An evaluation of the performance of the soil temperature simulation algorithms used in the PRZM model

Soil temperature simulation is an important component in environmental modeling since it is involved in several aspects of pollutant transport and fate. This paper deals with the performance of the soil temperature simulation algorithms of the well-known environmental model PRZM. Model results are compared and evaluated based on the basis of its ability to predict in situ measured soil temperature profiles in an experimental plot during a 3-year monitoring study. The evaluation of the performance is based on linear regression statistics and typical model statistical errors such as the root mean square error (RMSE) and the normalized objective function (NOF). Results show that the model required minimal calibration to match the observed response of the system. Values of the determination coefficient R² were found to be in all cases around the value of 0.98 indicating a very good agreement between measured and simulated data. Values of the RMSE were found to be in the range of 1.2 to 1.4°C, 1.1 to 1.4°C, 0.9 to 1.1°C, and 0.8 to 1.1°C, for the examined 2, 5, 10 and 20 cm soil depths, respectively. Sensitivity analyses were also performed to investigate the influence of various factors involved in the energy balance equation at the ground surface on the soil temperature profiles. The results showed that the model was able to represent important processes affecting the soil temperature regime such as the combined effect of the heat transfer by convection between the ground surface and the atmosphere and the latent heat flux due to soil water evaporation.

Keywords:

• Simulation models
• soil temperature
• agrometeorological parameters
• environmental models
• heat transfer.

Acknowledgments

The authors wish to thank Professor Aikaterini Chronopoulou-Sereli, Head of the Department of Sciences for providing useful comments and also Mrs. Fotoula Droulia for her technical assistance with the meteorological data.

Notes

∗RMSE is the root mean square error and NOF (normalized objective function) is the ratio of RMSE to the overall mean of the observed soil temperatures.