Application of the metabolic scaling theory and water-energy balance equation to model large-scale patterns of maximum forest canopy height

2016 ◽  
Vol 25 (12) ◽  
pp. 1428-1442 ◽  
Author(s):  
Sungho Choi ◽  
Christopher P. Kempes ◽  
Taejin Park ◽  
Sangram Ganguly ◽  
Weile Wang ◽  
...  
Trees ◽  
2021 ◽  
Author(s):  
Hans Pretzsch

Abstract Key message Prediction of tree growth based on size or mass as proposed by the Metabolic Scaling Theory is an over-simplification and can be significantly improved by consideration of stem and crown morphology. Tree growth and metabolic scaling theory, as well as corresponding growth equations, use tree volume or mass as predictors for growth. However, this may be an over-simplification, as the future growth of a tree may, in addition to volume or mass, also depend on its past development and aspects of the current inner structure and outer morphology. The objective of this evaluation was to analyse the effect of selected structural and morphological tree characteristics on the growth of common tree species in Europe. Here, we used eight long-term experiments with a total of 24 plots and extensive individual measurements of 1596 trees in monospecific stands of European beech (Fagus sylvatica L.), Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea (Matt.) Liebl.). Some of the experiments have been systematically surveyed since 1870. The selected plots represent a broad range of stand density, from fully to thinly stocked stands. We applied linear mixed models with random effects for analysing and modelling how tree growth and productivity are affected by stem and crown structure. We used the species-overarching relationship $$\mathrm{iv}={{a}_{0}\times v}$$ iv = a 0 × v between stem volume growth, $$\mathrm{iv}$$ iv and stem volume, $$v,$$ v , as the baseline model. In this model $${a}_{0}$$ a 0 represents the allometric factor and α the allometric exponent. Then we included tree age, mean stem volume of the stand and structural and morphological tree variables in the model. This significantly reduced the AIC; RMSE was reduced by up to 43%. Interestingly, the full model estimating $$\mathrm{iv}$$ iv as a function of $$v$$ v and mean tree volume, crown projection area, crown ratio and mean tree ring width, revealed a $$\alpha \cong 3/4$$ α ≅ 3 / 4 scaling for the relationship between $$\mathrm{iv}\propto {v}^{\alpha }$$ iv ∝ v α . This scaling corresponded with Kleiber’s rule and the West-Brown-Enquist model of the metabolic scaling theory. Simplified approaches based on stem diameter or tree mass as predictors may be useful for a rough estimation of stem growth in uniform stands and in cases where more detailed predictors are not available. However, they neglect other stem and crown characteristics that can have a strong additional effect on the growth behaviour. This becomes of considerable importance in the heterogeneous mixed-species stands that in many countries of the world are designed for forest restoration. Heterogeneous stand structures increase the structural variability of the individual trees and thereby cause a stronger variation of growth compared with monocultures. Stem and crown characteristics, which may improve the analysis and projection of tree and stand dynamics in the future forest, are becoming more easily accessible by Terrestrial laser scanning.


2007 ◽  
Vol 13 (3) ◽  
pp. 591-609 ◽  
Author(s):  
BRIAN J. ENQUIST ◽  
ANDREW J. KERKHOFF ◽  
TRAVIS E. HUXMAN ◽  
EVAN P. ECONOMO

Author(s):  
Shaun Lovejoy ◽  
Roman Procyk ◽  
Raphael Hébert ◽  
Lenin Del Rio Amador

2021 ◽  
Author(s):  
Roman Procyk ◽  
Shaun Lovejoy ◽  
Raphaël Hébert ◽  
Lenin Del Rio Amador

<p>We present the Fractional Energy Balance Equation (FEBE): a generalization of the standard EBE.  The key FEBE novelty is the assumption of a hierarchy of energy storage mechanisms: scaling energy storage.  Mathematically the storage term is of fractional rather than integer order.  The special half-order case (HEBE) can be classically derived from the continuum mechanics heat equation used by Budyko and Sellers simply by introducing a vertical coordinate and using the correct conductive-radiative surface boundary conditions (the FEBE is a mild extension).</p><div> <p>We use the FEBE to determine the temperature response to both historical forcings and to future scenarios.  Using historical data, we estimate the 2 FEBE parameters: its scaling exponent (<em>H</em> = 0.38±0.05; <em>H</em> = 1 is the standard EBE) and relaxation time (4.7±2.3 years, comparable to box model relaxation times). We also introduce two forcing parameters: an aerosol re-calibration factor, to account for their large uncertainty, and a volcanic intermittency exponent so that the intermittency volcanic signal can be linearly related to the temperature. The high frequency FEBE regime not only allows for modelling responses to volcanic forcings but also the response to internal white noise forcings: a theoretically motivated error model (approximated by a fractional Gaussian noise). The low frequency part uses historical data and long memory for climate projections, constraining both equilibrium climate sensitivity and historical aerosol forcings. <span>Parameters are estimated in a Bayesian framework using 5 global observational temperature series, and an error model which is a theoretical consequence of the FEBE forced by a Gaussian white noise.</span></p> <p>Using the CMIP5 Representative Concentration Pathways (RCPs) and CMIP6 Shared Socioeconomic Pathways (SSPs) scenario, the FEBE projections to 2100 are shown alongside the CMIP5 MME. The Equilibrium Climate Sensitivity = 2.0±0.4 <sup>o</sup>C/CO<sub>2</sub> doubling implies slightly lower temperature increases.   However, the FEBE’s 90% confidence intervals are about half the CMIP5 size so that the new projections lie within the corresponding CMIP5 MME uncertainties so that both approaches fully agree.   The mutually agreement of qualitatively different approaches, gives strong support to both.  We also compare both generations of General Circulation Models (GCMs) outputs from CMIP5/6 alongside with the projections produced by the FEBE model which are entirely independent from GCMs, contributing to our understanding of forced climate variability in the past, present and future.</p> <p>Following the same methodology, we apply the FEBE to regional scales: estimating model and forcing parameters to produce climate projections at 2.5<sup>o</sup>x2.5<sup>o</sup> resolutions. We compare the spatial patterns of climate sensitivity and projected warming between the FEBE and CMIP5/6 GCMs. </p> </div>


Sign in / Sign up

Export Citation Format

Share Document