Abstract
In order to know the main factors influence the infiltration parameters, based on the 344 sets of double-ring infiltration experiments in 101 different experimental sites in the Loess Plateau, obtained a large sample of Kostiakov-Lewis infiltration model parameters, analyzed the relationship between infiltration parameters and soil properties, established a multiple linear model, a nonlinear model and a BP neural network model to predict the infiltration parameters. The results showed that through Pearson correlation analysis, the main factors for parameter k was bulk density, soil water content of 0-10 cm, sand content, silt content and organic matter of 0-20 cm, the main factors influence parameter α was water content, sand content, silt content of 0-40 cm, and bulk density of 20-40 cm, and the main factors for parameter f0 was water content, sand content, silt content, of 0-40 cm, bulk density of 10-40 cm, and organic matter of 0-20 cm. Compared with previous studies, this paper added soil organic matter content as an independent variable to study the effect of soil chemical properties on soil infiltration capacity, which makes the model more reasonable, higher accuracy, and better prediction effect. Based on the effective test, result error analysis and comprehensive analysis, it was feasible to obtain the infiltration parameters in the Kostiakov-Lewis model using three Pedo-transfer functions. Under the condition of comprehensive consideration of forecast accuracy and stability applicability, it was recommended to use the nonlinear model as the prediction model of soil water infiltration parameters in the Loess Plateau.
Highlights
The Loess Plateau region is located in the north-central part of China
The field irrigation process refers to the determination of soil infiltration parameters using irrigation data, including the two-point method of Kostikov-Lewis model [5], the Philip model one-point method [6], the M method [7], the M-J
Estimation of the Infiltration Parameters to Soil Properties The Pedo-transfer function is constructed to establish a functional relationship between soil water infiltration parameters and soil physical and chemical parameters such as soil bulk density, initial volumetric water content, sand content, silt content, clay content, and organic matter content, that is, PTFs
Summary
The Loess Plateau region is located in the north-central part of China. It belongs to the temperate monsoon climate, with less rainfall and uneven rainfall, lack of water resources and drought. The indirect method includes field irrigation process method and soil Pedotransfer function method. The laboratory test method has great differences with the field soil in the construction of soil water infiltration model, and there are differences between the experimental and the actual irrigation parameters. The field irrigation process refers to the determination of soil infiltration parameters using irrigation data, including the two-point method of Kostikov-Lewis model [5], the Philip model one-point method [6], the M method [7], the M-J. method [8], and the M-Z method [9], etc. Based on the series of infiltration experiments of farmland scale in the Loess Plateau, this paper fits a large sample of Kostiakov-Lewis infiltration model parameters and related soil physical and chemical parameters and adopts multiple linear, nonlinear and BP neural network methods to establish the Pedo-transfer functions between each infiltration parameter and soil properties. The prediction of soil water infiltration model parameters is realized, which provides technical support for agricultural water resources management in the Loess Plateau
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