Extraction, processing, production and display of geographic data
Zahra Rabiee Gaffar; Hossein Asakereh; Uones Khosravi
Abstract
Extended Abstract Introduction The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability ...
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Extended Abstract Introduction The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability of different durations of precipitation in Iran over the past four decades (1977-2016). To achieve this goal, we used the third version of the Asfazari database. We defined a rainy day as a day when the precipitation is more than the average precipitation in a given place. The Markov chain method was employed to estimate the probability of precipitation duration from 1971 through 2016.Materials and MethodsWe adopted the daily data of 2188 stations under the supervision of Iran’s Meteorological Organization for the period 1971 through 2016. Accordingly, we estimated the probability of precipitation duration for 1-7 days for the entire period. We investigated the decadal changes in the probability of precipitation duration for the four study decades and compared them to the whole period under investigation. To understand the spatial features of these changes, we estimated the relationship between changes in the probability of precipitation duration for 1-7 days and spatial factors using multivariate regression models.Results and DiscussionOur findings revealed that as the duration of rainy days increased, the area affected by precipitation decreased. Therefore, the spatial distribution of the probability of precipitation duration for more than 7 days indicated the smallest area that received precipitation. The probability duration of precipitation lasting 4 days or more throughout Iran was very small, which can be attributed to the effects of local features on precipitation formation. The probability of 1-day precipitation for most regions of Iran was higher than other durations; however, there was only a probability of 1-day precipitation in half of Iran. The highest probability of precipitation duration occurred in the Caspian region, the only region that experienced all durations of precipitation, indicating the presence of various precipitation mechanisms in this area. The greatest probability of decadal changes was observed in the 1-7 day duration in the northern part of Iran, including the northwest to the east of the Caspian Sea and in the south of Alborz Mountain range. Additionally, the most changes in the probability durations of 1-7 days of precipitation in the south have been seen in Sistan and Baluchistan. The lowest probability of decadal changes was shown in large areas of the regions from the east, southeast, and southwest. Therefore, the changes in precipitation durations in the southern half of the regions were generally low; however, in the northern half, the changes were relatively significant.In general, during the four study decades, the relationship between changes in the probability of 1-7 day precipitation durations and spatial factors, particularly latitude, was positive. Thus, decreasing latitudes resulted in an increasing probability of 1-7 day precipitation.ConclusionThe most likely changes in precipitation duration were related to the western and eastern coast of the Caspian Sea and the northwestern region of Iran, as well as southern Alborz, where the probability of changes decreased. The least amount of possible changes was related to the south of Iran, where only two provinces, Sistan and Baluchistan, and Hormozgan, experienced the greatest change in the probability of one to seven days of precipitation. Thus, the possible changes in the spatial continuation of precipitation in the southern half of the country were primarily low. However, in the northern half, the possible changes in the duration of precipitation were more significant. changes in the duration of precipitation, along with changes in the intensity and frequency of precipitation, can have significant consequences in extreme events such as droughts and floods. Accurately depicting changes in precipitation duration can be helpful in addressing problems concerning precipitation.
Extraction, processing, production and display of geographic data
Hossein Asakereh; Somayeh Taheri Alam; Nosrat Farhadi
Abstract
Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological ...
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Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological phenomena, primarily vegetation, which reflects an intricate pattern of changes in climatic elements, particularly temperature, and precipitation. Although the substantial role of climatic elements on the density and geographical distribution of vegetation has been confirmed, it is arduous to estimate the relationship between climate changes and vegetation due to the complexity of the mechanism of different characteristics of climatic elements (such as the amount, type, intensity, season, continuity, etc.), feedback processes, and also the response time of the vegetation to climatic changes.
Materials and Methods
In the current research, the gridded data of the Normalized Difference Vegetation Index (NDVI), a product of the MODIS terra, was used from 2001 through 2016. The data were extracted from a GIOVANNI website. In the present study, Iran's vegetation density classes were determined based on quantitative methods, and the geographical distribution of two-half parts of the understudy periods was compared.
Results and Discussion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. The finding revealed that the NDVI has a direct relationship with the precipitation. Accordingly, the northern, northwestern, and western regions, as wet regions in Iran and comprise proper soil, included high NDVI.
Dividing NDVI data into two 8-year periods revealed that in the first 8 - year, despite the high amount of precipitation, the NDVI was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Conclusion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. Our finding showed that the spatial distribution of NDVI has a direct relationship with the precipitation. Comparing two - half of understudy data showed despite the high amount of precipitation, the NDVI in the first half was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Geographic Data
Mirnajaf Mousavi; Nima Bayramzadeh
Abstract
Extended Abstract
Introduction
Spatial inequalities in developing countries such as Iran are more visible due to various factors, so many Scientists (Dadashpour & Shojaei, 2022-Mosayebzadeh et al, 2021- Fotres & Fatemi Zardan, 2020- Dadashpour & Alvandipour, 2018- GhaderHajat & Hafeznia, ...
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Extended Abstract
Introduction
Spatial inequalities in developing countries such as Iran are more visible due to various factors, so many Scientists (Dadashpour & Shojaei, 2022-Mosayebzadeh et al, 2021- Fotres & Fatemi Zardan, 2020- Dadashpour & Alvandipour, 2018- GhaderHajat & Hafeznia, 2018) consider the most important feature of Iran's space organization to be spatial injustice, which is the manifestation of the country's center-periphery structure at micro-local and macro-national scales. In Iran, inequality and lack of balance in the optimal distribution of facilities as a result of unprincipled past policies in industrial-service locations, growth poles, and the trend of centralization in dominant regional cities, the spatial imbalance between national, regional, district, and local levels is one of the important issues, which has emerged under the influence of mechanisms governing economic, social and political structures, this anomaly and imbalance have increased with the increase of the government's role in the economy due to the nature of its concentration and departmentalism, and more planning has been provided to the government (Faraji et al, 2019). Finally, today, the issue of inequality in many countries is mentioned as a fundamental challenge in the path of development, So it is considered one of the main obstacles in the process of national development and disruption of regional balance, Therefore, the first step in development planning is to identify the position of each region in terms of development and inequalities (Amanpour and Mohammadi, 2021); Therefore, the main goal of this research is the spatial analysis of regional inequalities in Iran during the years 2011, 2016, and 2021.
Materials & Methods
The current type of research is applied and its research method is descriptive-analytical. The collection of data in this research is in the form of a library. The statistical population of this research is 31 provinces of the country based on the last administrative and political divisions of 2021. To evaluate the state of development, 47 indicators have been used in 3 main economic-infrastructural, educational-cultural, and health-treatment dimensions. The analysis of research data has been carried out quantitatively using GIS, EXCEL, and SPSS software. In this research, to rank the provinces from the VIKOR multi-indicator decision-making model, To weight the indices using the Shannon entropy method, For data clustering using the K-Means-Cluster method, To evaluate the changes of inter-provincial inequalities using the CV statistical method, To interpolate the development of the country using the Kriging method, To evaluate the spatial correlation and the type of clustering of the development of the provinces using the Spatial Autocorrelation method (Moran's I) and Geographically weighted regression method has been used to find the relationship between development as a dependent variable and population and area as an independent variable.
Results & Discussion
The results of this research show that in 2011 due to the strong concentration of administrative, political, economic, and industrial activities in Tehran, there was a sharp divergence between Tehran province and other provinces. The growth pole theory has entered the second stage and the degree of divergence has decreased and the degree of convergence between provinces has increased. According to the results of Moran's correlation, the clustering of the country is still multipolar and there is still regional inequality in the country, so the country's border and port provinces are in a worse situation than other provinces, despite their development potentials and capacities as border corridors. The geographic weighted regression model also shows that the influence of independent variables (area and population) is greater in the northwest of the country than in the southeast of the country, This issue is estimated at 76% in 2011, 35% in 2016 and 43% in 2021.
Conclusion
In general, the most important cause of Iran's regional inequality should be sought in the structure of the planning system and the pattern of regional spatial development of Iran. The formation of the planning system in Iran is based on neoclassical economic theories, the growth pole and the intense concentration of activities in the center of Iran, and this issue is very influential in creating regional inequalities, and on the one hand, due to top-down planning and lack of attention to environmental potential in the country's provinces, Actually, spatial injustice is spreading in the country and this issue can act as a dangerous factor in the direction of sustainable development of the country.
Extraction, processing, production and display of geographic data
Qadir Ashournejad
Abstract
Extended AbstractIntroductionRemote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts ...
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Extended AbstractIntroductionRemote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts in geospatial science is one of the dimensions of this development. The land cover product is one of these products that is used more than other remote sensing products. When presenting these products, their qualitative and quantitative characteristics, including their global accuracy, are also published. Expressing the accuracy of these products globally makes it necessary and necessary to re-evaluate their accuracy regionally for the users of these products in different regions of the world.Materials & MethodsIn this research, the accuracy of the European Space Agency's Copernicus Global Land Service (CGLS), GlobeLand30 and Esri's land cover product were evaluated for regional use in the north of Iran - Mazandaran province. After calculating the area of the classes for each of the land cover products, Pearson's correlation coefficient was used to calculate the correlation between them. For quantitative evaluation, the error matrix was used as one of the most common ways to evaluate the accuracy of land cover products. This method is based on the comparison of classified data and ground reality data. Also, the categorized random sampling method was used to select 1329 evaluation samples in Mazandaran province. For visual evaluation, three areas with dimensions of 6 x 6 km were selected.Results & DiscussionThe regional accuracy evaluation of the studied products shows opposite results compared to the global accuracy of these products. Based on the global accuracy reported for the studied products, the highest accuracy is calculated for the Esri product at 86%, followed by GlobeLand30 and CGLS at 83-85 and 80%. Meanwhile, based on the regional accuracy obtained from the results of this research, the highest regional accuracy for the CGLS product has been calculated at 84% and then for GlobeLand30 and Esri products at 81 and 75%. In evaluating the regional accuracy of the classes, all three studied products (CGLS, GlobeLand30 and Esri) have acceptable accuracy (above 90%) in the classes of snow and ice (100, 100 and 100%), forest (90, 95 and 98 percent), water (96, 94 and 90 percent) and impervious surface (94, 91 and 90 percent). For the agricultural class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers.In the 3 classes of shrubland, Impervious surface and wetland, the accuracy results are less than other classes for all three land cover products and in the amount of (29, 0 and 13 percent), (65, 66 and 42 percent) and (67, 38 and 0 percent).Conclusion By evaluating and comparing the regional accuracy of three CGLS products, GlobeLand30 and Esri, this research answered the question of whether the accuracy stated in global land cover products can be trusted for regional studies and planning. The results show that the regional accuracy of CGLS, GlobeLand30, and Esri are 84, 81, and 75 percent, respectively, compared to their global accuracy (80, 83, 85, and 86 percent). These results show the difference obtained for the Esri product more than the two products CGLS and GlobeLand30. Meanwhile, the remote sensing data used for the Esri product (Sentinel-2 data) and its pixel size (10 meters) are of higher quality and quantity than the other two products. In fact, these results show that only paying attention to the type of data used and the global accuracy is not enough to use products in regional scales and requires evaluations before using them.In addition, by evaluating the classes of each product and comparing them, the need for this evaluation before using these products seems necessary. The results showed that in the evaluation of the regional accuracy of the classes, all three studied products had an accuracy of over 90% in the classes of snow and ice, forest, water areas and human construction. For the agricultural land class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers. In the 3 classes of shrubland, herbaceous cover and wetland, the results show lower accuracy than other classes for all three land cover products. Significant results were also obtained in the visual evaluation, and it seems necessary to pay attention to this evaluation before the applications where it is important to pay attention to a particular class.
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.
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.
Saeed Farzaneh; Reza Shahhoseini; Iman Kordpour
Abstract
Introduction Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most ...
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Introduction Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most significant problems in drought monitoring is lack of proper spatial coverage for the collected data (due to unavailibility of field data in some regions) and also lack of a suitable time scale (observations and thus drought estimation is not always possible). Since satellite observations do not face challenges like lack of spatial scale which is quite common in field observations, remote sensing satellites can provide a better estimate of droughts. However, satellite observations alone are not capable of accurately estimating the occurrence of droughts. Therefore, a combination of field and satellite observations has been used recentely to reach a better estimate of hydrological problems. Materials & Methods Temporal and spatial complexity of droughts have made a new global index combining ground-based and satellite-based observations quite necessary. Given the kind of data used in MDI index, we cannot expect it to be global. However, its performance is still acceptable in similar environments and climates, and thus it has been used in the United States (Texas). Datasets selected for the present study have different temporal and spatial scales and thus, a common scale must be found before calculating the index. Data received from GRACE satellite and MODIS sensor were downloaded monthly, but precipitation data were collected on a daily basis. Thus, aritmatic mean of precipitation data was calculated to reach a monthly avarage. Regarding the spatial scale, one-degree precipitation data were received from GRACE and MODIS while precipitation data extracted from synoptic stations had a point-based nature. Therefore, Inverse Distance Weighting (IDW) method was used to produce a one-degree network. Three types of observations were used in the present study including data received from synoptic stations of Iran meteorological organization, GRACE mission satellite-based gravity data and MODIS remote sensing satellite-based data. These were selected to identify droughts over a 14-year time series. Results & Discussion The present study has calculated MDI drought index on a one-degree spatial scale and monthly temporal scale for 168 months using Precipitation, NDVI, and TWS data. Severe droughts in northwestern and central areas of Iran from 2004 to 2014 have led to a shortage of water in reservoirs. In addition to drought, too much water harvesting in northwestern Iran has resulted in a decrease in groundwater level and thus, increased water harvesting from rivers and canals leading to the Urmia Lake and reduced water level in this lake. The results of MDI drought index calculated for Iran over the period of 2000 to 2014 show a high correlation with the results of standardized precipitation-evapotranspiration drought index. According to the type of data used to calculate MDI index, it is expected to have a strong correlation with PDSI index due to its sensitivity to precipitation, area temperature and soil moisture content. Since GRACE and MODIS satellite-based data, and data received from synoptic stations were used, a strong correlation with MDI is also expected. It should be noted that PDSI index is higher than MDI index in Iran, although both show the drought trends accurately. For example according to PDSI index, the worst drought of the last two decades in Iran has occurred in 2008, and MDI index shows the same year. Conclusion The present study has introduced a new drought index using a combination of precipitation data, GRACE_TWS and NDVI. These data were selected because of their high sensitivity to drought. GRACE_TWS observations monitor hydrological drought and include surface and subsurface water sources. NDVI observations are mostly used to identify photosynthetic activities of vegetation cover and are therefore very useful for detecting agricultural drought. Precipitation value shows the amount of surface water in the study area. Precipitation can have relatively rapid effects and is therefore useful for monitoring meteorological drought. MDI index has identified several droughts in each region of the country in the period of 2003 to 2016. These identified droughts have generally covered the country over time. However, each drought has had a different impact on ecosystem. In Iran, the most severe droughts have occurred during 2008 to 2009 and 2011 to 2012. Since MDI correlates well with PDSI, both show a drought in these years. In order to develop the proposed algorithm, the effect of different zoning of the study area on MDI index can be studied.
Mahdi Sedaghat; Hamid Nazaripour
Abstract
Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction ...
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Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction results in the creation of centers susceptible to dust storms. With socio-economic impacts ranging from urban to intercontinental and from a few minutes to several decades, this can challenge regional development. The first estimate of potential dust sources is derived from the soil properties. With the reduction of surface soil moisture and the wind speedcrossing a certain threshold level, wind erosion process can cause the formation of dust storms. Field studies have proved that increasing the moisture content in soil from zero to about 3%, reduces the dust concentrationsignificantly. To understand the climatology of dust and develop related numerical predictive methods, continuous recording of dust storms is essential, which requires effective and continuous monitoring of the variations in surface soil moisture. Remote sensing technology is an effective method for calculating soil moisture. This technology was first used for the estimation of energy flux and surface soil moisture in the 1970s. To extract the surface soil moisture content, some remote sensing methods use surface radiation temperature and some others apply water transfer (soil/vegetation/air) (SVAT) model. Various indices have been developed for soil moisture monitoring, such as soil moisture (SM), soil water index (SWI), Temperature-Vegetation-Dryness Index (TVDI), Soil Moisture Index (SMI) and Perpendicular Soil Moisture Index (PSMI), all of which combine vegetation and surface temperature variables. Materials and Methods Soil moisture is considered to be a significant parameter in the exchange of mass and energy between the Earth surface and the atmosphere. Lack of soil moisture or decreased moisture in soil is considered to be a factoraccelerating the process of dust storm formation. During the previous decades, water stresses on the ecosystem of Hour-al-Azim have transformed this wetland into one of the main dust centers in the southwest Iran. Hour-al-Azim is one of the largest wetlands in southwestern Iran. This wetland is shared between in Iran and Iraq. It is located between N 30° 58´- N31° 50´ and E 47° 20´- 47° 55´. The Iranian part of this wetland encompassed an area of 64,100 ha in the 1970s, while in the 2000s, the area has decreased to only 29,000 ha. The present study aims to monitor the spatial-temporal variability of soil moisture in Hour-al-Azim wetland and to investigate the relation between these changes and dust storms in the southwest Iran. To reach this end, we used 8-day images obtained from the Aqua satellite in the period of 2003 to 2017 and also annual frequency of dust storms with a visibility of less than 1000 m in the period of1987–2017. A database consisting of 189 images of the red band, near-infrared band, and ground surface temperature (LST) was created, which contained 4 images per year (one image per season). The resolution of the red / near-infrared band data and daily LST values were 231.65 and 926.62 meters, respectively. Then, soil adjusted vegetation indices (SAVI) and perpendicular soil moisture index (PSMI) were extracted. SAVI index is used to reduce the effect of background soil on vegetation cover in semi-arid and arid environments with less than 30% vegetation cover.Compared to NDVI, SAVIwith L = 0.5reduces the effect of soil changes on green plants. In the next step, a trapezoidal method was used to calculate the PSMI index. In order to investigate changes in the soil moisture content of the Hour-al-Azim wetland, three time series obtained from regional mean of SAVI, LST and PSMI remote sensing indices and a time series consisting of the number of days with dust storms observed in the 9 stations were evaluated using simple linear regression test. Results and discussion Extracting Soil Adjusted Vegetation Index indicated that in the study period, the highest values of this index was observed with a regional mean of 0.15 on 4/7/2014 and the lowest values was observed with a regional mean of 0.08 on 1/1/2005. Land Surface Temperature survey showed that during the study period, the highest values of this index was observed with a regional mean of 54.42 ° C on 7/4/2010 and the lowest values was observed with a regional mean of 17.28 ° C on 1/1/2007. The regional mean of Perpendicular Soil Moisture Index indicates that despite winter is considered to bethe wettest season of the region, PSMI index with a regional mean of 0.2 has experienced the driest soil moisture conditionsat the beginning of winter (1/1/2016),while it had experienced the wettest soil moisture conditionsin the same season on 1/1/2009 with a regionalaverage of 0.13. Conclusion Finding of the present study indicate an increasing trend in the range of remote sensing indicators. The range of SAVI index is increasing, which means that the density of vegetation in the Wetland is decreasing. Perpendicular Soil Moisture Index values also show an increasing trend, indicating a decrease in soil moisture content. As a result of the decrease in soil moisture, the vegetation density also has decreased and the land surface temperature has increased. Results of statistical tests indicate the role of changes in environmental conditions of Hour-al-Azim wetland in the frequency of dust storms. Using findings of the present study, or studies such as Kim et al. (2017), it is possible to take advantage of soil moisture variations for the prediction of dust generation, its emission, and spread level.
Mahmoud Ahmadi; Abbas Ali Dadashi Rodbari; Behnaz Nassiri Khuzani; Tayebeh Akbari Azirani
Abstract
Introduction
Cloud is a special phenomenon formed by dynamic and thermodynamic changes of the general atmospheric circulation. Through dispersion and reflection of solar radiation, cloudschange energy balance of the Earth and affect its hydrologic cycleby producing rainfall in various forms. Determining ...
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Introduction
Cloud is a special phenomenon formed by dynamic and thermodynamic changes of the general atmospheric circulation. Through dispersion and reflection of solar radiation, cloudschange energy balance of the Earth and affect its hydrologic cycleby producing rainfall in various forms. Determining the state of clouds (in terms of clouds being liquid or ice) is crucial, sinceitaffects the atmosphere feedback mechanism. Moreover, the state of clouds is related with itsheight, i.e., higher clouds tend to have an icy state. Therefore, determiningtheir statusis especially important for the accuracy of elevation estimation. The present study seeks toinvestigatetemporal and spatial variation of liquid clouds in the geographical range of Iran using information received from meteorological stations and remote sensing techniques. It aims to find the feedback of cloudsin liquid phase and theirdominant condition.
Research Methodology
Data received from MODIS Sensor of TERRA Satellite (2001-2015) and Cloud mask (CM) algorithm were used in the present study. Moreover, long-term data of 31 synoptic meteorological stations collected during the period of 1960–2015 were used to compare satellite data. Followingdata decoding and required calculations, maps of each season were produced using Kriging method.
Results and discussion
Results indicate that maximum number of liquid clouds occurs in winter, while their minimum number occurs in summer. In winter, Rasht, Ramsar, Babolsar and Gorgan stations (with cumulative frequency of 174.33 to 305.66 days) have maximum frequency of liquid clouds.This country almost lacks liquid clouds in summer. Only in the coastal zone of the Caspian Sea, Rasht, Ramsar, Babolsar and Gorganstations with 153, 93.33, 77.66 and 26 days, respectively,had the maximum frequency of liquid clouds. The average thickness of liquid clouds in Iran was calculated on a seasonal scale. In winter, spring, summer and autumn, it was 22.23, 17.13, 14.11 and 16.7 microns, respectively. Results indicate that the average thickness of liquid clouds decreases in warm seasons. Maximum thickness of liquid clouds in winter, spring, summer and autumn was 33.04, 24.56, 24.85, 22.84 and minimum thickness of liquid clouds was 13.98, 6.82, 6.27, 8.09, respectively. In winter,maximum frequency of liquid clouds occurred in western Iran and the Caspian coastline, while maximum thickness of liquid clouds occurredin northwestern and western Iran.Moving from north to south and west to east,the frequency of liquid and icy clouds decreases. In contrast, maximum frequency of liquid clouds occurs in summer.
Conclusion
Results indicated that maximum frequency of winter and autumn liquid clouds mainly occur in high latitudes of northern regions, southern hillside of Alborz(west to east direction), and northwestern and western regions of the country. Maximum frequency of summer liquid clouds occurs in the Caspian Coasts, while maximum frequency of spring liquid clouds occursin the northern half and southeast regions of the country. This is well-justified due toactivities of the expected systems and local factors in each season. Liquid clouds of Iran have a nonlinear and possibly complex relationship, and factors such as hillside orientation, precipitation systems, distance from sources ofmoisture, lack of ascending factor, lack of sufficient moisture and many other factors contribute to this relationship.Evaluation of liquid clouds thickness indicated that elevated regions of central and western Zagros have the highest amount of liquid clouds in cold seasons, since low-pressure systems, fronts and mid-latitudewaves of atmosphere play a decisive role in the growthof cloud numbers in these seasons. This is also in consistencywith Masoudian (2011) results. Northwestern Iran and the Alborz belt are almost always affected by the western winds. Western winds pass over the Mediterranean Sea and its sufficient moisture resource, which play a significant role in the cloudiness of this area. Results are consistent with Alijani’sstudy(2010) that reported 120 cloudy days in Alborz Mountains, Khorasan and northern Azerbaijan altitudes. Increased cloudiness of southern and southeastern Iran during warm seasons is related with the monsoon system in July-September,which is also confirmed by Ghasemifar et al. (2018) and its mechanism is discussed by Yadva (2016). Results are also in consistency with the results of Ahmadi et al. (2018), which examined the cloud optical thickness (COT) and the total cloud cover (TCC) of Iran. In other words, results of Ahmadi et al.(2018) also confirm our findings.
Roohollah Karimi; Ali Reza Azmoude Ardalan; Siavash Yousefi
Abstract
Introduction
Components of verticaldeflection, i.e., North-South component and East-West component ,are used for accurate determination of geoid or quasigeoid. Moreover, vertical deflection components area useful source for determination of variations in subsurface density and geophysical interpretations. ...
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Introduction
Components of verticaldeflection, i.e., North-South component and East-West component ,are used for accurate determination of geoid or quasigeoid. Moreover, vertical deflection components area useful source for determination of variations in subsurface density and geophysical interpretations. Generally, there are two definitions for verticaldeflection. According to Helmert definition, vertical deflection at any given pointis the angle between the actualgravity vector (actual plumb line) and a line that is normal to the reference ellipsoid(a straight line perpendicular to the surface of reference ellipsoid). Another definition of vertical deflection is proposed by Molodensky. According this definition, vertical deflection at any given point is the angle between actualgravity vector and normal gravity vector (normal plumb line). Some relations have been introduced to convert Molodensky vertical deflection to Helmert vertical deflection. Helmert vertical deflection is estimated using astrogeodetic observations (combination of astronomical and geodetic observations).
Presently, global geopotential models (GGMs) have been expanded to the degree of2190, which is equivalenttoabout 5-min spatial resolution. Vertical deflectionat any point on the Earth can be calculated using the GGM. The resulting vertical deflection is consistent with Molodensky definition.Unfortunately, accuracy of GGMs is not sufficient for estimation of verticaldeflection.In other words, since GGMs are expanded up to a limited degree due to their resolution, omission error(or truncation error) occurs in computation of the earth’s various gravity field functionals, such as the geoidal height and verticaldeflection. Combining GGM with a digital terrain model (DTM) is a method used to reduce omission error.It should be noted that DTM has a higher spatial resolution as compared to GGM.In this method, the omitted signals of GGM can be modeled using residual terrain model (RTM) derived from subtracting high resolution DTM from a reference smooth surface. The reference smooth surface is obtained from eitherapplying average operator to DTM or expanding global topography into spherical harmonics. Fortunately, DTMs with spatial resolution of 3seconds or more,and reference smooth surface based on 2190 degree spherical harmonics are publicly available.
The present study seeks to assess vertical deflectionderived from a combination of GGM and DTM in Iran. Previously, Jekeli(1999) has studied EGM96 geopotential model with the aim of computingvertical deflection in the USA. Hirt(2010) and Hirt et al. (2010a) have assessed vertical deflection in Europe and the Alps using a combination of EGM2008 and RTM models.In Iran, GO_CONS_GCF_2_TIM_R4, a GOCE-only model, and EGM2008 geopotential model have been used toobtain vertical deflection and the results have been evaluated byKiamehr and Chavoshi-Nezhad(2014).
Materials & Methods
To implement the present study,a EGM2008 model with a spatial resolution of about 5-min is selected asGGM and a SRTM model with 3-sec spatial resolution is considered as DTM. To obtain RTM, DTM2006 model based on2190 degree spherical harmonicsis selected as the reference smooth surface.To compute the residual topography effect, prism method was used in an ellipsoidalmulti-cylindrical equal-area map projection system. First, we compute vertical deflectionusing EGM2008 model. It is also calculated using a combination of EGM2008 model and RTM(EGM2008/RTM method). In the next step, vertical deflection derived from the first method (EGM2008 model) and the second one (combination of EGM2008 model and RTM) are compared with vertical deflectionderived from astrogeodetic observations in 10 available Laplace stations in Iran.
Results & Discussion
Results indicate that there is a 1.2sec difference between North-South component of vertical deflection (i.e.) obtained from EGM2008 model and astrogeodetic observations.With RTM, this will reach 1 sec, which shows a 15% improvement. Moreover, there is a5.7secdifference between East-West component of vertical deflection () obtained from EGM2008 model and astrogeodetic observations, while this value will reach 5.6sec using RTM. Improvement in East-West component () is1.4%, which is smaller than the improvement of North-South component (). Based on the computations, we found that values of and in the Laplace stations canreach 17sec (RMS=7sec) and 15sec (RMS=8sec), respectively. Therefore, it is concluded that the relative error ofNorth-South component ()computation using EGM2008/RTM method is about 6% and the relative error ofEast-West component ()computation is about 37%.
Conclusion
The present research has studied the RTM effect on the improvement of GGM used for the determination of vertical deflectionin Iran. To performthe study, EGM2008 model with around 5-min spatial resolution was selected as GGM. RTM is also derived from subtracting the DTM2006 model (based on2190 degree spherical harmonics)from the 3-sec spatial resolutionSRTM model. Numerical findings indicate that a combination of RTM and GGM can improve the results of vertical deflectioncomputation, as compared to the results obtained from GGM-only approach. The improvement in North-South component of vertical deflection () is about15%and East-West component of the vertical deflection () undergoes about 1.4% improvement. In general, EGM2008 model and its combination with RTM have been more successful in the computation of component as compared to computationin the geographical region of Iran. There is no clear explanation for this difference, but it can be due to errors in theastronomical or geodetic observations oflongitude in Laplace stations.
Reza Sarli; Gholamreza Roshan; Stefan Grab
Abstract
Extended Abstract Introduction change monitoring is generally used to evaluate natural processes such as the long-term effects of climate change, which is affected by the interaction of the climatic system’s constructive components such as the biosphere, lithosphere, or factors that control the ...
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Extended Abstract Introduction change monitoring is generally used to evaluate natural processes such as the long-term effects of climate change, which is affected by the interaction of the climatic system’s constructive components such as the biosphere, lithosphere, or factors that control the climate changes outside the climatic system, over a long period of time, as well as the short-term processes that include vegetation sequence and geomorphological processes. Change monitoring is also used to evaluate the effects derived from human activities such as deforestation, agriculture and urban development. Remote sensing is a very useful technology, which can be used to obtain information layers from the soil and vegetation. Materials and Methods Land Cover Product was used to process the MODIS1 Satellite data which is one of the most frequently used products designed relating to MODIS Satellite, and is used annually. This Sensor with 250-500 meter and also 1-kilometer spatial resolution has 36 spectral bands in the range of visible, reflectional infrared and thermal infrared wavelengths, which can well be used for various applications of the surface, the Earth surface, atmosphere and the oceans. MOD12Q1, which is one of the MODIS products, was used to investigate and analyze the profile of the vegetation changes in Mazandaran province using the NDVI and EVI indicators from 2005 to 2017. The related images have been prepared annually with 500-meter resolution and sine coordinate system in the form of a combination of Terra and Aqua data. Given the standards provided by NASA, the changes were investigated using the “decision tree” classification method, and the map for the prediction of its changes was calculated using the Markov Chain Model. The ArcGis software was then used to analyze these changes in order to determine which use of land with what percentage of changes has been allocated to which area. Results and Discussion In 2005, land-uses associated with dense vegetation dominated an area of 398.77 m2. These land-uses include wasteland, dense vegetation and scattered vegetation. The estimation of the changes occurring in the aforementioned land-uses showed that the maximum changes relating to the low density vegetation with an average of 55.62% are in the northwestern and the eastern parts, and the minimum changes relating to the in dense vegetation with an average of 77.21% are in the central parts of the region, respectively. Furthermore, the observations of the images of the year 2005 show that the use of dense vegetation which has turned into low density vegetation in the image of the year 2017, has had the minimum changes. Finally, considering the prediction of the observed changes, it can be concluded that these changes were more related to the altitude range of 1400 m to 2260 m with the slope coefficients of 15% to 99%. The prediction carried out using the Markov Chain also suggests that the low-density land cover, which was over 864/80 km2 in 2017, will turn into barren lands in proportion to the changes occurringin 2022. Conclusion A major part of the vegetation changes in the area is due tothelack of job opportunities, extra labor attraction and the economic poverty of the inhabitants.In addition,the pressure on the meadow fields hasreached its highest limit by ranchers,which has resulted inthe reduction of grasslands. Eventually, it could be stated that the evaluationmethods and modelsof the vegetation changes have their own featuresand no method on its own is usable andappropriate for all cases, hence,the identification of an appropriate method for evaluating thevegetation changesneeds to be examined quantitatively and qualitativelyin order to provide the best result.
Mojtaba Rahiminasab; Yazdan Amerian
Abstract
Extended Abstract Introduction Rain is one of the most important atmospheric phenomena affecting human life. Rainfall prediction is important for various purposes such as planning for agricultural activities, forecasting floods, monitoring drought and providing resources for consumable water. The rapid ...
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Extended Abstract Introduction Rain is one of the most important atmospheric phenomena affecting human life. Rainfall prediction is important for various purposes such as planning for agricultural activities, forecasting floods, monitoring drought and providing resources for consumable water. The rapid expansion of using artificial neural networks (ANNs) as an experimental and efficient model in various sciences including meteorology and climatology implies the high value of studying these types of models. Materials and methods The purpose of this paper is to predict the monthly rainfall in Iran, using the combination of artificial neural networks and extendedKalman filter. For this purpose, the monthly average rainfall data of about 180 synoptic stations spreading across the country were used during the years 1951 to 2016, then, the monthly rainfall was predicted for the year 2017 using the article’s method. Artificial neural networks are a method for the approximation of the functions and prediction of the future state of various systems. Artificial neural networks discover the law latent in them and transfer it into the network by processing the experimental data. The smallest processing unit of information in the artificial neural network is neuron that builds the bases for the application of neural networks. Each neural network consists of a number of nodes which are the neurons, and the communication weights that connect the nodes together. Input data is multiplied by their corresponding weights, and their sum is entered into the neurons. Each neuron has a transfer function. This input data passes through the transfer function and specifies the output value of the neuron. The back propagation algorithm is one of the most commonly used algorithms for teaching these networks, but the back propagation algorithm depends on the selection of the number of hidden neurons. Also, the convergence speed of the back propagation algorithm is very slow comparing with the proposed method in this paper, and is very sensitive to the noises present in the input and output data set, which is used for teaching the neural network. In addition, it has a poor performance in modeling the complex processes. One of the most famous methods to eliminate the aforementioned defects is the use of the Kalman filter. The Kalman filter contains a set of mathematical equations that performs a repeated process, prediction and updates, and is also an optimal tool in minimizing the covarianceof the estimated error. The leading neural network can be considered as a nonlinear dynamic system with synaptic weights and equate the teaching of the neural network with the problem of estimating the state of the nonlinear system. Therefore, the extended version of the Kalman filter can be used to estimate the adjustable parameters of the artificial neural network like the neural network weights. Results and discussion The climatic zonation of Iran was used for the data separation by the method of Coupon-Geiger which has been conducted by other researchers, and Iran was divided into eight climatic zones. This zonation divides Iran into dry and hot desert, dry and cold desert, dry and hot semi-desert, dry and cold semi-desert, moderate with dry and hot summers, rainy moderate with warm summers, snowy with dry and hotarm summers, snowy with dry and warm summers climates. This artificial neural network which has been taught by the extended Kalmanfilter, was used for the prediction in each of the climatic zones. A multi-layered artificial neural network with two hidden layers has been used. There are 10 neurons in each of the hidden layers, and 7 neurons in the input layer, which are the same numbers as the network inputs. The methodology is that the year and number of months, the average monthly temperature, the average monthly wind speed and the geographic location of the synoptic stations are considered as the input parameters, and the average monthly precipitation as the output parameter. The difference between the observed and the predicted values of the monthly precipitation in 2017 was calculated for all stations and was considered as an error. The Root Mean Square Error (RMSE) of these differences was calculated for the 8 climatic zones. The RMSE is lower for dry and hot desert climate than for dry and cold desert climate. This RMSE is lower for dry and cold semi-desert climate than for dry and hot semi-desert climate. The RMSE is lower for moderate climate with dry and hot summers than for moderate rainy climate with warm summers. The RMSE is lower for snowy climate with dry and hotsummers than for snowy climates with dry and warm summers. Conclusion In most cases, the RMSE for hot climates is less than others that represents the efficiency of the proposed method for the prediction of monthly precipitation in hot climates.
Omid Reza Kefayat Motlagh; Mahmood Khosravi; Sayyed Abolfazl Masoodian
Abstract
1-Introduction The sun is the primary source of energy and life for Earth, and without solar radiation, there will be no atmospheric and climate processes on the Earth. Animal, human and plant life on the Earth depend on the energy received from the sun. Shortwave solar radiation is very important, due ...
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1-Introduction The sun is the primary source of energy and life for Earth, and without solar radiation, there will be no atmospheric and climate processes on the Earth. Animal, human and plant life on the Earth depend on the energy received from the sun. Shortwave solar radiation is very important, due to its role in biological processes, especially photosynthesis and human life. Outgoing Long Radiation (OLR), which is the result of heat reflection from the Earth’s surface, plays a vital role in the thermal balance of the Earth with regard to the presence of greenhouse gases. Part of the OLR goes out through atmospheric windows, but a large part of it is returned to the Earth by greenhouse gases, and plays an important role in the Earth’s thermal balance, especially during nights and in winters. Estimating Outgoing Long Radiation (OLR) is very difficult and remote sensing can be used to evaluate OLR on a planetary and regional scale. The purpose of this study is to examine long-term average of outgoing longwave radiation (OLR) over Iran using data received from the Iranian National Center for Oceanography and atmospheric science. Solar radiation is one of the most important parameters affecting the Earth atmosphere thermal balance (Isoman and Mayer, 2002). It also forms the basis for most of climate studies, because the process of evapotranspiration depends on the amount of available energy for evaporation (Alan et al, 1998). Since 99.8 percent of the energy at the Earth’s surface comes from the sun, the effect of solar radiation on evapotranspiration has been of great interest to researchers working in the field of agricultural science, especially irrigation sciences (De Souza et al, 2005). Some studies have used OLR trend to explore feedback and climate processes (Chu and Wang, 1997; Suuskind et al, 2012). Chuudi and Harrison studied El Niño’s impact on seasonal rainfall, temperature and atmospheric cycles’ anomalies in the U.S. using OLR. In another study, they have also estimated global seasonal rainfall anomalies related to El Niño and La niña using OLR (Chiody and Harrison, 2013, 2015). Knowing the amount of solar radiation in different locations is important for many practical issues such as estimating evapotranspiration, architectural design, agricultural products growth models, and etc. (Moradi, 2005; Alizadeh and Khalili, 2009; Mousavi Baygi et al, 2010). Considering the importance of climate change effects on the fluctuations of short wave and long wave radiations from the Earth surface and its relation with regional climate, research on this issue seems necessary. Since this issue has been underestimated in our country, and most researchers have only tried to find different coefficients and equations for estimating received solar radiation based on other meteorological parameters, making previous sporadic studies and researches on outgoing longwave radiation changes over Iran and other parts of the world applicable seems to be necessary. 2- Materials & Methods In this study, HIRS satellite data were used to analyze long-term average of OLR on planetary and regional scale. NOAA satellites were launched by the National Oceanic and Atmospheric Administration of the United States. The latest satellite in these series (version 19) was launched in February 2009. This polar-orbiting satellite circles the Earth from the North Pole to the South Pole 14 times a day. This allows NOAA-19 to observe the whole Earth twice every day (NOAA website). Since the purpose of the present study is to examine long-term average of outgoing longwave radiation over Iran based on data received from NOAA, daily OLR averages were retrieved from the CDR database with 1 arc degree resolution on a global scale for the period 1/1/1979 - 12/29/2016. Then, Iran long-term average of OLR and also its global average were calculated based on nearly 1 billion cells. The Gi* analysis method was also used to study the spatial distribution of outgoing long wave radiation over Iran. Since data received from outside Iranian territory were also included, we used “In polygon” function in MATLAB software to extract data specific to geographic borders of Iran. 3- Results & Discussion After calculating long-term average, results indicated that maximum OLR occurs between 30˚ north and south latitude, especially over the Middle East and North Africa, which is due to the radiation angle and ground cover. Results also showed that long-term average of the OLR was 222 W/m2. However, the mentioned areas have a reflection of more than 280 W/m2. Maximum OLR (289W/m2) occurs over Rub’ al-Khali desert and minimum OLR occurs over Antarctic glaciers (126 W/m2). These two points are one of the warmest and coldest areas on the Earth, respectively. They also have different ground cover. Therefore, it is natural to have a 173 W/m2 difference between the highest and lowest outgoing long-wave radiation over the Earth. Regional scale findings indicated that long-term average of OLR over Iran is 265 W/m2, which is 43 W/m2 (19 percent) higher than the global average. Results also showed that maximum OLR occurs to the west of Poshti region in Konnak city, Sistan and Baluchestan province (289 W/m2), and minimum OLR occurs over Ararat mountains in north-west Iran (approximately 235 W/m2). This 50 W/m2 difference is due to different latitude and altitude of these locations, which shows the significant role of temperature in the amount of outgoing long-wave radiation. 4-Conclusion Findings indicated that average global OLR is 222W/m2 and maximum reflection over the Earth surface occurs between 20˚ north and south latitude. This is because the average reflection between these latitudes reaches 270 W/m2, which can be attributed to the proximity of Tropic of Cancer and Tropic of Capricorn. Findings also showed that average long-wave radiation over Iran (264 W/m2) is %19 higher than the global long-term average. Although, maximum global OLR occurs in Rub’ al-Khali desert in Saudi Arabia (299W/m2), Iran is also considered to have a high level of OLR due to its geographic location and limited ground cover. With a reflection of more than 280 W/m2,vast regions in southern Iran are considered to have excessive energy and thus play an important role in environmental warming. Spatial analysis of hot and cold spots concentration patterns (above 90% level of confidence) showed that nearly 40 percent of Iran is considered to be hot spots, 17 percent neutral and 43 percent cold spots, the pattern of which is affected by difference in latitude and ground cover.
Faramarz Khoshakhlagh; Nemat Ahmadi; Mostafa Karimi
Abstract
Introduction The notion of climate change indicates a significant change in climate and environmental conditions over a long period of time (from a few decades to centuries). These changes can occur in mean radiation, temperature, precipitation, atmospheric patterns, wind, and other climate parameters. ...
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Introduction The notion of climate change indicates a significant change in climate and environmental conditions over a long period of time (from a few decades to centuries). These changes can occur in mean radiation, temperature, precipitation, atmospheric patterns, wind, and other climate parameters. Increased global average temperature and occurrence of climate extremes such as floods, storms, hails, tropical storms, heat waves, sea level rise and melting of polar ice caps are the most important effect of climate change. The present study sought to analyze the effect of climate change and global warming on temperature trends in Iran atmospheric levels. One advantage of the present study is that it investigates temperature changes at sea surface and other atmospheric levels, whereas many recent researches just emphasize on sea level. Materials and methods The present study used data received from the European Center for Medium-range Weather Forecast (ECMWF) for a period of 60 years, from 1951 to 2010, with a network resolution of 1 × 1° Latitude and Longitude for sea level data (Slp) and 850, 700 and 500 hPa levels. After converting extracted data using statistical extension of Net-cdf for excel 2007, the temperature trend for sea levels of 850, 700 and 500 hPa were calculated. The correlation between temperature and its anomalies was measured using elevation levels of 850, 700 and 500 hPa and the temperature anomaly maps and synoptic pattern were developed on a regional scale, and finally their relationship with temperature trends were analyzed and interpreted. Results and Discussion Iran had an average temperature of 18.06 °C during the 60 year period (1951 to 2010). 1999, with an average temperature of 20 C°, was the hottest year during this time. From 1993 onwards (except for 1997 and 2007), the average temperature was more than the 60-year average (18.06 C°). By comparing 30-year periods (from 1951 to 1980 and from 1981 to 2010) with each other, we observed that sea level temperature increase in the second 30-year period was more than the first period temperature increase at other atmospheric levels. This increase is most possibly due to the effects of global warming. Temperature increase in the first and second periods were 0.24 and 0.63 °C, respectively. Because of closeness to sea level and under the influence of surface conditions, 850 hPa level shows maximum temperature increase compared to other atmospheric levels (after sea level). Also due to the impact of sea level during the first and second periods, this factor is highly correlated with the sea level atmospheric condition. Despite the fact that correlation values of 850, 700 and 500 hPa levels were significant in both first and second periods at 1% level, they have increased in second period at all atmospheric levels. In other words, there is a clear increasing trend in the second period and few decreasing changes are observed. Regarding the patterns observed at sea level in the second period, two low-pressure closed cell trough which had been observed in the first period in India and Pakistan, merged in the second period. At 850 hPa, the subtropical high pressure located over Atlantic in the first period moved to East Africa in the second period and created a closed high pressure subtropical cell over Libya with an elevation of 1500 hPa. Compared to the first period, this high pressure cell has a higher altitude. At 700 hPa level, STHP ridge extended significantly in the second period, and in this period, central regions of Iran exhibit wide ranges of air sinking with a deep layer of warm air. Conclusion Over the 60 year-period, temperature of atmospheric levels in Iran have exhibited an increasing trend, which from 1993 onwards had a much steeper slope of increase. Compared to the first period (with almost normal periods of increasing and decreasing, and a slightly fluctuating rhythm), the second thirty-year period is expected to exhibit a constant and continuous increase. Additionally, warmer SLP at sea level and 850 hPa level, the northward expansion of the Hadley cell, and finally more intense subsidence of STHP toward lower atmospheric levels (above sea level and 850 hPa) exacerbate the effects of global warming on Iran atmosphere.
mohammad Rezaei; Elham Ghasemifar; Chenour Mohammadi
Abstract
Extended Abstract Introduction Vegetation plays an important role in the cycle of energy, carbon, hydrology and bio-geochemistry. The climate and vegetation have a mutual effect on each other. For example, the surface vegetation affects atmospheric patterns by affecting the surface albedo (which ...
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Extended Abstract Introduction Vegetation plays an important role in the cycle of energy, carbon, hydrology and bio-geochemistry. The climate and vegetation have a mutual effect on each other. For example, the surface vegetation affects atmospheric patterns by affecting the surface albedo (which determines the amount of radiation available for global warming, low atmosphere and evaporation as well). Therfore, the long-term study of the effect of the remot linking patterns on the varibility of vegetation is essential. So far, no study has been done on the effect of remote linking patterns on the varibility of vegetions.Therefore, the main objective of this study is to detect the vegetation changes in the month of May in Iran in relation to the remote linking patterns of the North Atlantic Oscillation. In this regard, remote linking patterns, such as El Nino have a significant effect on the surface climate with their periodic oscillations (Glantz, 1991). Many studies have been carried out in relation to the remote linking patterns and climatic elements on regional scale, but the role of remote linking patterns in the vegetation changes is a new topic which has been brought up lately (Wang et al., 2004). The normalized difference vegetation index (NDVI) obtained from the remote sensing satellite data is widely used to examine the vegetation features. Vicent Serrano et al. (2004) identified the positive and negative trends between NDVI and NAO in the Northern and Southern parts of Iberian Peninsula, respectively, by investigating the relation of NDVI, the North Atlantic Oscillation index (NAO) and the precipitation. Gouveia et al (2008) extracted the NAO correlation in the winter with vegetation activity in the spring and summer seasons by the combination of NDVI and luminosity temperature. Cook et al. (2004), Stockli and Vidale (2004), Sarkar and Kafatos (2004), Mennis, (2001), Erasmiet et al., (2009) also showed that there was a relationship between the remote linking patterns and vegetation in different parts of the world. Lu et al. (2012), showed that the vegetation impressibility in china in El Nino phase is greater than that of La Nino phase. Materials & Methods In order to investigate the relationship between the North Atlantic Oscillation and vegetation changes in the month of May in Iran, the normalized vegetation index products of MODIS sensor (MOD13A3) were used during the statistical period of 2001-2014. By applying the NDVI 0.2 threshold on the average long-term map of the vegetation index for the month of May in Iran, the area with larger and equal vegetation of the desired threshold was separated. Then, due to the severity and weakness of the NDVI values, the aforementioned area was divided into 3 areas based on the values of NDVI in order to assess the sensitivity of each area with regard to the remote linking patterns of the North Atlantic Oscillation which, helps identify the relationship between each vegetation category (namely, thinned, medium and dense vegetation) and the North Atlantic Oscillation index. Results & Discussion Due to the existence of vegetation-free deserts in Iran, an area susceptible to vegetation was first separated based on the threshold of at least 0.2 of the NDVI values. This region has about 38.2% of the country’s total area. Due to the high spatial variations in the NDVI values, the area was divided into 3 classes of thinned, medium and dense vegetation based on 0.2 to 0.5, 0.5 to 0.7 and higher than 0.7 ranges. It was assumed that the area with thinned and dense vegetation had the highest and lowest sensitivity respectively, with regard to the changes of the remote linking patterns. The positive and negative phases of the North Atlantic Oscillation (NAO) have significant effects on the climate of Iran. For example, the amount of vegetation, precipitation and humidity advection in many parts of the West, Northwest, and Northeast of Iran in the February 2010 (as a negative phase), were much higher than that in the February 2014 (as a positive phase). A 14-year time series was prepared from the NDVI values of the May for 18363 points in Iran and, each point was calculated with the variations in the values of the NAO index of January to May in a Pearson correlation coefficient matrix (assuming that the NAO changes in January influence the vegetation of May in Iran). The results showed that the positive and negative correlation values in terms of spatiality can be observed in all regions without a regular spatial pattern however, the maps showed that negative correlation values have covered a wider range of Iran in January and February. This indicates that, in the positive phase of the pattern, the higher values of sea level pressure in the Azore region, coinciding with the poor moisture transfer and precipitation systems, have caused less vegetation in a few months later (May) in Iran. Conclusion Given the highest coefficient of determination obtained in February(0.77) in East Azerbaijan province, the vegetation values of May can be estimated for the index points located in the Northwest and western provinces using the state of NAO in the months of winter.
Heydare Lotfi; Hosseyn Musazadeh
Abstract
Extended Abstract Introduction In order to analyze the reduction of the impacts of natural hazards, particularly the earthquakes, four basic constituents such as conceptual understanding of development, vulnerability, recognition of the concept of risk, conceptual understanding of capacity building, ...
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Extended Abstract Introduction In order to analyze the reduction of the impacts of natural hazards, particularly the earthquakes, four basic constituents such as conceptual understanding of development, vulnerability, recognition of the concept of risk, conceptual understanding of capacity building, and also, the recognition of risk management approaches are essential. Earthquakes and mass movements are a function of environmental factors, and identifying and segregation of seismic areas and hazard zonation is an important step in assessing environmental hazards. Therefore, Geographic Information Systems (GIS) and multi-criteria decision-making systems are the appropriate tools for zoning land in relation to seismic hazard. In this regard, the study of earthquakes and the areas with high seismic potential for all-round planning and management is imperative and inevitable. Therefore, the present research aims to study the vulnerable areas against natural hazards in Iran. Materials & Methods The present research is descriptive-analytical with regard to the nature of the problem and the study subject, and is a type of applied studies with an emphasis on quantitative methods. The purpose of the study is to investigate the vulnerable areas against natural hazards with an emphasis on earthquakes (Case study: Iran). In this research, the Modis Image (MOD11A1 product) of Terra satellite was used for the years 2000 to 2018. These images have a resolution of one kilometer. Therefore, each pixel of these images covers an area of about 100 hectares of land. To identify the sites with high seismic potential, criteria such as: altitude, earth temperature, the numbers of seismic events between the years of 2000 and 2018 were taken into consideration. In order to calculate the density and intensity of the earthquakes occurring, the data from the United States geological site (related to Iran) was used, and the final output was calculated by the interpolation methods of geo-statistical IDW model - a technique which predicts unknown points based on the correlation between the measured points and their spatial structure - and the arithmetic overlapping in the GIS environment. All processes and data analysis were used in the GIS environment and eventually the overlay of the final output was determined in the form of a map (vulnerability). Results & Discussion In order to measure the impact of effective factors on earthquakes, the analytical software mentioned in the research methodology section was used and also, to determine the indexes effective in determining low and high risk areas for identifying the seismicity and land evaluation for different types of activities and the amount of importance of each of these criteria relative to each other with regard to the present state and the collected information and the investigation and study of the books, previous plans and experts’ opinions have been implemented, which has ultimately entered into the GIS in the forms of information layers. In the next stage, the information layers are given weight proportional to the degree of importance and its effect on the selection of the appropriate field. In order to achieve these indices, a series of maps and databases were needed so these were prepared in the GIS environment. In this research, it is assumed that by analyzing long-term time series of satellite data, such modifications can be monitored. Therefore, in this research, the profile of temperature changes was analyzed using the MOD11A1 product of the Modis sensor during the years 2000 and 2018 in Iran. the results of the research show that the temperature variation pattern for each class is different, and in general, show the increase, stability, and then logical increment over the 18-year period, which can help researchers to identify temperature changes and consequently, to select the appropriate time period to take an image to investigate the changes in the coverage of the study area. Conclusion The study area, with regard to the mainly low topography, tectonic activity and high seismicity, diverse geological and climatic conditions, have the major natural conditions for the creation of a wide range of earthquakes, and these earthquakes bring a lot of financial losses to the region annually but unfortunately, all periodic studies have been carried out without accurate and efficient planning by the relevant authorities to date. Therefore, studying and zoning of susceptible seismic areas is necessary from an absolutely scientific view. Given the obtained finalized map and the study of temperature changes and occurring events, it can be concluded that parts of the south and southwest (Bushehr, Kermanshah, Hormozgan, Khuzestan, Ilam) are very vulnerable and exposed to severe damages. Also, the latitudes related to the central half and the southeast of the country (Kerman, Sistan and Baluchestan, southwest of southern Khorasan, east of Yazd) are exposed to moderate to high damages, and the northern part of the country (Golestan, Mazandaran, Gilan, Ardebil) are located in low damage zone because with regard to the thermal investigations within the framework of the seismic identification, they are located in the high to low thermal zones. Therefore. We find out that more than one third of Iran is faced with high, one third with moderate and one third with relatively low risk. One of the main causes of the earthquake is the high heat inside the Earth where is very hot and reaches to five to six thousand degrees Celsius. Wherever there is heat, there is movement as well, so the heat of the Earth’s center moves to the top layers and displace them.
Taghi Tavousi
Abstract
Extended Abstract
Introduction
Land degradation process that affects the arid, semi-arid and sub-humid zones of the globe has been interpreted as desertification that great many debates have grown up around the concept. A fundamental debate has been whether desertification actually exists? If ...
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Extended Abstract
Introduction
Land degradation process that affects the arid, semi-arid and sub-humid zones of the globe has been interpreted as desertification that great many debates have grown up around the concept. A fundamental debate has been whether desertification actually exists? If so, how it might be defined, measured and assessed (Herrmann and Hutchinson, 2005). In fact, the term "desertification" was used by Aubreville (1949) to describe the change of productive land into desert, which was the result of human activities in the tropical forest zone of Africa (Tavousi, 2010).However, the United Nations Conference on Desertification (UNCOD), held in Nairobi in 1977, launched the desertification issue into the global arena (Herrmann and Hutchinson, 2005). Desertification as defined in the United Nations Conference on Environment and Development (UNCED) and also in the United Nations Convection to Combat Desertification (UNCCD) is land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities (Cardy, 1993). Also, on the basis of this Convention, arid, semi-arid and sub-humid arid regions are regions in which the ratio of precipitation to potential evapotranspiration is in the range of 0.05 to 0.65 (Tavousi et al, 2010).
Determining the contribution of climatic variability to desertification is very complicated, and it is virtually impossible to separate the impacts of drought and desertification, because these processes often work together (Nicholson et al., 1998). Although now a more understanding of climatic variability has emerged, the understanding of the causes of this variability is still unfolding.
Two prevalent paradigms are expressed for climatic variability: One Internal feedback mechanisms such as Biophysical feedback mechanisms between land surface and precipitation due to modification of land cover characteristics in dry land regions and the other are External forcings, such as influence of the El-Nino Southern-Oscillation phenomenon and other major driving forces that promote changes in atmospheric circulations. Most probably, nor of these two prevalent paradigms (internal and external forcings) are mutually exclusive. Relative contributions of climate variability and human agency to desertification will likely depend on specific regional contexts (Herrmann and Hutchinson, 2005).
On the basis of UNEP index we observed that most areas of Iran have arid and semi-arid climates. With respect to the desertification intensity class, these two kinds of climates have classes of severe and very severe conditions. After those two kinds of climates, ultra arid, dry sub-humid, very humid and sub-humid climates cover most areas in Iran respectively (Alijani et al, 2015).
The purpose of this study was to investigate the trend of fluctuations in annual precipitation and the trend of UNEP aridity index of diverse climatic zones in the west and northwest of Iran.
Materials & Methods
In order to study the increase of aridity index in diverse climatic zones of the west and northwest of Iran, in the first step, the area was isolated by cutting 32 N latitude and 50 E longitude. Then, annual temperature average and total annual precipitation data was provided from 43 meteorological stations in the study area during the period of (1981-2010).
This period was divided into three decades: 1981-1990, 1991-2000 and 2001-2010. Then, for each decade, a zoning map was drawn.
In order to classify the climate, evaluate the Aridity Climatic Index and displacement of climatic zones in the northwest of Iran, the aridity index of UNEP (United Nation Environment Program) was used. Also, Kendall's nonparametric test was used to determine the significance of changes in annual precipitation.
Since the air temperature determines the potential evapotranspiration, the UNEP relationship is expressed based on the average total of annual precipitation relative to the average total of annual evapotranspiration.
Discussion and Results
In order to analyze the change in the Aridity Coefficient for each year, the UNEP index was calculated for 43 weather stations in the west and northwest of Iran. Based on the average UNEP index in each decade, the zoning map of the Aridity Index was drawn for three consecutive decades. Then, the UNEP Aridity index was subtracted in successive decades and the change occurred in the studied area was investigated. The spatial displacement of climatic zones over these three decades, represents the increase in the aridity coefficient and expansion of the territory of arid and semiarid climate in the area.
Conclusion
The results clearly indicate climate change from humid climate to semi-humid arid climate and semi-humid arid climate to arid climate. Based on Aridity Index of UNEP, in most parts of the northwest of Iran investigated in this study, the coefficient of Aridity has increased from the moderate risk class to severe and very severe Aridity. Although the results of Mann-Kendall test showed that 32 stations have a negative trend, this trend is significant for the 6 stations of Urmia, Tabriz, Khoy, Miandoab, Piranshahr and Sanandaj at = 0.05 .
abdolazim ghanghermeh; Gholamreza Roshan; smaeil shahkooeei
Abstract
Extended Abstract
Introduction
One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many ...
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Extended Abstract
Introduction
One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many applications in estimating required energy in cooling and heating section. It is notable that various studies around the world have used different temperatures to calculate HDD and CDD considering their climatic and geographical location. In Iran, 18 degrees centigrade is used for HDD and 24 degrees centigrade for CDD calculation, while climatic and geographical diversity of Iran causes new base temperatures to be recommended for HDD and CDD calculations. The present study plans to present a proper base temperature for calculating HDD and CDD with regard to specific characteristics of each city's climate.
Materials and Method
In the present study to determine the new threshold temperatures in order to provide the energy required for climatic comfort conditions, Olgyay diagram is used. Therefore, the average daily temperature and relative humidity data have been used to draw bioclimatic conditions. Since Iran has different climatic diversity, 10 stations that represent different climatic conditions of Iran were selected and analyzed (Figure 1). It should be mentioned that the duration of time series used includes the statistical period of 1950 to 2010 and these data was collected from Iran`s Meteorological Organization. Since hand drawing of each of the events on Olgyay diagram is cumbersome and time consuming considering the wide range of studied data, therefore, Olgyay diagram was digitalized to receive the output for each station quickly and easily. It is also noteworthy that in this study, Olgyay diagram is divided into 12 bioclimatic classes and the frequency of occurrence of each of the bioclimatic classes for each station in Table (1) has been reported. However, the most important section of this study is related to the determination of new base temperatures for calculating HDD and CDD indices of observational stations. Therefore, based on the days in the comfort zone, three regions in the form of percentile thresholds of 40 to 60 were selected as the representative of the central 20 percent of the data, percentile threshold of 25 to 75 percent as the representative of the dominant central 50% of the data, and finally percentile threshold of 10 to 90 as the central 80 % of the data were selected, and these domains were introduced as new thermal comfort for determining the base temperatures for HDD and CDD calculation (equation 1):
Equation 1:
In equation 1, LP is an equivalent for the threshold rank of the percentiles 10, 25, 40, 60, 75 and 90 percent, n is an equivalent for the number of samples and s is an equivalent for percentiles.
In the final step, after determining the base temperature, required cooling day-degree values (Equation 2) and heating (Equation 3) are calculated as follows:
Equation 2:
Equation 3:
In formula (2) and (3), cooling requirement is calculated by CDD and heating requirement is calculated by HDD for a given period of N days. In these formulae, T is the average daily temperature and è is the base temperature that with regard to the threshold of different percentiles, different numbers are proposed for each station.
Findings
Findings of this section showed that Shiraz and Esfahan have experienced the most ideal conditions of comfort with 35.22 and 33.22 percent of frequency of days in the comfort zone respectively and Babolsar with 83.2 percent of frequency has had the lowest percentage of days with thermal comfort. Among the observational stations, the most frequent occurrence experience of frost and freezing belongs to Sanandaj, and for the stations in Makoo, Shiraz, Tehran and Tabas, the most important preventive factor for the occurrence of comfort conditions is frost and freezing. But, Jask and Bushehr have had the most experience of the days with heat stroke risks and this factor is the most important preventive factor for comfort in these two stations. Although extreme dryness is the most important preventive factor for comfort in Ahvaz, but in Rasht and Babolsar, excess moisture is the most important factor of the lack of comfort. The results indicated that Olgyay diagram has perfectly shown the climatic and bioclimatic differences of various regions. For example, for the coastal cities of the Persian Gulf and Oman Sea, the type of data distribution on the diagram showed that climatic and bioclimatic characteristics of the two cities of Bushehr and Jask differ from Ahwaz, so that the dominant climatic regime of Bushehr and Jask due to the high humidity experience, are affected by the water zone of the Persian Gulf and Oman Sea, but Ahwaz is affected both by the water body of the Persian Gulf and hot and dry systems that pass directly through the Saudi Arabia.
Conclusion
Based on the main objective of this research, new thermal comfort thresholds for all study stations were proposed and the results showed that according to various percentiles, minimum base temperature for calculating HDD belonged to Babolsar station and maximum base temperature for calculating CDD belonged to Shiraz station. It is also worth noting that the sensitivity of the proposed method is such, that minimum differences in the domain and base temperature of thermal comfort are visible even for the stations located in a nearly similar geographical area, and this could indicate the validity of the proposed method. Finally, monthly and annual long-term average of HDD and CDD indices were calculated for the studied cities using proposed thresholds and base temperatures. The results of this section showed that in most observational stations, the months of January, December and February have had the maximum HDD requirements and the maximum CDD requirement was calculated for the months of July and August. The research findings reveal that maximum average annual HDD and CDD requirements belong to Makoo and Jask respectively. The results of this study point to the fact that the need for heating energy has been higher than the need for cooling energy for most of the studied cities. Therefore, the findings show that, based on the proposed method, which is derived from the climatic characteristics and experimental data of each station, a more logical thermal comfort thresholds for the studied stations are presented.
Mostafa Karampoor; Zahra ZareiCheghabaleki; Mansour Halimi; Mostafa Nouroozi Mirza
Abstract
Extended Abstract
Introduction
Global warming and climate change are terms for the observed century-scale rise in the average temperature of the Earth's climate system and its related effects. Multiple lines of scientific evidence show that the climate system is warming. Many ...
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Extended Abstract
Introduction
Global warming and climate change are terms for the observed century-scale rise in the average temperature of the Earth's climate system and its related effects. Multiple lines of scientific evidence show that the climate system is warming. Many of the observed changes since the 1950s are unprecedented over tens to thousands of years. In 2014, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report concluded that "It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. The largest human influence has been emission of greenhouse gases such as carbon dioxide, methane and nitrous oxide. Human activities have led to carbon dioxide concentrations above levels not seen in hundreds of thousands of years. Climate model projections summarized in the report indicated that during the 21st century, the global surface temperature is likely to rise a further 0.3 to 1.7 °C (0.5 to 3.1 °F) for the lowest emissions scenario and 2.6 to 4.8 °C (4.7 to 8.6 °F) in the highest emissions scenario. These findings have been recognized by the national science academies of the major industrialized nations and are not disputed by any scientific body of national or international standing.
Climate change is one of the main challenges that human being has faced since the 19th century. Anthropogenic changes in climate which leads to global warming and various side effects occurred and affected human life. The global warming leads to some significant changes in environmental, ecological and economic conditions. The spatiotemporal dynamics of vegetation colony and various biodiversity dynamics are also related to global warming. One of the main signal of global warming is the significant trends and changes in some climatic factors such as monthly, daily and annual temperature and rainfall. The spatial dynamics of climatic factors such as temperature and rainfall could also be related to global warming. In this study, we aimed to investigate the rainfall variations in different altitude ranges in Iran.
Precipitation varies from year to year and over decades, and changes in amount, intensity, frequency, and type (e.g. snow vs. rain) affect the environment and society. Steady moderate rains soak into the soil and benefit plants, while the same amounts of rainfall in a short period of time may cause local flooding and runoff, leaving soils much drier at the end of the day. Snow may remain on the ground for some months before it melts and runs off. Even with identical amounts, the climate can be very different if the frequency and intensity of precipitation differ, as illustrated, and in general the climate is changing from being more like that at Station (Stn) to that at Stn A. These examples highlight the fact that the characteristics of precipitation are just as vital as the amount, in terms of the effects on the soil moisture and stream flow. Hydrological extreme events are typically defined as floods and droughts. Floods are associated with extremes in rainfall (from tropical storms, thunderstorms, orographic rainfall, widespread extra-tropical cyclones, etc.), while droughts are associated with a lack of precipitation and often extremely high temperatures that contribute to drying. Floods are often fairly local and develop on short time scales, while droughts are extensive and develop over months or years. Both can be mitigated; floods by good drainage systems and drought by irrigation, for instance. Nonetheless, daily newspaper headlines of floods and droughts reflect the critical importance of the water cycle, in particular precipitation, in human affairs. World flood damage estimates are in the billions of U.S. dollars annually, with 1000s of lives lost; while drought costs are of similar magnitude and often lead to devastating wildfires and heat waves. The loss of life and property from extreme hydrological events has therefore caused society to focus on the causes and predictability of these events. Tropical cyclones typically have the highest property damage loss of any extreme event, and are therefore of great interest to state and local disaster preparedness organizations, as well as to the insurance industry.
Materials & Methods
The data of annual rainfall of 22 synoptic stations has been investigated during 1992 to 2012. First, we sorted these stations based on the altitude ranges into 4 classes, namely: Less than 500 meter, 500 to 1000 meters, 1000 to 1500 and more than 1500 meter above sea level. We used Man-Kendal’s nonparametric trend analysis test to detect any significant trend at 95 and 99 confidence levels (P value= 0.05 and 0.01, respectively).
Discussion and Results
The results indicated that the highest rainfall decrease was observed at the elevations below 500 meters, especially in March and in the annual scale. The highest precipitation at the elevations of 500 to 1000 meters was observed in the months of March, May and October, with the highest drop in rainfall at 1000 to 1500 meters in February and June. On the annual scale, all stations showed a negative trend in rainfall. Many stations, including Maragheh, Maku, Mahabad, Urmia and Birjand, showed a significant decrease in annual scale. The results of this study showed that elevations above 1000 meters have a higher relative stability in rainfall, while rainfall at stations below 500 meter elevations have a more time variability.
Conclusion
Based on the findings of this research, it can be concluded that the monthly and annual rainfall of stations located at elevations below 1000 meters have had greater and more significant changes than the rest of the stations. Thus, it can be said that the climate change has been more noticeable in the stations of this class.
Morteza Miri; Ghasem Azizi; Hossein Mohammadi; Mahdi Pourhashemi
Abstract
Extended Abstract
Introduction
The limited access to the atmospheric and terrestrial data such as rainfall, temperature, humidity and soil temperature is the most important problem in studying many climatological and hydrological in many parts of the world, particularly in developing countries, rural ...
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Extended Abstract
Introduction
The limited access to the atmospheric and terrestrial data such as rainfall, temperature, humidity and soil temperature is the most important problem in studying many climatological and hydrological in many parts of the world, particularly in developing countries, rural and mountainous areas. One of the solutions to overcome this obstacle is to use available gridded datasets that have proved their representativeness for many different parts of the world. Although the use of satellite data and gridded datasets is a reasonable alternative source for areas lacking station and data, since local effects can vary from region to region and can affect satellite and model performance, thus an dataset must be evaluated in a region before it is used as a decision-making tool in that region.
Materials and methods
The present study is aimed at the presentation of Global Land Data Assimilation System (GLDAS) and evaluates this model dataset against data measured by synoptic stations. The Global Land Data Assimilation System (GLDAS) has been developed jointly by scientists at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) in order to produce such fields. The goal of a land data assimilation system is to ingest satellite and ground-based observational data products, using advanced land surface modelling and data assimilation techniques. The uniqueness of GLDAS is that it is a global, high resolution, offline terrestrial modelling system incorporating ground and satellite observations. The temporal resolution for the GLDAS products is 3-hourly and Monthly with 0.25 and 1 degree spatial resolution its output is the result of four land surface models: the Community Land Model (CLM), NOAH, Mosaic, and the Variable Infiltration Capacity (VIC) model.The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces.
The representativeness and performance of GLDAS in estimate temperature amount at 66 Iranian synoptic stations distributed across the country is herein examined. To evaluate the performance of the considered dataset when compared to the observed temperature records at the considered locations we have used R squared, the Nash–Sutcliffe model efficiency coefficient (EF), RMSE, Bias, B slope of the regression and the standardized RMSE indicators. The performance of the dataset was also graphically represented through scatter plots of the established regression between GLDAS and observation at the selected stations.
Results and discussion
The results of the statistical indicators were represented through plotting the indicators over the map of Iran to ease displaying spatial tendency of the indicators and explaining the possible geographical role in controlling the spatial variation of the indicators. According to the results of the evaluation, the GLDAS data performs well in all of the studied stations with strong correlation coefficient. However, the Special physiographic and climatic characteristics is one of the main reasons for this overestimation in the coastal areas of the Caspian Sea. very likely due to not properly taking into account the complex topography of the region in its model parameterization or not being able to remove the effect of sea atmosphere in the stations nearby the seas. However, since the cloud of the estimated data for this region are distributed along the regression line, it can be said that the observed over-estimation could be resolved through establishing a statistical relationship between the observed and modeled datasets; thus such a mismatch might not be considered as a drawback of the modeled dataset. Considering that this model output is produced through combination of the modeled, observed and remotely sensed data, it could be confidentially used for mountainous areas and deserts of Iran that suffer from lack of weather stations or substantial missing values. This data-set might be considered as a superior dataset to be used for many climatological and hydrological subjects in Iran and thus should be seen as a promising tool for extending hydrological and climatological research areas in the country.
Conclusion
Statistical comparisons indicate that the GLDAS data perform well in all of the studied stations with strong Accuracy. Due to the Global coverage of the model dataset, A large number of climate-hydrological variables, and the results of this research that indicate the Good accuracy of the GLDAS model in Iran, It is suggested that all variables in the model to be evaluated.
sajad ferdowsi; Hamid Reza Shahmohammadi; Mahboobeh Jalali
Abstract
Extended Abstract
Introduction
In recent years, the economic benefits of tourism has attracted attention of many countries that have maritime border. In this regard, Caspian Sea which located in the northern part of Iran, has special potential in the field of tourism and it can be seen a bright future ...
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Extended Abstract
Introduction
In recent years, the economic benefits of tourism has attracted attention of many countries that have maritime border. In this regard, Caspian Sea which located in the northern part of Iran, has special potential in the field of tourism and it can be seen a bright future in the field of tourism just with a holistic approach in this field. Meanwhile, climate is the most important factor in tourism development. General Specifications of Destination Weather and daily, monthly and seasonal changes, temperature, precipitation, humidity, radiation, wind and other elements of the climate are important information for tourism destinations so that the applicant can plan in terms of travel time, type of clothing and equipment required. Tourism climatology includes a variety of topics about two applied issues of climate and tourism which is linked to the principles of atmospheric science, and in particular the study of climate on the one hand, and tourism, recreation and leisure on the other. In general, the present study seeks to answer the following question: What are the most appropriate timeframe for the development of tourism on the southern shores of the Caspian Sea?
Materials & Methods
In this research, the southern margin of the Caspian Sea, including three provinces of Golestan, Mazandaran and Gilan, as areas adjacent to the sea, have been studied and analyzed. Accordingly, 15 cities of Mazandaran province, 4 cities of Golestan province and 9 cities of Gilan province are located on the southern margin of the Caspian Sea that they are analyzed in terms of tourism climate index. The present study has been done on the method of descriptive- analytical and with aims of identifying the most desirable periods of climatic conditions for the development of tourism. The required data is obtained by method of library through documents, journals and books. In this regard, the required climatic parameters were collected from 2010 to 2014 in a 5-year period. In order to determination the appropriate timeframe for tourism development has been used from the method of TCI (Tourism Climate Indicator).
Results & Discussion
Based on the collected data, the TCI was calculated for each of cities along the Caspian Sea with a description of its descriptive class in different months of the year. The analysis of TCI indicate that respectively June and July are the most desirable time in terms of climate for the presence of tourists on the coast and the Caspian Sea. So that of the 28 neighboring cities of the Caspian Sea in June, there are 6 cities with very good climatic conditions, 6 cities with good climatic conditions, 12 cities with acceptable climatic conditions and 4 cities with low favorable climate conditions. Also In July, there are 6 points with very good conditions, 4 points with good conditions, 15 points with acceptable conditions and 3 points with low favorable climate conditions. In the meantime, in terms of the desirability of the climatic conditions, after June and July, the months of September, August, April, March, October, January, May, February, November and December are located.
Conclusion
In this research, the climatic conditions of the southern margin of the Caspian Sea, as one of the most important criteria for the development of tourism, were investigated. The results indicate that the southern margins of the Caspian Sea, each within a given time period, can provide favorable climatic conditions in order to attract tourists to these beaches. Somehow that in most of the months of the year you can see the favorable climate conditions for tourism in the area adjacent to the sea. The results of TCI indicate that over the years it can be seen areas with desirable climatic conditions in the Caspian Sea which it can be provided growth and development of tourism in this area with planning in appropriate time and place. Desirability of climatic conditions in the four months and coincided with the beginning of summer vacation, is a special opportunity to promote tourism and benefit from its significant economic advantages. In this regard the months of June, July, August and September is the most desirable periods for planning to presence of tourists and delivery of services to them. Also the months of April, March, October, January, May, February, November and December due to desirability climatic conditions are the next priority. Of course this does not mean that in these months, tourism remains silent, but the ratings is only Desirability of climate in different months. In fact, in almost all months of the year can be seen a favorable climate for the tourism in the area neighboring the sea.
Kamal Omidvar; Reza Ebrahimi; Ahmad Mazidi; Teymur Alizadeh
Abstract
Abstract[1]
Increasing demand for energy against the reduction of comprehensive energy resources along with the consequences of global warming, make the importance of a quantitative review of changes in the need for cooling, heating of the country in the past and in the future decades essential. First, ...
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Abstract[1]
Increasing demand for energy against the reduction of comprehensive energy resources along with the consequences of global warming, make the importance of a quantitative review of changes in the need for cooling, heating of the country in the past and in the future decades essential. First, the overall atmospheric circulation data was extracted from the EH5OM database. These data were under the A1B scenario of the International Climate Change Board and were downscaled with regional climate model data of average daily temperature of 0.27 x 0.27 degree, which covers approximately 30 x30 kilometer dimensions of Iran in the time interval of (2015-2050). The average daily temperature data of the past period were extracted from the ISFZARI databases during the statistical period of (1970-1970) on cells measuring 15 x 15 km. throughout the country. The temperature threshold of 11 degrees was used to calculate the heating degree day and the threshold of 18.3 to calculate the cooling degree day. The monthly average of these parameters was obtained on a matrix of 12 × 2140 (future) and 7187 * 12 (past), in which the rows represent the time (month of the year) and the columns represent the locations of the cells. Then the monthly average map of both periods was drawn and interpreted. The results indicate that the cooling of the air in the coming decades compared to the previous period in January and December in most parts of the country except for the coastal areas and the hinterlands, and the warming of the air in most parts of the country in the warm months of the year (June, July, August) will have significant effects on the amount of energy used for heating and cooling.
[1] - به دلیل کیفیت نامناسب ترجمه (چکیده مبسوط انگلیسیِ دریافتی) نشریه، به ناچار اقدام به ترجمه مجدد متن مختصر چکیده فارسی و انتشار آن به جای چکیده مبسوط انگلیسی نموده است.
Seyyed Keramat Hashemi-Ana; Mahmud Khosravi; Taghi Tavousi; Hamid Nazaripour
Abstract
Extended Abstract Introduction Precipitation is one of the vital climatic parameters that plays a major role in human life. Therefore, the impact of Precipitation in occurrence or non-occurrence of droughts and dry spells have been very effective. Identification and extraction length of dry spells ...
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Extended Abstract Introduction Precipitation is one of the vital climatic parameters that plays a major role in human life. Therefore, the impact of Precipitation in occurrence or non-occurrence of droughts and dry spells have been very effective. Identification and extraction length of dry spells in arid and semi-arid regions are very important. According to the most recent climate classification that has been done, about 90 percent of the areas of Iran are located in arid and semi-arid climate, and more than 40 percent are facing a severe water crisis. Therefore, understanding the behavioral mechanisms of dry spells have a great significance in arid and semi-arid areas like Iran, especially with the pose of the phenomenon of climate change that caused the worsening dryness and desertification in some of the regions. Many researches simulated dry spells with climate change approach and use of the output of AOGCM models. Researches in this category are in less numbers, but the most recent research has been done by the authors (hashmy titles et al., 2015), investigating and modeling the length of dry spells in the Southwestern area of Iran. The aim of this research is to examine the Validation of AOGCMs Capabilities for Simulation Length of Dry Spells under the Climate Change and Uncertainty in Iran Materials & Methods According to the aim of this research, we used two databases in this study. The first database involves collecting and analyzing all data base information (minimum temperature, maximum temperature, rainfall and sunshine) on a daily scale in 234 synoptic stations (with different statistical period). But the format for the data station and point during the period of statistical modeling was needed for more than 30 years, which has a large statistical defects were excluded, and finally 45 synoptic stations that have favorable conditions (the maximum area coverage and continuous and reliabledata) were selected for the final processing of the first data base. The period of 1981-2010 was used as the base period.The second database contains data provided by version 5 models (LARS-WG) and on emission scenarios (B1, A1B, A2) from AOGCM models for the 2050s to be downscaled. In fact, this data is the first data base (minimum temperature, maximum, precipitation and sunshine) prepared based on the format models for analysis and predicting climate change, after downscaling it. Because this research was based on study and extraction length of dry spells in the range of long-term with the approach to climate change, so the methodology is based on several stages. At first, verification (validation) of LARS-WG, to ensure efficiency in the process model simulation will be discussed. Then the performance and capabilities of 15 AOGCM models in the new version of Lars-wg will be assessed. At the end, the precipitation threshold is defined and extraction of the longest length of dry spells and comparing it with the maximum length of the dry spells will be simulated. Results & Discussion After calibrating the model of statistical properties (comparison tests T, F and P values (decision criteria), all stations were used to confirm the validity of the model. The results of this calibration indicate that in more than 96% of the stations, for the minimum and maximum temperature and sunshine model, show high accuracy (results of error in Dezful and Gorgan stations were greater). In all of these stations like Abadan station, variables significant (P-value) were at./05. It is acceptable that the data generated is random.Considering the bias error, at more than 95 percent of stations there were very good agreements between the observed and modeling data (for every 4 variables). Based on the principles of (1 to 3), and using statistical methods and indicators, the AOGCM models to simulate and extract during dry spells were examined and it was found that two models (Hadcm3 and GFDL-CM2.1) had maximum performance (correlation) and the lowest error in estimating for simulation data precipitation. The model (INM-CM3 and NCPCM) have least amount of correlation and efficiency. To estimate the maximum length of dry spells Hadcm3 results were used under scenario (A2 and B1) for the decade 2050 and the use of the results of other models was skipped in this research. Maximum dry spells in Iran comply with dryness condition in central and eastern areas. So that the country could be on the threshold of ./1 mm divided into 6 orbital regions of the northern circuit during the period of 37 days (in Rasht station) minimum and 351-day observation period in Southeastern Chabahar stations. The values show that the threshold of ./1 mm at more than 65 percent of the area’s dry spells over 7 months there was no rain on them yet. With a threshold of 5 mm needs maximum length of dry spells that lasted about a year with 364 days in Yazd station. That is roughly the size of 5 mm precipitation a year not registered at this station. Conclusion Modeling dry spells by computing scenarios of climate change and taking into consideration uncertain resources at the AOGCM models output, showed that based on the worst-case scenario (A2), and the most critical situation (2080), the average temperature of the country has increased 2.7 degrees (ºC) and Despite increased precipitation in some Stations, the average rainfall is facing a 33% reduction in the whole country. According to the most optimistic scenario (B1), the average temperature of the country is increasing by 1.4 (ºC) and the precipitation is decreasing by 14% in relation to the observation period. The results of the uncertainty examination for dry spells in Iran showed that in both 2050s and 2080s and based on all three scenarios (B1, A1B, A2), length of dry spells increases in all areas of Iran. Most of the changes in length of dry spells belong to the northwestern areas of Iran (Urmia, Khoy, Kermanshah, Hamedan and Lorestan).
Ali Ahmadabadi; Amanollah Fathnia; Saeed Rajaei
Abstract
Abstract[1]
Vegetation cover has a high relationship with climatic conditions. Identification of the seasonal variation of plant growth to determine the response of ecosystems to climate change in seasonal and inter-annual time scales is decisive.To present a prediction model, 7 climatic elements including ...
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Abstract[1]
Vegetation cover has a high relationship with climatic conditions. Identification of the seasonal variation of plant growth to determine the response of ecosystems to climate change in seasonal and inter-annual time scales is decisive.To present a prediction model, 7 climatic elements including precipitation, temperature and relative humidity (maximum, average and minimum) for a 20 year period (1987-2006) were converted into spatial data in 141 synoptic and climatological stations. The combination of maximum monthly NDVI values from NOAA-AVHRR images was extracted in the same period. Then climatic elements and NDVI entered the multivariate linear regression as independent variable and dependent variable respectively. The results showed that the highest correlation coefficient between climatic elements and the amount of NDVI was 0.82 and happens in May that is the peak of greenery. The least correlation in winter is due to the lack of sufficient tree growth. Taking into account the random error, the annual correlation coefficient of the model amount with computational mode is more than 93/0. In total, the computational value of May and June for 2004 and 2005 is close to the correlation coefficient of the model, but in the winter months, the correlation coefficient decreases due to lack of greenness.In 2006, there was less prediction due to more severe dryness in the late spring (June). In winter, the role of temperature control is more than rainfall and relative humidity, but with increasing temperature and decreasing precipitation and relative humidity, the role of precipitation and relative humidity becomes positive and temperature becomes negative from the beginning of May. In the autumn, the role of precipitation decreases and the temperature is increased.
[1] - به دلیل کیفیت نامناسب متن چکیده مبسوط انگلیسیِ ارائه شده توسط نویسنده مسئول مقاله، نشریه به ناچار اقدام به ترجمه مجدد متن چکیده فارسی و انتشار آن به جای چکیده مبسوط انگلیسی نموده است.
Reza Aghataher; Mahdi Samadi; Ilia Laliniat; Iman Najafi
Abstract
Abstract
Digital Elevation Models (DEM) enable researchers to perform geographical researches on a global and regional scale such as global changes, natural disasters, environmental hazards, environmental monitoring, etc. Therefore, DEM data plays a key role in scientific researches. SRTM and ASTER ...
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Abstract
Digital Elevation Models (DEM) enable researchers to perform geographical researches on a global and regional scale such as global changes, natural disasters, environmental hazards, environmental monitoring, etc. Therefore, DEM data plays a key role in scientific researches. SRTM and ASTER GDEM are two elevation datasets that cover nearly the entire land surface of the earth and are globally available (for almost 80% of the earth). Thus, it is necessary to evaluate the vertical accuracy of such data prior to their use and to select the appropriate data considering the research target. ASTER-based digital elevation model has spatial resolution of 30 meters, which seems to provide more precise elevation data than SRTM with 90 meters spatial resolution. Several studies have been performed for evaluating the accuracy of each of these two datasets in various countries of the world. The results of such studies indicate their advantages and limitations over each other. In this study, the vertical accuracy of these two DEMs are evaluated by ground control point in three zones of Iran with different topographic characteristics which are East Azerbaijan, Sistan and Baluchestan and Bushehr. Results show that the RMSE of SRTM as the index of error for the study area in East Azerbaijan, Sistan and Baluchestan and Bushehr are 6.1, 7.4 and 2.9 meters and in ASTER GDEM are 8.7, 8.3 and 7.2 meters respectively. Therefore, the vertical accuracy of STRM is higher than that of ASTER GDEM in all three zones. In this research, the relation between vertical error and land characteristics including slope and direction of slope has been studied and the results have been presented. The final findings of the research indicate higher vertical accuracy for SRTM compared to ASTER GDEM in Iran and it is concluded that SRTM is a more appropriate choice for various applications.
