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

Journal Pre-proof Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin Ali Darvishi Boloorani, Yasin Kazemi, Amin Sadeghi, Saman Nadizadeh Shorabeh, Meysam Argany PII:

S1352-2310(20)30041-8

DOI:

https://doi.org/10.1016/j.atmosenv.2020.117299

Reference:

AEA 117299

To appear in:

Atmospheric Environment

Received Date: 17 August 2019 Revised Date:

17 December 2019

Accepted Date: 19 January 2020

Please cite this article as: Boloorani, A.D., Kazemi, Y., Sadeghi, A., Shorabeh, S.N., Argany, M., Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin, Atmospheric Environment (2020), doi: https://doi.org/10.1016/ j.atmosenv.2020.117299. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Ali Darvishi Boloorani and Yasin Kazemi conceived and designed the research for the first draft; Yasin Kazemi Amin Sadeghi, and Saman Nadizadeh Shorabeh performed data

analysis and wrote the first draft; Meysam Argany edited the pre-draft; Yasin Kazemi and Ali Darvishi Boloorani re-designed the research, revised and edited the paper; all authors contributed to and approved the final manuscript.

1

Identification of dust sources using long term satellite and climatic data: a

2

case study of Tigris and Euphrates basin

3 4

Ali Darvishi Boloorania,b∗, Yasin Kazemib, Amin Sadeghic, Saman Nadizadeh Shorabehb,

5

Meysam Arganyb

6

a

7

Nanchang, Jiangxi, P.R. China,

8

b

9

Tehran, Iran

Key Laboratory of Digital Land and Resources, East China University of Technology,

Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran,

10

b

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Science and Research branch, Islamic Azad University, Tehran, Iran

12

Abstract

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Dust storms are considered as one of the most important environmental challenges in the

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West Asia region. In addition to the harmful impacts of dust storms on human health, they

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also have particular effects on socioeconomic and agroecological domains of human

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communities. Identify the sources of dust storms is the first step to combat against these

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devastating phenomena. Accordingly, the present study was conducted to determine dust

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sources of the Tigris and Euphrates basin using satellite and climatic data. Monthly LST and

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NDVI of MODIS, monthly wind speed, soil moisture, and absolute air humidity data from

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GLDAS, monthly TRMM precipitation, and soil texture data of FAO were used. The

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Analytic Hierarchy Process (AHP) model was applied to determine the weights of the

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collected data (i.e. criteria or drivers for dust storms formation). Susceptible Areas to Dust

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Storm Formation (SADSF) were determined using the Weighted Linear Combination (WLC)

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model for months of June, July, and August from 2000 to 2017. After performing SADSF

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analysis, five main dust sources were identified in the whole basin. To evaluate the accuracy

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of the results, the number of real Observed Dust Storms (ODS) in each source was compared

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to the repetition of allocation in SADSF for each pixel over the 18-year period of this study

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from 2000 to 2017. Results indicated that the area of SADSF has significantly grown for all

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three months since 2008. The areas of SADSF in June and July were almost the same, while ∗

Department of Remote Sensing and GIS, Faculty of Natural resources and Environment,

Corresponding Author ([email protected])

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they were significantly bigger than August. Among identified dust sources, the highest

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SADSF repetition was in the northwest of Iraq followed by eastern Syria, southern Iraq,

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southeast border of Iraq, and east border of Iraq, respectively. The correlation coefficient

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between the SADSF repetition with the number of ODS events in those recognized dust

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sources was equal to 0.88, 0.76, and 0.74 for June, July, and August, respectively, that shows

35

the accuracy of our results in comparison to actual data.

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Keywords: Dust sources mapping, satellite and climatic data, weighted linear combination,

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Tigris and Euphrates basin.

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1. Introduction

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Dust storms are the main consequences of wind erosion which annually caries about

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2000 million tons of soil into the atmosphere, where, 75 percent of these dust particles are

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deposited on land and 25 percent on the oceans (Shao et al., 2011). Dust particles affect the

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atmosphere, agricultural production, and ecosystem (Moghaddam et al., 2018). They also

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cause serious human health impacts like respiratory problems (Goudarzi et al., 2019; Kaiser,

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2005; Soleimani, Boloorani et al., 2019; Thalib & Al-Taiar, 2012), modifying the

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convectional activity and cloud formation (Kim, Chin, Kemp et al., 2017; Nenes et al., 2014),

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changing the rainfall pattern, water salinity, and reducing the surface and underground water

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quality (Nativ et al., 1997). Also, absorption and scattering of solar radiation by dust particles

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in the atmosphere can affect the air temperature and solar radiation budget of the Earth (Das,

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1988; Ghazi et al., 2014; Haywood et al., 2005; Saidan et al., 2016). Accelerated melting of

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snow and ice is also noticeable in the deposition areas of dust storms (Painter et al., 2007).

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Detecting dust sources and modeling the behavior of the most important drivers to

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form dust storms is the first step for struggling with the negative impacts of these phenomena

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(Ginoux et al., 2012; Nick Middleton & Kang, 2017; Soleimani, Teymouri et al., 2019). In

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earlier researches, several methods have been proposed using different data to identify dust

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storm sources. For instance, meteorological stations measurements were considered for

57

determining the dust source by J. Sun et al. (2001), Xin-fa et al. (2001), Gao et al. (2012),

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Rezazadeh et al. (2013), Hamidi et al. (2017), Rashki et al. (2017), Namdari et al. (2018).

59

Geochemical composition analysis of dust particles in the deposit area have been used by

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some researchers to find the potential sources of dust generation (Abouchami et al., 2013;

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Arimoto, 2001; Cesari et al., 2012; Engelbrecht & Jayanty, 2013; Nie et al., 2012; Reheis et

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al., 2002; Y. Sun et al., 2005; Xiaoye et al., 1996; Zarasvandi et al., 2011; Zhang et al., 2017).

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The numerical models are useful methods for dust storm prediction and dust sources

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identification. In these methods, the mathematical relationships of dust formation components

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are studied using wind erosion models which include all dust cycle stages: emission, transfer,

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and deposition (Gherboudj et al., 2017; Ginoux et al., 2001; Kim, Chin, Kemp et al., 2017;

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Sprigg et al., 2014; Tanaka & Chiba, 2006; Xi & Sokolik, 2016). Satellite data are the most

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common methods for tracking and determining dust storms sources. Some of the most

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extensively used satellite imagers for dust sources identification are as follows: NOAA-

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AVHRR (Bryant, 2003; Husar et al., 1997; Swap et al., 1996), Nimbus-TOMS (Esmaili et al.,

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2006; NJ Middleton & Goudie, 2001; Prospero et al., 2002), LANDSAT (Cao et al., 2015;

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Moridnejad et al., 2015; Rivera et al., 2010) and MODIS (Baddock et al., 2009; Moridnejad

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et al., 2015; Ni et al., 2005; Parajuli & Zender, 2017; Prospero et al., 2002). Hybrid methods

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have also been proposed for identifying dust sources by using different parameters involved

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in the dust process from different data sources, i.e. remote sensing imagery, meteorological

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data, numerical products, field measurements, etc. (Cao et al., 2015; Leys et al., 2011; Xi &

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Sokolik, 2016; Zoljoodi et al., 2013).

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The meteorological studies are very profitable for annual forecasting. However, the

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merely use of meteorological data for dust sources identification is insufficient and hardly

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can be cited by their results. Different dust sources usually have different chemical

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compositions (Scheuvens et al., 2013) and however geochemical methods cannot be applied

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as robust determinant of high certainty for dust source identification (Ahmady-Birgani et al.,

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2018; H. Wang et al., 2015; Yigiterhan et al., 2018). Although the simulation results of

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numerical models have been improved, they are still lacking in high certainty. Therefore,

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different models will produce different results that have been used for dust sources

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discrimination (Gherboudj et al., 2017; Nabavi et al., 2017). On the other hand, models that

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merely use remote sensing data are more useful to investigate the intensity and extent of dust

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events. Consequently determining the potential areas of dust emissions is complex and

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requires data from multi-sources like various satellite images, meteorological data and

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ground-based measurements (Akhlaq et al., 2012; Christopher et al., 2011).

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The semi-arid regions of west Asia within the Tigris and Euphrates basin are

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recognized as one of the main areas with high potential of wind erosion and dust generation

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(Ginoux et al., 2012; Parajuli et al., 2014). During the last two decades, there has been an

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increase in the number and frequency of dust storm events in this region, especially in spring

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and summer times (Khoshakhlagh et al., 2012). Tigris and Euphrates basin, with fine-grained

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alluvial deposits coincide closely with semi-arid and dry climate conditions, has very high

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potentials for dust storms formation in west Asia (Hamidi et al., 2013; Prospero et al., 2002).

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Dust sources that are located in this basin, directly have been impacting most countries,

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including, Iraq, Syria, Iran, and Kuwait and other countries of the Persian Gulf. Several

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studies have been carried out to identify dust sources in west Asia including the Tigris and

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Euphrates areas. Prospero et al. (2002) used TOMS data for Iran, Saudi Arabia, the regions

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near to the Caspian Sea, Aral Sea, and the Taklamakan Desert in China to identify dust

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sources, in addition to the Sahara region. Cao et al. (2015) identified sand and dust storms by

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HYSPLIT model and MODIS images. They used Landsat images as the basis for dust source

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mapping and the areas that were responsible for 70% of the regional dust storms that were

106

located in the Tigris and Euphrates basin. Moridnejad et al. (2015) developed the MEDI

107

index using the MODIS data to identify dust sources in Iraq and Syria. They identified 247

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dust sources from 2001 to 2012. Most of these sources were located in the north and

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northwest of Iraq and across its border with Syria. Gherboudj et al. (2017) identified the

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natural sources of dust storms from the North African to Pakistan and Afghanistan using

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Alfaro and Gomes (2001) relationships and parameters in monthly and annually scales from

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2011 to 2014.

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With respect to the importance of dust storm sources mapping in west Asia region, the

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objective of this study was to identify and analyze the spatial and temporal changes of the

115

dust sources in the Tigris and Euphrates basin. From our previous knowledge and the

116

available data of the region of interest, June, July, and August were selected as the illustrative

117

months in this study, that the regional intensity and frequency of dust storms are remarkable

118

in this months (Nabavi et al., 2016; Parajuli et al., 2014) due to the lack of precipitation and

119

intensity of wind. Therefore, by using Multi-Criteria Decision Analysis (MCDA) and long-

120

term data, it is possible to achieve good results with fewer data. This will result in better data

121

analysis, avoiding data redundancy and computation costs.

122

2. Material and methods

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2.1. Study area

124

The Tigris and Euphrates basin, that has been investigated in this study, is located in

125

36.72 to 52.05 degrees in east and 30.27 to 40.28 degrees in north. As far back as the 1980s,

126

the area was described as a major source of dust [Middleton, 1986]. Most of the basin is

127

located in Iraq and a part in Iran, Turkey, and Syria, and small parts are located in northern

128

Saudi Arabia and Kuwait. The boundary of the study area is in accordance with the

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hydrological boundary of these two great rivers of the west Asia region. Most of the dust

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storms from this basin are rising from Iraq, lowlands of the southwest of Iran, and the Persian

131

Gulf countries. Alluvial zones of the Tigris and Euphrates along with the An-Nafud desert are

132

referred as the main sources of dust in west Asia (Jish Prakash et al., 2015; Shao, 2008).

133 134

Fig. 1. The study area, Euphrates and Tigris (EuT) rivers basin

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2.2. Data

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The monthly NDVI and LST from MODIS with 0.05 degree spatial resolution;

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monthly precipitation product from TRMM products with 0.25 degree spatial resolution;

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average monthly wind speed, soil moisture, and absolute air humidity from GLDAS (NOAH

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model) with 0.25 degree spatial resolution; and soil erodibility data from FAO with 0.0085

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degree spatial resolution were collected from 2000 to 2017. The data had different spatial

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resolutions, therefore we converted them into 0.05 degrees to be used for the purpose of GIS

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spatial molding.

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2.3. Methodology

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The work methodology is displayed in Fig. (2). In the first step, a dataset of effective

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drivers (i.e. criteria) for the formation of dust sources was created using above mentioned

146

data. In the second step, using the minimum and maximum method (Jain et al., 2005; Snelick

147

et al., 2005) the maps of different criteria were standardized. The weights of criteria were

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determined using the Analytic Hierarchy Process (AHP) model. In the next step, the potential

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of dust production sources was determined by combining the criteria maps and the weight of

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each criterion using the Weighted Linear Combination (WLC) model at the pixel level. Then,

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the map of the Susceptible Areas to Dust Storm Formation (SADSF) was generated and their

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spatiotemporal variation has been analyzed to identify the major dust sources of the region.

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Finally, by visual inspection of MODIS images, the Observed Dust Storm (ODS) in each dust

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source was examined to evaluate the accuracy of the results.

155 156

Fig. 2. Data and method for dust sources mapping

157 158

•

Drivers of dust storm formation

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In this research, seven main drivers of dust formation were used to model the

160

behavior of the dust storm sources. These drivers are including: wind speed, NDVI, soil

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moisture, K-factor, precipitation, LST, and absolute air humidity. Based on our

162

investigations, expert opinions, and previous studies (Abdi Vishkaee et al., 2012; Kim, Chin,

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Remer et al., 2017; Najafi et al., 2014; Xi & Sokolik, 2016) wind speed and NDVI were

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selected as constraints criteria. The wind speed threshold is very variable for the formation of

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dust storms, both in time and space. For example, based on Xi and Sokolik (2016), 6.5 m.s-1

166

was considered as the standard threshold for wind erosion in Central Asia. While Pye and

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Tsoar (1987), Abdi Vishkaee et al. (2012) and Najafi et al. (2014) proposed that if the Shamal

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wind speed exceeds 6 m.s-1, dust storm could be formed in west Asia. Therefore, with respect

169

to the same study area, the wind speed of 6 m.s-1 was adapted as the threshold in this study.

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The threshold value of NDVI was also considered as 0.15 in various researches like Kim,

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Chin, Remer et al. (2017) and Tsolmon et al. (2008). According to direct or inverse

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relationship of all seven effective factors to the formation of dust storms, a number between

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zero to one was assigned for each pixel by normalization process, so that, we could compare

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these factors and apply algebraic calculations on them.

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Wind speed: this factor could reduce soil moisture content and make the land susceptible to

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erosion. In arid and semi-dry areas wind increases the rate of evaporation and leads to the

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reduction of moisture content of topsoil that is susceptible to erosion. In earlier researches it

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was established that in some areas like in west Asia there is a significant association between

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dust storms and near-surface wind speed and no wind erosion will occur without reaching to

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the required threshold (Csavina et al., 2014; Kok et al., 2012).

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NDVI: the density of vegetation cover controls soil erosion. However, the climate and

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ecological characteristics of the area, are changing toward dryness. Therefore, losing

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vegetation cover will result in the susceptibility of land against strong winds and

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consequently, the soil surface particles will be transferred easier by wind (He et al., 2007;

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Youssef et al., 2012).

186

Soil moisture: surface soil moisture is one of the most critical variables in hydrological

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processes, which is affected by water and the energy of the Earth's surface and atmosphere

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exchange (Fécan et al., 1999). The role of soil moisture in 1 to 2 meters of the topsoil has

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been emphasized and addressed as a key variable in soil erosion (Ravi et al., 2011). This

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factor helps vegetation maintaining and growing to more adhere soil particles will cause soil-

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resistant against wind erosion (Gao et al., 2012; Sotoudeheian et al., 2016; Xu et al., 2015).

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K-factor: soil erodibility factor indicates soil's potential for erosion by wind and runoff. This

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factor is a combination of texture, structure, organic carbon content, hydraulic properties, and

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moisture of soil function (Blanco Canqui & Lal, 2009). K-factor has different values due to

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the soil texture types that determines the size and proportion of soil material including sand,

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silt, and clay. In this study, for the mapping of soil erodibility, the coefficients presented in

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Table 1 were adapted with respect to the region's soil texture (Wischmeier & Smith, 1978). Soil texture class

K- factor

Sand

0.3

Loamy sand

0.1

Sandy loam

0.24

Sandy loam

0.34

Silty loam - Silt

0.42

Sandy clay loam

0.25

Clay loam

0.25

Silty clay loam

0.32

Clay- Silty clay

0.15

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Table 1. K-factor coefficient for different soil texture of the Tigris and Euphrates basin

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Precipitation: even small amounts of precipitation would affect the particles adhere together

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and consequently will result in the increase of soil-resistant against erosion. Generally, wind

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erosion often occurs in arid and semi-arid areas when the lack of precipitation causes soil to

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remain dry for long times (Taghavi, 2010).

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LST: Land Surface Temperature is a very important biophysical variable to show the amount

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of radiation emitted from the surface, and the exchange of energy between the earth surface

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and atmosphere (Weng et al., 2019). This factor reduces the soil moisture content and

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accelerates wind erosion by increasing the evaporation in dry areas (Taghavi, 2010).

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Following soil dryness due to high LST, the adhesion force between the soil particles will

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decrease and they will become more erodible.

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Absolute air humidity: the role of absolute air humidity in dust emission is complex.

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Generally, the lack of humidity causes the ascension of dry air and the formation of dust

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storms (Najafi et al., 2014) that will cause the soil particle less adhesion and higher

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erodibility (Csavina et al., 2014; Ravi et al., 2011).

213

•

Standardization of criteria

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Each criterion was standardized using their maximum and minimum values. The

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maximum was applied for the criterion that its higher value represents higher potential to the

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dust formation. The minimum method was used for the criterion which its lower values

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indicate a higher potential for dust generation. Therefore, LST, wind speed and K-factor have

218

been standardized using the maximum (eq. (1)) and precipitation, soil moisture content,

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absolute air humidity, and NDVI were standardized using minimum equation (2) (Firozjaei et

220

al., 2019; Y. Wang et al., 2011).

− −

=

221

Eq. (1)

−

=

Eq. (2)

− Where,

is the normalized value of the ith pixel for the jth criterion,

222

the value of the ith pixel for the jth criterion,

223

values of the jth criterion.

224

•

and

represents

are the maximum and minimum

Analytical Hierarchy Process (AHP)

225

AHP is known as one of the comprehensive approaches in MCDA (Satty, 1980). This

226

method facilitates the formulation of complicated problems by considering different

227

quantitative and qualitative criteria (Thomas L Saaty, 1986), in which, the weights of criteria

228

are obtained using a pair-wise comparison (Malczewski, 1999). According to Saaty and

229

Vargas (1991), a range is suggested for the comparison of criteria with quantities between 1

230

and 9. Each number within this range represents the relative importance for the corresponding

231

unit: 1 indicating similar (equal) importance, 3 moderate importance, 5 strong importance, 7

232

very strong importance, and 9 absolute importance. In addition, numbers 2, 4, 6 and 8

233

represent the intermediate values(Thomas Lorie Saaty & Vargas, 1991). In this study 20

234

experts were selected and asked to compare and allocate the weighting of the criteria.

235

•

Weighted Linear Combination (WLC)

236

WLC is a kind of multi-criteria decision making by using multiple spatial criteria and

237

scoring procedure (Aguayo, 2013). In WLC the decision maker assigns the weights of

238

relative importance directly to each attribute layer. The total score for each criterion is

239

obtained by multiplying the importance weight to each criterion. The scores are calculated for

240

all criteria using equation (3) (Malczewski, 2000).

241 i =n

A i = ∑W iX i i =1

242

Eq. (3)

243

Where, A is the suitability of criteria for dust storm formation, W is the criterion

244

weight, and X is the criterion score. In this study, the map of potential areas for dust

245

formation was created for warm months (i.e. June/July/August) by combining the values of

246

different criteria with the weight of each criterion using eq. (3).

247

•

Dust sources mapping

248

In order to identify and discriminate dust sources a clustering procedure has been

249

adapted as follows: (Fig. (3) schematically represent the adapted procedure for 3 exemplary

250

dust sources).

251

1. Count the number of SADSF repetition for each pixel,

252

2. Define the threshold of repetition of allocation in SADSF for each pixel,

253

3. Is the SADSF repetition greater than the defined threshold?

254

3.1. Yes, select the pixel as dust source and assign label “S” to it,

255

3.2. No, assign label “NS” as no dust source,

256

4. Repeat step 3 for all other pixels till all pixels have “S” or “NS” labels,

257

5. Assigned label “1” (i.e. dust source number one) to the first pixel with label “S”.

258

6. Examine the neighbor pixels of “1” in a moving window (n*n) to find other pixels

259

with the label “S”.

260

6.1. If yes, the neighbor pixel with label “S” will be allocated to the dust source

261

number 1,

262

6.2. If no, the geographical expansion of dust source number 1 will be closed.

263

Then, the remained pixels with label “S” will be examined for dust source number

264

2. Similar to dust source number 1 (steps 5 and 6), the same procedure will be

265

repeated for dust source 2. This procedure will be continued to create a raster map

266

with labels: “NS” and “1, 2, and 3”.

267

Within this study area, there are some non-permanent dust sources, with changeable

268

behavior year by year, that have not been considered in this work. Therefore, we set the

269

SADSF repetition threshold at six which means any labeled pixel (dust source with “S”) has

270

witnessed 6 years of dust creation out of 18 years from 2000 to 2017. We also examined

271

other thresholds and the verification results showed that 6 was the most suitable threshold for

272

SADSF repetition to allocate a pixel to a dust source. We also examined the effects of

273

different moving window sizes (i.e. 3*3, 5*5, 7*7, and 9*9) in the final dust sources map,

274

and our experiment revealed that 5*5 had the best results for identification and discrimination

275

of dust sources.

276 277 278

Fig. 3. Dust sources identification, discrimination, and mapping procedure

•

Observed Dust Storms (ODS)

279

Several methods have been proposed to detect dust sources by tracking dust storms

280

using MODIS satellite images. For instance, Ackerman (1997) used a combination of thermal

281

bands (8 and 12 µm) to separate dust from other features; Miller (2003) used a combination

282

of MODIS bands (0.645, 0.853, 11, and 12 µm) to detect dust from cloud and bright surfaces;

283

and Qu et al. (2006) developed normalized difference dust index (NDDI) using MODIS (2.13

284

and 0.469, 2.13, and 0.469 µm). In this work, MODIS false-color composite (RGB; 7,6,1)

285

were used to do accuracy assessment in two ways: (i) to discriminate the approximate borders

286

of dust sources to be used as ground truth for accuracy assessment, and (ii) to count the

287

number of dust events in each dust source to be compared with the SDASF repetition in each

288

identified dust source. Hence, MODIS-MOD09GA time series data from 2000 to 2017 (more

289

than 1600 images in June, July, and August) were visually examined to find the actual dust

290

events and we call them observed dust storms (ODS) as ground truth. Then the ODS events

291

were compared with the results obtained from the SADSF procedure. Also, our produced dust

292

sources map was compared with the available dust sources maps by Ginoux et al. (2012),

293

Darvishi Boloorani (2014), Cao et al. (2015), Moridnejad et al. (2015), and Nabavi et al.

294

(2016). Finally, the uncertainty of the identified dust sources was analyzed by using the

295

means of the numbers of allocated pixels in SADSF, mean of WLC means, and CV

296

(Coefficient of Variation) of WLC means from 2000 to 2017.

297

4. Results and Discussion

298

4.1. Criteria

299

As can be observed in Fig. 4, LST showed a monotone temporal pattern, while, it has

300

experienced an increasing trend. In a month-base comparison, August and July had almost

301

the same LST, while they are slightly higher than June. NDVI analysis also revealed that

302

some specific years like 2000, 2009, and 2012 have experienced less NDVI in compare to the

303

whole period of study. Despite the fact that June has shown higher soil moisture in compare

304

to August and July, but like LST, soil moisture also had experienced a monotone temporal

305

behavior in the monthly and seasonal patterns. Precipitation examination showed an irregular

306

temporal pattern. Time series analysis of absolute air humidity showed an almost a reducing

307

trend for the study period. Wind speed as one of the main drivers of dust storms (Abdi

308

Vishkaee et al., 2012; Najafi et al., 2014) showed an increasing pattern (Fig. 4) and these

309

changes are significant in some specific times like June 2008.

310

The influence of different drivers on dust storms formation is dissimilar (Csavina et

311

al., 2014). The influence of each parameter was determined with respect to the expert opinion

312

in the AHP model. The final weights of wind speed, NDVI, soil moisture, K-factor, LST,

313

absolute air humidity and precipitation were allocated as 0.223, 0.201, 0.164, 0.152, 0.050,

314

0.138, and 0.072, respectively. The inconsistency coefficient of weight determination due to

315

the AHP method was 0.05, which indicates the significance of the determined weight values

316

for different criteria at high confidence levels.

330

0.21

325

0.18 NDVI

LST (K)

317

320

June

July

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

0.12 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

315

0.15

August

June

14 12 10 8

July

4 2

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 June

August

July

August

4.5 Wind speed (m/s)

0.008 0.007 0.006 0.005

4

3.5

June

July

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

3 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

0.004

318

6

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 June

Absolute air humidity

August

8 precipitation (mm)

Soil moisture (kg/m2)

16

July

June

August

July

August

Fig. 4. Temporal pattern of criteria for dust sources formation

319 320

4.2. SADSF and dust sources mapping

321

By weighting the normalized criteria and using the WLC model, the potential of dust

322

storms formation was mapped for June, July, and August from 2000 to 2017 (Fig. 5, 6 and 7,

323

respectively). The color ramp from blue to red represents the lowest and highest potential

324

sources for dust formation, respectively. 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

20017

325

Fig. 5. SADSF maps in June 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

20017

326

327

Fig. 6. SADSF maps in July 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

20017

Fig. 7. SADSF maps in August

328

The SADSF areas have been mostly influenced by the two constraints, i.e. wind speed

329

and NDVI. When the wind speed exceeds 6 m.s-1 and NDVI get lesser than 0.15 than the

330

SADSF areas will be originated (Abdi Vishkaee et al., 2012; Najafi et al., 2014); and the

331

geographical expansion of the SADSF is tightly related to these two constraints. Accordingly,

332

the Turkey part of the basin, due to its high vegetation cover, shows no SADSF. In some

333

periods of time (like June 2003 and August 2000, 2003, and 2010) only one area of the whole

334

basin was identified as SADSF and in other times (for instance from 2011 to 2017) the basin

335

has witnessed numerous SADSF with greater geographical expansions.

336

The 18-year chart of SADSF areas (dust sources geographical expansion) for the three

337

months is represented in Fig. 8. Generally, the areas have increased in all months, and

338

especially the changes are noticeable after 2008. This increasing change on the SADSF may

339

be related to the harsh drought occurred in 2007 that caused more dust storm formation in this

340

area. Notaro et al. (2015) showed that after the period of inactivation of dust storms (1998–

341

2005), an active period of dust storms occurred between 2007- 2013 coincidence with the

342

severe drought in the region. For example, the SADSF trends of June showed a significant

343

increase in 2008 and 2011-2015 (Fig. 8).

344

SADSF area in August was less than Jun and July for all years. The largest expansion

345

of SADSF took place for June and July 2013 and for August 2012. The greater extent of

346

SADSF is not necessarily associated with more dust storms. In order to investigate which

347

susceptible area has a higher potential for dust formation, other influencing parameters also

348

must be considered. For instance, in 2013 (Fig. 5, 6, and7) the areas in the north and east of

349

Syria have blue color which demonstrates the areas having the required conditions for dust

350

storm formation, in terms of wind speed and NDVI, but due to other parameters, they are less

351

suitable for dust formation. In general, SADSF has shown high potentials for dust formation

352

in the northwest of Iraq and eastern Syria. The SADSF geographical expansion was

353

controlled by several criteria based on their weights. As mentioned before, the highest weight

354

was given to the wind speed parameter followed by NDVI. Consequently, these two

355

parameters had the highest impacts on the geographical expansion of the SADSF area (Ta et

356

al., 2004; Xi & Sokolik, 2016). It is clearly observed that in the second half period of the

357

study (after 2008), NDVI is lower and wind speed is higher than the first half period (before

358

2008) (Fig. 4), which resulted in bigger areas of the SADSF in the second half period (Fig.

359

8). The repetition of SADSF for each pixel is presented in Fig. 9 and each pixel can have

360

eventually 18 possible numbers. The dust sources of the northwest Iraq and east of Syria have

361

the highest repetition of allocation in SADSF, and the three other sources have clearly less

362

allocation of SADSF. Considering Fig. (9) and the procedure explained in section3.2.5, five

363

main dust sources were identified in the Tigris and Euphrates basin (Fig. 10).

364

250000

Area (Km2)

200000

150000

100000

50000

July

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

June

365 366

2001

2000

0

August

Fig. 8. SADSF areas in June, July, and August from 2000 to 2017.

367 June

368

July

August

Fig. 9. The number of years that each pixel is allocated to SADSF.

369 370

Fig. 10. Identified dust sources in the Tigris and Euphrates basin (dust source 1; northwest of

371

Iraq, dust source 2; east of Syria, dust source 3; southeastern of Iraq, and dust source 4;

372

southeastern borderlands of Iraq, dust source 5; eastern of Iraq)

373 374

4.3. Accuracy Assessment

375

Using visual observations of MODIS (MOD09GA) false color composite (RGB:

376

7,6,1) images, the number of ODS events was determined in all dust sources (Fig. 11).

377

MODIS visual investigation revealed that in addition to those five identified dust sources,

378

there is a place in the western part of Iraq, which occasionally creates dust storms, but it was

379

not recognized as a dust source by the presented model of this study (Fig. 11). The repetition

380

of allocation in SADSF for each pixel is strongly related to the changes in the number of

381

ODS events for all months. The highest SADSF repetition for each pixel in June was

382

allocated to the pixels in the dust sources 1 and 2. In these 18 years, the pixels in source 1

383

witnessed 16 of SADSF repetitions. Therefore, it has more potential for dust storm formation.

384

The number of ODS events obtained from MODIS images showed more than 110 dust storms

385

in source 1, in June 2000 to 2018. The SADSF repetition for the pixels of the source 2 (Fig.

386

9) showed the second most active dust source of the basin. The other three dust sources

387

approximately have the same SADSF repetition and ODS numbers. In general, the correlation

388

coefficient between the number of ODS events and the SADSF repetition were 0.88, 0.76 and

389

0.74 for June, July, and August, respectively. June

390

July

August

Fig. 11. The number of ODS in each dust source from 2000 to 2017.

391 392

In July, the highest and lowest SADSF repetition was allocated to the sources

393

numbers 2 and 5, respectively (Fig. 9). In this month, the maximum SADSF repetition was 17

394

out of 18 years of the study period. Changes in the ODS events in other sources were the

395

same as June. In sources numbers 5 and 4, by decreasing the number of SADSF repetition,

396

the ODS events were also decreased. In source 3, there is no significant change in SADSF

397

repetition, and also the numbers of ODS events were constant between 70 to 90. The ODS

398

events in August were much less than June and July. The highest ODS events of this month

399

were observed in source 1 (i.e. 45 <), followed by source 2 (35-45 ODS). The maximum

400

SADSF repetition was 12, while in June and July it was 16 and 17, respectively. This

401

indicates that the ODS events in all sources were well associated with SADSF repetition over

402

time.

403

The dust sources in eastern and southern parts of Iraq (sources 5 and 3), identified in

404

our study, were very well coordinated to the sources discriminated in the National Action

405

Plan to Combat Desertification in Iraq (2015)1. The source B (appendix 1) was partly

406

matched with the identified dust source 1 (Fig. 10). However, in June and July (after 2008),

407

where the SADSF area increased these two maps well matched to each other. Source F and G

408

(appendix 1) were very well matched with sources 5 and 3 (Fig. 10), respectively. Appendix

409

(1) also shows some other dust sources in the southeast (E), center (F), and west (D) parts of

410

Iran that have not been identified in our study. While source (A) is well matched with the

411

identified source 1 in east Syria by our study.

412

Uncertainty analysis was carried out using the mean of the SADSF repetition, the

413

mean of WLC means, and the CV of WLC means from 2000-2017 (Table 2). The higher the

414

value of these parameters indicates the lesser uncertainty of the identified dust sources. The

415

highest mean of the SADSF repetition was (10.24 out of 18) in dust source 1, which indicates

416

the highest certainty of this source, followed by dust sources 1, 3, 4, and 5, respectively. The

417

mean of WLC means was obtained by using all maps in Fig. 5, 6, and 7, in which, the higher

418

value the more intensity of dust events in a dust source. The results showed that sources 1 and

419

2 have the highest certainty followed by 3, 5 and 4. In the CV of WLC means also the higher

420

value indicates the higher certainty degree of the source of dust. Likewise, CV of WLC

421

means showed that dust sources numbers 1, 2, 3, 4, and 5, had respectively the highest to the

422

lowest level of certainty (Table 2).

423

Table 2. uncertainty analysis of identified dust sources Dust sources Southeast

Eastern

border of

border of

Iraq (4)

Iraq (5)

5.41

4.02

3.97

0.51

0.38

0.32

0.35

11.66

7.48

5.60

5.25

Northwest

Eastern

Southern of

Iraq (1)

Syria (2)

Iraq (3)

Mean of SADSF repetition

10.24

9.31

Mean of WLC means

0.57

CV of WLC means from

16.73

Uncertainty parameter

1

. ‫اق‬

‫ا‬

‫ا‬

‫ا‬

‫ا‬

424 425

4.4. Analysis of identified dust sources •

Northwestern Iraq (source 1)

426

According to Fig. 11, the ODS events in the northwest of Iraq is more than other dust

427

sources in all three months. On the other hand, this source has the highest repetition in

428

SADSF. This area is known as the main dust source in the Euphrates and Tigris basin. For

429

instance, in Ginoux et al. (2012), Darvishi Boloorani et al. (2014), Cao et al. (2015),

430

Moridnejad et al. (2015), and Nabavi et al. (2016) have mentioned the same results as this

431

undertaken work. Clay soil texture, low soil moisture content and very sparse vegetation

432

cover are the main characteristic features of this dust storms source. Also, LST is high and

433

the conditions for severe wind erosion are provided. Consequently, the high wind speed and

434

its accompaniment with other factors have created the most active dust storm source in the

435

basin.

436

•

Eastern Syria (source 2)

437

The eastern Syria dust source is located on both sides of the Euphrates river and

438

covers a relatively large area. Fig. 5, 6, and 7, along with the ODS events number in Fig. 11,

439

confirm that this source is the second most active source in the Tigris and Euphrates basin.

440

Ginoux et al. (2012), Cao et al. (2015), and Shahraiyni et al. (2015) have also identified this

441

region as an active dust source in west Asia. The geographical expansion of this source is

442

changing over time and is going to spread out to the borders of Iraq in some years. The

443

maximum wind speed was observed for this source that in company with bare soil, sparse

444

vegetation, and the lack of absolute air humidity caused the ascension of dry air and the

445

formation of dust storms in this basin (Najafi et al., 2014).

446

•

Southern Iraq (source 3)

447

The third most active source of dust is in the southern center of Iraq that originates

448

from the Al-Muthanna desert (Fig. 11). This source with high SADSF repetition also

449

witnessed high ODS events. The remarkable feature of this source is the high K-factor of soil

450

which provides the condition for wind erosion and dust storms. Nabavi et al. (2016)

451

expressed that this area is a permanent dust source in west Asia. High dust intensity in the

452

southeast of Iraq and northern Arabian Peninsula can be attributed to the fact that this region

453

is not only the origin of dust storms but it is also hit by dust storms coming from upstream

454

sources in the northwest of Iraq (Nabavi et al., 2017). However, some studies did not identify

455

a significant dust source in this area (Abdi Vishkaee et al., 2012; Ginoux et al., 2012;

456

Moridnejad et al., 2015; Najafi et al., 2014).

457

•

The southeastern border of Iraq (source 4)

458

This dust source is located in the west of Khuzestan of Iran and the east and southeast

459

of Basra of Iraq (Fig. 10). Most of the dust storms activities of this source are in June. This

460

source has been formed because of the destruction of wetlands, drying up of the rivers, and

461

the decline of vegetation cover during the last two decades. This source also is known as one

462

of the most active dust sources in Iran that resulted in dust storms in the Khuzestan province

463

and neighboring areas. High LST is the most influential parameter that in combined with

464

other parameters caused the formation of dust storms.

465

•

Eastern border of Iraq (source 5)

466

This source is located in the eastern part of Iraq near the border of Iran. This source is

467

in Maysan province of Iraq with common borders with Ilam province of Iran (Moridnejad et

468

al., 2015) (Fig. 10). The high K-factor of this region plays a remarkable role in dust storms

469

formation. Due to the wind direction of this region, most of the time, dust storms blowing

470

toward Ilam and Khuzestan provinces of Iran (Akbary & Farahbakhshi, 2015). This dust

471

source covers smaller area and lower dust events in comparison to other identified dust

472

sources in this research.

473

Cao et al. (2015) identified regional dust sources (6 in Syria and 10 in Iraq) from 2000

474

to 2013 (appendix 2); in which dust source number 2 in Syria is well matched with dust

475

source number 1 (Fig. 10) of our study. In Iraq, dust sources 6 and 8 (appendix 2) were well

476

matched with 3 and 4 (Fig. 10), respectively. Also, dust sources 1 and 2 (appendix 2) were

477

partly matched dust source 2 (Fig. 10) and dust sources 9 and 10 (appendix 1) were partly

478

matched dust source 4 (Fig. 10). Darvishi Boloorani (2014) adapted the similarity criteria of

479

different dust sources and made three clusters of dust sources in the basin. These clusters

480

include several dust sources that have not been discriminated. Dust cluster 1 (appendix 3)

481

covered dust sources 1 and 2 (Fig. 10) and cluster 3 covers dust source 3 of our study.

482

Moridnejad et al. (2015) identified 247 dust sources from 2001 to 2012 (appendix 4) and also

483

Nabavi et al. (2016) have discriminated some dust sources from 1986 to 2016 (appendix 5)

484

using the data with spatial resolutions of 1000 m and 1.25 degree, respectively, that both are

485

not well comparable with the spatial resolution (i.e. 0.05 degree) of our dataset. Ginoux et al.

486

(2012) produced a global map of dust sources, in which, the natural sources were

487

discriminated from anthropogenic sources of dust. Appendix (6) shows a big cluster of dust

488

sources in Iraq, east Syria, and west Iran that are comparable with our results.

489

5. Conclusion

490

This study was conducted to identify the main dust sources in the Tigris and

491

Euphrates basin. According to the results, there are five main dust sources in the Tigris and

492

Euphrates basin, including north-west of Iraq, east of Syria, south of Iraq, southeast border

493

regions of Iraq with Iran, and east border of Iraq near the borders of Iran. In a monthly-based

494

comparison, dust sources in August were found to have lower intensity and frequency of dust

495

storms occurrences in comparison to June and July. The largest extent of suitable area for

496

dust storm formation was for 2008 and 2011 - 2015. The correlation coefficient between the

497

obtained results of this study with the real observed dust events was 0.88, 0.76, and 0.74 for

498

June, July, and August, respectively. In addition, the results were compared to the previously

499

mapped dust sources. The comparison revealed that three sources in northwest, south, and

500

east of Iraq are very well matched with appendices 1 and 2. The other two identified dust

501

sources (i.e. east of Syria and southeast of Iraq) are also matched with appendices 3, 4, and 6.

502

All of the examined references (appendices 1-6) and the undertaken study for dust sources

503

mapping in west Asia revealed the main reasons for the unmatching of dust sources maps,

504

including, the difference in spatial and temporal resolutions, different data sources and

505

methodologies, and the study objective. Considering these limitations we have developed our

506

methodology with enough flexibility to to be adaptable with a variety of data resources for

507

dust storms source mapping. Results of this study and their confirmation using evaluation

508

methods indicated that, by the use of climate and satellite data along with MCDA, it is

509

possible to identify the dust sources. In addition, it is recommended to conduct further studies

510

using time series models to investigate the spatiotemporal variation of SADSF.

511

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512 513 514 515 516

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Appendices

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Appendix 1. Active sand and dust storms source (National Action Plan to Combat

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Desertification in Iraq (2015) modified)

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Appendix 2. Dust source map identified by Cao et al. (2015) (modified)

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Appendix 3. Dust source map identified by Darvishi Boloorani et al. (2014),

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Appendix 4. Dust source map identified by Nabavi et al. (2016)

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Appendix 5. Dust source map identified by Moridnejad et al. (2015)

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Appendix 6. Dust source map identified by (Ginoux et al., 2012)

Highlights

• A temporal Geoinformatics-based dust storms sources identification is developed. • Dust storms sources map of the Tigris and Euphrates basin is created. • Significant difference in the activities of dust sources in the Tigris and Euphrates basin was modeled. • Remote sensing-based data for just three months of June, July, and August was enough to map the sources of dust in the Tigris and Euphrates basin.