Can a physically-based land surface model accurately represent evapotranspiration partitioning? A case study in a humid boreal forest

One of the roles of Land Surface Models (LSMs) is to partition evapotranspiration (E) into overstory transpiration (E-T), understory evapotranspiration (E-G), and wet canopy evaporation (E-C). Unfortunately, only a handful of studies have evaluated the performance of LSMs with 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 Canadian Land Surface Scheme (CLASS) in simulating evapotranspiration and its components by applying detailed observations of water pathways and residence times in a forest canopy using a variety of methods (eddy covariance; 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 approximate to 1600 mm of annual precipitation. The field campaign was conducted during the summers of 2017 and 2018. To improve the accuracy of the simulations, we adjusted the definition of maximum canopy water storage, S = c x LAI, by using c = 0.49 mm instead of the default value of 0.20 mm. This simple modification improved the performance of CLASS when simulating components of evapotranspiration, indicated by the increase in the modified Kling-Gupta efficiency (KGE') with rises between 0.01 and 0.23. Overall, CLASS performed well in simulating evapotranspiration and overstory transpiration in dry canopy conditions at half-hourly time steps with KGE' values between 0.67 and 0.81. The contribution of simulated E-T, E-G, and E-C to total E during the two sampling periods were 46%, 24%, and 30%, respectively, which were comparable with the field observations (35%, 22%, and 25%, respectively). In conclusion, CLASS was able to simulate E partitioning in dry and wet canopy conditions reasonably well at both seasonal and half-hourly time scales.