TY - JOUR
T1 - Estimating saturated hydraulic conductivity along a south-north transect in the Loess Plateau of China
AU - Yang, Yang
AU - Jia, Xiaoxu
AU - Wendroth, Ole
AU - Liu, Baoyuan
N1 - Publisher Copyright:
© Soil Science Society of America.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - A precise description of saturated hydraulic conductivity (Ks) and its spatial variability is required for modeling soil and water transport in the vadose zone. Nevertheless, the direct measurement of Ks is expensive and laborious especially for large domains crossing hundreds of kilometers. The objective was to estimate Ks 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 Ks 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 Ks 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 Ks over the entire transect. The adjusted coefficients of determination (R2 adj) for the statespace models were all greater than 0.9, whereas the corresponding R2adj weremuch 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 R2 adj of ~1 but rather poorly in validation with R2 adj typically >0.4. The ANN method exhibited the best Ks estimations at all depths. Both wavelet coherency and state-space modeling quantified the spatial correlations of Ks with the eight factors investigated and manifested consistent results, that is, bulk density, clay content, and topography were the primary properties controlling Ks 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.
AB - A precise description of saturated hydraulic conductivity (Ks) and its spatial variability is required for modeling soil and water transport in the vadose zone. Nevertheless, the direct measurement of Ks is expensive and laborious especially for large domains crossing hundreds of kilometers. The objective was to estimate Ks 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 Ks 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 Ks 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 Ks over the entire transect. The adjusted coefficients of determination (R2 adj) for the statespace models were all greater than 0.9, whereas the corresponding R2adj weremuch 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 R2 adj of ~1 but rather poorly in validation with R2 adj typically >0.4. The ANN method exhibited the best Ks estimations at all depths. Both wavelet coherency and state-space modeling quantified the spatial correlations of Ks with the eight factors investigated and manifested consistent results, that is, bulk density, clay content, and topography were the primary properties controlling Ks 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.
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U2 - 10.2136/sssaj2018.03.0126
DO - 10.2136/sssaj2018.03.0126
M3 - Article
AN - SCOPUS:85054855247
SN - 0361-5995
VL - 82
SP - 1033
EP - 1045
JO - Soil Science Society of America Journal
JF - Soil Science Society of America Journal
IS - 5
ER -