Assessing the Impact of Using Different Land Cover Classification in Regional Modeling Studies for the Manaus Area, Brazil

Sameh Adib Abou Rafee^1*, Ana Beatriz Kawashima¹, Marcos Vinícius Bueno de Morais¹, Viviana Urbina³, Leila Droprinchinski Martins², Jorge Alberto Martins¹

¹Department of Physics, Federal University of Technology-Parana, Londrina, Brazil.
²Department of Chemistry, Federal University of Technology-Parana, Londrina, Brazil.
³Department of Atmospheric Sciences, University of S?o Paulo, S?o Paulo, Brazil.

DOI: 10.4236/gep.2015.36013                

Abstract

Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.

Keywords

Land Use and Land Cover Classification, Regional Modeling Studies, Urban Air Quality

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Oke, T.R. (1987) Boundary Layer Climates. 2ed Edition, Methuen, New York, 435 p.
[2]	Kesselmeier, et al. (2000) Atmospheric Volatile Organic Compounds (VOC) at a Remote Tropical Forest Site in Central Amazonia. Atmospheric Environment, 34, 4063-4072. http://dx.doi.org/10.1016/S1352-2310(00)00186-2
[3]	Gehlhausen, S.M., Schwartz, M.W. and Augspurger, C.K. (2000) Vegetation and Microclimatic Edge Effects in Two Mixed-Mesophytic Forest Frag-ments. Plant Ecology, 147, 21-35. http://dx.doi.org/10.1023/A:1009846507652
[4]	IBGE—Instituto Brasileiro de Geografia e Estatística. Censo Demográfico. http://cidades.ibge.gov.br/xtras/perfil.php?codmun=130260
[5]	Skamarock, W.C., et al. (2008) Description of the Advanced Research WRF Version 3. National Center for Atmospheric Research Boulder, Colorado.
[6]	Hansen, M. and Reed, B.C. (2000) A Comparison of the IGBP Discover and University of Maryland Global Land Cover Products. International Journal of Remote Sensing, 21, 1365-1374. http://dx.doi.org/10.1080/014311600210218
[7]	Schneider, A., Friedl, M.A. and Potere, D. (2009) A New Map of Global Urban Extent from MODIS Data. Environmental Research Letters, 4. http://dx.doi.org/10.1088/1748-9326/4/4/044003
[8]	Kalnay, E. and Cai, M. (2003) Impact of Urbanization and Land-Use Change on Climate. Nature, 423, 528-531. http://dx.doi.org/10.1038/nature01675
[9]	Pielke, R.A. (2002) Mesoscale Meteorological Modeling. 2nd Edition, International Geophysics Series, Vol. 78, 676.
[10]	Hallak, R. and Perreira Filho, A.J. (2001) Metodologia para análise de desempenho de simulacaes de sistemas convectivos na regiao metropolitana de Sao Paulo com o modelo ARPS: sensibilidade a variacoes com os esquemas de adveccao e assimilacao de dados. Revista Brasileira de Meteorologia, 26, 591-608.