Abstract

Soil temperature and moisture have a close relationship, the accurate controlling of which is important for crop growth. Mechanistic models built by previous studies need exhaustive parameters and seldom consider time stochasticity and lagging effect. To circumvent these problems, this study designed a data-driven stochastic model analyzing soil moisture-heat coupling. Firstly, three vector autoregression models are built using hourly data on soil moisture and temperature at the depth of 10, 30, and 90 cm. Secondly, from impulse response functions, the time lag and intensity of two variables’ response to one unit of positive shock can be obtained, which describe the time length and strength at which temperature and moisture affect each other, indicating the degree of coupling. Thirdly, Granger causality tests unfold whether one variable’s past value helps predict the other’s future value. Analyzing data obtained from Shangqiu Experiment Station in Central China, we obtained three conclusions. Firstly, moisture’s response time lag is 25, 50, and 120 h, while temperature’s response time lag is 50, 120, and 120 h at 10, 30, and 90 cm. Secondly, temperature’s response intensity is 0.2004, 0.0163, and 0.0035 °C for 1% variation in moisture, and moisture’s response intensity is 0.0638%, 0.0163%, and 0.0050% for 1 °C variation in temperature at 10, 30, and 90 cm. Thirdly, the past value of soil moisture helps predict soil temperature at 10, 30, and 90 cm. Besides, the past value of soil temperature helps predict soil moisture at 10 and 30 cm, but not at 90 cm. We verified this model by using data from a different year and linking it to soil plant atmospheric continuum model.

Highlights

  • The hydrothermal conditions of a wheat-soil system are essential to the dynamic balance of heat, moisture, and organic matter within the entire system and the thriving of wheat (Yang, Shang & Guan, 2012; Sun et al, 2018)

  • Impulse response analysis and Granger causality tests are tools which have been proposed for disentangling the relations between the variables in a vector autoregression model (Droumaguet, Warne & Wozniak, 2017; Bouri et al, 2018)

  • Soil moisture is helpful in terms of predicting soil temperature at the depth of 10, 30, and 90 cm

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Summary

Introduction

The hydrothermal conditions of a wheat-soil system are essential to the dynamic balance of heat, moisture, and organic matter within the entire system and the thriving of wheat (Yang, Shang & Guan, 2012; Sun et al, 2018). Most of the models employed to simulate moisture-heat coupling of soil are mechanistic models which call for complex parameters on soil properties and neglect time stochasticity. Philip & De Vries (1957) took soil evaporation under different temperature into consideration and brought up the theory of moisturegas-heat coupling transport under mass and energy balance. They did not consider the time lag effect and temporal heterogeneity.

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