| Abstract: | Net ecosystem productivity (NEP), net primary productivity (NPP), and water vapour exchange of a mature Pinus ponderosa forest (44°30′ N, 121°37′ W) growing in a region subject to summer drought were investigated along with canopy assimilation and respiratory fluxes. This paper describes seasonal and annual variation in these factors, and the evaluation of two generalized models of carbon and water balance (PnET‐II and 3‐PG) with a combination of traditional measurements of NPP, respiration and water stress, and eddy covariance measurements of above‐and below‐canopy CO2 and water vapour exchange. The objective was to evaluate the models using two years of traditional and eddy covariance measurements, and to use the models to help interpret the relative importance of processes controlling carbon and water vapour exchange in a water‐limited pine ecosystem throughout the year. PnET‐II is a monthly time‐step model that is driven by nitrogen availability through foliar N concentration, and 3‐PG is a monthly time‐step quantum‐efficiency model constrained by extreme temperatures, drought, and vapour pressure deficits. Both models require few parameters and have the potential to be applied at the watershed to regional scale. There was 2/3 less rainfall in 1997 than in 1996, providing a challenge to modelling the water balance, and consequently the carbon balance, when driving the models with the two years of climate data, sequentially. Soil fertility was not a key factor in modelling processes at this site because other environmental factors limited photosynthesis and restricted projected leaf area index to ～1.6. Seasonally, GEP and LE were overestimated in early summer and underestimated through the rest of the year. The model predictions of annual GEP, NEP and water vapour exchange were within 1–39% of flux measurements, with greater disparity in 1997 because soil water never fully recharged. The results suggest that generalized models can provide insights to constraints on productivity on an annual basis, using a minimum of site data. |
