[1] We assess Canada's gross primary productivity (GPP) and net primary productivity (NPP) using boreal ecosystem productivity simulator (BEPS) at 250 m spatial resolution with improved input parameter and driver fields and phenology and nutrient release parameterization schemes. BEPS is a process-based two-leaf enzyme kinetic terrestrial ecosystem model designed to simulate energy, water, and carbon (C) fluxes using spatial data sets of meteorology, remotely sensed land surface variables, soil properties, and photosynthesis and respiration rate parameters. Two improved key land surface variables, leaf area index (LAI) and land cover type, are derived at 250 m from Moderate Resolution Imaging Spectroradiometer sensor. For diagnostic error assessment, we use nine forest flux tower sites where all measured C flux, meteorology, and ancillary data sets are available. The errors due to input drivers and parameters are then independently corrected for Canada-wide GPP and NPP simulations. The optimized LAI use, for example, reduced the absolute bias in GPP from 20.7% to 1.1% for hourly BEPS simulations. Following the error diagnostics and corrections, daily GPP and NPP are simulated over Canada at 250 m spatial resolution, the highest resolution simulation yet for the country or any other comparable region. Total NPP (GPP) for Canada's land area was 1.27 (2.68) Pg C for 2008, with forests contributing 1.02 (2.2) Pg C. The annual comparisons between measured and simulated GPP show that the mean differences are not statistically significant ( p > 0.05, paired t test). The main BEPS simulation error sources are from the driver fields.

The DayCENT model was employed to simulate the effects of conventional tillage (CT) and no‐till (NT) practices on the dynamics of soil organic carbon (SOC) over 9 yr in a rotational cropping system in Southern Ontario, Canada. Observations of site properties and eddy covariance measurements were used to assess crop productivity, net ecosystem productivity (NEP), and SOC changes. The validated model captured the dynamics of grain yield and net primary production, which indicated that DayCENT can be used to simulate crop productivity for evaluating the effects of tillage on crop residues and heterotrophic respiration (Rh) dynamics. The simulation suggested that CT enhanced the annual Rh relative to NT by 38.4, 93.7 and 64.2 g C m−2 yr−1 for corn (Zea mays L.), soybean [Glycine max (L.) Merr], and winter wheat (Triticum aestivum L.), respectively. The combined effect of incorporating crop residues and increased cultivation factors enhanced Rh in CT by 35% relative to NT after disk cultivation in the spring. The simulated NEP varied with crop species, tillage practices, and timing/length of the growing season. The seasonal variation of the total SOC pool was greater in CT than NT because of tillage effects on C transfer from the active surface SOC pool to the active soil SOC pool at a rate of 50 to 100 g C m−2 yr−1. The NT method practiced during the study period accounted for a 10.7 g C m−2 yr−1 increase in the slow SOC pool. The validated DayCENT model may be applied for longer‐term simulations in similar ecosystems for a variety of climate change experiment.

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Land surface models are useful tools for estimating the contribution and response to climate change of C dynamics in various terrestrial ecosystems. In many land surface models, plant phenological algorithms are incorporated based on eld studies in forests. However, to simulate adequately the C cycle over a large area, there is a need to include and validate algorithms for other ecosystems. e Carbon and Nitrogen-coupled Canadian Land Surface Scheme (CN-CLASS) is a land surface model that has been applied successfully to the study of C stocks in forest ecosystems. e objective of this study is to incorporate a simple crop phenology algorithm into CN-CLASS and validate its ability to simulate C cycles at an agricultural site in southern Ontario, Canada. e model was validated on a corn crop (Zea mays L.) in 2005 and 2008 based on measurements of aboveground biomass and net ecosystem productivity (NEP), as well as a well-tested agricultural model, DayCENT (the daily time-step version of the CENTURY model). e modi ed CN-CLASS showed similar dynamics of biomass allocation compared with eld measurements and DayCENT simulations. Regression analysis indicated that the modi cations improved the NEP simulation for a corn eld, with the coe cient of determination (R 2 ) relating simulated and observed NEP increasing from 0.51 in the original CN-CLASS to 0.78 in the modi ed model. Other crop species could be further validated to expand the model application to crop rotation studies and large areas covered by forests and crop elds in consideration of land management practices.

Water‐use restrictions during the summer of 2016 in Guelph, ON, Canada, prevented the irrigation of natural turf soccer fields and provided a unique opportunity to study the effects on soil volumetric water content and surface hardness on actively used youth soccer fields. Soil volumetric water content and surface hardness were tested on a weekly basis from July through September 2016. Areas of the turf that became brown were compared with the areas that remained green. Surface hardness within areas of brown turfgrass cover frequently exceeded 100g (level of concern for player safety) even though soil volumetric water content was similar between the areas of green and brown turfgrass cover. Therefore, irrigation of sports fields during times of drought is recommended to preserve the cushioning effect of green turfgrass cover.