Estimating Temperature Effects on Water Flow in Variably Saturated Soils using Activation Energy
- Fucang Zhangab,
- Renduo Zhang *ab and
- Shaozhong Kangac
- a Key Lab. of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest Science and Technology Univ. of Agriculture and Forestry, Yangling, Shaanxi, 712100, P.R. China
b Dep. of Renewable Resources, Univ. of Wyoming, Laramie, WY 82071-3354, USA, Also at Dep. of Water Resources, Wuhan Univ., Wuhan 430072, P.R. China
c College of Water Resources and Civil Engineering, China Agriculture Univ., Beijing, 100083, P.R. China
Temperature effects on soil water flow are attributable to various factors in the soil water system, such as fluid viscosity, soil water content, and other soil physical and chemical properties. To account for the factors as a whole and quantify the temperature effects, the first objective was to apply the concept of activation energy and to estimate apparent activation energies of steady-state saturated flow and horizontal as well as vertical infiltration. The second objective was to predict the water flow processes under different temperatures. Soil column experiments were conducted to measure discharge of steady-state saturated flow and processes of cumulative infiltration using four soils at four temperatures. The parameters of the flow equations were related to temperature and apparent activation energies. Based on the relationships, apparent activation energies of the water flow processes were estimated using the experimental data. The sensitivities of temperature effects on the steady-state saturated flow and infiltration parameters were linearly related to the activation energy and inversely proportional to the absolute temperature. In general, temperature effects on the water flow processes were larger in the fine-textured soils and/or with higher soil water saturation. Using the parameters estimated from measured water flow processes at two temperatures, we predicted the processes at other temperatures and compared the predicted results with the measured data. The predicted results were highly correlated with the measured data with coefficients of determination (r 2) larger than 0.990 and the relative errors of the predicted processes were within 12%.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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