Incorporating canopy physiology into a hydrological model: photosynthesis, dynamic respiration, and stomatal sensitivity
Introduction
Physically based hydrological models represent vegetation controls of transpiration either in terms of one bulk stomatal resistance or using a series of resistances from the root system to the leaves, where each resistance is described by a semi-empirical function based on field data (Neitsch et al., 2001, Devonec and Barros, 2002). Thus, the biological control of evapotranspiration (ET) is passive and depends entirely on physical variables, neglecting feedbacks involving photosynthesis, soil moisture and transpiration. Even when the temporal evolution of vegetation parameters is taken into consideration via data assimilation of remote-sensing products, the interaction between soil and vegetation in the models is unidirectional: vegetation changes affect soil moisture dynamics via evapotranspiration, and the surface energy balance via albedo and surface roughness changes, but soil moisture does not affect photosynthesis, and therefore primary productivity (Yildiz and Barros, 2005).
Biome and terrestrial biogeochemistry models like PnET (Aber and Federer, 1992), IBIS (Foley et al., 1996), SECRETS (Sampson and Ceulemans, 2000) or BIOME-BGC (White et al., 2000) are used to simulate the carbon uptake by ecosystems from local to global scales. These terrestrial biogeochemical models utilize variations of a photosynthesis model proposed by Farquhar et al. (1980), hereafter referred to as FM. FM calculates CO2 assimilation as a function of the carboxylation and oxygenation velocities, photosynthetic electron transport and dark respiration. However, moisture availability controls in the atmosphere (water vapor pressure) and soil (soil water content) are not accounted for. Previous work on moisture availability controls includes Jarvis (1976), Farquhar and Wong (1984), Stewart (1988), Collatz et al. (1991), Friend (1995), Leuning (1995), and Friend et al. (1997). Typically, the actual photosynthesis rate is taken as the lowest rate between the ones calculated for each of the main photosynthetic factors (i.e., Rubisco carboxylation and RuBP regeneration rates), but the effects of soil water stress are not included. This approximation can lead to overestimating carbon assimilation (Al) when soil moisture is below the level necessary to reach potential assimilation rates.
Another common weakness of hydrological and terrestrial biochemical models is the lack of representation of the timing of vegetative processes including budburst, leaf elongation and leaf flushing (i.e., plant phenology). Although parameterization schemes based on chilling–heat (Cannell and Smith, 1983) and degree-day thresholds (Cumming and Burton, 1996, Roltseh et al., 1999) have been used with some success, the applicability of those schemes is restricted to the specific biomes for which they were empirically derived at annual and interannual time-scales, and therefore lack generality.
Sellers et al. (1992) and Evans (1996) discuss how trees may theoretically optimize their photosynthetic capacity by distributing the nitrogen and Rubisco contents in leaves through the tree crown with the same gradient as the time-average photosynthetically active radiation (PAR). In order to estimate this gradient, they utilize parameterizations to estimate Rubisco content, photosynthesis rates, and temperature for every canopy layer. Similar geometry effects should be taken into consideration for transpiration. Nevertheless, most hydrological and biochemical models rely on point measures of LAI and assume that transpiration is uniform within the canopy independent of the height of each foliage layer.
In as much as vegetation has the capacity to modulate and, possibly amplify hydrometeorological and climate forcing, improvements in modeling the effects of vegetation on the water and energy cycles are indispensable for assessing climate induced changes in regional hydrological regimes via land–atmosphere interactions (Hutjes et al., 1998, Foster, 2001, Devonec and Barros, 2002, Yildiz and Barros, 2005). Furthermore, the representation of vegetation mechanisms is also necessary to characterize the dynamics of ecohydrological gradients, to estimate water transfers and allocation in natural landscapes, and to assess the impact of human-induced change in the environment.
In this paper, we present new developments to an existing land surface hydrological model (LSHM) (Barros, 1995, Devonec and Barros, 2002, Yildiz and Barros, 2005) by integrating a photosynthesis model (Farquhar et al., 1980, Friend, 1995) complemented with a dynamical representation of the diurnal cycle of the photosynthetic capacity (Evans, 1996), and a substrate-structure dark respiration parameterization (Thornley and Cannell, 2000). A species-specific stomatal sensitivity parameter was also included in the calculation of evapotranspiration in order to introduce active control by plants (Mackay et al., 2003). The objective is to provide the hydrological model with a capacity to describe interactions between hydrology and canopy physiology with the ultimate goal of investigating the links between seasonal to interannual climate variability and ecosystem response at the catchment scale (Yildiz and Barros, 2005).
In order to test whether the new developments in the LSHM are indeed capable of simulating photosynthetic and respiratory processes under realistic environmental conditions, one-year long simulations were conducted using 1987 data from Cabauw, The Netherlands (Beljaars and Bosveld, 1997). The performance of the LSHM model on daily and seasonal time scales, for the same location, was evaluated previously by Devonec and Barros (2002). Here, we focus mainly on hydrological controls of vegetation processes.
Section snippets
Model description
The modeling approach consists of integrating the physically based land surface hydrological model, a biochemical model for leaf photosynthesis, a substrate-structure separation model for respiration, including parameterizations of the diurnal cycle of Rubisco concentration and of species-specific stomatal conductance (resistance). The column implementation of the LSHM (atmospheric boundary-layer and contiguous soil profile) used here is described in detail by Devonec and Barros (2002), who
Modeling experiments
The LSHM, including vegetation processes as described above, was evaluated using data from Cabauw, The Netherlands. The Cabauw site is a characteristic of mid-latitude climatic regions (51°58′N, 4°56′E), and although grass is the predominant vegetation cover in the direct vicinity of the observation tower, neighboring patches are covered by forest. Data were obtained from an in situ measurement tower recording mainly meteorological data, temperature and radiation fluxes, measured every 30 min
Summary
In order to improve our understanding and quantification of soil–vegetation–atmosphere interactions, it was necessary to integrate hydrological and photosynthesis processes into a single model, able to estimate not only dry matter production but also the influence of vegetation dynamics on soil moisture and transpiration (Devonec and Barros, 2002). The original photosynthesis model of Friend (1995) was complemented with a dynamical representation of the photosynthesis capacity (Evans, 1996) and
Acknowledgments
This work was funded by a Merck Foundation Faculty Fellowship at Harvard University awarded to the second author and director of the project. The first author was a research assistant in the project. The model is available from the second author upon request. We thank two anonymous reviewers for their thoughtful comments and suggestions.
Glossary: Parameters values were obtained from three sources: Friend (1995) for vegetation parameters for photosynthesis; Thornley and Cannell (2000) for respiration parameters; Mackay et al. (2003) for stomatal sensitivity parameters.
(a) Model parameters
- αpar
- fraction of PAR absorbed by leaf (0.85)
- constant to calculate effect of T for kc (2.897E14 molelectron mol−1chl s−1)
- constant to calculate effect of T for ko (4.397E07 molelectron mol−1chl s−1)
- constant to calculate effect of T for RdT (1.658E06 molelectron mol−1chl s−1)
- constant to calculate effect of T for jmax,chl (3.48E13 molelectron mol−1chl s−1)
- Cair
- CO2 concentration outside the leaf boundary layer (0.0145 mol m−3)
- δ
- species-specific sensitivity of stomatal conductance to water
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