IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p8862-d610564.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/8862/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/8862/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    2. Yuri B. Kirsta & Ol’ga V. Lovtskaya, 2021. "Good-quality Long-term Forecast of Spring-summer Flood Runoff for Mountain Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 811-825, February.
    3. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    4. Wenxin Xu & Jie Chen & Xunchang J. Zhang, 2022. "Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3609-3625, August.
    5. Du, Yu & Lin, Xiaodong & Pham, Minh & Ruszczyński, Andrzej, 2021. "Selective linearization for multi-block statistical learning," European Journal of Operational Research, Elsevier, vol. 293(1), pages 219-228.
    6. Miriyala, Srinivas Soumitri & Subramanian, Venkat & Mitra, Kishalay, 2018. "TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study," European Journal of Operational Research, Elsevier, vol. 264(1), pages 294-309.
    7. Zhuoqi Wang & Yuan Si & Haibo Chu, 2022. "Daily Streamflow Prediction and Uncertainty Using a Long Short-Term Memory (LSTM) Network Coupled with Bootstrap," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4575-4590, September.
    8. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    9. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
    10. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    11. Gabriele Vissio & Marco Turco & Antonello Provenzale, 2023. "Testing drought indicators for summer burned area prediction in Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 1125-1137, March.
    12. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Jin-Cheng Fu & Hsiao-Yun Huang & Jiun-Huei Jang & Pei-Hsun Huang, 2019. "River Stage Forecasting Using Multiple Additive Regression Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4491-4507, October.
    14. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    15. Yong Huang & Kehan Miao & Xiaoguang Liu & Yin Jiang, 2022. "The Hysteresis Response of Groundwater to Reservoir Water Level Changes in a Plain Reservoir Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4739-4763, September.
    16. Jihong Qu & Kun Ren & Xiaoyu Shi, 2021. "Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1029-1045, February.
    17. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
    18. Fangqin Zhang & Yan Kang & Xiao Cheng & Peiru Chen & Songbai Song, 2022. "A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3673-3697, August.
    19. Yanmin Shuai & Yanjun Tian & Congying Shao & Jiapeng Huang & Lingxiao Gu & Qingling Zhang & Ruishan Zhao, 2022. "Potential Variation of Evapotranspiration Induced by Typical Vegetation Changes in Northwest China," Land, MDPI, vol. 11(6), pages 1-19, May.
    20. Hsin Hsu & Paul A. Dirmeyer, 2023. "Soil moisture-evaporation coupling shifts into new gears under increasing CO2," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8862-:d:610564. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.