A modified Jarvis-Stewart model of canopy transpiration (Ec) was tested over five ecosystems differing in climate, soil type and species composition. The aims of this study were to investigate the model's applicability over multiple ecosystems; to determine whether the number of model parameters could be reduced by assuming that site-specific responses of Ec to solar radiation, vapour pressure deficit and soil moisture content vary little between sites; and to examine convergence of behaviour of canopy water-use across multiple sites. This was accomplished by the following: (i) calibrating the model for each site to determine a set of site-specific (SS) parameters, and (ii) calibrating the model for all sites simultaneously to determine a set of combined sites (CS) parameters. The performance of both models was compared with measured Ec data and a statistical benchmark using an artificial neural network (ANN). Both the CS and SS models performed well, explaining hourly and daily variation in Ec. The SS model produced slightly better model statistics [R2=0.75-0.91; model efficiency (ME)=0.53-0.81; root mean square error (RMSE)=0.0015-0.0280mm h-1] than the CS model (R2=0.68-0.87; ME=0.45-0.72; RMSE=0.0023-0.0164mm h-1). Both were highly comparable with the ANN (R2=0.77-0.90; ME=0.58-0.80; RMSE=0.0007-0.0122mm h-1). These results indicate that the response of canopy water-use to abiotic drivers displayed significant convergence across sites, but the absolute magnitude of Ec was site specific. Period totals estimated with the modified Jarvis-Stewart model provided close approximations of observed totals, demonstrating the effectiveness of this model as a tool aiding water resource management. Analysis of the measured diel patterns of water use revealed significant nocturnal transpiration (9-18% of total water use by the canopy), but no Jarvis-Stewart formulations are able to capture this because of the dependence of water-use on solar radiation, which is zero at night.