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.
Shahriar Khaledi; Ghasem Keikhosravi; Farzaneh Ahmadibarati
Abstract
Extended AbstractIntroductionAmong the climatic elements, the effect of temperature in an area and its changes is the perception of land reclamation and can be maintained and land use of a place. Mean while, surface temperature is an important factor in global warming studies and as a representative ...
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Extended AbstractIntroductionAmong the climatic elements, the effect of temperature in an area and its changes is the perception of land reclamation and can be maintained and land use of a place. Mean while, surface temperature is an important factor in global warming studies and as a representative for climate change and radiation balance estimation in energy balance studies. Due to the special heat that each cover has on the ground. Vegetation land uses, barren lands, water resources, residential areas, absorb some of the sun's radiant energy and increase the temperature of the earth's surface. Finally, this heat is emitted from the surface of various coatings to the environment in the form of long wavelength radiation. If the surface temperature is calculated in different periods, the process of increasing or decreasing the surface temperature of different types of surface coverings can be modeled. MethodologyIn this study, to study the changes in land cover, MODIS images related to land cover from 2001 to 2019 were received. Surface cover product (MCD12Q1) Surface temperature product (MOD11) was prepared on a daily scale for both Terra and Aqua satellites to provide a variety of surface temperature indicators in the Google Earth engine system. In environmental studies, we often deal with observations that are not independent of each other and their interdependence with each other is due to the location and location of the observations in the study space. For this purpose, to reveal the effect of land cover on surface temperature components, global Moran correlation analysis tool was used and to analyze clusters and non-clusters, local Moran insulin index was used. In the last step, to evaluate the relationship between circadian surface temperature, daily temperature and night temperature After converting NDVI and LST raster maps to vector maps, Pearson correlation coefficient, regression relationship and significant value between variables in R programming environment were calculated.DiscussionBased on the land cover product of Modis 5 sensor, the predominant cover including shrubs, grasslands, agricultural lands, scattered vegetation and residential areas were identified between 2001 and 2019. The largest area of the region is scattered vegetation (50%) and secondarily grasslands (20%). During these 19 years, the cover of shrublands and the cover layer of scattered plants has an increasing trend and the cover of grasslands and arable lands has a decreasing trend. The surface temperature of this region has a spatial structure and is distributed in the form of clusters, so it has a spatial relationship with the natural features of the region. Spatial patterns of spatial data on surface temperature are divided into three categories: hot spots, cold spots, and clusters. Low-lying areas of the south and part of the east and west of the area, hot spots, high-altitude areas that include parts of the central areas in the south and north of the area, cold spots and cold spots margin, clusters (foothills) they give. On the 24-hour surface temperature scale, the land use layer of settlements and agricultural lands shows the most significant relationship between the types of land surface cover. In the daily temperature scale, the land use layers, grasslands and scattered vegetation have a decreasing trend and the use layer of shrubs and settlements has an increasing temperature. At night surface temperature scale, the trend of significant surface coatings in relation to the microclimatic element of surface temperature intensifies so that field cover, scattered vegetation and habitat layer have the highest correlation with increasing night surface temperature Show them selves. Therefore, in the study of spatial pattern of surface temperature, latitude and altitude are the most influential factors and in the study of the effects of land cover, the layer of settlements in three surface temperature parameters (minimum, maximum, average) of the highest temperature increase compared to others. Uses have been enjoyed. ConclusionLand use type and land use changes and vegetation have a significant effect on land surface temperature changes. In the northeastern region of the country, shrub cover, grasslands, arable lands, scattered vegetation cover and residential areas are the dominant cover of the region. During 19 years, the increase in the area of scattered vegetation and barren shrubs indicates negative changes in the ecosystem of the region. In such a way that the area of other classes such as arable lands and grasslands has been reduced and the area of these classes has been increased. The surface temperature of this region has a spatial structure and is distributed in the form of clusters in 3 clusters. Hot clusters, low-lying areas, cold clusters, high-altitude areas and inconveniences covered the foothills. Elevation factor, latitude are influential in the distribution of clusters. In studying the effects of land cover on the surface temperature of the land, during 19 years, the circadian temperature of the settlement layer has increased by about 1.12 degrees and the arable land layer by 0.41 degrees Celsius. On the daily temperature scale, the settlement layer has a temperature increase of about 1 degree. At night surface temperature scale, arable land cover, scattered vegetation cover and habitat layer recorded 6.2, 0.8 and 0.6 ° C temperature increase, respectively.