Patterns and controls of water-use efficiency in an old-growth coniferous forest Yueyang Jiang1, John B. Kim2, Christopher J. Still1, Bharat Rastogi1, Steve Voelker3, Frederick Meinzer2 1Department of Forest Ecosystems & Society, Oregon State University, Corvallis, OR; 2Pacific Northwest Research Station, USDA Forest Service; 3Department of Plants, Soils and Climate, Utah State University, Logan, UT Oregon State University Introduction Water use efficiency (WUE) has been widely recognized as an important physiological link between carbon and water cycling, and used to track forest ecosystem responses to climate change and rising atmospheric CO2 concentrations. Numerous studies have been conducted to estimate forest-scale WUE 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 CO2 collected at the Wind River AmeriFlux site were used to validate model performance. Results The calibrated ED2.1-WR model well captured carbon and water fluxes, and both model and eddy covariance data indicated that the Wind River forest functioned as carbon sink during 1998-2015. The unique physiological properties of Pacific Northwest late-successional conifer trees play an important role in determining old growth forest demography, thereby largely affecting ecosystem carbon budget. Figure 3. ED2.1-WR simulated and tower derived 18 years average daily GPP, Reco and NEP at the Wind River Canopy Crane Research Facility site. Figure 4. Comparison of ED2 modeled and field measured daily transpiration (mmol/m2/s) for year 2002 at Wind River Canopy Crane Research Facility site. Figure 5. The ED2.1-WR modeled Ci/Ca ratio under clear and cloudy sky conditions for northern pine (left) and late conifer (right) trees. ED2.1-WR Eddy covariance 52 m 19 m Douglas-fir W. hemlock Wind River tower Figure 1. The Wind River Canopy Crane Research Facility, and species dominancy. Methods We calibrated the ED2 model to the Wind River forest site by adjusting several key physiological parameters for two dominant PFTs (further referred as ED2.1-WR). Based on eddy covariance, isotope data and ED2.1-WR simulations, we Tatm Tcanopy Tsurface Tsoil_1 Tsoil_i Tsoil_n eatm ecanopy esurface esoil_1 esoil_i esoil_n CO2 Rin Rout Evaluate ED2.1-WR performance in simulating carbon, water and energy fluxes. Characterize WUE, sapflow and stomatal responses (ci/ca) in wet and dry years. Contrast WUE, sapflow and stomatal response between species & between cohorts. Rank relative importance of varied predictors in determining WUE using machine learning methods. Figure 6. Relative importance of different predictors in determining WUE. Ongoing Work To explore how various WUE metrics (e.g., intrinsic WUE, inherent WUE) from both measurements and model predictions vary with meteorology through different time scales. To investigate the relative importance of different predictors in determining various WUE metrics, using machine learning methods. To scale up forest WUE across the Pacific Northwest and conducted model simulations (e.g., ED2.1-WR, 3-PG and FVS-Climate) to project future WUE under projected climate scenarios. Different PFTs have distinct WUE, sapflow and stomatal responses to short-term meteorology and radiation including clear and cloudy days, and long-term climate. Multiple linear regression and machine learning methods indicated that the relative importance of predictor for WUE shown different patterns in ED2.1-WR and eddy covariance data. Figure 2. ED2 framework derived from Medvigy et al. (2009).