Improving the accuracy of soil temperature time series prediction at different depths using spectral analysis and Box-Jenkins models

Articles in Press

Document Type : Research Paper

Authors

1 Department of Civil Engineering, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 School of Agricultural Engineering, Faculty of Agriculture, Isfahan University of Technology, Isfahan. Iran.

Abstract

Soil temperature is an important dynamic parameter that plays a key role in surface hydrological processes. In this study, a combination of spectral analysis and Box–Jenkins time series models was used to improve the prediction of thermal behavior in soil layers at various depths. Two main scenarios were considered for modeling: the first relying on soil temperature data, and the second including meteorological variables as auxiliary inputs. Model performance was evaluated using MAE, RMSE, R², and AIC criteria. After performing various tests, deterministic components in the time series were identified, and their intensity was assessed using different statistical parameters. The statistical analysis indicated that seasonality plays a more significant role than trend in the soil temperature time series. The developed models showed that combining spectral analysis with ARMA and ARIMA structures significantly improves soil temperature prediction accuracy. At the depth of 100 cm, this method achieved better performance with R² = 0.9750, MAE = 0.83, RMSE = 1.06, and AIC = –221.38, compared to multivariate models. Although in some scenarios the inclusion of meteorological variables such as evapotranspiration, wind speed, and solar radiation improved the results, univariate models based on soil temperature data provided more stable performance. Ultimately, this study demonstrated that combining spectral methods with time series models is an effective and reliable approach for predicting soil temperature at various depths.

Keywords

Main Subjects

Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 11 May 2025

Files

Share

How to cite

Statistics