Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin

Dust storms are considered as one of the most important environmental challenges in the West Asia region. In addition to the harmful impacts of dust storms on human health, they also have particular effects on socioeconomic and agroecological domains of human communities. Identify the sources of dust storms is the first step to combat against these devastating phenomena. Accordingly, the present study was conducted to determine dust sources of the Tigris and Euphrates basin using satellite and climatic data. Monthly LST and NDVI of MODIS, monthly wind speed, soil moisture, and absolute air humidity data from GLDAS, monthly TRMM precipitation, and soil texture data of FAO were used. The Analytic Hierarchy Process (AHP) model was applied to determine the weights of the collected data (i.e. criteria or drivers for dust storms formation). Susceptible Areas to Dust Storm Formation (SADSF) were determined using the Weighted Linear Combination (WLC) model for months of June, July, and August from 2000 to 2017. After performing SADSF analysis, five main dust sources were identified in the whole basin. To evaluate the accuracy of the results, the number of real Observed Dust Storms (ODS) in each source was compared to the repetition of allocation in SADSF for each pixel over the 18-year period of this study from 2000 to 2017. Results indicated that the area of SADSF has significantly grown for all three months since 2008. The areas of SADSF in June and July were almost the same, while they were significantly bigger than August. Among identified dust sources, the highest SADSF repetition was in the northwest of Iraq followed by eastern Syria, southern Iraq, southeast border of Iraq, and east border of Iraq, respectively. The correlation coefficient between the SADSF repetition with the number of ODS events in those recognized dust sources was equal to 0.88, 0.76, and 0.74 for June, July, and August, respectively, that shows the accuracy of our results in comparison to actual data.