Agricultural Meteorology
Nasrin Ebrahimi; Azar Zarrin; Abbas Mofidi; Abbasali Dadashi-Roudbari
Abstract
IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake ...
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IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake basin is one of the most vulnerable areas to climate change. Urmia lake basin has received the special attention of policymakers and planners since it is the location of Lake Urmia, and it also holds nearly 7% of Iran's water resources. A huge program of dam construction and irrigation networks has been started in this basin in the northwest of Iran since the late 1960s. Despite the increasing attention to Lake Urmia since 1995, the water level of this lake has decreased. During the drought of 1990-2001, Lake Urmia experienced a decrease in its level without any recovery and is decreasing at an alarming rate. Therefore, it is necessary to project the future climate of the Urmia Lake basin and especially extreme precipitation based on the latest climate change models. Materials and MethodsThe CMIP6 models were used to investigate the future projection of extreme precipitation in the Lake Urmia basin. Considering the horizontal resolution, availability of daily data, and climate sensitivity, we selected five models including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The horizontal resolution of all five models is 0.5o. The 25-year historical period (1990-2014) and the 25-year projection period for the near future (2026-2050) were chosen to analyze the extreme precipitation in the Urmia Lake Basin. The future projection was considered under three shared socioeconomic pathways (SSPs) scenarios. These scenarios include SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Mean bias error (MBE) and Normalized Root Mean Square Error (NRMSE) were computed to evaluate the individual models and the multi-model ensemble generated by Bayesian Model Average (BMA) method. To assess extreme precipitation, we used four indices including the Number of heavy precipitation days (R10mm), the number of very heavy precipitation days (R20mm), the Maximum 1-day total precipitation (Rx1day), and the Simple Daily Intensity Index (SDII). Results and DiscussionThe performance of five CMIP6 individual models and the multi-model ensemble in the Lake Urmia basin during the period of 1990 to 2014 was evaluated against eight ground stations. The investigation of the annual precipitation showed that this variable is underestimated in CMIP6 models in the basin averaged. The maximum and minimum bias values model was seen in Saqez station by -9.64 mm for the MRI-ESM2-0 and -0.43 mm for the UKESM1-0-LL, respectively. The highest average MBE in the Urmia Lake basin was respectively obtained for GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL models. Among the examined models, MPI-ESM1-2-HR has shown the highest efficiency among the examined individual models.Variations in the number of heavy precipitation days during the historical period (1990-2014) have distinguished three main areas for the Lake Urmia basin. The main hotspot of heavy precipitations in the Urmia Lake basin is located in the southwest of Kurdistan province with a long-term average of 25.4 days. The next hotspots are the northwest and the northeast of the basin. In the historical period (1990-2014), the precipitation intensity index Rx1day experienced considerable variability. Based on CMIP6-MME, the value of the Rx1day index in the Urmia Lake basin is estimated between a minimum of 16.3 mm and a maximum of 63.3 mm. The maximum variation of this index is seen in the southern areas of the basin, especially on the border with Iraq. ConclusionEvaluation of individual CMIP6 models showed that these models underestimated precipitation in the Lake Urmia basin during the historical period (1990-2014). The CMIP6-MME has significantly improved precipitation estimation. The results of the investigation of days with heavy and very heavy precipitation showed that the two indices R10mm and R20mm are increasing in most areas of the Lake Urmia basin by the middle of the 21st century. Trend analysis showed that the days with heavy and very heavy precipitation will increase under different SSP scenarios in most areas of the Lake Urmia basin, especially in the northern and western regions. Also, days with heavy and very heavy precipitation will have a greater contribution than normal precipitation days in the future. It is expected that the intensity of precipitation will increase in the coming decades in the Lake Urmia basin, and this increase is more for the western and northern regions than for other regions of the basin. This result may potentially increase the flood risk in Lake Urmia.
Agricultural Meteorology
Sh. Katorani; M. Ahmadi; A. Dadashi-Roudbari
Abstract
IntroductionDust emission is considered as one of the environmental hazards in arid and semi-arid regions. Understanding the effective variables in increasing dust mass density is very important for early warning and reducing its imposing damages. One of the main and effective variables in the occurrence ...
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IntroductionDust emission is considered as one of the environmental hazards in arid and semi-arid regions. Understanding the effective variables in increasing dust mass density is very important for early warning and reducing its imposing damages. One of the main and effective variables in the occurrence of dust is the geographical and climate characteristics of the origin areas and areas affected by this phenomenon. Feeding the great rivers of Mesopotamia, it has reduced soil moisture. Also, the wind component is one of the reasons for the increase in dust in these areas. This study examines the relative importance of climatic variables to investigate seasonal and monthly changes in dust emission in West Asia and parts of South and Central Asia. Materials and MethodsThis study has examined West Asian dust from three perspectives spatial distribution, trends, and their relationship with climate variables. For this purpose, the Dust Column Mass Density (DUCMASS) variable output of the MERRA-2 dataset was used to investigate the spatial distribution of the dust mass density trend, and the AgERA5 dataset was used to investigate the seasonal and monthly changes of precipitation, wind speed, and temperature variables from 1981 to 2020. In this study, the modified Mann-Kendall (MMK) trend test method was used to investigate the trend of dust occurrence in the study area, and the Sen's slope estimator (SSE) test was used to investigate the slope of the trend and to better display the changes in dust mass density in the western region. the results of the SSE test have been examined on a decade scale. Results and DiscussionInvestigating the possible climate drivers in the changes of dust mass density for different regions by calculating the correlation between the time series of dust mass density and the variables of temperature, precipitation, and wind speed has been investigated. The results showed that there is an inverse correlation between dust mass density and precipitation and a direct relationship between dust mass density and temperature and wind speed. The highest correlations between dust mass density and temperature have been calculated, and this value has reached 0.9 in the warm months of the year. On the other hand, the highest negative correlations have been calculated in the cold period of the year (winter and autumn seasons) between dust concentration and precipitation with a value of -0.7. The correlation coefficient between dust mass density and wind speed in the months of January to May and November to December was mostly above 0.6. This value shows a lower correlation in the summer season.In most months of the year, dust mass density shows an increasing trend in most regions, from March to July, an increasing trend in active dust springs in Mesopotamia, the deserts of Iraq and Syria, the desert of Rub' Al Khali, Ad-Dahna and Al Nufud Al Kabir were observed in Arabia and Thar desert in Pakistan. This increasing trend started cyclically from the beginning of spring and reaches its peak in June and July, and the intensity of the trend decreases from September and reaches its minimum value in December. The important point is that the cycle of changes in the monthly trend of dust mass density coincides with the cycle of changes in dust mass density. The northern parts of Iran and Turkey have the highest frequency among different months of the year with a decreasing trend of dust mass density. The increasing trend of dust mass density in the spring and summer seasons in Mesopotamia, the deserts of Iraq, Syria, and Yemen, the Sistan Plain, and the Thar desert in Pakistan and the southeast of Iran was significant at the level of 0.05. ConclusionThe results revealed that the seasonal changes in dust mass density show well the active sources of dust in the studied area. In the spring and summer seasons, the activity of the dust centers located in the west of the study area, including the Rub' al Khali, Ad-Dahna and Al Nufud Al Kabir deserts, Mesopotamia, the deserts of Iraq and Syria, increases and on the arrival of dust to the west and southwest Iran affects. The investigation showed that climate variables play a key role in the variability of dust mass density in the study area so the areas corresponding to the summer north wind and the 120-day wind of Sistan have shown the highest dust mass density in annual variability. The correlation coefficient between dust mass density with temperature and direct wind speed and its correlation with negative precipitation have been obtained. The results showed that dust mass density has an increasing trend in most of the regions, so from March to August (spring and summer), the increasing trend of dust mass density is significant at the level of 0.05. The highest intensity of the increasing trend was observed in the spring and summer seasons in Mesopotamia, the deserts of Iraq, Syria, and Yemen, the Sistan Plain, and the Thar desert in Pakistan and southeast Iran.
Agricultural Meteorology
N. Torabinezhad; A. Zarrin; A.A. Dadashi-Roudbari
Abstract
Introduction
Drought is a costly natural hazard with wide-ranging consequences for agriculture, ecosystems, and water resources. The purpose of this research is to determine the characteristics of drought and its types in Iran during the last four decades. Drought turns into different types in the water ...
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Introduction
Drought is a costly natural hazard with wide-ranging consequences for agriculture, ecosystems, and water resources. The purpose of this research is to determine the characteristics of drought and its types in Iran during the last four decades. Drought turns into different types in the water cycle and imposes many negative consequences on natural ecosystems and different socio-economic sectors. According to International Disaster Database (EM-DAT), drought accounts for 59% of the economic losses caused by climate change. Many parts of the world have experienced extensive and severe droughts in recent decades. In Iran, droughts have occurred frequently during the last four decades and have become more severe in the last decade.
Materials and Methods
In this research, we used precipitation, temperature, wind speed, and sunshine hours of 49 synoptic meteorological stations during 1981-2020. Drought has been investigated with The Standardized Precipitation-Evapotranspiration Index (SPEI) in four scales of 3, 6, 12, and 24 months, which represent meteorological, agricultural, hydrological, and socio-economic droughts. To calculate the SPEI, the precipitation variable (P) is analyzed with the cumulative difference between P and potential evapotranspiration (PET). In other words, surplus/deficit climate water balance (CWB) is considered. The FAO Penman-Monteith method was used to calculate PET. Then, using the RUN theory, the characteristics of drought, including its magnitude, duration, intensity, and frequency, were determined for all four investigated scales.
Results and Discussion
The results showed that the frequency of drought events fluctuates from a minimum of 12.13% to a maximum of 18.13% in different regions of the country during 1981-2020. The climatological study of drought characteristics shows that the most frequent drought events occurred in the west, southwest, and southern coasts of the Persian Gulf and northwest of Iran compare to other regions of the country. This is while the duration of the drought period is longer in the eastern and interior regions of Iran. Examining the types of droughts shows that more than 60% of the droughts occurring in Iran are moderate droughts. Moderate and severe droughts are mostly seen in the west, southwest, and northwest of Iran. The duration of Iran's drought varies from at least 3 months in meteorological drought to more than 8 months in socio-economic drought. Therefore, droughts are more frequent in the western regions and longer in the eastern regions. The intensity of drought is also higher in the eastern and interior regions than in the western and northwestern regions of Iran. The decadal changes of drought show that the duration and magnitude of drought in Iran have increased and the severity of the drought has decreased during recent decades.
Conclusion
The intensity, magnitude, and duration of the drought period in Iran increased with the increase of the investigated scales from 3 months to 24 months. Examining the average frequency of drought showed that as we move from meteorological drought to socio-economic drought, the frequency of drought increases, which confirms the previous findings. The eastern and southeastern parts of Iran have experienced a longer duration and larger magnitude of drought than the western and northwestern Iran, which can be caused by the climate conditions of this region, i.e., high temperature and evapotranspiration and less precipitation, and seasonality.
The maximum magnitude of drought in Iran is related to socio-economic drought (SPEI-24) followed by hydrological drought (SPEI-12). This characteristic has increased especially in the last two decades (2001-2020) compared to the previous decades (1981-2000). This is while the magnitude of meteorological (SPEI-3) and agricultural (SPEI-12) droughts do not increase much in the last two decades compared to the previous decades.
Anthropogenic activities play a more prominent role in increasing the magnitude of socio-economic (SPEI-24) and hydrological (SPEI-12) droughts than natural forcing. With the construction of many dams and the digging of countless deep wells, as well as changing the direction of rivers, the water cycle has been completely affected by human activities during the last four decades in Iran. Obviously, anthropogenic activities play an important role in increasing the magnitude of hydrological and socio-economic droughts. In contrast, meteorological and agricultural droughts have not shown many changes in Iran.
The results of the decadal average of drought intensity showed that this characteristic of drought in the last decade (2011-2020) has decreased compared to previous decades (1981-2010). On the other hand, as mentioned earlier, the magnitude and duration of drought, especially for hydrological and socio-economic droughts, have increased in the last two decades (2001-2020). Therefore, the reason for the decrease in the severity of the drought has a statistical explanation before it has a climatic reason because the severity of the drought is calculated by dividing the magnitude of the drought by its duration.