[A-CG37] Biogeochemical Cycles in Land Ecosystem

[ACG37-10] Wind Erosion/Dust Emission Conditions Revealed through Observations at Tsogt-Ovoo, Mongolia in the Gobi Desert

*Yasunori Kurosaki¹, Ishizuka Masahide², Batdelger Gantsetseg³, Batjargal Buyantogtokh³, Jing Wu¹, Kaman Kong⁴, Thomas Sekiyama⁵ (1.Arid Land Research Center, Tottori University, 2.Faculty of Engineering and Design, Kagawa University, 3.Information and Research Institute of Meteorology, Hydrology and Environment (IRIMHE), 4.Center for Computational Science, RIKEN, 5.Meteorological Research Institute, Japan Meteorological Agency)

Keywords:wind erosion, dust emission, soil crust, stone, dead vegetation

Wind erosion/dust emission is one of biogeochemical cycles of minerals, carbon, nitrogen included in soil, which occur mainly in drylands. Land ecosystems such as dead vegetation are major conditions for them. A dust monitoring station was installed by Arid Land Research Center, Tottori University (TU-ALRC) at Tsogt-Ovoo, Mongolia in the Gobi Desert in March 2012. We have focused on land surface elements such as soil crusts, stones, and dead vegetation, and have examined their effects on erodibility (susceptibility of land surface for wind erosion/dust emission). We have employed threshold wind speed or threshold friction velocity (wind speed at which wind erosion/dust emissions initiate. Friction velocity is a wind speed considering turbulence) as an index of erodibility. So far, we have obtained the following results. For crusts, we reported their disaggregation decreases the threshold, and we proposed a hypothesis that freeze-thaw is a mechanism causing the disaggregation (Ishizuka et al. 2012; Abulaiti et al. 2014; Kong et al. 2021). Buyantogtokh et al. (2022) examined its spatial extents using SAR satellite data. For stones, Buyantogtokh et al. (2021) investigated the relationship between their roughness density and the threshold. The result was consistent with Raupach's model, which is often employed in community models such as WRF/Chem. They also found less stones in a topographic depression and more ones in mountains. Sekiyama et al. (2023) installed the stone effect, which suppress wind erosion/dust emissions by stones, into their numerical dust model (NHM-Chem). They confirmed that the installation of stone effect improves the model accuracy. For vegetation, Gantsetseg et al. (2017) demonstrated vegetation flourishes in the topographic depression in a summer of high rainfall because rainwater accumulates into it. NDVI was high (0.56) only in the depression area even in a desert. This suggests that such vegetation remains as dead vegetation in the following years and suppresses wind erosion/dust emissions (dead vegetation effect). Wu et al. (2021) compared dead grass coverage with various vegetation indices. They found a high correlation between dead vegetation coverage and Soil Tillage Index (STI), which is a ratio of MODIS bands 6 and 7 (STI = B6/B7). Using this result, Wu et al.(2023) examined the spatiotemporal differences in the effect of dead vegetation on wind erosion/dust emission.

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Buyantogtokh et al. (2021) https://doi.org/10.1007/s40333-021-0072-7
Buyantogtokh et al. (2022) https://doi.org/10.1016/j.jaridenv.2022.104772
Gantsetseg et al. (2017) https://doi.org/10.1007/s40333-016-0090-z
Ishizuka et al. (2012) https://doi.org/10.2151/sola.2012-032
Kong et al. (2021) https://doi.org/10.1016/j.aeolia.2021.100716
Sekiyama et al. (2023) https://doi.org/10.1029/2022JD037295
Wu et al. (2021) https://doi.org/10.1016/j.jag.2021.102417
Wu et al. (2023) https://doi.org/10.2151/jmsj.2023-004