Evaluation and Development of Pedo-Transfer Functions for Predicting Soil Saturated Hydraulic Conductivity in the Alpine Frigid Hilly Region of Qinghai Province

Abstract

In recent years, Pedo-Transfer Functions (PTFs) have become a commonly used tool to predict the hydraulic properties of soil. As an important index to evaluate the function of forest water conservation, the prediction of saturated hydraulic conductivity (KS) on the regional scale is of great significance to guide the vegetation construction of returning farmland to forest area. However, if the published PTFs are directly applied to areas where the soil conditions are different from those where the PTFs are established, their predictive performance will be greatly reduced. In this study, 10 basic soil properties were measured as input variables for PTFs to predict KS in the three watersheds of Taergou, Anmentan, and Yangjiazhai in the alpine frigid hilly region of Qinghai Province, China. The parameters of the eight published PTFs were modified by the least-squares method and new PTFs were also constructed, and their prediction performance was evaluated. The results showed that the KS of coniferous and broad-leaved mixed forests and broad-leaved pure forests in the study area were significantly higher than those of pure coniferous forests, and grassland and farmland were the lowest (p > 0.05). Soil Organic Matter plays an important role in predicting KS and should be used as an input variable when establishing PTFs. The Analysis-Back Propagation Artificial Neural Network (BP ANN) PTF that was established, with input variables that were, Si·SOM, BD·Si, ln2Cl, SOM2, and SOM·lnCl had a better predictive performance than published PTFs and MLR PTFs.

The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back‐propagation neural network model

“…Generally, the range (3–12) of hidden layers is obtained by empirical formula, and the number of hidden layers is determined by trial‐and‐error method (Figure 3). By increasing the number of hidden layers, the mean square error (MSE) of each hidden layer is judged, and the lowest MSE is regarded as the best hidden layer structure (Zuo & He, 2021). The training learning rate and number of iterations were 0.01 and 1000, respectively. where h is the number of hidden layers, m and l are the numbers of the input layer and output layer, respectively, and α is a constant between 1 and 10.…”

Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks

“…The SWC is typically measured in the lab as series of equilibrium states obtained during drainage, with one water content value assigned to the applied pressure. The results of such small-scale experiments are not sensitive to structural pores that can be found at the field scale (Romero-Ruiz, et al, 2018) and can thus be expressed as function of basic soil properties (texture, bulk density, content of organic material) using pedotransfer functions (PTF; Zuo & He, 2021). Because these PTFs ignore the effects of soil structures including macropores and cracks (Basile, et al, 2019) and are trained with data from small samples with artificially high initial saturation conditions, their applicability to model dynamic processes in the field is limited.…”

A Comparison of Saturated Hydraulic Conductivity (Ksat) Estimations from Pedotransfer Functions (PTFs) and Field Observations in Riparian Seasonal Wetlands

“…PTFs require a wide range of soil physical characteristics to generate accurate estimations of Ksat [12]. Therefore, it is essential to understand the relationship between soil properties and Ksat to choose an appropriate PTF [17]. Previous researchers used the Pearson correlation matrix and Principal Component Analysis (PCA) to identify the strength and relationship of soil properties with Ksat [17][18][19][20][21].…”

“…Therefore, it is essential to understand the relationship between soil properties and Ksat to choose an appropriate PTF [17]. Previous researchers used the Pearson correlation matrix and Principal Component Analysis (PCA) to identify the strength and relationship of soil properties with Ksat [17][18][19][20][21]. For example, Gamie and De Smedt [19] analyzed the relationship between Ksat and soil properties in desert soils of Egypt and showed that soil structure had a stronger correlation with Ksat than soil texture.…”