Mapping erodibility in dust source regions based on geomorphology, meteorology, and remote sensing

Abstract

Mineral dust in the atmosphere has implications for Earth's radiation budget, biogeochemical cycles, hydrological cycles, human health, and visibility. Currently, the simulated vertical mass flux of dust differs greatly among the existing dust models. While most of the models utilize an erodibility factor to characterize dust sources, this factor is assumed to be static, without sufficient characterization of the highly heterogeneous and dynamic nature of dust source regions. We present a high-resolution land cover map of the Middle East and North Africa (MENA) in which the terrain is classified by visually examining satellite images obtained from Google Earth Professional and Environmental Systems Research Institute Basemap. We show that the correlation between surface wind speed and Moderate Resolution Imaging Spectroradiometer deep blue aerosol optical depth (AOD) can be used as a proxy for erodibility, which satisfactorily represents the spatiotemporal distribution of soil-derived dust sources. This method also identifies agricultural dust sources and eliminates the satellite-observed dust component that arises from long-range transport, pollution, and biomass burning. The erodible land cover of the MENA region is grouped into nine categories: (1) bedrock: with sediment, (2) sand deposit, (3) sand deposit: on bedrock, (4) sand deposit: stabilized, (5) agricultural and urban area, (6) fluvial system, (7) stony surface, (8) playa/sabkha, and (9) savanna/grassland. Our results indicate that erodibility is linked to the land cover type and has regional variation. An improved land cover map, which explicitly accounts for sediment supply, availability, and transport capacity, may be necessary to represent the highly dynamic nature of dust sources in climate models.

Assessing Landscape Dust Emission Potential Using Combined Ground‐Based Measurements and Remote Sensing Data

“…Interactions between erosive and resisting forces are complex and result in dust emission being spatially and temporally highly heterogeneous (e.g., Bryant et al, ; Gillette, ; Gillies, ; Mahowald et al, ; Taramelli et al, ). Improvements in dust emission modeling remains an important contemporary research goal since existing models have a limited capacity to accurately account for the spatiotemporal variability of dust emission within dust sources (Haustein et al, ; Parajuli et al, ; Shao et al, ).…”

“…A surface erodibility factor is typically used in dust models to constrain the observed spatial heterogeneity of emissions (Zender et al, ). Several dust emission mapping schemes at the landscape scale have attempted to account for erodibility as a regulator of emission potential for use in dust models (e.g., Ashpole & Washington, ; Baddock et al, ; Bullard et al, ; Parajuli et al, ; Parajuli & Zender, ). The erodibility factor has typically been based on various physical assumptions of the influence of geomorphology, topography, and hydrology on dust emission (Ginoux et al, ; Zender et al, ).…”

Quantifying the effect of geomorphology on aeolian dust emission potential in northern China

“…This scheme has been evaluated at several active dust sources using satellite remote sensing data (e.g. Given the increasingly important role of human impact on soil erodibility, several studies have used satellite-based dust indicators allied with land cover maps to attribute dust emission to natural or anthropogenic sources (Ginoux et al, 2012;Lee et al, 2012;Parajuli et al, 2014). Given the increasingly important role of human impact on soil erodibility, several studies have used satellite-based dust indicators allied with land cover maps to attribute dust emission to natural or anthropogenic sources (Ginoux et al, 2012;Lee et al, 2012;Parajuli et al, 2014).…”

Elevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity

“…Although q can vary greatly over space and time (Sterk and Stein, 1997;Chappell et al, 2003), we hypothesized that embedded spatial patterns of q could be modeled when integrated over multiple seasons by approximating local environmental factors related to aeolian sediment transport (Okin et al, 2006;Miller et al, 2012;Parajuli et al, 2014;Webb et al, 2014). Prior efforts have estimated wind erosion from spatial representations of vegetation cover and height, soil particle size, and soil moisture, but had very limited validation data and coarse grids of~50-100 km (Shao and Leslie, 1997;Lu and Shao, 2001;Shao, 2008).…”