Desert Roughness Retrieval Using CYGNSS GNSS-R Data

The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° × 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.

Seasonal dynamics of threshold friction velocity and dust emission in Central Asia

An improved model representation of mineral dust cycle is critical to reducing the uncertainty of dust-induced environmental and climatic impact. Here we present a mesoscale model study of the seasonal dust activity in the semiarid drylands of Central Asia, focusing on the effects of wind speed, soil moisture, surface roughness heterogeneity, and vegetation phenology on the threshold friction velocity (u_*t ) and dust emission during the dust season of 1 March to 31 October 2001. The dust model WRF-Chem-DuMo allows us to examine the uncertainties in seasonal dust emissions due to the selection of dust emission scheme and soil grain size distribution data. To account for the vegetation effects on the u_*t , we use the Moderate Resolution Imaging Spectroradiometer monthly normalized difference vegetation index to derive the dynamic surface roughness parameters required by the physically based dust schemes of Marticorena and Bergametti (1995, hereinafter MB) and Shao et al. (1996, hereinafter Shao). We find the springtime u_*t is strongly enhanced by the roughness effects of temperate steppe and desert ephemeral plants and, to less extent, the binding effects of increased soil moisture. The u_*t decreases as the aboveground biomass dies back and soil moisture depletes during summer. The u_*t dynamics determines the dust seasonality by causing more summer dust emission, despite a higher frequency of strong winds during spring. Due to the presence of more erodible materials in the saltation diameter range of 60-200 µm, the dry-sieved soil size distribution data lead to eight times more season-total dust emission than the soil texture data, but with minor differences in the temporal distribution. On the other hand, the Shao scheme produces almost the same amount of season-total dust emission as the MB scheme, but with a strong shift toward summer due to the strong sensitivity of the u_*t to vegetation. By simply averaging the MB and Shao model experiments, we obtain a mean estimate (Exp_mean) of season-total dust emission of 255.6 Mt (megaton), of which 26.8%, 50.4%, and 22.8% are produced in spring (March-April-May), summer (June-July-August), and autumn (September-October), respectively. The Exp_mean estimate identifies the Ustyurt Plateau, dried seabed of Aral Sea (called Aralkum), Caspian Sea coast, and loess deserts as the strongest dust source areas in Central Asia. The spatial distribution and seasonality of the Exp_mean estimate are in general agreement with ground station dusty weather observations and satellite aerosol optical depth and absorbing aerosol index products. Compared to Cakmur et al. (2006), the Exp_mean estimate suggests Central Asia contributes 10-17% to the global dust emission in 2001.

KEY POINTS

The WRF-Chem-DuMo model is used to study dust seasonality in Central Asia An accurate representation of u_*t is critical for dust seasonality Multiexperiment mean dust emission estimate agrees with observations.

Radar remote sensing of wind-driven land degradation processes in northeastern Patagonia

Wind-driven land degradation negatively impacts on rangeland production and infrastructure in the Valdes Peninsula, northeastern Patagonia. The Valdes Peninsula has the most noticeable dunefields of the Patagonian drylands. Wind erosion has been assessed at different scales in this region, but often with limited data. In general, terrain features caused by wind activity are better discriminated by active microwaves than by sensors operating in the visible and infrared regions of the electromagnetic spectrum. This paper aims to analyze wind-driven land degradation processes that control the radar backscatter observed in different sources of radar imagery. We used subsets derived from SIR-C, ERS-1 and 2, ENVISAT ASAR, RADARSAT-1, and ALOS PALSAR data. The visibility of aeolian features on radar images is mostly a function of wavelength, polarization, and incidence angle. Stabilized sand deposits are clearly observed in radar images, with defined edges but also signals of ongoing wind erosion. One of the most conspicuous features corresponds to old track sand dunes, a mixture of active and inactive barchanoid ridges and parabolic dunes. This is a clear example of deactivation of migrating dunes under the influence of vegetation. The L-band data reveal details of these sand ridges, whereas the C-band data only allow detecting a few of the larger tracks. The results of this study enable us to make recommendations about the utility of some radar sensor configurations for wind-driven land degradation reconnaissance in mid-latitude regions.

Modeling aerosol suspension from soils and oceans as sources of micropollutants to air

Soil and marine aerosol suspension are two physical mass transfer processes that are not usually included in models describing fate and transport of environmental pollutants. Here, we review the literature on soil and marine aerosol suspension and estimate aerosol suspension mass transfer velocities for inclusion in multimedia models, as a global average and on a 1 x 1 scale. The yearly, global average mass transfer velocity for soil aerosol suspension is estimated to be 6 x 10(-10)mh(-1), approximately an order of magnitude smaller than marine aerosol suspension, which is estimated to be 8 x 10(-9)mh(-1). Monthly averages of these velocities can be as high as 10(-7)mh(-1) and 10(-5)mh(-1) for soil and marine aerosol suspension, respectively, depending on location. We use a unit-world multimedia model to analyze the relevance of these two suspension processes as a mechanism that enhances long-range atmospheric transport of pollutants. This is done by monitoring a metric of long-range transport potential, phi-one thousand (phi1000), that denotes the fraction of modeled emissions to air, water or soil in a source region that reaches a distance of 1000 km in air. We find that when the yearly, globally averaged mass transfer velocity is used, marine aerosol suspension increases phi1000 only fractionally for both emissions to air and water. However, enrichment of substances in marine aerosols, or speciation between ionic and neutral forms in ocean water may increase the influence of this surface-to-air transfer process. Soil aerosol suspension can be the dominant process for soil-to-air transfer in an emission-to-soil scenario for certain substances that have a high affinity to soil. When a suspension mass transfer velocity near the maximum limit is used, soil suspension remains important if the emissions are made to soil, and marine aerosol suspension becomes important regardless of if emissions are made to air or water compartments. We recommend that multimedia models designed to assess the environmental fate and long-range transport behavior of substances with a range of chemical properties include both aerosol suspension processes, using the mass transfer velocities estimated here.