Application of the Penman-Monteith Equation under Contrasting Climate Conditions.

The Penman-Monteith equation was used for calculating the actual transpiration of optimum-managed maize crops under the contrasting climate conditions of Brazil, Germany and Israel. In-situ measurements of light interceptance and stomatal conductance were correlated and the resulting light response function was scaled to the canopy level using a light penetration model. The transpiration model was tested against 10634 independent sap flow measurements in maize stems. Measured and calculated 15-minute or 30-minute averaged transpiration rates are highly correlated (r²=0.95) and the slope of the regression line is close to unity (y=0.989x+0.002). The overall root mean square error (RMSE) of calculated transpiration minus measured sap flow is 0.08 mm/hr and dominated by its variance component (0.005 {mm/hr}²). Measured sap flow consistently lagged behind calculated transpiration, because plant hydraulic capacitance delayed the change of leaf water potential that drives water uptake. Calculated transpiration significantly overestimated sap flow during morning hours (mean = 0.068 mm/hr) and underestimated it during afternoon hours (mean = -0.065 mm/hr).

Calculated daily transpiration closely followed the measured trends at the three locations during two successive years. The RMSE of daily calculated transpiration minus measured sap flow was 0.6 mm / day. It was dominated by its variance component (variance=0.3 {mm/day}²; bias=0.2 mm/day). No significant differences were found between seasons and locations. Statistical uncertainties of canopy conductance parameterizations led to computation errors of up to 2.1 mm/day. The model responded most sensitively to a 30 percent change of net radiation (Absolute bias error =1.6 mm/day), followed by corresponding alterations of canopy resistances (0.8 mm/day), vapour pressure deficits (0.5 mm/day) and aerodynamic resistances (0.34 mm/day). The Penman-Monteith approach as implemented in the present study is sufficiently sensitive to detect small differences between transpiration and water uptake and provides a robust tool for managing plant water supply under unstressed conditions.