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Probability of a dust emission event during the period that the soil active layer remains wet after a precipitation event

Citation

Okin, Gregory (2022), Probability of a dust emission event during the period that the soil active layer remains wet after a precipitation event, Dryad, Dataset, https://doi.org/10.5068/D1997M

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.          (Equation 1)       

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

A histogram was constructed for each cell (0.25° x 0.25°) of the length of time between each precipitation event (3B42RT > 0, GES DISC, 2016) and the next period during which the 10-m MERRA2 (Molod et al., 2015) wind exceeded 7 m/s (e.g. Marsham et al., 2011).  If the (3-hour) wind exceeded threshold during the same time step as the precipitation event, a 0 was recorded. If wind exceeded the threshold during the time step immediately following the rain event, a 1 was recorded. If wind exceeded the threshold during two timesteps following the rain event, a 2 was recorded, and so on. To account for the uncertainty of when the above-threshold wind occurred during the 3-hour TRMM period, Drying Time (DT) was also rounded down to the nearest 3-hour period (e.g., 4 hours rounded down to 3 hours) and up to the nearest 3-hour period (e.g., 4 hours rounded up to 6 hours). For the season with the highest DUP, the probability that a wind event occurs after a rain event but before the soil is dry, and can therefore suppress emission, is given by: Psuppress = P (Tinterval ≤ DT).

Batjes, N. H. (2016). Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma, 269, 61-68. https://www.sciencedirect.com/science/article/pii/S0016706116300349

GES DISC. (2016). RMM (TMPA-RT) Near Real-Time Precipitation L3 1 day 0.25 degree x 0.25 degree V7. http://disc.gsfc.nasa.gov/datacollection/TRMM_3B42RT_Daily_7.html. Retrieved from: http://disc.gsfc.nasa.gov/datacollection/TRMM_3B42RT_Daily_7.html 

Marsham, J. H., Knippertz, P., Dixon, N. S., Parker, D. J., & Lister, G. M. S. (2011). The importance of the representation of deep convection for modeled dust-generating winds over West Africa during summer. Geophysical Research Letters, 38(16), L16803. http://dx.doi.org/10.1029/2011GL048368

Molod, A., Takacs, L., Suarez, M., & Bacmeister, J. (2015). Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev., 8(5), 1339-1356.

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 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. 

Figure 3A: Probability of a wind event while the soil is wet, DT=DT: windy_season_prob_wind_while_wet_m2wind_3b42rtrain_1440_480.flt.tif

Figure 3B: Probability of a wind event while the soil is wet, DT=0.5DT: windy_season_prob_wind_while_wet_drytimex0.5_m2wind_3b42rtrain_1440_480.flt.tif

Funding

Army Research Office, Award: 67787‐EV