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预测土被丘陵坡面的土壤厚度。

Predicting soil thickness on soil mantled hillslopes.

机构信息

Department of Geosciences, Idaho State University, Pocatello, ID, 83209, USA.

Department of Biological Sciences, Idaho State University, Pocatello, ID, 83209, USA.

出版信息

Nat Commun. 2018 Aug 20;9(1):3329. doi: 10.1038/s41467-018-05743-y.

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 (r = 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.

摘要

土壤厚度是许多地球科学学科的基本变量,因为它在许多水文和生态过程中起着关键作用,但很难预测。在这里,我们在爱达荷州的一个野外地点展示了土壤厚度与山坡曲率之间的强线性关系(r=0.87,RMSE=0.19 m),该关系适用于汇聚和发散部分的地形。我们在不同的地形中发现了类似的线性关系(n=6),这些关系的斜率随集水区曲率标准差的变化而变化。这种土壤厚度-曲率方法的效率显著更高,与基于克里金的方法一样准确,但仅需要高分辨率的高程数据和一个土壤剖面。高效获取的、空间连续的土壤厚度数据集可改善土壤碳、水文学、风化和景观演化的模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/940f/6102209/52318f047c54/41467_2018_5743_Fig1_HTML.jpg

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