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• W4285087236 abstract "Abstract Soils in vegetation‐covered areas are of high variability in space and time, and the variability is crucial for hydro‐geophysical processes at small and large scales. Conventionally, the spatial heterogeneity at large scale is visualized as a regional division map of soil properties by extrapolating sample measurements. No formalized method is available for assigning parameters for soils at the resolution of a small grid cell. This study formulates a method of establishing parameter space for vegetated soil by a single index ( μ ) derived from the unified grain size distribution (GSD). The index μ varies in space as a random variable and defines soil as a granular field. The μ ‐division represents the concentration of fine grains in response to root–soil interactions. Coupling μ ‐division with moisture distribution suggests that the gradient of μ plays a potential role in rooted soil hydraulics. It is further found that μ satisfies the Weibull distribution, which makes it possible to establish the μ ‐field for a soil space at large scale using the random dataset of μ . A case study is conducted in two vegetated slopes based on high‐resolution digital elevation models (DEM) derived from unmanned aerial vehicle survey to create the field and illustrate its implications in morphology and hydrology. It is expected that the μ ‐field may derive parameter fields of various soil properties as functions of the GSD parameters and thus equip DEM with a complete dataset of soil. The paradigm proposed in the study fills the gap of establishing high‐resolution data space for soil at large scale for dynamics of surface processes." @default.
• W4285087236 created "2022-07-14" @default.
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• W4285087236 date "2022-07-28" @default.
• W4285087236 modified "2023-10-15" @default.
• W4285087236 title "The probability‐based granular field of vegetated soils" @default.
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• W4285087236 doi "https://doi.org/10.1002/esp.5446" @default.
• W4285087236 hasPublicationYear "2022" @default.
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