Canopy Conductance Model Research Articles

Effects of forest thinning on sap flow dynamics and transpiration in a Japanese cedar forest

Historically, forest thinning in Japan was conducted to obtain high-quality timber from plantations. Today, in contrast, thinning is also motivated by forest water balance and climate change considerations. It is in this context that the present study examines the effects of thinning on the ecophysiological responses of remaining trees, which are inadequately understood, especially in relation to changes in the magnitude and duration of transpiration. Sap flux densities were measured in both outer and inner sapwood to obtain stand-scale transpiration for two years in the pre-thinning state and three years post-thinning. The effects of thinning on transpiration were quantitatively evaluated based on canopy conductance models. The larger increases in outer sap flux density were found in the first year after the treatment, while those in inner sap flux density were detected in the second and third years. The remaining trees required a few of years to adjust to improved light conditions of the lower crown, resulting in a delayed response of inner sap flux density. As a result of this lag, transpiration was reduced to 71 % of the pre-thinning condition in the first year, but transpiration recovered to the pre-thinning levels in the second and third years due to compensating contributions from inner sap flow. In terms of more accurately chronicling the thinning effect, the distribution of sap flux density with respect to its radial pattern, is necessary. Such measurements are key to more comprehensively examining the ecophysiological response of forest plantations to thinning and, ultimately, its effect on the forest water balance.

Validation of a simple canopy conductance model for estimating transpiration of different citrus species under non-limiting soil water conditions

Validation of a simple canopy conductance model for estimating transpiration of different citrus species under non-limiting soil water conditions

The counteracting effects of large-scale vegetation restoration and increased precipitation on drought in the Huang-Huai-Hai-Yangtze River basin

Quantifying the causes of drought trends is critical for disaster prevention and water resource management. In recent years, not only climatic variables, such as temperature and precipitation (PRE), have changed, but also environmental factors, including vegetation restoration, increased CO2 concentration, and land cover conversion. However, the role of diverse factors, particularly large-scale vegetation restoration, in influencing drought trends is still indeterminate. In this study, a Ball-Berry canopy conductance model was incorporated into the Shuttleworth–Wallace (S-W) model to estimate the potential evapotranspiration (PET) in the Huang-Huai-Hai-Yangtze River basin from 2001 to 2019. The standardized precipitation evapotranspiration index (SPEI), which considers both water supply (from PRE) and demand (from PET), was adopted to identify drought conditions. Given the complex non-linear relationships in the model, an interpretable machine learning model was applied to the attribution of PET and SPEI trends. The results were as follows: (1) The PRE and PET showed significant upward trends, whereas the change rate of PET (60 mm/10a, p < 0.01) was higher than that of PET (44 mm/10a, p < 0.1) in the region; (2) The SPEI at an annual scale non-significantly decreased at a rate of −0.12/10a (p > 0.1). Increasing PET dominated SPEI trends with a contribution of −0.28/10a (p < 0.01), whereas increasing PRE contributed 0.16/10a (p > 0.1); (3) The interpretable machine learning model performed better than multiple linear regression in attributing PET trends; (4) Large-scale vegetation restoration (the increased leaf area index, LAI) and wind speed, were the two dominant factors influencing PET trends with contributions of 32.4 mm/10a (32%) and 19.6 mm/10a (20%), respectively; (5) In terms of the trends in drought across the entire region, the increase in LAI almost offset the positive contributions of PRE to SPEI variations, hence, combined with the influence of the other factors, drought was non-significantly accelerated.

Development of a Solar-Induced Fluorescence─Canopy Conductance Model and Its Application to Stomatal Reactive Nitrogen Deposition

Development of a Solar-Induced Fluorescence─Canopy Conductance Model and Its Application to Stomatal Reactive Nitrogen Deposition

A canopy conductance model with temporal physiological and environmental factors

Canopy conductance, one of the key variables in simulating evapotranspiration, is strongly influenced by the physiological status of a plant and environmental factors, including photosynthetically active radiation, vapor pressure deficit, air temperature, soil moisture and so on. However, the restrictive functions used to represent these factors rarely consider the dynamics of physiological and environmental factors. This study proposed an improved canopy conductance model by regarding radiation and vapor pressure deficit as the two main influencing factors, quantifying the temporal variation in stomatal responses to radiation that notably adjust stomatal behavior, parameterizing maximum stomatal conductance with plant type-specific functions and proposing a new restrictive function for the VPD. The improved canopy conductance model was incorporated in a surface conductance model for estimating surface conductance and evapotranspiration at 8 flux stations at the Heihe River Basin and the Haihe River Basin. The estimated results were the most accurate when comparing to two other models. Furthermore, the model performance was acceptable when most of the parameters were assumed to be constant across the sites except the reference canopy conductance Gc, ref and the soil evaporation parameter αs, which suggests that the improved canopy conductance model could be used as a parsimony model for improving canopy conductance predictions and water use efficiency over typical climate zones and underlying surface types in North of China.

Quantifying the contribution of biophysical and environmental factors in uncertainty of modeling canopy conductance

Canopy conductance (Gc) is a crucial variable in accurately estimating latent heat flux and evapotranspiration (ET) over vegetated surfaces. Gc is highly dependent on the plant species and the surrounding environment; consequently, it is difficult to accurately simulate Gc using a simplified universal model. A number of empirical parameters and functional forms have been introduced to appropriately account for various plant species. This study examines the efficacy of a widely adopted Gc model and Penman-Monteith (PM) approach over a selection of vegetated sites in arid and semi-arid regions of China to investigate the factors that influence the accuracy of the model. Comprehensive micrometeorological and land surface monitoring datasets from eight eddy covariance (EC) stations in the Hei and Hai river basins were used for our analysis. ET calculated from a widely used surface conductance and PM method was compared with EC measurements, and the residuals of the comparison were analyzed with factors potentially influencing the residuals using their correlations and regression coefficients to identify factors accounting for the errors caused via Gc. The results showed that the vapor pressure deficit and the maximum stomatal conductance (gsm) are most significantly correlated with uncertainties in Gc for all plant species. The Gc error vs. vapor pressure deficit exhibited distinctive sensitivities among the C3 crop/grass, C4 crop and C3 tree groups. The maximum stomatal conductance showed variation with the leaf age, reaching the maximum level in the mid-growth stage for most types of plant species except for coniferous trees. The residuals increased with radiation levels for sites covered with trees, which appeared to be caused by the decreasing trend of the stomatal conductance vs. radiation at high radiation levels for trees. Finally, we propose potential methods of improving canopy conductance models based on our error analysis.

Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence

Vegetation transpiration (T) is the process of plant water loss through the stomata on the leaf surface and plays a key role in the energy and water balance of the land surface, especially with dense vegetation cover. To date, however, estimation of ecosystem-scale T is still rather uncertain mainly due to errors in modeling canopy resistance or conductance. Considering the intrinsic link between photosynthesis and chlorophyll fluorescence, the recent available remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a valuable opportunity to estimate plants T at large scales. In this study, we demonstrate how remote sensing of SIF relates to canopy stomatal conductance and transpiration at diurnal and seasonal scales with continuous ground measurements of SIF at three flux sites in forest, cropland and grassland ecosystems. The results show that both ground and spaceborne SIF observations are good indicators of canopy conductance at both diurnal and seasonal scales (R2 = 0.57 and 0.74 for forest, R2 = 0.62 and 0.80 for cropland, R2 = 0.52 and 0.63 for grassland, respectively). Then, empirical SIF-based canopy conductance models are employed to estimate hourly and daily transpiration. We evaluate our ecosystem T estimations against latent heat fluxes measured by eddy covariance systems with more satisfactory results for forest (R2 = 0.57 and 0.71), and cropland (R2 = 0.77 and 0.83) than for grassland (R2 = 0.13 and 0.22) at hourly and daily time scales. Our results suggest the potential of remotely-sensed SIF for estimating canopy conductance and plant transpiration, but a more mechanistic understanding is needed for their link.

A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops

The temporal dynamics of optimum stomatal conductance (gsmax), as well differences between C3 and C4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance (Gc) models. To address this issue, a RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China. RST-Gc included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of gsmax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m−2 s−1 (gsm, 2000). Results showed that the parameters of RST-Gc differed between C3 and C4 crops. RST-Gc successfully simulated variations in Penman–Monteith (PM)-derived daytime Gc with R2 = 0.57 for both C3 and C4 crops. RST-Gc was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R2 = 0.79, RMSE (root mean standard error) = 20.66 W m−2 for daily ET and R2 = 0.87, RMSE = 15.32 W m−2 for 16-day ET) and GPP (R2 = 0.83, RMSE = 2.49 gC m−2 d−1 for daily GPP and R2 = 0.86, RMSE = 1.96 gC m−2 d−1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived gsm, 2000 outperformed the fixed gsm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP.

Optimization of canopy conductance models from concurrent measurements of sap flow and stem water potential on Drooping Sheoak in South Australia

AbstractCanopy conductance (gc) is a critical component in hydrological modeling for transpiration estimate. It is often formulated as functions of environmental variables. These functions are climate and vegetation specific. Thus, it is important to determine the appropriate functions in gc models and corresponding parameter values for a specific environment. In this study, sap flow, stem water potential, and microclimatic variables were measured for three Drooping Sheoak (Allocasuarina verticillata) trees in year 2011, 2012, and 2014. Canopy conductance was calculated from the inversed Penman‐Monteith (PM) equation, which was then used to examine 36 gc models that comprise different response functions. Parameters were optimized using the DiffeRential Evolution Adaptive Metropolis (DREAM) model based on a training data set in 2012. Use of proper predawn stem water potential function, vapor pressure deficit function, and temperature function improves model performance significantly, while no pronounced difference is observed between models that differ in solar radiation functions. The best model gives a correlation coefficient of 0.97, and root‐mean‐square error of 0.0006 m/s in comparison to the PM‐calculated gc. The optimized temperature function shows different characteristics from its counterparts in other similar studies. This is likely due to strong interdependence between air temperature and vapor pressure deficit in the study area or Sheoak tree physiology. Supported by the measurements and optimization results, we suggest that the effects of air temperature and vapor pressure deficit on canopy conductance should be represented together.

Efeitos da rega e do regime hídrico em olival super intensivo no Alentejo

This study characterizes the effects of a typical farmer irrigation management and one of deficit irrigation imposed to a hedgerow olive orchard (1, 35 x 3,75 m) in the region of Evora, Southern Portugal on a Eutric Cambisol soil (FAO, 1998). The two irrigation regimes and their effects on tree transpiration, soil water content and stem and leaves water potential, a physiological parameter indicator of plant water stress, are analysed. With deficit irrigation DI imposed after pit fruit hardening, it was observed a progressive reduction on the values of tree transpiration, stem and leaf water potential and soil water content predominantly towards the end August and September. For the normal irrigation treatment (FI) transpiration values slightly declined for the months of July and August, although less than the ones for treatment DI. Results obtained with the “big leaf” model of Penman-Monteith (Jones, 1992) and bulk canopy conductance model of Orgaz et al. (2007) indicate that once validated they can be used to predict the transpiration of non-stressed hedgerow olive orchards in Alentejo. Predicting transpiration with the remotely sensed vegetation index NDVI proved difficult for lack of a strong correlation between the two parameters.

A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006

We applied a satellite remote sensing–based evapotranspiration (ET) algorithm to assess global terrestrial ET from 1983 to 2006. The algorithm quantifies canopy transpiration and soil evaporation using a modified Penman‐Monteith approach with biome‐specific canopy conductance determined from the normalized difference vegetation index (NDVI) and quantifies open water evaporation using a Priestley‐Taylor approach. These algorithms were applied globally using advanced very high resolution radiometer (AVHRR) GIMMS NDVI, NCEP/NCAR Reanalysis (NNR) daily surface meteorology, and NASA/GEWEX Surface Radiation Budget Release−3.0 solar radiation inputs. We used observations from 34 FLUXNET tower sites to parameterize an NDVI‐based canopy conductance model and then validated the global ET algorithm using measurements from 48 additional, independent flux towers. Two sets of monthly ET estimates at the tower level, driven by in situ meteorological measurements and meteorology interpolated from coarse resolution NNR meteorology reanalysis, agree favorably (root mean square error (RMSE) = 13.0–15.3 mm month−1; R2 = 0.80–0.84) with observed tower fluxes from globally representative land cover types. The global ET results capture observed spatial and temporal variations at the global scale and also compare favorably (RMSE = 186.3 mm yr−1; R2 = 0.80) with ET inferred from basin‐scale water balance calculations for 261 basins covering 61% of the global vegetated area. The results of this study provide a relatively long term global ET record with well‐quantified accuracy for assessing ET climatologies, terrestrial water, and energy budgets and long‐term water cycle changes.

Intercomparison of a ‘bottom-up’ and ‘top-down’ modeling paradigm for estimating carbon and energy fluxes over a variety of vegetative regimes across the U.S.

Biophysical models intended for routine applications at a range of scales should attempt to balance the competing demands of generality and simplicity and be capable of realistically simulating the response of CO 2 and energy fluxes to environmental and physiological forcings. At the same time they must remain computationally inexpensive and sufficiently simple to be effectively parameterized at the scale of application. This study investigates the utility of two modeling strategies for quantifying coupled land surface fluxes of carbon and water, which differ distinctly in their description of CO 2 assimilation processes. ‘Bottom-up’ models of land–atmosphere carbon exchange are based on detailed mechanistic descriptions of leaf-level photosynthetic processes scaled to the canopy whereas ‘top-down’ scaling approaches neglect the behavior of individual leaves and consider the canopy response to its environment in bulk. Effective intercomparisons of a light-use-efficiency (LUE)-based model of canopy conductance and a mechanistic model of leaf photosynthesis–stomatal response that employs a ‘two-leaf’ scaling strategy are facilitated by embedding both canopy sub-models in the Atmosphere–Land Exchange (ALEX) surface energy balance model. Water and carbon flux simulations are evaluated across time scales of hours, days, seasons and years for a variety of natural and agricultural ecosystems, using micrometeorological data from several AmeriFlux sites across the U.S. While both modeling paradigms reproduced observed magnitudes and variances of carbon and water vapor exchange on hourly and daily timescales with acceptable accuracy, the simpler LUE-based model often performed better than the more detailed scaled-leaf model, which has many adjustable species-specific model parameters. Actual light-use efficiencies vary significantly in response to changing environmental conditions and the success of LUE-based modeling frameworks rely on their ability to realistically respond to changes in light environment, atmospheric humidity, CO 2 concentration and a desiccating environment.

Intercomparison of a ‘bottom-up’ and ‘top-down’ modeling paradigm for estimating carbon and energy fluxes over a variety of vegetative regimes across the U.S.

Biophysical models intended for routine applications at a range of scales should attempt to balance the competing demands of generality and simplicity and be capable of realistically simulating the response of CO 2 and energy fluxes to environmental and physiological forcings. At the same time they must remain computationally inexpensive and sufficiently simple to be effectively parameterized at the scale of application. This study investigates the utility of two modeling strategies for quantifying coupled land surface fluxes of carbon and water, which differ distinctly in their description of CO 2 assimilation processes. ‘Bottom-up’ models of land–atmosphere carbon exchange are based on detailed mechanistic descriptions of leaf-level photosynthetic processes scaled to the canopy whereas ‘top-down’ scaling approaches neglect the behavior of individual leaves and consider the canopy response to its environment in bulk. Effective intercomparisons of a light-use-efficiency (LUE)-based model of canopy conductance and a mechanistic model of leaf photosynthesis–stomatal response that employs a ‘two-leaf’ scaling strategy are facilitated by embedding both canopy sub-models in the Atmosphere–Land Exchange (ALEX) surface energy balance model. Water and carbon flux simulations are evaluated across time scales of hours, days, seasons and years for a variety of natural and agricultural ecosystems, using micrometeorological data from several AmeriFlux sites across the U.S. While both modeling paradigms reproduced observed magnitudes and variances of carbon and water vapor exchange on hourly and daily timescales with acceptable accuracy, the simpler LUE-based model often performed better than the more detailed scaled-leaf model, which has many adjustable species-specific model parameters. Actual light-use efficiencies vary significantly in response to changing environmental conditions and the success of LUE-based modeling frameworks rely on their ability to realistically respond to changes in light environment, atmospheric humidity, CO 2 concentration and a desiccating environment.

Quantitative comparison of canopy conductance models using a Bayesian approach

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.

An operational remote sensing algorithm of land surface evaporation

Partitioning of solar energy at the Earth surface has significant implications in climate dynamics, hydrology, and ecology. Consequently, spatial mapping of energy partitioning from satellite remote sensing data has been an active research area for over two decades. We developed an algorithm for estimating evaporation fraction (EF), expressed as a ratio of actual evapotranspiration (ET) to the available energy (sum of ET and sensible heat flux), from satellite data. The algorithm is a simple two‐source model of ET. We characterize a landscape as a mixture of bare soil and vegetation and thus we estimate EF as a mixture of EF of bare soil and EF of vegetation. In the estimation of EF of vegetation, we use the complementary relationship of the actual and the potential ET for the formulation of EF. In that, we use the canopy conductance model for describing vegetation physiology. On the other hand, we use “VI‐Ts” (vegetation index‐surface temperature) diagram for estimation of EF of bare soil. As operational production of EF globally is our goal, the algorithm is primarily driven by remote sensing data but flexible enough to ingest ancillary data when available. We validated EF from this prototype algorithm using NOAA/AVHRR data with actual observations of EF at AmeriFlux stations (standard error ≅ 0.17 and R2 ≅ 0.71). Global distribution of EF every 8 days will be operationally produced by this algorithm using the data of MODIS on EOS‐PM (Aqua) satellite.

Regulation of canopy conductance and transpiration and their modelling in irrigated grapevines

Whole-vine transpiration was estimated for well-watered nine-year-old Sultana grapevines (Vitis vinifera L. cv. Sultana) from xylem sap flow measured with Granier's heat-dissipation probes. Canopy conductance of the grapevine was calculated by inverting the Penman-Monteith equation. Transpiration from grapevine canopies was strongly controlled by the canopy conductance. Canopy conductance decreased exponentially with increasing vapour pressure deficit (VPD) except in the morning when solar radiation was less than 200 W m-2 and the canopy conductance was predominantly limited by the solar radiation. A non-linear model of canopy conductance as a function of the solar radiation and VPD explained > 90% of the variation observed in canopy conductance. Under contrasting VPD conditions (daytime maximum of 3 kPa vs 8 kPa), grapevines were able to regulate their canopy conductance from 0.006 to 0.001 m s-1 to maintain a near constant transpiration. Whole-canopy transpiration calculated from modelled canopy conductance using the Penman-Monteith equation was highly correlated with the measured transpiration (sap flow) values over the range of 0-0.20 mm h-1 (R2 > 0.85). Cross-validation shows that these mechanistic models based on solar radiation and VPD provide good predictions of canopy conductance and transpiration under the conditions of the study.

Physiological tradeoffs in the parameterization of a model of canopy transpiration

We examined physiological parameter tradeoffs in modeling stomatal control of transpiration from a number of forest species. Measurements of sapflux, micrometeorology, and leaf area index were made in stands representing 85% of the forest ecosystems around the WLEF eddy flux tower in northern Wisconsin. A Jarvis-based canopy conductance model was used to simulate canopy transpiration ( E C) for five tree species from these stands. They consisted of conifers and deciduous species in both upland and wetland locations. Parameter estimation was used to assess the tradeoffs between physiological parameters used in the calculation of stomatal conductance. These tradeoffs were then evaluated against current theory on stomatal regulation of leaf water potential. The results show that the best simulations of E C were obtained with values of maximum stomatal conductance ( g Smax) and stomatal sensitivity to vapor pressure deficit ( δ) that closely followed this hydraulic theory. The model predictions reveal a large variation in the strategies used to regulate water potential among species. Aspen showed the greatest tendency towards efficiency, indicating that it has high E C under low vapor pressure deficit ( D) conditions, but is susceptible to rapid E C decline at moderate to high D. Other species showed more conservative water use. The results indicate that inter-specific differences in dynamic response to D can produce large spatial variation in E C under typical environmental conditions.

On the information content of forest transpiration measurements for identifying canopy conductance model parameters

AbstractGenerally, forest transpiration models contain model parameters that cannot be measured independently and therefore are tuned to fit the model results to measurements. Only unique parameter estimates with high accuracy can be used for extrapolation in time or space. However, parameter identification problems may occur as a result of the properties of the data set. Time‐series of environmental conditions, which control the forest transpiration, may contain periods with redundant or coupled information, so called collinearity, and other combinations of conditions may be measured only with difficulty or incompletely. The aim of this study is to select environmental conditions that yield a unique parameter set of a canopy conductance model. The parameter identification method based on localization of information (PIMLI) was used to calculate the information content of every individual artificial transpiration measurement. It is concluded that every measurement has its own information with respect to a parameter. Independent criteria were assessed to localize the environmental conditions, which contain measurements with most information. These measurements were used in separate subdata sets to identify the parameters. The selected measurements do not overlap and the accuracies of the parameter estimates are maximized. Measurements that were not selected do not contain additional information that can be used to further maximize the parameter accuracy. Thereupon, the independent criteria were used to select eddy correlation measurements and parameters were identified with only the selected measurements. It is concluded that, for this forest and data set, PIMLI identifies a unique parameter set with high accuracy, whereas conventional calibrations on subdata sets give non‐unique parameter estimates. Copyright © 2001 John Wiley &amp; Sons, Ltd.

A canopy conductance and photosynthesis model for use in a GCM land surface scheme

Land surface schemes are vital components of general circulation models (GCMs) which provide the fluxes of heat, water and momentum at the land–atmosphere interface. The fluxes simulated by these schemes are especially dependent on the way in which the canopy (or `bulk stomatal') conductance for plant transpiration is modelled. Considerable research has been carried out into the dependences of canopy conductance on the local environment, and empirical relationships for such dependences have been obtained by fitting the data collected in field and laboratory experiments. However, observed leaf level relationships between stomatal conductance and net photosynthesis suggest an alternative approach. Given an appropriate algorithm for scaling these values up to canopy level, such relationships allow canopy conductance values to be derived from (comparatively) well validated models of leaf photosynthesis. This approach is likely to become especially attractive as land surface schemes are extended to simulate CO 2 fluxes, since the shared environmental dependences of canopy conductance and photosynthesis reduce the number of model parameters which require independent specification. This paper is concerned with the evaluation of canopy level relationships between photosynthesis and conductance using data from the first international satellite land surface climatology project (I slscp) field experiment (F ife). Simultaneous measurements of CO 2 and water vapour fluxes, taken over a predominantly C 4 grassland, are used to independently test the conductance and photosynthesis models. Based on these tests, a simple coupled model of canopy conductance and photosynthesis is developed, which produces a good match to the experimental data.

Modelling the surface conductance of a broad‐leaf canopy: effects of partial decoupling from the atmosphere

The transpiration of a mature beech (Fagus sylvatica L.) forest was measured over a whole season by the heat pulse velocity technique and the results analysed in terms of a new analytical canopy conductance model, which takes into account the effects of partial decoupling from the atmosphere on the local humidity environment experienced by the canopy. Stand daily transpiration ranged from 0·62 to 2·97 mm d–1, with a seasonal mean value of 1·97 mm d–1. Maximum canopy conductance was 18·5 mm s–1, with a mean estimated value of 5·0 mm s–1; computed values were little affected by the assumption of neutral atmospheric conditions. The decoupling coefficient Ω varied greatly on a daily and seasonal basis, with an average value of 0·28. As a result of partial decoupling, the estimated vapour pressure deficit at the notional canopy surface exceeded the values measured above the canopy by 380 Pa on average. When correctly expressed in terms of humidity at the canopy surface, the model explained 80% of the variance in half‐hourly transpiration measurements. Upon cross‐validation it still explained 72% of the variance, as compared to only 40% when correction for partial decoupling was not introduced. A baseline canopy conductance of 0·7 mm s–1, not modulated by the environment, was estimated. The implications of the model are discussed for the representation of canopy conductance and transpiration of broad‐leaf forests.