Climate change is responsible for changes in the world’s vegetation. This study was aimed to investigate the effect of long-term variations in the air temperature, precipitation, and dust ...concentration on normalized difference vegetation index (NDVI) variations in the spring, summer, and growing season over arid regions of Iran. The results showed that the precipitation had a positive association with the NDVI in the spring and growing seasons (
β
> + 0.28;
P
< 0.05), while air temperature had a negative relationship with the NDVI changes in these seasons (
β
> − 0.34;
P
= 0.013). Our results also showed a negative relationship between the accumulated dust concentration (ADC) and NDVI in the spring (
β
= − 0.23;
P
= 0.09) and growing seasons (
β
= − 0.24;
P
= 0.003). Spatially, the strongest linkage between NDVI and climatic variables was observed in about two-thirds of the study region. In total, our findings indicate that the use of dust concentration, air temperature, and precipitation helps us to make a more accurate approach for evaluating the impacts of climate change on vegetation variations in the arid environments.
In recent years, climate change has widely affected different ecosystem conditions, especially natural wetlands across different regions of the world. This study was aimed at evaluating the effects ...of climate change on the drought characteristics of natural wetlands in Iran, based on the fifth assessment report scenarios of IPCC (Intergovernmental Panel on Climate Change). The Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI), and effective Reconnaissance were compared to assess the future droughts according to their mean difference and the relationship between temperature anomaly (TA) and drought indices. Furthermore, the effect of increasing temperature on drought risk was analyzed using the return period of drought severity and duration. The results showed that SPEI and RDI performed better than eRDI to assess the future drought conditions of wetlands in Iran. The findings also indicated that all wetlands will experience decreasing trend in the rainfall, while only few of them will experience a similar trend in temperature, in future. According to the projected droughts, long-term droughts will have higher severity and duration than the base period in the eastern wetlands compared to those in western ones in Iran. It was also found that the southeastern wetlands will experience severe droughts in the future. This study can be useful to evaluate the projection of climatic parameters and to provide practical solutions for water resources management in Iran.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The study was carried out to assess meteorological drought on the basis of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) evaluated in ...future climate scenarios. Yazd province, located in an arid region in the centre of Iran, was chosen for analysis. The study area has just one synoptic station with a long‐term record (56 years). The impact of climate change on future drought was examined by using the CanESM2 of the CMIP5 model under three scenarios, that is, representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. Given that a drought is defined by several dependent variables, the evaluation of this phenomenon should be based on a multivariate analysis. For this purpose, two main characteristics of drought (severity and duration) were extracted by run theory in a past (1961–2016) and future (2017–2100) period based on the SPI and SPEI, and studied using copula theory. Three functions, that is, Frank, Gaussian and Gumbel copula, were selected to fit with drought severity and duration. The results of the bivariate analysis using copula showed that, according to both indicators, the study area will experience droughts with greater severity and duration in future as compared with the historical period, and the drought represented by the SPEI is more severe than that associated with the SPI. Also, drought simulated using the RCP8.5 scenario was more severe than when using the other two scenarios. Finally, droughts with a longer return period will become more frequent in future.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This study aims to employ joint modeling for drought and dust hazards. Long-term monthly precipitation and temperature were used to calculate drought characteristics based on the standardized ...precipitation- evapotranspiration index. Three-hourly horizontal visibility and wind erosion event codes were also obtained to detect dust events. The results of drought hazard assessment indicated that about 52% of the total area of Iran was affected by moderate hazard. Other classes of drought hazard i.e., severe, low, and very severe, covered approximately 24, 18, and 6%, respectively. The assessment of dust hazard was conducted by weighting the frequency of dust events based on the horizontal visibility. The findings on dust hazard showed that approximately 18% of the total area of Iran was affected by the more intense hazard, which covered the southeastern sectors
.
Bivariate copulas were considered for joint modeling of drought and dust hazards. The copulas of Student’s t, Gaussian, Gumbel, and others were applied to construct a joint model. Bivariate assessment of drought and dust hazard illustrated that the eastern part of Iran, which covers around 47% of the country, exposed at greater hazard than other parts. The copula-based conditional probability was also obtained to quantify the probability of dust occurrence under different drought hazard levels. The conditional probability values showed that by intensification of drought conditions the probability of dust hazard increased. In addition the frequency of dust occurrences increased when the region faced very severe droughts. Generally, the prolonged and severe droughts were identified as the most important reasons for the increased hazard of dust and drought in the study region. This study results indicated that the joint risk of drought and dust hazard along with vulnerability factors, can be used for possible mitigation of the risks of these natural hazards.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Natural disasters such as dust storms are random phenomena created by complicated mechanisms involving many parameters. In this study, we used copula theory for bivariate modeling of dust storms. ...Copula theory is a suitable method for multivariate modeling of natural disasters. We identified 40 severe dust storms, as defined by the World Meteorological Organization, during 1982–2017 in Yazd province, central Iran. We used parameters at two spatial vertical levels (near-surface and upper atmosphere) that included surface maximum wind speed, and geopotential height and vertical velocity at 500, 850, and 1000 hPa. We compared two bivariate models based on the pairs of maximum wind speed–geopotential height and maximum wind speed–vertical velocity. We determined the bivariate return period using Student t and Gaussian copulas, which were considered as the most suitable functions for these variables. The results obtained for maximum wind speed–geopotential height indicated that the maximum return period was consistent with the observed frequency of severe dust storms. The bivariate modeling of dust storms based on maximum wind speed and geopotential height better described the conditions of severe dust storms than modeling based on maximum wind speed and vertical velocity. The finding of this study can be useful to improve risk management and mitigate the impacts of severe dust storms.
•The spatial and seasonal variations of Dust Storm Index in semi-arid regions of central Iran were investigated.•The association between climate changes and vegetation covers with sand-dust events ...were identified using Ridge Regression.•The surface winds speed had a significant effect on sand-dust events in the summer of the whole study period.•The vegetation degradation has led to the intensification of dust emissions in the spring of the second period.•The highest activity of dust events occurred in the border region of Iran and Turkmenistan.
Atmospheric conditions and physical characteristics of the earth surface have an important effect on the spatiotemporal variations of sand-dust events. The main objective of the present study was to investigate the effect of these variables on the seasonal variation of these events in semi-arid regions of Central Iran Zone (CIZ). The Ridge Regression (RR) method was used to analyze the relationship between seasonal variations of precipitation, surface winds speed, air temperature, and Enhanced Vegetation Index (EVI) with Dust Storm Index (DSI) for two different periods (2001–2008 and 2009–2016). The dusty winds direction around the study area was also determined using the dust roses. The results showed that the annual DSI changes in the study area had a week incremental trend with a rate of 0.07/8 yrs in the previous period while it followed a strong increasing trend with a rate of 0.22/8 yrs in the latter period. It was also found that the activity of sand-dust storms in the second period was greater than the first period, especially in the border region of Iran and Turkmenistan. According to RR analysis, DSI had a significant positive association with the surface winds speed in the summer (β = +0.48; p-value < 0.05) and the winter precipitation (β = −0.3; p-value < 0.05) over the previous period. During this period, there was no significant relationship between the temperature and EVI with DSI in other seasons (p-value > 0.05). In the second period, the surface winds speed was positively correlated with the DSI in the spring (β = +2.04), summer (β = +2.6) and autumn (β = +2.08). The significant negative relationship between EVI and DSI changes was observed only in the spring season (β = −0.7; p-value < 0.05). Our findings also indicated that dusty winds direction in the northeast, northwest, and southeast parts of the study area were from the northwest, southeast, and west, respectively. These findings can help to mitigate the negative consequences of dust emissions and improve the wind erosion management in semi-dry lands of CIZ.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
It is necessary to predict wind erosion events and specify the related effective factors to prioritize management and executive measures to combat desertification caused by wind erosion in arid ...areas. Therefore, this work aimed to evaluate the applicability of nine machine learning (ML) models (including multivariate adaptive regression splines, least absolute shrinkage and selection operator, k-nearest neighbors, genetic programming, support vector machine, Cubist, artificial neural networks, extreme gradient boosting, random forest) and their average for predicting the seasonal dust storm index (DSI) during 2000–2018 in arid regions of Iran. The results showed that the averaging method outperformed the other individual ML models in predicting DSI changes in all seasons. For instance, the averaging methods improved the prediction accuracies for winter, spring, summer, autumn, and dusty seasons by 22%, 39%, 28%, 32%, and 26%, respectively, compared to the multivariate adaptive regression splines. Furthermore, the most important factors in predicting DSI were detected as follows: wind speed for winter, enhanced vegetation index for spring, maximum wind speed for summer, autumn and dusty seasons. In general, our results indicate that the combining of the individual ML models by averaging method help us to develop a more accurate approach for predicting the temporal changes of the dust events in arid regions. Furthermore, the obtained results in this study can be applicable for prioritizing measures in order to minimize the dangers of wind erosion based on the major driving factors.
•Predicting of the seasonal dust storm index variations in arid regions of Iran using nine machine learning models.•Identifying the relative importance of driving forces on dust storm events using sensitivity analysis.•Granger-Ramanathan averaging method performed better than other individual machine learning models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study was carried out to determine whether the changes in dust concentration (DC) in Iran were attributed to changes in aridity index (AI) from 1990 to 2018. Long-term precipitation and ...potential evapotranspiration were used to compute the United Nations Environment Program AI and horizontal visibility data were used to calculate the DC in the study area. The DC spatial distribution maps were prepared to better identify areas affected by sand-dust events. Pearson's correlation analysis was performed to explore the association between DC and AI in Iran and study stations during the monitoring period. Spatiotemporal variations in DC showed that more than 60 percent of Iran's area experienced severe wind erosion events in 2001, 2008, and 1990 when AI was less than 0.2. The results also showed that approximately 45 percent of Iran's area has suffered from the average DC over the study period (565 µg/m
3
). The incremental changes in DC were observed in almost half of the entire area of Iran during the second period (2000-2008) and one third of this area during the third period (2009-2018). Areas affected by decreasing changes in AI during the second and third periods were 90 percent and 73 percent of the area affected during the previous period, respectively. Overall, there was a significant negative correlation between AI and DC in Iran over the monitoring period (r = −0.51, p value < 0.01). Insignificant positive correlations were observed in humid regions, whereas negative correlations were mostly detected in arid and semiarid regions of Iran.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Vegetation cover plays a key role in reducing wind erosion and improving air quality in different parts of the world. However, little is known about the long-term period seasonal changes in ...vegetation anomalies and their effects on wind erosion in Iran. Therefore, in the current research, the seasonal changes in vegetation cover and the level of wind erosion activity across the country from 2000 to 2018 have been identified in detail using the Normalized Difference Vegetation Index anomaly (NDVIA) and the dust concentration anomaly (DCA), respectively. Furthermore, the nonparametric Mann–Kendall test and Sen's slope estimator were used to evaluate the variable trend changes. The bivariate correlation analysis was performed to determine the relationship between NDVIA and DCA in the spring, summer, autumn, winter, and growing season. The agricultural drought was further monitored for the season when the strongest correlation between vegetation changes and the activity of wind erosion events was observed. The NDVIA and DCA maps were spatially analyzed to identify areas affected by drought and wind erosion, respectively. Results indicated that across the whole country, the NDVIA increased significantly in the autumn and growing season (Z>+2; α < 0.05) while the DCA had a significant decreasing trend in the spring and growing season (Z < -2.5; α < 0.05). The results of correlation analysis indicated that NDVIA corresponded well with the DCA in the spring (R = −0.58; P < 0.05), summer (R = −0.52; P < 0.05) and growing season (R = −0.67; P < 0.01) while no meaningful correlation was found between them in the winter and autumn (R<−0.25; P>0.05). Agricultural drought monitoring using the growing season NDVIA showed that about 85%, 72%, 67%, 63%, 62%, and 61% of Iran has experienced this phenomenon over the years 2008, 2000, 2018, 2002, 2006, and 2003, respectively. The DCA maps displayed more than 54% of the country has suffered from the intensification of the wind erosion over the years. Our findings also demonstrated that areas with NDVIA<0 have had a significant impact on dust produced by wind erosion in areas with DCA>0 (R = −0.5; P = 0.03). Spatially, these areas were mainly observed in the eastern half of Iran, areas that were most affected by the drought over the monitoring years. This work encourages us to use the NDVIA and DCA to evaluate the linkage between seasonal vegetation variations and wind erosion activity in arid and semi-arid regions. This study could also be useful for recognizing areas at risk of drought and wind erosion as well as for mitigating their adverse effects.
•Seasonal variations of the NDVI anomaly and its association with the DC anomaly over Iran were investigated.•Agricultural drought and wind erosion were monitored for a long-term period (2000–2018).•The linkage between areas that have suffered from both droughts and soil erosion has been identified.•The significant linkages between NDVIA and DCA variations were observed in the warm seasons of the monitoring period.•Drought-affected areas have played an important role in dust production, especially in areas with positive DC anomaly.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Aim: The main goal of the current research is to analyze the temporal changes in air quality in these areas based on an index of both types of events called the Pollution Dust Storm Index.Material & ...Method: This study calculated the PDSI using the frequency of local and extra-local dust events. The Mann-Kendall test was used to evaluate the trend of temporal changes in this index at different time scales.Finding: The highest dust pollution in the analyzed statistical period belonged to the cities of Zahedan, Chabahar, Nehbandan, and Yazd, and the lowest air pollution was in Baft, Shahrood, and Shahrbabak stations. The significant incremental changes in dust events occurred for most of the stations in June. On a seasonal scale, maximum and minimum dust pollution occurred in spring and autumn, respectively. The increasing trend of dust occurrences in the spring season and their decreasing trend in the summer season was observed for most of the stations. Conclusion: The findings of evaluating the trend of changes in dust occurrences based on the PDSI index in dry areas of the country, which are affected by dust with different frequencies and intensities throughout the year, can be used to identify sensitive areas and provide necessary measures to prevent and reduce adverse effects caused by dust events.Innovation: In addition to local dust events, extra-local events also impact the air quality of dry areas. Analyzing changes in the PDSI based on both types of events can enhance our understanding of air quality in these regions. This study aims to analyze air pollution changes using this index, which has not been done before for dry areas. The findings can help identify areas with higher air pollution levels and lead to more effective solutions to improve air quality in arid regions.
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