Modeling daily gas exchange of a Douglas-fir forest: comparison of three stomatal conductance models with and without a soil water stress function

Wijk, Mark T. van; Dekker, Stefan C.; Bouten, W.; Bosveld, Fred C.; Kohsiek, W.; Krämer, K.; Mohren, G.M.J.

doi:10.1093/treephys/20.2.115

Cited by 98 publications

(50 citation statements)

References 39 publications

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“…However, the success of the second approach depends critically on the choice of the model comparison metric. Often the comparison metric is purely a quantitative measure of the goodness of fit, such as the coefficient of determination, R 2 , calculated through linear regression between model output and observed data [e.g., Katul et al, 2000;van Wijk et al, 2000;Moriana et al, 2002;Misson et al, 2004]. However, like the transpiration models used here, semiempirical models are often nonlinear, and all of the models included in a comparison may not use the same predictor variables, therefore, the most appropriate way to calculate R 2 uniformly for all the models is not always clear [Healy, 1984;Kvålseth, 1985;Anderson-Sprecher, 1994;Mitchell, 1997].…”

Section: Overview Of Model Comparison and Selection Approaches: Ratiomentioning

confidence: 99%

Quantitative comparison of canopy conductance models using a Bayesian approach

Samanta

Clayton

Mackay

et al. 2008

Water Resources Research

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[1] A quantitative model comparison methodology based on deviance information criterion, a Bayesian measure of the trade-off between model complexity and goodness of fit, is developed and demonstrated by comparing semiempirical transpiration models. This methodology accounts for parameter and prediction uncertainties associated with such models and facilitates objective selection of the simplest model, out of available alternatives, which does not significantly compromise the ability to accurately model observations. We use this methodology to compare various Jarvis canopy conductance model configurations, embedded within a larger transpiration model, against canopy transpiration measured by sap flux. The results indicate that descriptions of the dependence of stomatal conductance on vapor pressure deficit, photosynthetic radiation, and temperature, as well as the gradual variation in canopy conductance through the season are essential in the transpiration model. Use of soil moisture was moderately significant, but only when used with a hyperbolic vapor pressure deficit relationship. Subtle differences in model quality could be clearly associated with small structural changes through the use of this methodology. The results also indicate that increments in model complexity are not always accompanied by improvements in model quality and that such improvements are conditional on model structure. Possible application of this methodology to compare complex semiempirical models of natural systems in general is also discussed.

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Section: Overview Of Model Comparison and Selection Approaches: Ratiomentioning

confidence: 99%

Quantitative comparison of canopy conductance models using a Bayesian approach

Samanta

Clayton

Mackay

et al. 2008

Water Resources Research

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“…The surrounding area is characterized by the presence of broadleaved and coniferous tree species, distributed in blocks around the tower site (Bosveld and Bouten, 2001). Within a 500 m radius it is possible to find native tree species such as beech (Fagus sylvatica L.), pedunculate oak (Quercus robur L.) and Scots pine (Pinus sylvestris L.), as well as the introduced species hemlock (Tsuga heterophylla (Rafinesque) Sargent) and Japanese larch (Larix kaempferi (Lambert) Carriére) (Erisman et al, 1998;Raj et al, 2014;Su et al, 2009;Bosveld and Bouten, 2001;Tietema et al, 2002;Van Wijk et al, 2000;Weligepolage et al, 2013). Canopy heights differ between cover types depending on species and growing stage.…”

Section: Study Sitementioning

confidence: 99%

“…The topography is slightly undulating with smooth height differences (Raj et al, 2014), a well-drained soil, and a groundwater table below 40 m depth (Tiktak and Bouten, 1994). The soil texture ranges from fine sand to sandy loam (Weligepolage et al, 2012;Tietema et al, 2002;Van Wijk et al, 2000).…”

Section: Study Sitementioning

confidence: 99%

Technical note: Using distributed temperature sensing for Bowen ratio evaporation measurements

Schilperoort

Coenders-Gerrits

Luxemburg

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Rapid improvements in the precision and spatial resolution of distributed temperature sensing (DTS) technology now allow its use in hydrological and atmospheric sciences. Introduced by Euser et al. (2014) is the use of DTS for measuring the Bowen ratio (BR-DTS), to estimate the sensible and latent heat flux. The Bowen ratio is derived from DTS-measured vertical profiles of the air temperature and wet-bulb temperature. However, in previous research the measured temperatures were not validated, and the cables were not shielded from solar radiation. Additionally, the BR-DTS method has not been tested above a forest before, where temperature gradients are small and energy storage in the air column becomes important.In this paper the accuracy of the wet-bulb and air temperature measurements of the DTS are verified, and the resulting Bowen ratio and heat fluxes are compared to eddy covariance data. The performance of BR-DTS was tested on a 46 m high tower in a mixed forest in the centre of the Netherlands in August 2016. The average tree height is 26 to 30 m, and the temperatures are measured below, in, and above the canopy. Using the vertical temperature profiles the storage of latent and sensible heat in the air column was calculated.We found a significant effect of solar radiation on the temperature measurements, leading to a deviation of up to 3 K. By installing screens, the error caused by sunlight is reduced to under 1 K. Wind speed seems to have a minimal effect on the measured wet-bulb temperature, both below and above the canopy. After a simple quality control, the Bowen ratio measured by DTS correlates well with eddy covariance (EC) estimates (r 2 = 0.59). The average energy balance closure between BR-DTS and EC is good, with a mean underestimation of 3.4 W m −2 by the BR-DTS method. However, during daytime the BR-DTS method overestimates the available energy, and during night-time the BR-DTS method estimates the available energy to be more negative. This difference could be related to the biomass heat storage, which is neglected in this study.The BR-DTS method overestimates the latent heat flux on average by 18.7 W m −2 , with RMSE = 90 W m −2 . The sensible heat flux is underestimated on average by 10.6 W m −2 , with RMSE = 76 W m −2 . Estimates of the BR-DTS can be improved once the uncertainties in the energy balance are reduced. However, applying, for example, Monin-Obukhov similarity theory could provide independent estimates for the sensible heat flux. This would make the determination of the highly uncertain and difficult to determine net available energy redundant.

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“…Canopy photosynthesis was estimated from LAI and leaf-level photosynthesis (Sellers et al, 1992). The latter was described using the model developed by Farquhar et al (1980) for both carboxylation and electron transport processes together with a stomatal conductance model (Leuning, 1995;Van Wijk et al, 2000;Chang, 2012). See Wu et al (2009) for more details.…”

Section: Model Descriptionmentioning

confidence: 99%

Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model

Nie

et al. 2015

Tellus B: Chemical and Physical Meteorology

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A B S T R A C T To improve models for accurate projections, data assimilation, an emerging statistical approach to combine models with data, have recently been developed to probe initial conditions, parameters, data content, response functions and model uncertainties. Quantifying how many information contents are contained in different data streams is essential to predict future states of ecosystems and the climate. This study uses a data assimilation approach to examine the information contents contained in flux-and biometric-based data to constrain parameters in a terrestrial carbon (C) model, which includes canopy photosynthesis and vegetationÁsoil C transfer submodels. Three assimilation experiments were constructed with either net ecosystem exchange (NEE) data only or biometric data only [including foliage and woody biomass, litterfall, soil organic C (SOC) and soil respiration], or both NEE and biometric data to constrain model parameters by a probabilistic inversion application. The results showed that NEE data mainly constrained parameters associated with gross primary production (GPP) and ecosystem respiration (RE) but were almost invalid for C transfer coefficients, while biometric data were more effective in constraining C transfer coefficients than other parameters. NEE and biometric data constrained about 26% (6) and 30% (7) of a total of 23 parameters, respectively, but their combined application constrained about 61% (14) of all parameters. The complementarity of NEE and biometric data was obvious in constraining most of parameters. The poor constraint by only NEE or biometric data was probably attributable to either the lack of long-term C dynamic data or errors from measurements. Overall, our results suggest that flux-and biometric-based data, containing different processes in ecosystem C dynamics, have different capacities to constrain parameters related to photosynthesis and C transfer coefficients, respectively. Multiple data sources could also reduce uncertainties in parameter estimation if these data sources contain complementary information.

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Modeling daily gas exchange of a Douglas-fir forest: comparison of three stomatal conductance models with and without a soil water stress function

Cited by 98 publications

References 39 publications

Quantitative comparison of canopy conductance models using a Bayesian approach

Quantitative comparison of canopy conductance models using a Bayesian approach

Technical note: Using distributed temperature sensing for Bowen ratio evaporation measurements

Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model

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