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• We evaluated a land surface scheme (CLASS) for its simulation of transpiration. • CLASS adequately simulates daily transpiration over the growing season in the boreal forest. • CLASS daily transpiration was however underestimated during the tree rehydration period. • CLASS overestimated transpiration during droughts. • A new equation that increases CLASS sensitivity to decreasing soil water was proposed and tested. Transpiration, a key component of the hydrological cycle, contributes greatly to the climate system by transferring large amount of water from soils to the atmosphere. Its correct representation within Land Surface Schemes in climate models is crucial to provide accurate and reliable climate projections. In this study, transpiration simulated by the Canadian Land Surface Scheme (CLASS) was compared to long-term observations of sap flow measurements in two boreal forest sites of eastern Canada dominated by balsam fir and black spruce. In general, CLASS adequately models daily transpiration during the growing season for most of the years at both sites. During the tree rehydration period (preceding the growing season), modeled transpiration was greatly underestimated because of overestimating the duration of the snowpack, the latter restricting transpiration. Moreover, CLASS did not capture the impact of extreme events on tree physiology and maintained high transpiration rates during a heat stress and a drought. During both observed and simulated drought events, transpiration modeled using CLASS was overestimated, due to insensitivity to substantial decreases in soil water content; modeled transpiration being strictly controlled by atmospheric variables (vapour pressure deficit and radiations). Thus, we also proposed and implemented a new equation that was able to increase the sensitivity of CLASS to decreasing soil water content. However, this equation needs to be further tested on different sites and tree species. [ABSTRACT FROM AUTHOR]

Abstract: Soil moisture is a critical variable in determining crop productivity in the Canadian prairies. Methods to estimate and measure soil moisture extremes all have limitations related to spatial coverage, data accuracy and temporal record length. Three soil moisture data sets (in situ, satellite and modelled) were evaluated to determine how well they capture extreme moisture conditions that impact crop productivity. The limited temporal baseline of satellite datasets appears to provide a relatively robust measure of normal conditions, with a bias toward capturing drier than normal conditions. In situ soil moisture measurements showed few weeks with a significant relationship to agricultural yield, but the relationship was strong when it was significant, suggesting that accurate soil moisture could be a good indicator of yield anomalies if more spatially distributed data were available. Surface satellite and surface layer modelled soil moisture from the Canadian Land Surface Scheme (CLASS) showed a stronger relationship to yield during critical growth stages than did modelled soil moisture at depth. This observation indicates that improvements in the CLASS soil hydrology estimation procedure are needed, particularly for deeper soil layers. Overall, while models and satellite-detected soil moisture show promise for capturing crop yield variation, improvements are needed to increase accuracy in both approaches if they are to be used in routine agricultural monitoring. The use of agricultural productivity as a means of evaluating errors and limitations of soil moisture data sets provides an alternative to traditional data validation as it focuses on the application of the data rather than the absolute accuracy. [Copyright &y& Elsevier]