Dust storm source detection using ANP and WRF models in southwest of Iran

In recent years, dust storms with huge adverse impacts on the environment have become more frequent and intense in the southwest of Iran. The first step to control or influence the dust storm process is source identification. The objective of this study is to detect the major sources of dust storms...

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Veröffentlicht in:Arabian journal of geosciences 2021-08, Vol.14 (15), Article 1529
Hauptverfasser: Abbasi, Esmaeil, Etemadi, Hana, Smoak, Joseph M., Amouniya, Hamaid, Mahoutchi, Mohammad Hassan
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container_title Arabian journal of geosciences
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creator Abbasi, Esmaeil
Etemadi, Hana
Smoak, Joseph M.
Amouniya, Hamaid
Mahoutchi, Mohammad Hassan
description In recent years, dust storms with huge adverse impacts on the environment have become more frequent and intense in the southwest of Iran. The first step to control or influence the dust storm process is source identification. The objective of this study is to detect the major sources of dust storms in Bushehr Province of Iran using the analytic network processes (ANP) and the weather research and forecasting (WRF) models. Five synoptic stations for this purpose were examined over 17 years from 2001 to 2017. The spatial data includes land use, NDVI, slope, aspect-slope, elevation, and soil used as the major layers. The layers were weighted by applying the paired comparison and analytic hierarchy process methods. Also, local scale pressure systems were identified using the WRF numerical model. Results revealed that pressure systems at the local scale in different seasons are located exactly over areas prone to dust storm generation within the study area. Furthermore, the WRF model correctly showed the atmospheric pressure and wind field locations at a local scale. Based on ANP output, more than 25% of Bushehr Province has been active as dust-prone regions at a local scale. The ANP model identified the zones of erosion-prone areas, and the WRF model determined the location of permanent or semi-permanent pressure systems. Results demonstrated that applying the WRF and ANP models provided a useful tool to identify and validate the local dust sources with high accuracy in the study sites.
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The first step to control or influence the dust storm process is source identification. The objective of this study is to detect the major sources of dust storms in Bushehr Province of Iran using the analytic network processes (ANP) and the weather research and forecasting (WRF) models. Five synoptic stations for this purpose were examined over 17 years from 2001 to 2017. The spatial data includes land use, NDVI, slope, aspect-slope, elevation, and soil used as the major layers. The layers were weighted by applying the paired comparison and analytic hierarchy process methods. Also, local scale pressure systems were identified using the WRF numerical model. Results revealed that pressure systems at the local scale in different seasons are located exactly over areas prone to dust storm generation within the study area. Furthermore, the WRF model correctly showed the atmospheric pressure and wind field locations at a local scale. Based on ANP output, more than 25% of Bushehr Province has been active as dust-prone regions at a local scale. The ANP model identified the zones of erosion-prone areas, and the WRF model determined the location of permanent or semi-permanent pressure systems. 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Based on ANP output, more than 25% of Bushehr Province has been active as dust-prone regions at a local scale. The ANP model identified the zones of erosion-prone areas, and the WRF model determined the location of permanent or semi-permanent pressure systems. 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subjects Analytic hierarchy process
Atmospheric correction
Atmospheric models
Atmospheric particulates
Dust
Dust storms
Earth and Environmental Science
Earth science
Earth Sciences
Environmental impact
Identification
Land use
Mathematical models
Numerical models
Original Paper
Pressure
Spatial data
Storms
Weather forecasting
title Dust storm source detection using ANP and WRF models in southwest of Iran
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