Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis

Taramelli, Andrea; Pasqui, Massimiliano; Barbour, J. R.; Kirschbaum, Dalia; Bottai, Lorenzo; Busillo, C.; Calastrini, F.; Guarnieri, F.; Small, Christopher

doi:10.1002/esp.3321

Cited by 15 publications

(28 citation statements)

References 89 publications

(135 reference statements)

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“…The dynamics of mineral dust emission are fundamentally controlled by a combination of the power of the wind to erode (erosivity) and the resistance of an emitting surface to erosion (erodibility; Webb & Strong, ). 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, ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…() and Taramelli et al . (). Potential dust‐emitting pixels were identified using a combination of global digital datasets along with remote sensed and field validation analysis.…”

Section: Methodsmentioning

confidence: 96%

“…Second, as highlighted by the fine spatial structure of potential dust sources validated with the remote sensing analysis presented in Taramelli et al . (), dust emission and consequent transport can not be resolved by coarse resolution global atmospheric models: this reduces the overall reliability of represented dust storm dynamics and, in particular, the critical wind speed at the surface for dust emission.…”

Section: Introductionmentioning

confidence: 99%

Dust emission in northern China: atmospheric emission–dispersion modelling of a major dust event

Pasqui

Taramelli

Barbour

et al. 2013

Earth Surf Processes Landf

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Comprehensive modelling of dust events requires a full physical representation of small‐scale emission mechanisms and description of long‐range transport dynamic. In this paper we propose a simulation system that integrates three different models in order to represent the whole dust cycle. The RAMS atmospheric model configuration has two nested grids, at 50 km and 10 km horizontal resolution, and is used to force both the dust emission model DUSTEM, and the transport model CAMx. The performance of the three‐model simulation system was evaluated using a major dust storm that occurred in March 2002 in the desert of the Alashan Prefecture (Inner Mongolia, China) and which had a significant impact over a large area in northern China. In order to identify potential active dust sources, a specific remote sensed analysis, calibrated through a field campaign in the Alashan Prefecture region, has been assimilated in the modelling system. Simulated dust storm features, from the higher resolution grid, are in good agreement with observed data: surface wind values discrepancies are less than 2 m/s in the Alashan area and less than 5 m/s along the dust storm track. In comparison to ground observations, the modelled dust surface concentration peaks in Beijing differ by only 2 mg/m3 although the timing of dust peaks is delayed in the model. As a consequence this integrated numerical model, along with the remote sensed land surface characterization, is suggested to be a practical and flexible tool for simulating and analysing the whole dust storm dynamics. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Every pixel in the image was assigned to a certain class using a threshold value in fractional cover (i.e., ≥ 0.5552 for the wet class; ≥ 0.5950 for the soil class; ≥ 0.4992 for P. australis ; ≥ 0.5507 for E. athericus ; and ≥ 0.8945 for pioneer vegetation). The LiDAR elevation ranges for each vegetation type, which were obtained from the RWS vegetation map, were used to determine the vegetation class for the pixels where the threshold was not reached and, therefore, could not be classified (Taramelli & Melelli, ; Taramelli, Pasqui, et al, ). These pixels were classified by performing principal component analysis between the three fraction maps and the elevation value of the LiDAR survey provided by RWS based on information on the distribution of vegetation classes in the LIDAR‐elevation range value (Hladik et al, ; Taramelli, Valentini, et al, ).…”

Section: Methodsmentioning

confidence: 99%

Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

et al. 2018

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Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high‐resolution vegetation map (derived from aerial photography) and lower‐resolution satellite imagery processed with linear spectral mixture analysis. The patch‐size distribution in all vegetation types corresponded to a power law, suggesting the presence of self‐organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development.

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Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis

Cited by 15 publications

References 89 publications

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

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

Dust emission in northern China: atmospheric emission–dispersion modelling of a major dust event

Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

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