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ORIGINAL RESEARCH

Resolving Structural Influences on Water-Retention Properties of Alluvial Deposits

^a U.S. Geological Survey, 345 Middlefield Road, MS 421, Menlo Park, CA 94025
^b U.S. Geological Survey, 5735 Kearny Villa Road, San Diego, CA 92123
^* Corresponding author (jrnimmo{at}usgs.gov)

With the goal of improving property-transfer model (PTM) predictions of unsaturated hydraulic properties, we investigated the influence of sedimentary structure, defined as particle arrangement during deposition, on laboratory-measured water retention (water content vs. potential [()]) of 10 undisturbed core samples from alluvial deposits in the western Mojave Desert, California. The samples were classified as having fluvial or debris-flow structure based on observed stratification and measured spread of particle-size distribution. The () data were fit with the Rossi–Nimmo junction model, representing water retention with three parameters: the maximum water content (_max), the -scaling parameter (_o), and the shape parameter (). We examined trends between these hydraulic parameters and bulk physical properties, both textural—geometric mean, M_g, and geometric standard deviation, _g, of particle diameter—and structural—bulk density, _b, the fraction of unfilled pore space at natural saturation, A_e, and porosity-based randomness index, _s, defined as the excess of total porosity over 0.3. Structural parameters _s and A_e were greater for fluvial samples, indicating greater structural pore space and a possibly broader pore-size distribution associated with a more systematic arrangement of particles. Multiple linear regression analysis and Mallow's C_p statistic identified combinations of textural and structural parameters for the most useful predictive models: for _max, including A_e, _s, and _g, and for both _o and , including only textural parameters, although use of A_e can somewhat improve _o predictions. Textural properties can explain most of the sample-to-sample variation in () independent of deposit type, but inclusion of the simple structural indicators A_e and _s can improve PTM predictions, especially for the wettest part of the () curve.

Abbreviations: OG, Oro Grande • PSD, particle-size distribution • PTM, property-transfer model • SC, Sheep Creek

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