Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate

Abstract

Applications of ecosystem flux models on large geographical scales are often limited by model complexity and data availability. Here we calibrated and evaluated a semiempirical ecosystem flux model, PREdict Light-use efficiency, Evapotranspiration and Soil water (PRELES), for various forest types and climate conditions, based on eddy covariance data from 55 sites. A Bayesian approach was adopted for model calibration and uncertainty quantification. We applied the site-specific calibrations and multisite calibrations to nine plant functional types (PFTs) to obtain the sitespecific and PFT-specific parameter vectors for PRELES. A systematically designed cross-validation was implemented to evaluate calibration strategies and the risks in extrapolation. The combination of plant physiological traits and climate patterns generated significant variation in vegetation responses and model parameters across but not within PFTs, implying that applying the model without PFT-specific parameters is risky. But within PFT, the multisite calibrations performed as accurately as the sitespecific calibrations in predicting gross primary production (GPP) and evapotranspiration (ET). Moreover, the variations among sites within one PFT could be effectively simulated by simply adjusting the parameter of potential light-use efficiency (LUE), implying significant convergence of simulated vegetation processes within PFT. The hierarchical modelling of PRELES provides a compromise between satellite-driven LUE and physiologically oriented approaches for extrapolating the geographical variation of ecosystem productivity. Although measurement errors of eddy covariance and remotely sensed data propagated a substantial proportion of uncertainty or potential biases, the results illustrated that PRELES could reliably capture daily variations of GPP and ET for contrasting forest types on large geographical scales if PFT-specific parameterizations were applied. K E Y W O R D S evapotranspiration, geographical variations, gross primary production, inverse modelling, light-use efficiency, multisite calibration, plant functional type S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Tian X, Minunno F, Cao T, Peltoniemi M, Kalliokoski T, Mäkelä A. Extending the range of applicability of the semi-empirical ecosystem flux model PRELES for varying forest types and climate. Glob Change

Estimating canopy gross primary production by combining phloem stable isotopes with canopy and mesophyll conductances

“…However, a positive N‐fertilization effect on GPP was not always observed. At our site, previous studies showed no effect of N supply on GPP when measured from biometrics (Lim et al, 2015) or shoot‐scale gas exchange (Tarvainen, Lutz, Räntfors, Näsholm, & Wallin, 2016), but Tian et al (2020), who used EC data to parametrize a model, did find higher GPP in the fertilized plot than in the reference plot. Thus, the GPP results have been mixed, depending on which method was used.…”

“…Using Bayesian calibration, we adjusted parameters of PRELES according to their ability to reproduce EC observations (Tian et al, 2020). The Bayesian framework treated all terms in the model calibrations and predictions as probability distributions (Clark, 2007; Dietze, 2017).…”

“…The required EC data are available from many sites around the world (Baldocchi, 2003). PRELES has been reported to work well in all boreal forests (Minunno et al, 2016;Tian et al, 2020). Based on this assessment, we felt justified in using it, with calibration from 2014 to 2017 and environmental data from 2012 to 2013, to model carbon fluxes in 2012 to 2013.…”

“…One of the advantages of PRELES is that it estimates ecosystem fluxes (GPP and evapotranspiration) by using routinely measured weather data. It means that GPP PRELES can be estimated everywhere with no additional measurement than weather conditions (Tian, Minunno, Cao, Kalliokoski, & Mäkelä, 2020). This approach allows one to go back in time for estimating GPP of the boreal forest in years for which EC are not available (Minunno et al, 2016).…”

“…The understorey contribution from PRELES is in the process of being analysed. However, understorey GPP represents rather little of ecosystem GPP in a closed-canopy boreal forest (Kulmala et al, 2011;Palmroth et al, 2019, Tian et al, 2020 and the sap flow/isotopic method should therefore give similar results. The GPP iso/SF method can also provide information on how GPP iso/SF responds to fertilization in terms of assimilation and g S .…”