A state-space modeling approach to estimating canopy conductance and associated uncertainties from sap flux density data

Abstract

Uncertainties in ecophysiological responses to environment, such as the impact of atmospheric and soil moisture conditions on plant water regulation, limit our ability to estimate key inputs for ecosystem models. Advanced statistical frameworks provide coherent methodologies for relating observed data, such as stem sap flux density, to unobserved processes, such as canopy conductance and transpiration. To address this need, we developed a hierarchical Bayesian State-Space Canopy Conductance (StaCC) model linking canopy conductance and transpiration to tree sap flux density from a 4-year experiment in the North Carolina Piedmont, USA. Our model builds on existing ecophysiological knowledge, but explicitly incorporates uncertainty in canopy conductance, internal tree hydraulics and observation error to improve estimation of canopy conductance responses to atmospheric drought (i.e., vapor pressure deficit), soil drought (i.e., soil moisture) and above canopy light. Our statistical framework not only predicted sap flux observations well, but it also allowed us to simultaneously gap-fill missing data as we made inference on canopy processes, marking a substantial advance over traditional methods. The predicted and observed sap flux data were highly correlated (mean sensor-level Pearson correlation coefficient = 0.88). Variations in canopy conductance and transpiration associated with environmental variation across days to years were many times greater than the variation associated with model uncertainties. Because some variables, such as vapor pressure deficit and soil moisture, were correlated at the scale of days to weeks, canopy conductance responses to individual environmental variables were difficult to interpret in isolation. Still, our results highlight the importance of accounting for uncertainty in models of ecophysiological and ecosystem function where the process of interest, canopy conductance in this case, is not observed directly. The StaCC modeling framework provides a statistically coherent approach to estimating canopy conductance and transpiration and propagating estimation uncertainty into ecosystem models, paving the way for improved prediction of water and carbon uptake responses to environmental change.

Bridging the Flux Gap: Sap Flow Measurements Reveal Species‐Specific Patterns of Water Use in a Tallgrass Prairie

“…Sap flux of probe i at time t was modeled as where J t is the average sap flux at time t , a i is the random effect associated with probe i , and S is the Gaussian observation variance. As opposed to the previous studies of trees using the StaCC model (Bell et al, ; Ward et al, ), we did not utilize a sapwood depth submodel due to the small sizes of stems (<7 mm) and a lack of information regarding radial reductions in sap flux. Similarly, because these small‐stemmed plants do not likely store an appreciable amount of stem water (Nobel & Jordan, ), we also did not utilize the additional capacitance submodel described in Bell et al ().…”

“…Next, G s,t was used to calculate actual canopy conductance ( G C ), assuming that G C is dependent on previous conductance and a time interval, d t (Rayment et al, ; Ward et al, ): where d t = 30 min and V t = 1 − exp(− d t /τ) and τ is the stomatal lag in minutes. The V t term accounts for stomatal lags using a stomatal time constant ( τ ), where τ = 10 min, following Bell et al (). Canopy conductance was then used to calculate E L,t , (kg m −2 s −1 ) as …”

“…which includes air temperature ( T t ), VPD, and several constant terms (Bell et al, ). Finally, E L was scaled to E C with the following equation: where A L is the leaf area index (LAI) and A s is the conducting tissue area index (CAI).…”

A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

“…5c-d). Synthesizing sap flow dynamics within a tree, for the whole tree, for groups of functionally related trees, and across the pantropical region enables improved understanding of ecohydrological processes in hyperdiverse tropical forests (Goldstein et al, 1998;Meinzer et al, 2001;Meinzer et al, 2004;Meinzer et al, 2005;Bell et al, 2015). The multiscale measurement position hierarchy facilitates such spatially extensive analyses because observations are defined by their position on the landscape and are linked by unique measurement position, tree (location), and site identifiers.…”

Regional evapotranspiration from image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous United States

“…Monteith (1981) highlighted the fact that the biophysical conductances (i.e., gA and gC) regulating ET are heavily temperature dependent, after which a stream of research demonstrated the dominant control of TR into gC and associated canopy-scale aerodynamics (Moffett and Gorelick, 2012;Blonquist et al, 2009). Somewhat surprisingly, the idea of integrating TR into the PM model was never attempted because of complexities associated with gC 20 parameterization (Bell et al, 2015;Matheny et al, 2014), until the concept of STIC was formulated (Mallick et al, 2014;Mallick et al, 2015). The recent version of STIC, STIC1.2, combines PM with the Shuttleworth-Wallace (SW) model (Shuttleworth and Wallace, 1985) to estimate the source/sink height temperature and vapour pressure (T0 and e0) (Mallick et al, 2016).…”