Land-surface evapotranspiration derived from a first-principles primary production model

Evapotranspiration (ET) links the water and carbon cycles in the atmosphere, hydrosphere, and biosphere. In this study, we develop an ET modelling framework based on the idea that the transpiration and carbon uptake are closely coupled, as predicted by the 'least-cost hypothesis' that canopy conductance acclimates to environmental variations. According to eco-evolutionary optimality theory, which has been previously applied in monitoring and modelling land-surface processes, the total costs (per unit carbon fixed) for maintaining transpiration and carboxylation capacities should be minimized. We calculate gross primary production (GPP) assuming that the light- and Rubisco-limited rates of photosynthesis, described by the classical biochemical model of photosynthesis, are coordinated on an approximately weekly time scale. Transpiration (T) is then calculated via acclimated canopy conductance, with no need for plant type- or biome-specific parameters. ET is finally calculated from T using an empirical function of light, temperature, soil water content and foliage cover to predict the T/ET ratio at each site. The GPP estimates were well supported by (weekly) GPP data at 20 widely distributed eddy-covariance flux sites (228 site-years), with correlation coefficients (r) = 0.81 and root-mean-square error (RMSE) = 18.7 gC week^-1 and Nash-Sutcliffe efficiency (NSE) = 0.61. Predicted ET was also well supported, with r =0.85, RMSE = 5.5 mm week^-1 and NSE = 0.66. Estimated T/ET ratios (0.43-0.74) showed significant positive relationships to radiation, precipitation and green vegetation cover and negative relationships to temperature and modelled T (r = 0.84). Aspects of this framework could be improved, notably the estimation of T/ET. Nonetheless, we see the application of eco-evolutionary principles as a promising direction for water resources research, eliminating the uncertainty introduced by the need to specify multiple parameters, and leveraging the power of remotely sensed vegetation cover data as a key indicator of ecosystem function.