A canopy scale test of the optimal water use hypothesis

Authors:

Stanislaus J. Schymanski, Michael L. Roderick, Murugesu Sivapalan, Lindsay B. Hutley, Jason Beringer

Abstract:

Common empirical models of stomatal conductivity often incorporate a sensitivity of stomata to the rate of leaf photosynthesis. Such a sensitivity has been predicted on theoretical terms by Cowan and Farquhar, who postulated that stomata should adjust dynamically to maximize photosynthesis for a given water loss. In this study, we implemented the Cowan and Farquhar hypothesis of optimal stomatal conductivity into a canopy gas exchange model, and predicted the diurnal and daily variability of transpiration for a savanna site in the wet–dry tropics of northern Australia. The predicted transpiration dynamics were then compared with observations at the site using the eddy covariance technique.The observations were also used to evaluate two alternative approaches: constant conductivity and a tuned empirical model. The model based on the optimal water-use hypothesis performed better than the one based on constant stomatal conductivity, and at least as well as the tuned empirical model. This suggests that the optimal water-use hypothesis is useful for modelling canopy gas exchange, and that it can reduce the need for model parameterization.

Reference:

  • Plant, Cell and Environment, Vol. 31, 97-111.
  • Weblink to publisher's web page.
  • Postprint of this manuscript (accepted version of the paper formatted by author).
  • BibTex entry.

Figure 1: Relationship between Et and Ag for a fixed electron transport rate (JA) and atmospheric vapour deficit (Dv), but variable stomatal conductivity (Gs). The upper limit for Ag is determined by JA, while the initial slope of the relationship is determined by Dv.

Figure 2: Comparison of observed (thick, grey lines) and modelled (thin, black lines) daily transpiration rates (Et) during daylight hours in July 2004. Daylight hours are defined as time intervals with mean Ia > 100 μmol s-1 m-2. (a) Model based on constant λ; (b) model based on constant Gs; and (c) calibrated model. The arrows highlight a period between 9 and 13 July when data were not available because of equipment failure, and the ‘observed’ values were estimated using a neural network model. The insets show 1:1 plots of the observed and modelled daily values. MAE, mean absolute error in mm d-1.

Figure 3: Comparison of observed (thick, grey lines) and modelled (thin, black lines) daily transpiration rates (Et) during daylight hours in October 2004. Daylight hours are defined as time intervals with mean Ia > 100 μmol s-1 m-2. (a) Model based on constant λ; (b) model based on constant Gs; and (c) calibrated model. The insets show 1:1 plots of the observed and modelled daily values. MAE, mean absolute error in mm d-1.

Figure 4: Evidence of an abrupt change in λ after a rainfall event in October 2004. The match between observed (thick, grey line) and modelled (thin, black line) daily daytime Et was significantly improved if λ was set to different values before and after the first occurrence of a rainfall event (cf. Fig. 3a). The two days with more than 20 mm of precipitation are indicated by vertical arrows, and the periods over which λ was held constant are indicated by horizontal arrows. The inset shows a 1:1 plot of the observed and modelled daily values. MAE, mean absolute error in mm d-1.

Figure 5: Comparison of observed (thick, grey lines) and modelled (thin, black lines) daily transpiration rates (Et) during daylight hours in January 2005. Daylight hours are defined as time intervals with mean Ia > 100 μmol s-1 m-2. (a) Model based on constant λ; (b) model based on constant Gs; and (c) calibrated model. The insets show 1:1 plots of the observed and modelled daily values. MAE, mean absolute error in mm d-1.

Figure 6: Comparison of observed (thick, grey lines) and modelled (thin, black lines) daily transpiration rates (Et) during daylight hours in February 2005. Daylight hours are defined as time intervals with mean Ia > 100 μmol s-1 m-2. (a) Model based on constant λ; (b) model based on constant Gs; and (c) calibrated model. The insets show 1:1 plots of the observed and modelled daily values. MAE, mean absolute error in mm d-1.

Figure 7: Ensemble means of diurnal transpiration rates (Et) during daylight hours. Grey lines, observed values; solid black lines without markers, Et modelled assuming constant λ (values given in mol mol-1); solid black lines with markers, Et modelled assuming constant Gs (values given in mol m-2 s-1); dashed lines, calibrated Ball–Berry–Leuning model. Means were computed for all days of the respective month. Error bars are left out for clarity.