Remote Sensing (RS)
Heshmat Karami; Hadi Abdolazimi
Abstract
Extended AbstractIntroductionWetlands are considered valuable resources of the environment. Despite the importance of wetlands, they are currently threatened by intensive water harvesting for irrigation, industrial development, deforestation, construction of dam reservoirs, and changing rainfall patterns. ...
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Extended AbstractIntroductionWetlands are considered valuable resources of the environment. Despite the importance of wetlands, they are currently threatened by intensive water harvesting for irrigation, industrial development, deforestation, construction of dam reservoirs, and changing rainfall patterns. Monitoring can determine the changes in the location, extent, and quality of the wetland and therefore plays an important role in the maintenance and protection of the wetland. Ecosystem monitoring with remote sensing methods offers the advantage of difference, frequent and uniform coverage of large areas. The study of effective parameters or up-to-date maps that show spatial and temporal changes in the sub-basin of Horul Azim Wetland is not available. Therefore, considering that currently, this wetland is struggling with various problems to continue its survival, the purpose of this research is to use Google Earth Engine and satellite data to study the process of wetland changes.Materials & MethodsThis study was done on the platform of Google Earth Engine open source system. In this study, the data of water area, vegetation cover, precipitation, evaporation, and surface temperature were coded in the Google Earth Engine system in a standard way and their time series was obtained. Also, the NASA GRACE data analysis tool (DAT) was used for time series of groundwater levels. In this research, the Mann-Kendall test and Spearman's correlation were used in order to evaluate the changes in different parameters. In this research, the period from 2000 to 2022 was considered to investigate the trend of the data according to the available time range of the data. Finally, to check the fact that the changes in the zones were affected by floods, the data of the Global Surface Water of Water Occurrence (GSWE) probe was used.Results, discussion, and conclusion The results of the analysis graph of the water area data trend showed that from 2007 to 2019 the water area trend is increasing, with 2007 being the minimum year and 2019 being the maximum year, and the reason for this was the 90% water withdrawal of the Hor al-Azim wetland in the Iranian part. Also, the reason for the increase in the water area in 2017 is heavy rains that lead to floods and overflowing of the Karkheh dam in the sub-basin of the Hor al-Azim wetland. In 2017 and 2020, 2021, the water area shows a significant increase, which is due to the change in climatic behavior and the occurrence of floods in these years. Finally, the trend of the blue zone will be downward until July 2022. The results of a careful analysis of the data trend by the Mann-Kendall test showed that the trend of the available time period was observed. Kendall's tau value also confirms the increasing trend. It seems that the increasing trend of the water area in the years 2019 to 2021 in this study using the Google Earth Engine system is the result of the floods of the last few years, that Considering only this parameter and these data leads to errors in the study and investigation of the condition of Hor-al Azim wetland. No significant trend was observed in the time series of vegetation cover, but according to the positive Mann-Kendall vegetation cover statistic, one of the causes of the non-significant decrease in the groundwater level could be the increase of pastures and agricultural lands. Kendall's tau value for the surface temperature also showed a negative value (-0.24). According to this result and the sensitivity of the evaporation parameter to temperature, we can point to the role of this parameter in reducing evaporation in the sub-basin of the Hor al-Azim wetland. The northwest and southeast regions have the highest temperature up to a part of the central region of the sub-basin. The western part, which includes the border of the Hor al-Azim wetland, has the lowest temperature, and most of the central part has the lowest temperature, one of the causes of which can be the presence of vegetation and the development of agricultural lands. The time series graph of precipitation showed that the parameter of precipitation in the years 2017 to 2020 had an upward trend, which led to recent floods in the studied area. The results of the Mann-Kendall test for the general trend of evaporation and transpiration parameters, ground surface temperature, and precipitation in the sub-basin of the Hor al-Azim wetland did not show a significant trend. Using the Global Surface Water Explorer (GSWE) data, the occurrence of water, the intensity of water changes, and the seasonal change of water on the wetland were studied for the period of 1984-2021. The study of this dataset confirmed the human interference (creating the Karkheh Dam and draining its lake) and the occurrence and effects of the flood on the sub-basin of the Hor-al Azim wetland. The results of Spearman's correlation test also showed that climate changes such as changes in precipitation patterns and human activities can become factors that affect the surface of the water body of Hor al-Azim Wetland. The results of this research can be used in the management of Hor al-Azim wetland and wetlands with similar conditions.
Yousef Alipour; Naser Bayat; Ali Osanlu
Abstract
Extended AbstractIntroductionTemperature is considered to be an important element of climate whose changes have important consequences for human life. The present study seeks to detect trends and significant changes in the temperature at the 1000 hPa level in Iran. Due to its geographical location, Iran ...
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Extended AbstractIntroductionTemperature is considered to be an important element of climate whose changes have important consequences for human life. The present study seeks to detect trends and significant changes in the temperature at the 1000 hPa level in Iran. Due to its geographical location, Iran climate is affected by various patterns of sea level pressure such as subtropical high-pressure, Siberian high-pressure, Monsoon low-pressure, the Mediterranean low pressure, Black Sea low pressure and Sudan low pressure during warm and cold seasons. These patterns have changed in different time series leaving adverse effects such as decreased precipitation and increased temperature, while probably changing Iran climate from semi-arid to arid and causing climate hazards. Having enough information on the temperature characteristics and its future trends, it is possible to decide on macro politics and a comprehensive method for the management of an area. Therefore, the present study aims to detect trends and significant changes in air temperature at the 1000 hPa level. Materials & Methods45 ° to 64 ° Eastern longitude and 45 ° to 64 ° latitude were selected to study temperature changes at the 1000 hPa level in Iran. In this study, temperature data of 1000 hPa level recorded in a 70-year statistical period (1950 to 2020) and data retrieved from NCEP/NCAR with a spatial resolution of 2.5 by 2.5 degrees have been used to prepare time series and necessary maps. The Kendall Man test was used to analyze the trend of time series. The 70-year statistical period (1950 - 2020) was divided into 10 decades and average seasonal temperature was used. Results & DiscussionThe average temperature of Iran at the 1000 hPa level is rising by 1.34° C per century and its standard deviation has reached its maximum value in recent decades. In the last two decades of the statistical period, 30 ° C contour line has approached Iran from southwest. Temperature trend at the 1000 hPa level is investigated in 4 different seasons of Iran.Summer: according to the Mann-Kendall test, average temperature in summer shows a significant trend and has increased by 0.2 ° C every decade.Autumn: time series of temperature data in autumn shows a significant trend and the slope of the regression line (temperature) has increased with a rate of 0.0451 ° C every decade.Winter: average temperature has decreased at the beginning of the study series and increased at the end of the series. 15.26 ° C and 8.18 ° C (in 1966 and 1972) were the highest and the lowest average temperature recorded in winter, respectively.Spring:The average temperature in Iran has increased by 0.197 ° C every decade. In this 70-year statistical period, average temperature of Iran in this season was 24.37 ° C with the highest annual average temperature recorded as 27.18 ° C in 2008 and the lowest annual average temperature recorded as 21.83 ° C in 1972 and 1992. ConclusionAverage temperature in Iran is raising with a rate much higher than the global average (0.74 ° C per hundred years), due to wide fluctuations in the general circulation patterns of the atmosphere and changes in sea level pressure pattern. Thus, it can be predicted that the temperature in southern Iran may reach over 60 ° C by the end of the century threatening southern riparian provinces with dangerously rising water level and the risk of drowning. Wildfires will still be common in Iranian forests, the number and intensity of floods will increase sharply, and water resources will reach a critically low status.
Mahsa Polroudimoghadam; Saeid Hamzeh; Madjid Vazifehdoust
Abstract
Abstract
Nowadays, considering the reduction of water resources and the existing water crisis, it is necessary and important to pay attention to the proper and integrated water resources management, especially in border areas. One of the basic measures in this field is to know the amount of rainfall ...
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Abstract
Nowadays, considering the reduction of water resources and the existing water crisis, it is necessary and important to pay attention to the proper and integrated water resources management, especially in border areas. One of the basic measures in this field is to know the amount of rainfall and runoff and the trend of its changes in the watershed basins.
However, the lack of access to sufficient field data in the border areas poses a major problem. Remotely sensing data and global land models can be used to overcome this problem. The aim of this research is to investigate the trend of rainfall-runoff changes in the Doosti dam basin - which is important to decision–makers in Iran- using the Global Land Surface Model System (GLDAS). For this purpose GLDAS data were used in 7 pixels 1.5*1.5 degree between the Latitudes of 35-36.5 N and Longitude of 59.5-67 W. The type of changes and trend of model data were investigated seasonally and annually through simulation, Pearson correlation coefficient, Mann-Kendall and Mann-Kendall sequential tests over a period of 10 years from 2004 to 2013. The results of data analysis showed that the correlation between rainfall and runoff is weaker in the East and the Southeast of the studied basin than in other areas. Also, at 95% of the confidence level for annual rainfall data, the trend for the rainfall is negative only in pixel 7 and for runoff in pixels 6 and 7. Regarding seasonal data, the trend was detected to be negative for the rainfall only in spring in pixels 5 and 7, and for the runoff in winter and summer in pixel 7. The results of this model show that the GLDAS model can be very useful and practical for studying rainfall-runoff in areas with difficult access to terrestrial data because it is possible to study vast areas at low cost.
Madjid Montazeri
Volume 22, Issue 88 , January 2014, , Pages 58-61
Abstract
Miqan wetland is a natural hole formed between Zagros and Central (Markazi) Iran mountain ranges. This wetland is expanded in the center of this basin and in the heart of Markazi province. In Iran comprehensive water plan, this basin is classified under 7-1-5 study code. The basin covers an area of 5514 ...
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Miqan wetland is a natural hole formed between Zagros and Central (Markazi) Iran mountain ranges. This wetland is expanded in the center of this basin and in the heart of Markazi province. In Iran comprehensive water plan, this basin is classified under 7-1-5 study code. The basin covers an area of 5514 km2 and its average annual precipitation is about 280 mm. This basin has experienced lots of fluctuations in precipitation, so that it spent a low rainfall period during the 60s. In order to investigate the system of precipitation changes in this basin, monthly precipitation data was exploited from weather stations and was transformed into zone data using Kringing interpolation method and a cell size of 5*5. Finally, 220 cells covered the basin boundaries and a data matrix of 220*12 was obtained. In order to identify precipitation changes, standard scores of the monthly precipitations’ time series were calculated and Mann-Kendall non parametric trend test was applied. Results indicate the presence of a trend in the basin’s precipitation in March, July, September and October.
Madjid Montazeri; Leyla Dadkhah
Volume 22, SEPEHR , July 2013, , Pages 89-91
Abstract
Dust has always been one of the most important environmental hazards and it leaves adverse environmental consequences. The present article seeks to identify and analyze dusty days’ trend in Bushehr station during the last 55 years.
In this regard, monthly and annual statistical data of dusty days ...
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Dust has always been one of the most important environmental hazards and it leaves adverse environmental consequences. The present article seeks to identify and analyze dusty days’ trend in Bushehr station during the last 55 years.
In this regard, monthly and annual statistical data of dusty days in Bushehr station between 1951 and 2005 was applied. First, normality test was performed using Ncss and homogeneity test was performed using Runs Test. After proving data abnormality, nonparametric test of Mann-Kendall was chosen.
Findings indicate that except for June, other months show an increasing trend of dusty days even in annual scale. Noteworthy, the increasing trend in cold months is more obvious than warm months of the year so that March and November with respectively 3.71 and 4.4 show an increasing trend in 99.9 percent significant level.
