Iranian Journal of Range and Desert Research

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A Study of the Relationship Between Maximum Dust Values and Climatic Variables Using Remote Sensing Data (Case Study: Hormozgan Province)

Document Type : Research Paper

Authors

1 Assistant Professor, Hormoz Studies and Research Center, University of Hormozgan, Bandar Abbas, Iran

2 Assistant Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran.

3 Assistant Professor, Department of Water Sciences & Engineering, Minab Higher Education Complex, University of Hormozgan, Minab, Iran

Abstract

Dust phenomenon is one of the natural disasters that is considered as a serious environmental hazard, especially in arid and semi-arid regions due to the great damage it causes every year. The present study aimed to investigate the relationship between 14 climatic variables with the maximum monthly aerosol optical depth (AOD) due to the dust events in Hormozgan province. First, by coding in the Google Earth Engine (GEE) environment, a satellite image was retrieved from the MODIS aerosol products for each day, and while preparing the AOD time series, the average maximum monthly dust values for a 17-year period (2000-2017) was extracted. Also, monthly climate and water balance products of University of Idaho including actual and reference evapotranspiration, minimum and maximum temperature, precipitation accumulation, soil moisture, Palmer drought severity index, climate water deficit, downward surface shortwave radiation, vapor pressure, vapor pressure deficit, and wind speed, as well as land surface temperature (LST) and vegetation index (EVI) were extracted and, while sampling these images, the relationship between the average maximum monthly dust values with them were computed using the ordinary least squares (OLS) and geographic weighted regression (GWR) methods. Then, the global Moran's I statistics was employed to analyze the spatial autocorrelation and distribution of dust over the province. The results showed that the GWR model with the root mean square error of 0.14, the sum of residual squares of 11.3, the coefficient of determination of 0.82, and the corrected Akaike information criterion of -570.19, performed better than the OLS method. The evaluation of the coefficients of the GWR model showed that the variables of vegetation cover, soil moisture and precipitation had the greatest effect on the amount of dust, respectively. Also from the perspective of spatial autocorrelation, a cluster pattern was observed for dust distribution over the province.

Keywords

References
Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A. and Hegewisch, K.C., 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5: 170191.
Nohegar, A., Khoorani, A., and Tamassoki, E., 2013. Climate analysis of suspended dust storms in Sar-Pol-Zohab Station (1986 to 2009). Geography and Environmental Hazards, 2 (6): 89-102
Jafari, M., Zehtabian, G., Mesbahzadeh, T., 2019. Statistical analysis the phenomenon of Dust Storm (case study: Isfahan). Iranian Journal of Rangeland and Desert Research, 25 (4): 863-876
Brunsdon., C., Fotheringham., A.S. and Charlton., M., 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28 (4): 281-298.
Dai, X., Guo, Z., Zhang, L. and Li, D., 2010. Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, China. Stochastic Environmental Research and Risk Assessment, 24 (2): 247-257.
Danesh-Shahraki, M., Shahriari, A., Ganjali, M. and Bameri, A., 2017. Seasonal and spatial variability of airborne dust loading rate over the Sistan plain cities and its relationship with some climatic parameters. Journal of Water and Soil Conservation, 23 (6): 199-215.
Farajzadeh Asl, M. and Alizadeh, KH., 2012. Spatial and temporal analysis of dust storms in Iran, Journal of Spatial Planning, 15 (1): 65-84.
Foody, G.M., 2004. Spatial nonstationarity and scale‐dependency in the relationship between species richness and environmental determinants for the sub‐Saharan endemic avifauna. Global Ecology and Biogeography, 13 (4): 315-320.
Fotheringham, A.S., Brunsdon, C. and Charlton, M., 2003. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.
Guo, J.P., Zhang, X.Y., Che, H.Z., Gong, S.L., An, X., Cao, C.X., Guang, J., Zhang, H., Wang, Y.Q., Zhang, X.C. and Xue, M., 2009. Correlation between PM concentrations and aerosol optical depth in eastern China. Atmospheric Environment, 43 (37): 5876-5886.
Gupta, P., Christopher, S.A., Wang, J., Gehrig, R., Lee, Y.C. and Kumar, N., 2006. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40 (30): 5880-5892.
He, Q., Gu, Y. and Zhang, M., 2019. Spatiotemporal patterns of aerosol optical depth throughout China from 2003 to 2016. Science of The Total Environment, 653: 23-35.
Khajeh, M., Kheyrandish, H., and Pishdad, H., 2015. Review the phenomenon of dust and study of effective winds on it (Case Study: Bandar Abbas, Hormozgan province). Environmental Erosion Research Journal, 4 (4): 37-48.
Kazemi, M., Nafarzadegan, A., Mohammadi, F., 2019. Studying changes in heat islands and land uses of the Minab city using the random forest classification approach and spatial autocorrelation analysis. Journal of RS and GIS for Natural Resources, 10 (4): 38-56.
Masatoshi, Y., 2002. Climatology of yellow sand (Asian sand, Asian dust or Kosa) in East Asia. Science in China Series D: Earth Sciences, 45 (1): 59-70.
Mehrabi, S., Soltani, S., Jafari, R., 2015. Analyzing the Relationship Between Dust Storm Occurrence and Climatic Parameters. Journal of Water and Soil Science. 19 (71): 69-81.
Mitchel, A., 2005. The ESRI Guide to GIS analysis, Volume 2: Spatial measurements and statistics. ESRI Press, Redlands, California
Namdari, S., Valizade, K.K., Rasuly, A.A. and Sarraf, B.S., 2016. Spatio-temporal analysis of MODIS AOD over western part of Iran. Arabian Journal of Geosciences, 9 (3): 191.
Ranjbar, H., Bazgir, M., Namdar Khojasteh, D., Rostaminia, M., 2019. Identification of dust sources in Ilam province. Iranian Journal of Rangeland and Desert Research, 26 (3): 675-688.
Rashki, A., Kaskaoutis, D.G., Goudie, A.S. and Kahn, R.A., 2013. Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Science of the Total Environment, 463: 552-564.
Rayegani, B. and Kheirandish, Z., 2018. Utilization of time series of satellite data in order to validate the identified dust storm sources in Alborz province. Journal of Spatial Analysis Environmental Hazards, 4 (4): 1-18.
Sadeghinia, A., Alijani, B., Ziaeian, P., Khaledi, Sh., 2014. Application of spatial autocorrelation techniques in analysis of heat islands in Tehran. Journal of Applied Researches in Geographical Sciences, 30: 67-90.
Sepahvand, A., Almasian, F., Zand, M., 2019. Investigating the effect of climatic variables (rainfall, temperature and pressure) on the occurrence of dust in the west of the country. 7th National Conference on Rainwater Catchment Systems. Iran, 20-21 February, 178-187.
Shirazi, M., Akhavan Ghalibaf, M., Matinfar, H., Nakhkesh, M., 2019. Evaluation of Landsat OLI data for estimating Aerosol Optical Thickness over deserts. Iranian Journal of Rangeland and Desert Research, 26 (4): 855-867.
Tao, M., Li, R., Wang, L., Lan, F., Wang, Z., Tao, J. and Chen, L., 2020. A critical view of long-term AVHRR aerosol data record in China: Retrieval frequency and heavy pollution. Atmospheric Environment, 223: 117246.
Tu, J. and Xia, Z.G., 2008. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation. Science of the Total Environment, 407 (1): 358-378.
Wang, Q., Ni, J. and Tenhunen, J., 2005. Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global ecology and biogeography, 14 (4): 379-393.
Zhang, W., He, Q., Wang, H., Cao, K. and He, S., 2018. Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change. Ecological Indicators, 93: 458-469.
Iranian Journal of Range and Desert Research
Volume 27, Issue 4
December 2020
Pages 792-801
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