Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

The results of previous studies have suggested that estimated daily global radiation (R(G)) values contain an error that could compromise the precision of subsequent crop model applications. The following study presents a detailed site and spatial analysis of the R(G) error propagation in CERES and WOFOST crop growth models in Central European climate conditions. The research was conducted i) at the eight individual sites in Austria and the Czech Republic where measured daily R(G) values were available as a reference, with seven methods for R(G) estimation being tested, and ii) for the agricultural areas of the Czech Republic using daily data from 52 weather stations, with five R(G) estimation methods. In the latter case the R(G) values estimated from the hours of sunshine using the Ångström-Prescott formula were used as the standard method because of the lack of measured R(G) data. At the site level we found that even the use of methods based on hours of sunshine, which showed the lowest bias in R(G) estimates, led to a significant distortion of the key crop model outputs. When the Ångström-Prescott method was used to estimate R(G), for example, deviations greater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 per cent of cases. The precision of the yield estimates and other crop model outputs was lower when R(G) estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating R(G) from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the R(G) data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that R(G) estimates based on diurnal temperature range or its combination with daily total precipitation produced a bias of to 30 per cent in the mean winter wheat grain yields in some regions compared with simulations in which R(G) values had been estimated using the Ångström-Prescott formula. In contrast to the results at the individual sites, the methods based on the diurnal temperature range in combination with daily precipitation totals showed significantly poorer performance than the methods based on the diurnal temperature range only. This was due to the marked increase in the bias in R(G) estimates with altitude, longitude or latitude of given region. These findings in our view should act as an incentive for further research to develop more precise and generally applicable methods for estimating daily R(G) based more on the underlying physical principles and/or the remote sensing approach.