1. ChEAS 2005 D.S. Mackay June 1-2, 2005 Reference canopy conductance through space and time: Unifying properties and their conceptual basis D. Scott Mackay 1 Brent E. Ewers 2 Eric L. Kruger 3 Jonathan Adelman 2 Mike Loranty 1 Sudeep Samanta 3 1 SUNY at Buffalo 2 University of Wyoming 3 UW-Madison NSF Hydrologic Sciences EAR-0405306 EAR-0405381 EAR-0405318

2. ChEAS 2005 D.S. Mackay June 1-2, 2005 Problem Prediction of water resources from local to global scales requires an understanding of important hydrologic fluxes, including transpiration Current understanding of these fluxes relies on “center-of-stand” observations and “paint-by- numbers” scaling logic Spatial gradients are ignored, but this is an unnecessary simplification New scaling logic is needed that includes linear or nonlinear effects of spatial gradients on water fluxes

3. ChEAS 2005 D.S. Mackay June 1-2, 2005 Why is canopy transpiration important to hydrology? Average annual precipitation: 800 mm Growing season precipitation: 300-500 mm Growing season evapotranspiration: 350-450 mm Canopy transpiration (forest): 150-200 mm Canopy transpiration (aspen): 300 mm Ewers et al., 2002 (WRR) Mackay et al., 2002 (GCB)

4. ChEAS 2005 D.S. Mackay June 1-2, 2005 Assumptions Transpiration is too costly to measure everywhere, and so appropriate sampling strategies are needed The need for parameterization (e.g., sub-grid variability) will never go away Both forcing on and responses to transpiration are spatially related (or correlated), but this correlation is stronger in some places Human activities may increase or decrease this correlation

5. ChEAS 2005 D.S. Mackay June 1-2, 2005 Transpiration [mm (30-min) –1 ] 0.05.1.15.2 What if we increase edge effects? Center-of-Stand Basis Spatial Gradient Basis

6. ChEAS 2005 D.S. Mackay June 1-2, 2005 Why is Transpiration a Nonlinear Response? Relative Response Relative water demand Stomatal Conductance (Jarvis, 1980; Monteith, 1995) Transpiration (No stomata) “hydraulic failure” Reference Conductance Transpiration (With Stomata) “prevents hydraulic failure” Prevention of hydraulic failure is a key limiting factor for carbon gain and nutrient use by woody plants.

7. ChEAS 2005 D.S. Mackay June 1-2, 2005 Conceptual Basis of Spatial Reference Conductance G S = G Sref – mlnD m = 0.6G Sref (Oren et al., 1999) Environmental Gradient Canopy stomatal control of leaf water potential Hydraulic “Universal” line Mapping from spatial domain into a linear parameter domain

8. ChEAS 2005 D.S. Mackay June 1-2, 2005 Mackay et al., 2003 (Advances in Water Resources)

9. ChEAS 2005 D.S. Mackay June 1-2, 2005 Hypothesis 1 G Sref varies in response to spatial gradients within forest stands, but the relationship between G Sref and m remains linear Note that 1/D  1- 0.6ln(D) for 1 ≤ D ≤ 3 kPa; error is maximum of 16% at 2 kPa Thus many empirical stomatal conductance models are applicable, but discrepancies will occur at moderate mid-day D when it is hydrologically most relevant

10. ChEAS 2005 D.S. Mackay June 1-2, 2005

11. ChEAS 2005 D.S. Mackay June 1-2, 2005

12. ChEAS 2005 D.S. Mackay June 1-2, 2005

13. ChEAS 2005 D.S. Mackay June 1-2, 2005 Hydraulic constraint Light sensitivity Some model realizations follow hydraulic theory Best dynamic response Agricultural and Forest Meteorology (in review)

14. ChEAS 2005 D.S. Mackay June 1-2, 2005 These models preserve plant hydraulics and represent the regional variability for Sugar maple Agricultural and Forest Meteorology (in review)

15. ChEAS 2005 D.S. Mackay June 1-2, 2005 Aspen flux study, northern Wisconsin Wetland Transition Upland X – sample point X - Aspen Funded by NSF Hydrological Sciences

16. ChEAS 2005 D.S. Mackay June 1-2, 2005 Aspen Restricted Simulations Funded by NSF Hydrological Sciences

17. ChEAS 2005 D.S. Mackay June 1-2, 2005 Lodgepole pine study, Wyoming A1, riparian zone Row 4, lower slope Row 5, mid-slope Row 6, mid-slope Row 7, mid-slope Row 8, upper slope X – sample point X – Lodgepole pine

18. ChEAS 2005 D.S. Mackay June 1-2, 2005 A1, riparian zone Row 4, lower slope Row 5, mid-slope Row 6, mid-slope Row 7, mid-slope Row 8, upper slope Basal area crowding Lodgepole Pine Restricted Simulations

19. ChEAS 2005 D.S. Mackay June 1-2, 2005 Reference Canopy Conductance Water availability Index low high low high xericmesic Hydraulic Constraint Index Summary of Ecohydrologic Constraints highlow

20. ChEAS 2005 D.S. Mackay June 1-2, 2005 Hypothesis 2 Variation in leaf g S within and among species and environments is positively related with leaf nitrogen content and leaf-specific hydraulic conductance The relative response of g Smax to light intensity (Q) is governed in large part by  leaf, and this dependence underlies stomatal sensitivity to D –Corollary i: g S will increase with increasing Q until it reaches a limit imposed  leaf, which for a given leaf is mediated primarily by D –Corollary ii: The limit imposed on relative stomatal conductance (g/g Smax ) by  leaf (relative to the threshold linked to runaway cavitation,  crit ) is consistent within and among species

21. ChEAS 2005 D.S. Mackay June 1-2, 2005 Hypothesis 3 The model complexity needed to accurately predict transpiration is greater in areas of steep spatial gradients in species and environmental factors Model complexity (e.g. number of functions, non- linearity) should be increased when absolutely necessary, and it should subject to a penalty We should gain new knowledge whenever we are forced to increase a model’s complexity

22. ChEAS 2005 D.S. Mackay June 1-2, 2005

23. ChEAS 2005 D.S. Mackay June 1-2, 2005