Assessment of the impact of dust aerosols on crop and water loss in the Great Salt Desert in Iran

Boroughani, Mahdi; Mohammadi, Maziar; Mirchooli, Fahimeh; Fiedler, Stephanie

doi:10.1016/j.compag.2021.106605

Cited by 10 publications

(9 citation statements)

References 42 publications

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“…With each of these methods we see dust aerosol in different color and quality in the MODIS images on the 28 days. After combining the four methods in the software ENVI, we choose the method that shows the dust plume in the MODIS image more clearly as the best method (Boroughani et al, , 2022. This method is based on a cone of dust diffusion seen in the processed MODIS images, where the apex denotes the dust's source (Lee et al, 2009;Walker et al, 2009).…”

Section: Dust Aerosol Mapmentioning

confidence: 99%

“…5. These classes are set as in earlier studies (Mosavi et al, 2020;Boroughani, Mohammadi, Mirchooli, & Fiedler, 2022). The results of the model evaluation using ROC indicates that the RF model with an accuracy of 75.0% provides the most accurate outputs.…”

Section: 3 Spatial Distribution Of Dust Source Susceptibilitymentioning

confidence: 99%

“…The areas that falls under the category of both kind of land degradation might be most vulnerable concerning local self-sufficiency for food security and sustainability of human activities. For instance, dust storms drive water loss through failure of agricultural crops in Iran (Boroughani et al 2022). Moreover, the adverse impacts of water-induced soil erosion is known from numerous other regions (Lal and Moldenhauer 2008, Gao et al 2015, Standardi et al 2018.…”

Section: Land Degradation Susceptibilitymentioning

confidence: 99%

“…Dust storms, which are a natural hazard, are associated with soil erosion. This phenomenon has detrimental impacts in the Earth system, e.g., for food security (Boroughani et al 2022), human health (Moridnejad et al, 2015), geochemical conditions (Gholami et al, 2020b), and the Earth's carbon cycle (Gherboudj et al, 2017). Identifying dust sources as potential areas of dust emission is therefore necessary in developing a better understanding of land degradation.…”

Section: Introductionmentioning

confidence: 99%

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Mapping land degradation risk due to wind and water erosion

Boroughani

Mirchooli

Hadavifar

et al. 2023

Preprint

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Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation, the topic receives often relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the metric Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The maps for water and aeolian soil erosion are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.

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Section: Dust Aerosol Mapmentioning

confidence: 99%

Section: 3 Spatial Distribution Of Dust Source Susceptibilitymentioning

confidence: 99%

Section: Land Degradation Susceptibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping land degradation risk due to wind and water erosion

Boroughani

Mirchooli

Hadavifar

et al. 2023

Preprint

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show abstract

“…Dust storms, which are a natural hazard, are associated with soil erosion. This phenomenon has detrimental impacts on the Earth system, e.g., for food security (Boroughani et al, 2022), water supply (Duniway et al, 2019), human health (Moridnejad et al, 2015), geochem-ical conditions (Gholami et al, 2020b), and the Earth's carbon cycle (Gherboudj et al, 2017). Identifying dust sources as potential areas of dust emission is therefore necessary for developing a better understanding of land degradation.…”

Section: Introductionmentioning

confidence: 99%

Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Boroughani¹,

Mirchooli²,

Hadavifar³

et al. 2023

SOIL

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Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation due to wind and water erosion in some areas of the world, the combined study of both types of erosion in the same area receives relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran, encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing the artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the area under the curve (AUC) of the receiver operating characteristic (ROC) metric. The water and aeolian soil erosion maps are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for in an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.

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