Tsiros Ioannis X, Droulia Fotoula, Thoma Eleni, Psiloglou Basil
a Meteorology Laboratory , Agricultural University of Athens , Athens , Greece.
b Institute of Environmental Research and Sustainable Development, National Observatory of Athens , Athens , Greece.
J Environ Sci Health A Tox Hazard Subst Environ Eng. 2017 Jul 29;52(9):837-841. doi: 10.1080/10934529.2017.1312184. Epub 2017 Apr 27.
The ability of various semi-analytical models to predict soil temperature profiles in an experimental plot during a 16-year monitoring study for soil depths up to 120 cm is evaluated. The models are developed from an analytical model by replacing the steady-state soil temperature with easily obtained hourly and daily average temperature values. Such values include the hourly air temperature, the daily average air temperature, the hourly soil temperature of selected soil depths from three daily observations, the daily average of the soil temperature profile and the hourly soil temperature for the bottom depth. The performance evaluation results show that, in principle, all models exhibit high correlation (R values in the range 0.85-0.97), indicating a very good agreement between measured and predicted values. In addition, error statistics reveal that the best performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is the model based on the daily average of the soil temperature profile with MAE values in the range of 0-0.4°C and RMSE values in the range of 0.1-1.5°C.
评估了各种半解析模型在一项为期16年的监测研究中预测实验地块土壤温度剖面的能力，该研究针对深度达120厘米的土壤。这些模型是在一个解析模型的基础上开发的，通过用易于获取的每小时和每日平均温度值取代稳态土壤温度。这些值包括每小时气温、每日平均气温、从三次每日观测中选取的选定土壤深度的每小时土壤温度、土壤温度剖面的每日平均值以及底部深度的每小时土壤温度。性能评估结果表明，原则上所有模型都具有高度相关性（R值在0.85 - 0.97范围内），表明测量值与预测值之间非常吻合。此外，误差统计显示，就平均绝对误差（MAE）和均方根误差（RMSE）而言，性能最佳的是基于土壤温度剖面每日平均值的模型，其MAE值在0 - 0.4°C范围内，RMSE值在0.1 - 1.5°C范围内。
