Physical Crust Formation on Sandy Soils and Their Potential to Reduce Dust Emissions from Croplands

Abstract

The sandy croplands in the Free State have been identified as one of the main dust sources in South Africa. The aim of this study was to investigate the occurrence and strength of physical soil crusts on cropland soils in the Free State, to identify the rainfall required to form a stable crust, and to test their impact on dust emissions. Crust strength was measured using a fall cone penetrometer and a torvane, while laboratory rainfall simulations were used to form experimental crusts. Dust emissions were measured with a Portable In-Situ Wind Erosion Laboratory (PI-SWERL). The laboratory rainfall simulations showed that stable crusts could be formed by 15 mm of rainfall. The PI-SWERL experiments illustrated that the PM10 emission flux of such crusts is between 0.14% and 0.26% of that of a non-crusted Luvisol and Arenosol, respectively. The presence of abraders on the crust can increase the emissions up to 4% and 8% of the non-crusted dust flux. Overall, our study shows that crusts in the field are potentially strong enough to protect the soil surfaces against wind erosion during a phase of the cropping cycle when the soil surface is not protected by plants.

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

“…However, we know a priori that there are at least two very weak assumptions in the dust emission modeling: that the soil surface is covered homogeneously with an infinite supply of loose erodible material, which when mobilized by sufficient wind friction, causes dust emission. This approach assumes energy‐limited dust emission which is rarely justified in dust source regions where the soil surface is rough due to soil aggregates, rocks, or gravel, sealed with biological or physical crusts, or loose sediment is simply unavailable (Vos et al., 2020; Webb & Strong, 2011). Consequently, we follow the recently established approach (Hennen et al., 2022) and bypass those weak assumptions by using observed dust emission frequencies at DPS data locations to parameterize the entrainment threshold frequency distribution $$${Q}_{\text{DPS}}(\omega )=C\frac{{\rho }_{a}}{g}{u}_{s\ast }^{3}\,P(\text{DPS} > 0),$$$ $$${F}_{\text{DPS}}(\omega )={\sum }_{d}{A}_{s}{A}_{f}M{Q}_{\text{DPS}}{10}^{\left(0.134{\text{clay}}_{\%}-6.0\right)}\,\text{with}\,0\%\,< \,\text{clay}\%\,< 20\%,$$$ using the established calibration for this region (Hennen et al., 2022) $$${\mathrm{Log}}_{10}\left({\text{AEM}}_{\text{cal}}\right)=0.88\,{\mathrm{Log}}_{10}\left({F}_{\text{DPS}}\right)-2.02,$$$ where AEM cal is the adjustment of modeled F DPS values using the calibration.…”

“…However, we know a priori that there are at least two very weak assumptions in the dust emission modeling: that the soil surface is covered homogeneously with an infinite supply of loose erodible material, which when mobilized by sufficient wind friction, causes dust emission. This approach assumes energy-limited dust emission which is rarely justified in dust source regions where the soil surface is rough due to soil aggregates, rocks, or gravel, sealed with biological or physical crusts, or loose sediment is simply unavailable (Vos et al, 2020;Webb & Strong, 2011). Consequently, we follow the recently established approach (Hennen et al, 2022) and bypass those weak assumptions by using observed dust emission frequencies at DPS data locations to parameterize the entrainment threshold frequency distribution…”

“…Furthermore, it was assumed for simplicity that the sediment entrainment was at the grain scale, static over time and a function of dry, loose and erodible spherical particle diameters discretized across sizes (Shao & Lu, 2000) so that soil texture data (typically aggregated over depth) could be used. Similarly, dust emission models assumed for simplicity that the soil surface had an infinite supply of dry, loose erodible sediment, despite soil surfaces, particularly in drylands, being sealed/crusted and/or with loose sediment occurring sporadically over space and intermittently over time (Vos et al, 2020;Webb & Strong, 2011). These simplifying assumptions in dust emission models represent the parsimony of implementation more than the fidelity of the dust emission processes.…”

Spatial variation of soil temperature fields in a urban park

“…As a rule, the intensity of rain at this time is low, but drops of light rain have a greater destructive power than drops of intense rain, because in the latter case, intense rainfall results in a thin layer of water covering the soil, which has a protective function. In low-intensity rainfall, no protective layer of water is formed, the soil aggregates are destroyed and the soil is crusted due to colmatation [66]. The soil crust has a poor capacity for water infiltration, so it activates the lateral runoff [67], which greatly increases the role of microrelief as a factor regulating the distribution of moisture in space [68].…”

Evaluating dust emission model performance using dichotomous satellite observations of dust emission

“…In supplylimited areas, once these fine materials are deposited, there exists a finite period of increased dust emission potential. During the intervening periods, supply is either exhausted or protected from erosive winds by the formation of biogeophysical crusts (Vos et al, 2020) or surface 'armouring'. Accordingly, dust source areas, like the Sistan Basin, Tigris-Euphrates Basin (Syria/Iraq), and the Kuiseb River catchment (Namibia), where ephemeral or fluvial systems (with variable flow rates) occur, will tend to be limited by the production of fine materials (von Holdt and Eckardt, 2018).…”