期刊
JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
卷 114, 期 -, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2008JF001168
关键词
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资金
- USDA Forest Service
- Division Of Environmental Biology
- Direct For Biological Sciences [823293] Funding Source: National Science Foundation
Shallow landslides are a significant hazard in steep, soil-mantled landscapes. During intense rainfall events, the distribution of shallow landslides is controlled by variations in landscape gradient, the frictional and cohesive properties of soil and roots, and the subsurface hydrologic response. While gradients can be estimated from digital elevation models, information on soil and root properties remains sparse. We investigated whether geomorphically controlled variations in ecology affect the spatial distribution of root cohesion by measuring the distribution and tensile strength of roots from soil pits dug downslope of 15 native trees in the southern Appalachian Mountains, North Carolina, United States. Root tensile strengths from different hardwood tree species were similar and consistently higher than the only native shrub species measured (Rhododendron maximum). Roots were stronger in trees found on noses (areas of divergent topography) relative to those in hollows (unchanneled, convergent topography) coincident with the variability in cellulose content. This cellulose variability is likely related to topographic differences in soil water potential. For all species, roots were concentrated close to the soil surface, with roots in hollows being more evenly distributed in the soil column than those on noses. Trees located on noses had higher mean root cohesion than those in hollows because of a higher root tensile force. R. maximum had the shallowest, weakest roots suggesting that recent expansion of this species due to fire suppression has likely lowered the root cohesion of some hollows. Quantification of this feedback between physiologic controls on root growth and slope hydrology has allowed us to create a curvature-based model of root cohesion that is a significant improvement on current models that assume a spatially averaged value.
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