Background/Question/Methods To assess the impact of climate change on forest ecosystems, general circulation models are used, in which Land Surface Models (LSMs) simulate soil-vegetation-atmosphere water exchanges. One of the roles of LSMs is to partition evapotranspiration (E) into overstory transpiration (ET), understory evapotranspiration (EG), and wet canopy evaporation (EC). Unfortunately, only a handful of studies have evaluated the performance of LSMs at E partitioning. Unlike dry canopies which are dominated by transpiration, wet canopies lead to the evaporation of intercepted water. In this respect, there is no better testing site for LSMs than the humid boreal forest, which is characterized by frequent precipitation and sustained evapotranspiration. This study assesses the performance of the physically-based Canadian Land Surface Scheme (CLASS) in simulating evapotranspiration and its components by comparing with detailed observations of water pathways and residence times in a forest canopy using a variety of methods (eddy covariance; species-calibrated sap flow, throughfall and stemflow measurements; and the stem compression approach). The study site was located in Montmorency Forest, in Quebec, Canada, a balsam fir boreal forest with ≈ 1600 mm of annual precipitation. The field campaign was conducted during the summers of 2017 and 2018.
Results/Conclusions Our results show the importance of calibrating the maximum canopy water storage (a linear function of the leaf area index) to significantly improve the model performance. At the seasonal scale, our results show that the observed evapotranspiration partitioning (ET = 46%, EG = 24%, and EC = 30%) is reasonably well reproduced by CLASS (35%, 22%, and 25%). It is at finer temporal scales that things become more difficult. For example, CLASS performed poorly in modeling transpiration during rainfall events, because of its default assumption that there is zero transpiration during wet canopy conditions, while observations point to the contrary. Second, we noted that CLASS tends to simulate earlier daily transpiration rises and peaks compared to observations, suggesting different responses to ET drivers between the model and field observations. While our study is reassuring about the overall performance of the model, it identifies clear areas for improvement.