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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. 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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