A critical evaluation of simple, flexible, and scalable models of daily transpiration in forested biomes

Abstract

<p>Transpiration (<em>T</em>) makes up the bulk of total evaporation over vegetated land yet remains challenging to predict at landscape-to-global scale.  Model improvements often occur at the expense of model parsimony and an increased dependence on input data that is difficult to acquire at large scale.  <em>T</em> models intended for these scales should ideally be easily scalable using routine meteorological and/or remote sensing data as input.  </p><p>Here, we critically evaluate several “big leaf”-type models ranging in their complexity to simulate daily <em>T</em> in a variety of forest biomes.  All these models use input data streams furnished by readily available global reanalysis or satellite-based remote sensing products.   We develop and evaluate a novel moisture stress method based on the Antecedent Precipitation Index (API) serving as proxy for soil moisture supply, motivated by the challenge of acquiring reliable soil moisture and other soil physical property data at large spatial and temporal scales.</p><p>We rely on independent estimates of <em>T</em> derived from co-located sap flow and eddy-covariance measurement systems.  The triple collocation technique is employed to quantify error metrics when treating modeled <em>T</em> as a third, independent measurement.</p><p>Preliminary results suggests that models that explicitly account for the aerodynamic coupling between canopy surfaces and the atmosphere generally perform better than those that do not, and that the API-based approach to modeling constraints related to soil moisture stress appears as a valid alternative when soil moisture information is unavailable.  </p>