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Spatial and quantitative assessment of wind erosion from adjacent dunes with different surface cover ratios.

Sema KaplanMustafa BasaranOguzhan UzunAmin NouriFeras YoussefSelen Deviren SaygınA Ugur OzcanGunay Erpul
Published in: Environmental monitoring and assessment (2020)
Direct measurements, models, and risk maps play significant roles in assessment and monitoring of wind erosion cases. Although active and passive traps allow researchers to measure point sediment transports directly, it is also possible to make geostatistical analysis of wind erosion with grid and random sampling at multiple points. Geostatistical models can be used in multi-sample eolian researches to improve model success and update model parameters. The present study was conducted for case-based geostatistical analysis of sediment transport rates (STRs) over two adjacent dunes (plot A and B) with different vegetation cover rates between 22 May and 15 June 2011. The plot A has a vegetative cover ratio of 30%, while the plot B has a vegetation cover ratio of 2% and sand content of the plots is 88%. Actual mass transports were measured with BEST sediment traps. A total of 19 BEST sediment trap assemblies were placed randomly over the plot A and 21 were placed over the plot B. A climate station was installed over the research site to record climate data throughout the experimental period. There were two wind erosion cases during the research period. U test indicated that differences in sediment transport rates of the plots for each case were significant (p < 0.00). Spatial analyses of STRs (kg m-1 h-1) also exhibited case-based differences. While nugget effect was observed in case 1 of the plot B, the other case in both plots were modeled with spherical model. Maximum likelihood distances in plot A and B were respectively identified as 61 m and 1 m in the first case and as 13 m and 30 m in the second case. Total mass transport was measured as 112 kg m-1 in plot A and as 2162 kg m-1 in plot B. Consequently, it was found that 30% vegetation cover reduced the total mass transport dramatically.
Keyphrases
  • climate change
  • heavy metals
  • polycyclic aromatic hydrocarbons
  • machine learning
  • artificial intelligence
  • data analysis
  • organic matter