simple model for predicting daily mean soil temperatures

• Published in: Journal of agronomy and crop science (1986) Vol. 163; no. 5; pp. 312 - 318
• Main Author: Langholz, H
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.12.1989; Blackwell
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agricultural and forest meteorology; Agronomy. Soil science and plant productions; air temperature; Biological and medical sciences; Climatic models of plant production; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production; mathematical models; Physical properties; Physics, chemistry, biochemistry and biology of agricultural and forest soils; prediction; regression analysis; seasonal variation; Soil science; soil temperature; Structure, texture, density, mechanical behavior. Heat and gas exchanges; German Federal Republic; Crop establishment; Climate; Linear regression; Europe; Growing season; Property of soil; Environmental factor; Clay soil; Sandy soil; Forecast model; West Germany; Air temperature; Peat soil; Cloudiness; Germany; Soil temperature
• ISSN: 0931-2250; 1439-037X
• DOI: 10.1111/j.1439-037X.1989.tb00773.x

Based on measurements in 3 different types of soil (clay, sand, peat) linear regression equations between daily air temperature (2 m) and soil temperature (2, 5, 10, 20, 50 cm depth) are calculated for all months of the growing season. The equations show a significant seasonal dependence and the best correlations in the upper 10 cm of soil. Differences depending on the type of soil are relatively small. Correction terms involving cloudiness and thermal inertia of the soil during a sudden warming or cooling period complete the prediction model. Standard deviations between predicted and measured values have been found within 1.5 K in most cases. Lastly a generally applicable method for calculating regression equations at any station is introduced. The application of this method to different sites and types of soil in Bavaria and other regions of Germany shows a good agreement with measured values.