Hydrological models featuring root water uptake usually do not include compensation mechanisms such that reductions in uptake from dry layers are compensated by an increase in uptake from wetter layers. We developed a physically based root water uptake model with an implicit compensation mechanism. Based on an expression for the matric flux potential (M) as a function of the distance to the root, and assuming a depth‐independent value of M at the root surface, uptake per layer is shown to be a function of layer bulk M, root surface M, and a weighting factor that depends on root length density and root radius. Actual transpiration can be calculated from the sum of layer uptake rates. The proposed reduction function (PRF) was built into the SWAP model, and predictions were compared to those made with the Feddes reduction function (FRF). Simulation results were tested against data from Canada (continuous spring wheat [(Triticum aestivum L.]) and Germany (spring wheat, winter barley [Hordeum vulgare L.], sugarbeet [Beta vulgaris L.], winter wheat rotation). For the Canadian data, the root mean square error of prediction (RMSEP) for water content in the upper soil layers was very similar for FRF and PRF; for the deeper layers, RMSEP was smaller for PRF. For the German data, RMSEP was lower for PRF in the upper layers and was similar for both models in the deeper layers. In conclusion, but dependent on the properties of the data sets available for testing, the incorporation of the new reduction function into SWAP was successful, providing new capabilities for simulating compensated root water uptake without increasing the number of input parameters or degrading model performance.

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Two weather generators -LARS-WG, developed at Long Ashton Research Station (UK), and AAFC-WG, developed at Agriculture and Agri-Food Canada -were compared in order to gauge their capabilities of reproducing probability distributions, means and variances of observed daily precipitation, maximum temperature and minimum temperature for diverse Canadian climates. Climatic conditions, such as wet and dry spells, interannual variability and agroclimatic indices, were also used to assess the performance of the 2 weather generators. AAFC-WG performed better in simulating temperature-related statistics, while it did almost as well as LARS-WG for statistics associated with daily precipitation. Using empirical distributions in AAFC-WG for daily maximum and minimum temperatures helped to improve the temperature statistics, especially in cases where local temperatures did not follow normal distributions. However, both weather generators had overdispersion problems, i.e. they underestimated interannual variability, especially for temperatures. Overall, AAFC-WG performed better.

The interest in using crop growth simulation models for estimating large area yields in western Canada has led to a requirement for daily values of solar radiation on an historical and a real time basis. Because such data are usually not readily available, an equation was developed which relates solar transmissivity (ζ) (the ratio of incoming global solar radiation at the earth surface (Q) to solar radiation at the top of the atmosphere (Q0)) to daily observations of maximum and minimum air temperature and total precipitation (P):[Formula: see text]where a, b, c and d are empirical coefficients which vary with time of year and ΔT is the range in daily temperature extremes. During the late fall-winter period, correlation coefficients between observed and calculated transmissivities were less than 0.5 with relative large root mean square errors (RMSE). However, during the growing season when the equation would be of most use, correlation coefficients were 0.7 or higher with RMSEs of 0.12 or lower. The coefficients a, b, c and d were found not to be site-specific during the growing season. No significant differences were found between wheat yields estimated with observed solar radiation and those estimated with the calculated solar radiation from the equation. Key words: Solar radiation, transmissivity, crop growth modelling, wheat yield

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