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Estimating Saturated Hydraulic Conductivity along a South‐North Transect in the Loess Plateau of China
Author(s) -
Yang Yang,
Jia Xiaoxu,
Wendroth Ole,
Liu Baoyuan
Publication year - 2018
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2018.03.0126
Subject(s) - transect , hydraulic conductivity , soil science , pedotransfer function , spatial variability , loess , water content , soil water , environmental science , bulk density , hydrology (agriculture) , plateau (mathematics) , geology , geomorphology , geotechnical engineering , mathematics , statistics , mathematical analysis , oceanography
Core Ideas K s was estimated using MLR‐type PTFs, ANN‐type PTFs and state‐space analysis. State‐space modeling was scale‐sensitive in estimating K s in Loess Plateau. Spatial correlations revealed in state‐space analyses were consistent with wavelet coherency. Bulk density, clay content and topography dominated K s spatial distribution. A precise description of saturated hydraulic conductivity ( K s ) and its spatial variability is required for modeling soil and water transport in the vadose zone. Nevertheless, the direct measurement of K s is expensive and laborious especially for large domains crossing hundreds of kilometers. The objective was to estimate K s from easily accessible soil properties and environmental factors using pedotransfer functions (PTFs) and state‐space analysis. Along an 860‐km south–north transect in the Loess Plateau of China, soil cores for K s measurements were collected at depths of 0 to 10, 10 to 20, and 20 to 40 cm at 10‐km intervals from 15 Apr. to 15 May 2013. Multiple linear regression (MLR) and artificial neural network (ANN) were used to derive PTFs for K s estimation. Based on the eight factors of bulk density, soil organic carbon, sand content, clay content, mean annual precipitation and temperature, slope gradient and elevation, the state‐space analysis appeared to outperform the PTFs in calibrating K s over the entire transect. The adjusted coefficients of determination ( R 2 adj ) for the state‐space models were all greater than 0.9, whereas the corresponding R 2 adj were much lower for the MLR‐ and ANN‐type PTFs (ranging from 0.398 to 0.880). However, the state‐space approach is quite scale‐sensitive, and overfitting occurred when it was cross‐validated with a leave‐one‐out procedure. It performed almost perfectly in calibration as implied in the R 2 adj of ∼1 but rather poorly in validation with R 2 adj typically >0.4. The ANN method exhibited the best K s estimations at all depths. Both wavelet coherency and state‐space modeling quantified the spatial correlations of K s with the eight factors investigated and manifested consistent results, that is, bulk density, clay content, and topography were the primary properties controlling K s distribution. These findings are critical for hydrological modeling and irrigation management in the Loess Plateau of China and possibly other arid and semi‐arid regions.