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Global sand, silt, and clay fractions of surface soils and derived drying time (min)

Citation

Okin, Gregory (2022), Global sand, silt, and clay fractions of surface soils and derived drying time (min), Dryad, Dataset, https://doi.org/10.5068/D1PM40

Abstract

Soil moisture in the active aeolian layer (the top ~2 mm of the soil) impacts dust emission by increasing the threshold for emission, and thus precipitation has the potential to suppress dust emission. The purpose of this study was to use reanalysis and satellite data similar to those used in global and regional dust emission models to calculate the probability that a high wind event happens during the period that antecedent precipitation would have left the active layer wet. The results indicate that the answer to this question is more strongly related to regional climate than soil texture. For more than half of the globe with mean annual precipitation < 500 mm/year, the probability of precipitation influencing dust emission is greater than 30 – 40%. Thus, rain-derived soil moisture in the active layer should not be ignored in models throughout much of the world’s dust-producing regions.

Methods

A global map of sand, silt, and clay fractions was derived from the global WISE v. 3.1 30 x 30 arcsec database which is based on the Harmonized World Soil Database [Batjes, 2016]. For each cell of this dataset, there are up to nine different soil components. For each cell, percent sand, silt and clay were derived by calculating the average of all available components, weighted by their fractional area in the cell. These average fractions were resampled to 0.25° x 0.25°, the same resolution as the precipitation data, and drying time was calculated using Equation 1. 

Ravi et al. [2006] conducted experiments on six soils that provide estimates of the time it takes the active layer to dry (i.e., for a wet surface to return come into equilibrium with ambient relative humidity). A parameterization for these drying times (DTs) for soils based on soil texture data created by regressing DT against fractions of sand, silt, and clay:

                DT (minutes) = 15.95 % Sand + 28.05 % Silt + 20.28 % Clay – 1494.                 

This relationship fits the published values from Ravi et al. [2006] with R2 = 0.99.

Batjes, N. H. (2016). Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma, 269, 61-68. 10.1016/j.geoderma.2016.01.034

Ravi, S., Zobeck, T. M., Over, T. M., Okin, G. S., & D'Odorico, P. (2006). On the effect of wet bonding forces in air-dry soils on threshold friction velocity of wind erosion. Sedimentology, 10.1111/j.1365-3091.2006.00775.x

Usage Notes

Data are in TIFF/GeoTIFF Format and can be opened in standard image processing (for remote sensing data) or GIS (e.g., ArcGIS, QGIS) software. Because the images are raw floating point  data and not image data, an image viewer (suitable for photos, for example) will not display the data correctly and may fail to open the image altogether.  The .tfw file is the "world" file that must accompany the .tif image (in the same directory) when opening in order for the image to be properly geographically registered. 

Funding

Army Research Office, Award: 67787‐EV