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

Abstract

Modeled estimates of eolian dust emission can vary by an order of magnitude due to the spatiotemporal heterogeneity of emissions. To better constrain location and magnitude of emissions, a surface erodibility factor is typically employed in models. Several landscape‐scale schemes representing surface dust emission potential for use in models have recently been proposed, but validation of such schemes has only been attempted indirectly with medium‐resolution remote sensing of mineral aerosol loadings and high‐resolution land surface mapping. In this study, we used dust emission source points identified in Namibia with Landsat imagery together with field‐based dust emission measurements using a Portable In‐situ Wind Erosion Laboratory wind tunnel to assess the performance of schemes aiming to represent erodibility in global dust cycle modeling. From analyses of the surface and samples taken at the time of wind tunnel testing, a Boosted Regression Tree analysis identified the significant factors controlling erodibility based on Portable In‐situ Wind Erosion Laboratory dust flux measurements and various surface characteristics, such as soil moisture, particle size, crusting degree, and mineralogy. Despite recent attention to improving the characterization of surface dust emission potential, our assessment indicates a high level of variability in the measured fluxes within similar geomorphologic classes. This variability poses challenges to dust modeling attempts based on geomorphology and/or spectral‐defined classes. Our approach using high‐resolution identification of dust sources to guide ground‐based testing of emissivity offers a valuable means to help constrain and validate dust emission schemes. Detailed determination of the relative strength of factors controlling emission can provide further improvement to regional and global dust cycle modeling.

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

“…[Colour figure can be viewed at wileyonlinelibrary.com] (2003b) (Parajuli and Zender, 2017). Similar disagreements between the SSM and observational data were also found in the Namib Desert by von Holdt et al (2019). However, no clear correlation was found between our measured fluxes and extracted SSM values (Figure 8).…”

“…In contrast, dry lake beds and stone pavements had relatively high SSM values (geometric means:~0.26), but exhibited low emission potentials as evidenced by the PI-SWERL data. Similar disagreements between the SSM and observational data were also found in the Namib Desert by von Holdt et al (2019). In this context, incorporation of field measurements is a clear priority in order to reliably represent the relationship between landform type and dust emission potential when using dust source schemes such as those of Parajuli and Zender (2017) and Bullard et al (2011).…”

“…Characterization of the relative emissivity of different landform types is an important step forward for regional/global erodibility mapping and prescribing dust sources in regional/global models (Bullard et al, 2011;Parajuli and Zender, 2017). Owing to its small size and portability, the PI-SWERL offers great advantages in measuring small-scale variability in dust emission von Holdt et al, 2019). Our results demonstrate the spatial and temporal heterogeneity in the characteristics and potential of PM 10 emission (Figures 4 and 5).…”

“…This scheme has been evaluated at several active dust sources using satellite remote sensing data (e.g. Bullard et al, 2011;Lee et al, 2012;Baddock et al, 2016;von Holdt et al, 2019). 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).…”

“…King et al, 2011;Sweeney et al, 2011), fluvial surfaces (e.g. Sankey et al, 2011;von Holdt et al, 2017) and grasslands (e.g. Munkhtsetseg et al, 2016Munkhtsetseg et al, , 2017.…”