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Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area

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  • Jinlin Li

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Lanhui Zhang

    (Key Laboratory of West China’s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

Abstract

The accurate estimation of moisture content in deep soil layers is usually difficult due to the associated costs, strong spatiotemporal variability, and nonlinear relationship between surface and deep moisture content, especially in alpine areas (where complications include extreme heterogeneity and freeze-thaw processes). In an effort to identify the optimal method for this purpose, this study used measurements of soil moisture content at three depths (4, 10, and 20 cm) in the upper parts of the Babao River basin in the Qilian Mountains, Northwest China. These measurements were collected in the HiWATER (Heihe watershed allied telemetry experimental research) program to test four vertical extrapolation methods: exponential filtering (ExpF), linear regression (LR), support vector regression (SVR), and the application of a type of artificial neural network, the radial basis function (RBF). SVR provided the best predictions, in terms of the lowest root mean squared error and mean absolute error values, for the 10 and 20 cm layers from surface layer (4 cm) measurements. However, the data also confirmed that freeze-thawing is an important process in the study area, which makes the infiltration process more complex and highly variable over time. Thus, we compared the vertical extrapolation methods’ performance in each of the four periods with differing infiltration characteristics and found significant among-period differences in each case. However, SVR consistently provided the best estimates, and all methods provided better estimates for the 10 cm layer than for the 20 cm layer.

Suggested Citation

  • Jinlin Li & Lanhui Zhang, 2021. "Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8862-:d:610564
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    References listed on IDEAS

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    1. Jinlin Li & Lanhui Zhang & Chansheng He & Chen Zhao, 2018. "A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China," Sustainability, MDPI, vol. 10(8), pages 1-18, August.
    2. S. Aggarwal & Arun Goel & Vijay Singh, 2012. "Stage and Discharge Forecasting by SVM and ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3705-3724, October.
    3. Sha Zhou & A. Park Williams & Benjamin R. Lintner & Alexis M. Berg & Yao Zhang & Trevor F. Keenan & Benjamin I. Cook & Stefan Hagemann & Sonia I. Seneviratne & Pierre Gentine, 2021. "Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands," Nature Climate Change, Nature, vol. 11(1), pages 38-44, January.
    4. Wong, Wai-Tak & Hsu, Sheng-Hsun, 2006. "Application of SVM and ANN for image retrieval," European Journal of Operational Research, Elsevier, vol. 173(3), pages 938-950, September.
    5. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
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