TY - JOUR
T1 - Clays Are Not Created Equal: How Clay Mineral Type Affects Soil Parameterization
AU - Lehmann, Peter
AU - Leshchinsky, B.
AU - Gupta, Surya
AU - Mirus, Benjamin B.
AU - Bickel, Samuel
AU - Lu, Ning
AU - Or, Dani
PY - 2021/10/28
Y1 - 2021/10/28
N2 - Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes
AB - Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes
UR - http://dx.doi.org/10.1029/2021gl095311
U2 - 10.1029/2021gl095311
DO - 10.1029/2021gl095311
M3 - Article
SN - 0094-8276
JO - Geophysical Research Letters
JF - Geophysical Research Letters
M1 - e2021GL095311
ER -