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Predicting soil thickness on soil mantled hillslopes

Author

Listed:
  • Nicholas R. Patton

    (Idaho State University)

  • Kathleen A. Lohse

    (Idaho State University
    Idaho State University)

  • Sarah E. Godsey

    (Idaho State University)

  • Benjamin T. Crosby

    (Idaho State University)

  • Mark S. Seyfried

    (Northwest Watershed Research Center)

Abstract

Soil thickness is a fundamental variable in many earth science disciplines due to its critical role in many hydrological and ecological processes, but it is difficult to predict. Here we show a strong linear relationship (r2 = 0.87, RMSE = 0.19 m) between soil thickness and hillslope curvature across both convergent and divergent parts of the landscape at a field site in Idaho. We find similar linear relationships across diverse landscapes (n = 6) with the slopes of these relationships varying as a function of the standard deviation in catchment curvatures. This soil thickness-curvature approach is significantly more efficient and just as accurate as kriging-based methods, but requires only high-resolution elevation data and as few as one soil profile. Efficiently attained, spatially continuous soil thickness datasets enable improved models for soil carbon, hydrology, weathering, and landscape evolution.

Suggested Citation

  • Nicholas R. Patton & Kathleen A. Lohse & Sarah E. Godsey & Benjamin T. Crosby & Mark S. Seyfried, 2018. "Predicting soil thickness on soil mantled hillslopes," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05743-y
    DOI: 10.1038/s41467-018-05743-y
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    Cited by:

    1. Chen, Lu & Luo, Yong & Tang, Jialiang & Zhang, Xifeng & Liu, Haowen & Cui, Junfang & Zheng, Jing & Dong, Xiaoming, 2024. "Determination of optimum solum thickness of sloping cropland for maize plantation in an Entisol based on water use strategy and plant traits," Agricultural Water Management, Elsevier, vol. 299(C).
    2. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," 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. 109(1), pages 471-497, October.

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