the sign and magnitude of WUE response to climate variability are still highly uncertain, and can vary with the time scale of analysis. This study employed the Ecosystem Demography model version 2 (ED2) to explore patterns and physiological and biophysical controls of WUE in an old-growth coniferous forest in Pacific Northwest. Long-term eddy covariance flux measurements and the stable carbon isotope composition of COcollected at the Wind River AmeriFlux site were used to validate model performance. We characterized and contrasted WUE responses based on flux and isotope data between wet and dry years at the site, and also quantified how model predictions of WUE varied across these years. We explored how various WUE metrics from both measurements and model predictions vary with site meteorology and radiation, including clear and cloudy days. We also investigated how different species (e.g., Douglas-fir and western hemlock) and their respective age/size cohorts differ in modeled WUE patterns through time.


Our results showed that various WUE metrics from measurements and model predictions varied with site meteorology and radiation, including clear and cloudy days. Multiple linear regression and random Forest analysis indicated that the importance of different predictors (e.g., CO2 concentration, vapor pressure deficit) varied across different time scales (i.e. half hourly, daily, monthly). Fluxes measurements indicated that atmospheric CO2 concentration, air temperature and radiation was the most important predictor for WUE at monthly, daily and half-hourly time scale, respectively. In contrast, the ED2 model shown that vapor pressure deficit was consistently the most important predictors across the three time scales.