Contribution of sapwood traits to uncertainty in conifer sap flow as estimated with the heat-ratio method

Inferring whole-tree sap flow rates (Q) with thermometric sap flow sensors requires specification of physiological and structural attributes of trees. Using sap temperature measurements to estimate Q with the heat-ratio method (HRM) requires quantification of the water content (m_c), basic density (ρ_b), and depth (R_s) of sapwood. Values of m_c and ρ_b serve to estimate sapwood thermal diffusivity (k), a necessary variable in the calculation of heat-pulse velocity (V_h) that is often set to a nominal value (k_nom); m_c and ρ_b are also used to convert V_h to sap velocity (V_s). The sapwood area across which V_s is integrated is often estimated on the basis of R_s. Because m_c and ρ_b are correlated and influence Q through estimation of k and the conversion of V_h to V_s, we sought to quantify the potential error introduced when Q is calculated with k_nom rather than with k as estimated from measurements of m_c and ρ_b in five coniferous species. We also examined how variation in m_c, ρ_b, and R_s across sampling scales may contribute to uncertainty in estimated Q. Across the observed range of m_c and ρ_b, the two traits contributed to a net decline in the process of estimating V_s from V_h (with k_nom). This suggests that the use of k_nom rather than a calculated k may result in overestimation of Q when m_c and ρ_b co-vary as they did in our study. Variability in m_c and ρ_b across sampling scales could induce errors greater than 10% in V_s (and hence Q), while within- and among-tree variability in R_s could impart even greater errors (up to 130%). We propose that this uncertainty be represented in the expression of error in whole-tree sap flow estimates and in statistical analyses involving those estimates.