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A Simple Model for Predicting Daily Mean Soil Temperatures

A Simple Model for Predicting Daily Mean Soil Temperatures 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Agronomy and Crop Science Wiley

A Simple Model for Predicting Daily Mean Soil Temperatures

Journal of Agronomy and Crop Science , Volume 163 (5) – Dec 1, 1989

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References (7)

Publisher
Wiley
Copyright
Copyright © 1989 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0931-2250
eISSN
1439-037X
DOI
10.1111/j.1439-037X.1989.tb00773.x
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Agronomy and Crop ScienceWiley

Published: Dec 1, 1989

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