Assessment of Dust Event Frequency in the North Lake Urmia Based on MAIAC-Derived Aerosol Retrievals | ||
| پژوهش های آبخیزداری | ||
| Article 8, Volume 38, Issue 4 - Serial Number 149, January 2026, Pages 128-145 PDF (1.3 M) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmrj.2025.369926.1627 | ||
| Authors | ||
| Maryam Fathi1; Hesam Ahmady Birgani* 2 | ||
| 1Masters’ Student, Rangeland and Watershed Management Group, Faculty of Natural Resources, Urmia University, Urmia, Iran | ||
| 2Associate Professor, Rangeland and Watershed Management Department, Natural Resources, Urmia University, Urmia, Iran | ||
| Abstract | ||
| Introduction and Goal Dust storms, as one of the major environmental hazards, have widespread impacts on human health, agriculture, and ecosystem sustainability. In recent years, the incidence of this phenomenon has increased significantly in the areas surrounding Lake Urmia, particularly in the northern parts. This trend is due to numerous factors, the most important of which is the widespread drying of the bed of Lake Urmia following a severe reduction in water resources and climate change. The decrease in the amount of water entering the lake and the increase in evaporation duo to climatic change have caused the water level to drop and a large portion of the lake bed to become visible. Today, this dried bed serves as a source of dust particles in the region, with many claims being made about the dust it causes to the surrounding environment. Therefore, accurate monitoring and spatiotemporal trends of dust storm on the northern margin of Lake Urmia is a great importance for adopting management measures and environmental policies. Given the importance of accurate monitoring of this phenomenon, the use of remote sensing technologies and atmospheric correction algorithms such as MAIAC allows for more accurate and up-to-date analyses. In this regard, the main objective of this research was to analyze the temporal and spatial patterns trends in the frequency of dust day on the northern Lake Urmia during the period 2001 to 2024 using MAIAC-corrected dust algorithm data. Using the results of this research, understanding of the processes affecting dust generation and transport has been improved and appropriate management strategies can be provided to reduce its negative effects. Materials and Methods In this study, moderate resolution MODIS satellite images were collected between 2001 and 2024 for the northern margin of Lake Urmia. By utilizing the high capability of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) in accurately separating airborne particles, dusty days were identified. The extracted data were analyzed using Google Earth Engine (GEE) platform and ArcMap software (version 10.8) and divided into four frequency classes based on the number of dust days: low (0–50 dusty days per year) (class 1), moderate (50–100 dusty days per year) (class 2), high (100–150 dusty days per year) (class 3), and very high (more than 150 dusty days per year) (class 4). Subsequently, spatial and temporal distribution maps of each class were carefully prepared. Also, in order to identify and categorize years with similar patterns in terms of the frequency of dust day, the K-Means clustering method was used in SPSS software (version 30). Using this analysis, years were well separated into cluster groups based on similar frequencies. Results and Discussion The results of this study showed that changes in the spatial distribution and intensity of dust events were significant between 2001 and 2024. In the early years of the study (2001-2008), the frequency of dust occurrence on the floor was low, but from 2012 onwards, the area of floors with medium and high frequency increased significantly. Although in some years, such as 2019 and 2020, the abundance of dust decreased relatively, in previous years the trend of this phenomenon was increasing. The results of cluster analysis using SPSS software (version 30) showed that the highest frequency of dusty days in 2014-2015 and 2015-2016 belonged to cluster group 2. Also, the lowest frequency of dusty days in the years 2001-2002 to 2007-2008, 2019-2020, and 2020-2021 was related to cluster 4. Also, validation result using data from the Shabestar synoptic station, (the nearest station to the northern margin of Lake Urmia), indicated that the relationship between station observations (codes 5, 6, and 8) and the area of pixels with high dust storm frequency in remote sensing image was weak and not statistically insignificant. These findings indicated that dust particles rising from the dried lake bed are unable to be transported over long distances and settle very close to their point of origin. Conclusion and Suggestions The result of the study of the spatial and temporal frequency of dust storm events from 2001 to 2024 on the northern margin of Lake Urmia showed that the annual changes in the frequency of dusty days in the mentioned region were significant. Therefore, the role of climatic, geological, soil and environmental factors is important. The results of this study indicate the need for continuous monitoring and updating of data in the mentioned region so that the damaging trend of dust events in the region can be identified and managed in a timely manner. Therefore, it is suggested that the results of this research be used to design management strategies and reduce environmental risks in the region so that favorable decisions can be made at the local and regional levels. It is also suggested that data updates be carried out continuously on the northern shore of Lake Urmia to identify harmful trends in a timely manner and to make local and regional decisions with more accuracy and effectiveness. On the other hand, by integrating climatic, geological, and soil information, it is possible to help develop early warning systems and preventive management and minimize the negative effects of this phenomenon on the environment and local communities. | ||
| Keywords | ||
| Dust storm frequency; K-Means clustering; Lake Urmia; MAIAC algorithm; remote sensing | ||
| References | ||
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