Spatial interpolation of air temperature using environmental context: Application to a crop model

The air temperature is one of the main input data in models for water balan ce monitoring or crop models for yield prediction. The different phenologic al stages of plant growth are generally defined according to cumulated air temperature from the sowing date. When these crop models are used at the re gional scale, the meteorological stations providing input climatic data are not spatially dense enough or in a similar environment to reflect the crop local climate. Hence spatial interpolation methods must be used. Climatic data, particularly air temperature, are influenced by local environment. Me asurements show that the air above dry surfaces is warmer than above wet ar eas. We propose a method taking into account the environment of the meteoro logical stations in order to improve spatial interpolation of air temperatu re. The aim of this study is to assess the impact of these "corrected clima tic data" in crop models. The proposed method is an external drift kriging where the Kriging system is modified to correct local environment effects. The environment of the meteorological stations was characterized using a la nd use map summarized in a small number of classes considered as a factor i nfluencing local temperature. This method was applied to a region in south- east France (150 x 250 km) where daily temperatures were measured on 150 we ather stations for two years. Environment classes were extracted from the C ORINE Landcover map obtained from remote sensing data. Categorical external drift kriging was compared to ordinary kriging by a cross validation study . The gain in precision was assessed for different environment classes and for summer days. We then performed a sensitivity study of air temperature w ith the crop model STICS. The influence of interpolation corrections on the main outputs as yield or harvest date is discussed. We showed that the met hod works well for air temperature in summer and can lead to significant co rrection for yield prediction. For example, we observed by cross validation a bias reduction of 0.5 to 1.0 degreesC (exceptionally 2.5 degreesC for so me class), which corresponds to differences in yield prediction from 0.6 to 1.5 t/ha.