Extraction, processing, production and display of geographic data
Seyed Hossein Mirmousavi
Abstract
Extended Abstract
Introduction
In hydrological drought, water scarcity spreads through the hydrological cycle and can subsequently reduce groundwater levels, surface water and lake levels, and this means that hydrological drought dominates those areas, leading to long-term effects. In addition, due ...
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Extended Abstract
Introduction
In hydrological drought, water scarcity spreads through the hydrological cycle and can subsequently reduce groundwater levels, surface water and lake levels, and this means that hydrological drought dominates those areas, leading to long-term effects. In addition, due to climate changes and rainfall and temperature anomalies, droughts have increased in frequency and severity in many regions of the world. The predicted changes for the coming years show that climate variables will not have uniform changes in all regions and regional changes in the amount of precipitation may lead to the creation of hydrological patterns much different from the current conditions.
The present study was also carried out with the aim of spatial analysis of drought effects on water level changes in the catchment area of Bakhtegan, Tashk and Maharlo lakes. In this research, an attempt has been made to identify temporal and spatial patterns of changes in the level of this lakes by using satellite images and spatial analysis models.
Materials and Methods
In the present study, Landsat 5(TM), 7(ETM+) and Landsat 8(OLI) satellite images with a resolution of 30 meters have been used in the period of 2000-2021 to investigate water level changes. Due to the fact that the water level of the studied lakes changes drastically with the rainfall of different months, therefore, it is difficult to determine the amount of water cover for a year without considering the fact that a part of this cover is seasonal and when the rainfall decreases, a part of the lake Dry may not provide accurate results. Based on this, in the present study, one image was used for each month for each year studied to evaluate the changes in the water level of the lakes in all months of the year.
Conclusion and Discussion
The investigation of the changes in the water level of Maharlo Lake shows that in the drought of 2108 and 2017, the permanent water level of the lake has decreased to 1.8 square kilometers. Meanwhile, in the severe and very severe drought of 2005 and 2004, the permanent water level reached 170.4 square kilometers. Examining the changes in the area of Tashek Lake in 15 years of drought shows that the area of the waterless part of this lake has increased more than the seasonal and permanent water. The highest amount in this field was in 2021 with a very severe drought, which shows that this lake has more critical conditions in terms of permanent dryness than Maharlo Lake. This lake has been in a terrible state for 5 years. Comparing the changes in the area of Bakhtegan lake in different years shows that this lake has a more critical situation than its neighboring lakes (Maharlo and Tashk), so that in a significant number of years (12 years) the lake lacked permanent water and only With monthly or seasonal rains, some water has been temporarily collected on its surface, but it has a short shelf life between 2 to 6 months (November to May).
Results
The results of the evaluation and analysis of the role of drought in the water level changes of the Bakhtegan, Tashk and Maharlo catchment lakes showed that the area of these lakes has decreased significantly during the studied period, so that over time the area of the water area has decreased. It has been permanently reduced and added to the dry and waterless area. The maximum decrease in the water level of all three investigated lakes occurred during a 6-year drought between 2008 and 2013, in such a way that the area of the part with permanent water was greatly reduced and the area of the dry part of the lakes was increased.
Geographic Data
Zahra Heydari monfared; Seyed Hossein Mirmousavi; Hossein Asakereh; Koohzad Raisipour
Abstract
Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis ...
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Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis is more visible. Due to the fact that most of the rainfall falls in the form of snow in mountainous areas, the management of snow resources in these areas is very important, and knowing the different aspects of variability and geographical patterns governing the phenomenon of snow is a scientific and practical need. It is considered special in water resources and in the agricultural sector. Thus, in the current research, the spatio-temporal patterns governing the annual average of snow density in different decades and the difference of each of the decades compared to the entire time period have been estimated and analyzed using spatial statistics methods.
Materials & Methods: The studied area with an area of about 151,771.91 square kilometers is located between 34°44' to 39°25' north latitude from the equator and 44°3' to 49°52' east longitude from the Greenwich meridian. In order to investigate the spatial autocorrelation changes of the average snow density in northwest Iran during the years 1982-2022 from the data obtained from the database of the European Center for Medium-Range Atmospheric Forecasting ECMWF4/ ERA5 based on daily data, and to identify and understand the spatial patterns of density Barf, based on statistical and graphic models have been used in the geographic information system environment. In the study of temporal-spatial changes of the average snow density of the region in different time periods including 4 decades ((1982-1992), (1992-2002), (2002-2012), (2012-2022)) and the whole period of 41 years (2022) -1982)), general Moran's I and Getis-Ord Gi* statistics were used. Also, in the current research, in order to investigate the effect of changes in Extreme snow precipitation on the amount of snow density in the northwest region, it has been done to determine the snow threshold. In order to estimate snow drift, a threshold was defined. Since the station snowfall amount data has a high dispersion, values above the mean cannot be accurate for defining the threshold of freezing snow. In this way, the 99th percentile index has been used to determine the snow threshold.
Results & Discussion: The aim of the current research is to investigate the spatial autocorrelation changes of the annual mean snow density in the northwest of Iran. For this purpose, the annual snow density data during the statistical period of 1982-2022 was obtained from the ECMWF/EAR5 database with a resolution of 0.25 x 0.25 degrees, and then divided into four ten-year periods. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettys-RDJ) were used at the significance level of 90, 95 and 99%. Also, in order to investigate the effect of extreme precipitation on changes in the level of snow density, the 99th percentile statistical index was used, and based on this index, the freezing threshold of each synoptic station in the region was determined during the last decade (2012-2022) and the interval the entire statistical period (1982-2002) was carried out. The results of the present research showed that in the studied area, snow density has spatial autocorrelation and a strong cluster pattern. With a density threshold less than 0.10 kg/m3, from the first decade to the end of the fourth decade, the area (number of pixels) and the amount of snow density in the northwest have decreased. The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly during the last decade of the study, and this has caused the snow density to increase relatively in the last decade compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has significantly decreased during the last four decades.
Conclusion: The evaluation of the temporal changes of snow density also strengthened the hypothesis of the occurrence of freezing snow precipitation leading to an increase in snow density in the months of cold seasons during the last decade. This point was confirmed by examining the statistical index of the 99th percentile of snowy days of each synoptic station in the region during the last decade (2009-2018) compared to the entire period of station statistics (2000-2018). The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly in the last decade of the study and this has caused the snow density in the last decade to increase relatively compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has decreased significantly during the last four decades. Moran's statistic was used to explain the pattern governing snow density in northwest Iran. The results of Moran's index about the annual average of snow density showed that the values related to different time periods have a positive coefficient and are close to one, which indicates that the snow density data has spatial autocorrelation and has a cluster pattern. Also, the results of standard Z score and P-value confirmed the cluster significance of the spatial distribution of snow density in the northwest. Finally, the analysis of hot spots has been a clear confirmation of the continuation of concentration and clustering of snow density in northwest Iran in space with the increase of the time period, which mountainous areas have the first rank in the formation of hot clusters with a probability of 99%. have given.
Extraction, processing, production and display of geographic data
Seyed Hossein Mirmousavi
Abstract
Extended AbstractIntroductionThe planetary boundary layer (PBL) as the lowest part of the troposphere is the most dynamic part of the atmosphere that is directly affected by the interactions of the atmosphere and the surface of the Earth (Stell, 2012 and Gert, 1992). These atmospheric surface interactions ...
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Extended AbstractIntroductionThe planetary boundary layer (PBL) as the lowest part of the troposphere is the most dynamic part of the atmosphere that is directly affected by the interactions of the atmosphere and the surface of the Earth (Stell, 2012 and Gert, 1992). These atmospheric surface interactions occur in short periods of time and play an important role in the development of the boundary layer. The height of this layer is also influenced by atmospheric conditions, topography characteristics, and type of land cover, and is an important parameter for many meteorological phenomena that have various applications such as monitoring air quality, cloud formation and evolution, surface fluids, and atmospheric hydrological cycles (Garrett 1994). Since the height of the boundary layer indicates the depth of turbulent vertical mixing, it is very effective in increasing or decreasing the concentration of pollutants near the surface and is considered as an essential parameter in air quality monitoring (Su and Khan, 2018). In addition, the height of this layer is a key factor in numerical weather forecasts. Since the height of the base of clouds is usually close to the height of the boundary layer, this layer determines the extent of cloud development and causes the transition from shallow convection to deep in the clouds. MaterialsThe data used in this study included re-analysis data on the monthly time scale of the planetary boundary layer height for the entire Iranian region with a resolution of 0.25×0.25 which was obtained from the ERA5 version of ECMWF site during the period 1959-2021. In order to analyze the relationship between different climatic variables (mean temperature, mean relative humidity and air pressure), the meteorological data of 187 synoptic weather stations during the statistical period 2000-2022 has been used.MethodsIn this study, in order to prepare the data using programming capabilities in MATLAB software, maps with an average of 62 years old have been prepared and then using ARC GIS software to map the monthly average height of the boundary layer in Iran. In the next step, spatial statistics index of Getis-Ord Gi* was used to analyze the spatial changes in the height of the boundary layer in different months. In order to analyze the effective variables in elevation changes in the boundary layer temperature, relative humidity, soil moisture, etc. Multivariate standard regression method was used.Conclusion and DiscussionThe annual average elevation map of the boundary layer also shows that the maximum height of this layer in Iran is 1600 m which is located in the south of Iran in Kerman province and south of Sistan and Baluchestan province and in general, the southern half of Iran with the exception of a narrow strip of southern coasts is higher than the northern half. The lowest elevation between 520 and 1000 meters is mainly located in the northern half, the eastern part and a narrow strip of southern coast. The average height of the entire boundary layer of Iran during the year is 1131 meters. The height of the boundary layer in different months of the year has significant changes in Iran and in terms of spatial changes it follows severe cluster patterns. Analysis of hot and cold spots showed that the spatial distribution of the height of the boundary layer has completely homogeneous spatial patterns so that the northern half of the country, especially the northwest and northeastern regions of the country, have a high significance as cold spots in most months of the year.ResultsThe results of this study showed that the elevation of the boundary layer in Iran during the year has a lot of spatial and temporal changes due to geographical diversity and climatic characteristics in different regions of the country. The existence of diverse topography, expansion in latitude, large differences in relative moisture content and soil moisture content are among the factors that have caused significant changes in the height of the boundary layer at different times and places. The results of multivariate regression analysis showed that the height of this layer is mainly affected by six parameters in particular, temperature and relative humidity.
