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Volume 5, Issue 4 (3-2019)
Volume 5, Issue 4 (3-2019)
Abstract
Extended Abstract
Cold and frost is one of the most important climatic parameters in the agricultural climate, and the damage caused by them reduces the possibility of producing many agricultural and horticultural products in vulnerable areas. Cold and frost is one of the climatic hazards that annually causes damage to various activities. The agricultural sector is the most important part of the damage that is most seriously damaged by frost. Cold and frosty weather for many crops and gardens results in harmful and destructive consequences, in some years billions of rials damage farmers, farmers and, ultimately, the national interests of the country. Considering that the northwest region of Iran suffers a lot of financial losses each year due to atmospheric hazards especially cold and frost. Identification and zoning of areas with high potential of cold and frost hazard and prediction of their occurrence can provide valuable and valuable information for preventing and mitigating damages. In this study using HadCM3 global model under two scenarios A2 and B1 and The LARS-WG microscope model is dealt with this.
It is important to check the time of occurrence and predict their future changes. For this purpose, general atmospheric circulation (GCM) models are designed that can simulate future climate parameters. In this study, the output data of the HadCM3 general circulation model under two scenarios of A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in northwest of Iran. The results of this study were based on the base period (1980-1989) and The 2020 decade (2030-2011) was evaluated for two climate variables: minimum temperature and maximum temperature. Then the history of the first and last frost and cold of autumn and spring was extracted and their date of occurrence was calculated in the future.
The monthly average of the minimum temperature of the stations studied in the course of the 2020s and the base period shows that the temperature has been increased according to both scenarios and increased in all months and at most study stations compared to the base period. The maximum changes in the minimum temperature in the study area are based on the average scenarios in this decade related to Abhar, Ardebil, Khoy and Urumieh stations at 0.8 degrees Celsius; In fact, the minimum temperatures that occurred at these stations during the base period have not been observed in the next period and the heating process has shown that its rate in the region of the study area in the 2020s is between 0.4 and 0.8 It will be in the base period. The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 degrees Celsius. The results of the first and last glacial survey in the decade of 2020 indicate that the first glacial precipitate of autumn occurs between 2 and 9 days later, with the least change in the history of frost occurring in two stations of Qazvin and Meshkinshahr each with 2 The change day is relative to the base period. The last frost of late spring also will be 3-10 days earlier on the surface of the region. However, the duration of the ice free period will be reduced at all stations, which is the highest decrease for Khoy station with 16 days, then the stations of Urmia and Ardebil each Two with 14 days and the lowest decrease is due to Meshkinshahr station for 6 days. Based on the results of changes in the date of early ice ages, changes are less than the late frost. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. The least changes were observed in the south-east of the study area, Meshkinshahr and Sarab regions, and the most changes in the glacial period were related to Khoy, Urmia, Tabriz and Ahar areas. According to the results of most studied areas, averaging between 10 and 12 days decrease in length The ice age will experience the base course.
The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 C. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. Also, the length of the cold and freezing period is decreasing, which may reflect the consequences of climate change at study stations. The results of this study are based on the studies of Grasick and Dodwilich (2015) in Poland, Medella et al. (2016) in Texas, Hosseini and Ahmadi (1395) in Saqez, Aqa Shariatmadari et al. (1395) in West Iran, Sobhani et al. (1396). ) In Ardebil and Khalili et al. (1396) in Iran.
Dr Manouchehr Farajzadeh, Miss Zahra Kazemnezhad, Dr Reza Borna,
Volume 5, Issue 4 (3-2019)
Volume 5, Issue 4 (3-2019)
Abstract
Abstract
Climate change in one area has severe impacts on water resources and, consequently, agriculture in that area. Therefore, studying the extent of the vulnerability of regions to adopting policies to reduce or adapt to new conditions is of particular importance. One of the methods for assessing the extent of damage to agricultural activities is the calculation of the vulnerability index. In this study, with the aim of assessing agricultural vulnerability to climate change, The CVI index was calculated for 16 cities in Guilan province.
The results showed that the cities of Rasht (61.58) and Talesh (55.21) had the highest vulnerability and, accordingly, had the least adaptive power to climate change compared to other cities. And Langrood County (29.51) has the lowest number of vulnerabilities. The average value of the calculated index is 40.42 in Guilan province. In component R, the most vulnerable were Talesh (99.66) and lowest for Lahijan (2.27), In component M, the highest vulnerability was for Rudbar (97.21) and the lowest for Talesh (24.30), In component A, the most vulnerable were Rasht (89.99) and the lowest for Anzali (2.21), In component C, the most vulnerable were Shaft (66.66) and lowest for Anzali (1.89), In component U, the most vulnerable were Rasht (67.55) and the lowest for Astara (28.92), In component E, the highest vulnerability was for Talesh (76.49) and lowest for Lahijan (22.69), In component G, the most vulnerable was reported to Rasht (53.05) and the lowest vulnerability was reported for Sunnelk (23.24).
Mostafa Yaghoobzadeh, Abbas Khashei, Yousof Ramezani, Seyyedeh Atefeh Hosseini,
Volume 6, Issue 4 (2-2020)
Volume 6, Issue 4 (2-2020)
Abstract
Evaluation the best of selective base period of GCM models to determine meteorological variables of Birjand station in future periods
Abstract:
Nowadays, determining the effect of a climate change in the various aspects of human life is quite evident. In such a situation, it is very important to determine the base period, which determines the effects of a climate change than in this period. Choosing a course-based course plays an important role in choosing future courses to conduct research on the effects of climate change. Many researchers in the research use the LARS-WG dynamic downscale method or the statistical method to measure the weather variables, which should be the same for the years of the base period and the upcoming period.
This research was conducted to select the appropriate base course for estimating minimum temperature, maximum temperature and precipitation at the synoptic station in Birjand. The station is located at latitude 32 degrees and 53 degrees east and 59 degrees and 17 degrees north latitude. In order to evaluate and accuracy of the methods in this research, seven criteria for estimating root mean square error (RMSE) and mean absolute error (MAE), relative error (RD), mean relative error of the month of the year (MRDM), average relative error of the month in the year (RDMM), PBIAS and RSR. In this study, using GCM models, we assessed the selected base courses for the synoptic station in Birjand. To doing in the research, an amount of 27 base courses from 35 models of the fifth report of the change were compared with similar periods obtained from the station in Birjand.
The results showed about precipitation that the duration of the base periods such as 1960-2005 and 1960-2000 is less of the RMSE and MAE errors than the rest of the courses, and the base period of 1965-1990 between periods less than 30 years and the period The 1990-1960s are also well suited to the precipitation data of the synoptic station. The maximum temperature of the 1960-1990, 1960-1985 and 1960-1995 is the lowest RMSE error. However, short-term courses of 1980-1960 and 1965-1985 present satisfactory results.In the case of minimum temperatures, periods of 21 and 31 years 1960-1980, 1960-1985, 1960-1990 and 1965- 1985 have a percentage error of RMSE and a lower percentage of PBIAS. Variable variation range can also be used to show the appropriate base course. The result showed that the periods 1960-2005 and 1970-2005 had a lower range of rainfall variation than the other variables and seems to be more suitable. However, courses such as 1990-2000, 1975-1995, and 1995-2005 have less certainty. The more courses that go into periods with shorter periods of time, the more modest and less certainty they will be. Also, if you look at changes in the 1975-2005 periods and the 1965-1995 periods, it will be clear how much each year towards the years closest to 2005 will be deducted from the precipitation daily average.
The results also show that maximum temperature changes are better than precipitation, and all courses have less variation range. Nevertheless, the period of 1960-2005 has the highest degree of certainty and the period of 1975-2005 has the least degree of certainty compared to the rest of the courses. In contrast to precipitation, there are periods such as 1970-1990, which, if considered as the basis for research, provide more certainty than the longer period of 1965-2005 for maximum temperature. Also, what's most clear about the maximum temperature is the higher the period with years closer to 2005, the temperature increases, which will increase the temperature over time.
The process of minimum temperature variations also indicates that in addition these changes are similar to the change in temperature, with the difference that the range of variations in the minimum temperature is somewhat higher than the maximum temperature. The period of 1960-2005 has the best degree of certainty and the period from 1975-2005 has the least degree of certainty than the rest of the courses. Although long periods of time are less certain than short periods, the result is that the longer the interval between periods increases, the more precise the results will be. The result is not entirely correct, 1975-2000 is less certainty than the 1965-2000 period and has better results in minimum temperatures. Therefore, the evaluation of selected periods of GCM models with similar periods from observations of Birjand station shows that for rainfall variables, periods with a number of years yield more satisfactory results, but for two variables the minimum temperature and maximum temperature of the periods, not long or short periods, provide less risk of RMSE and PBIAS than long periods.
Keywords: climate change, GCM model, base period, meteorological variable, emotion scenario
Hassan Zohrevandi, Ali Mohamad Khorshid Dost, Behroz Sari Saraf,
Volume 7, Issue 1 (5-2020)
Volume 7, Issue 1 (5-2020)
Abstract
Prediction of Climate Change in Western of Iran using Downscaling of HadCM3 Model under Different Scenarios
Hassan Zohrehvandi 1, Ali Mohammad Khorshiddoust 2, Behrouz Sari Sarraf 3
1- Ph.D student of Climatology, University of Tabriz, Email: H.zohrehvandi@gmail.com
Mobile number:+989181502513
2 - Associate Professor of Climatology, University of Tabriz, Email:
Mobile number:
3- Associate Professor of Climatology, University of Tabriz, Email:
Mobile number:
Abstract
Considering that water resources are at risk from climate change, the study of temperature and precipitation changes in the coming years can lead to droughts such as droughts, sudden floods, high evaporation and environmental degradation. To this end, global climate models (GCMs) are designed to assess climate change. The outputs of these models have low spatial accuracy. In order to increase the spatial accuracy of this data, downscaling methods are used which are divided into statistical and dynamic methods. One of the reasons for using these models is their quick and easy operation compared to other methods. Our study area consists of Kurdistan, Kermanshah and Hamedan provinces in the west of the country. In this study, observational data of minimum temperature, maximum temperature, precipitation and radiation of 6 synoptic stations in the studied area in the statistical period of 1961 until 2005. In this study, the LARS-WG model was used for downscaling of HadCM3 global model data. The LARS-WG model is one of the most popular weather generator models that which to generation for maximum and minimum temperature, rainfall and radiation are used daily under current and future climate conditions. This model as a downscaled version of the same process less complex and simulated data input and output, high ability to predict climate change. The HadCM3 model is also a type of atmospheric- oceanic circulation model developed at the Hadley Center for Climate Prediction and Research, which has a 2.5 degree latitude network at 3.75 degrees longitude. Also, three climate change scenarios A1B, A2 and B1 have been used, each of which reflects the characteristics of the world's economic growth, the world's population and social awareness. The methodology is that the model receives the monitored data of the basic course; by examining them the statistical characteristics of the data are extracted. Then, in order to validate and ensure the model's capability for the basic statistical period, the model is implemented to re-establish a series of artificial data in the base period. Then the outputs to evaluate the performance of the model in the reconstruction of the data, the statistical characteristics of observations to test and compare various criteria. MAE, MSE, RMSE and R2 criteria were used to evaluate and analyze the performance of the downscaling model. The results showed that the accuracy of the model varies in different stations and parameters, so that the model in simulation of temperature and radiation is more suitable than rainfall simulation. Also, the model has more successful in simulation of maximum temperature in comparison with minimum temperature. In sum, the results of different evaluation criteria indicate that the LARS-WG model has a good accuracy for the downscaling of the parameters studied in the study area. After evaluating the LARS-WG model and ensuring its appropriateness, the data was generated by the model for three climate change scenarios using the HadCM3 model. The results of the monthly review of the parameters studied at the station indicate that precipitation in the 2050s at all stations except Saralpul Zahab and Sanandaj stations according to the three scenarios studied in most months except December, January And at some stations, sometimes in November and February, they were lower than the base period, and rainfall is expected to decrease over the 20 years period (2046-2065), but the situation for Sanandaj and Saralpul Zahab stations is somewhat different, which, according to some scenarios, has increased in most months of the year, and according to some scenarios, rainfall has decreased in some months and it seems that the precipitation pattern is shifted The end of the warm season. But the rainfall situation is completely different in the 2080s, and rainfall has decreased in all stations and in most months of the year. The average monthly of the minimum and maximum temperatures as well as the amount of radiation shows that all three parameters will increase in all months of the year based on all three scenarios, as well as in the two decades studied (2080 and 2050) And its rate would increase in the decade than in the previous decade. According to the results, the amount of precipitation decreases in study area and the temperature and radiation will increase as well. The rate of precipitation decrease in the following periods will be 7.7% in the region than in the base period, and the minimum and maximum temperatures in the long-term was increase at the region 3.4 and 3.4 degrees Celsius, respectively, compared to the average period of the base. The radiation increase was 0.38 mJ /m2 in Area level. The results of this research can help to solve the challenges of water resource managers and planners in future periods.
Keywords: Climate Change, downscaling, west of Iran, General Circulation model, LARS-WG
Nima Sohrabnia, Dr Bohlol Alijani, Dr Mehry Akbari,
Volume 7, Issue 2 (8-2020)
Volume 7, Issue 2 (8-2020)
Abstract
Modeling the discharge of rivers in selected watersheds of Guilan province during climate change
Abstract
In this essay, we investigated the effects of climate change on the rivers of selected basins of Guilan province, one of the northern provinces of Iran for the period 2020 to 2050 under three climate scenarios: RCP2.6, RCP4.5, RCP8.5. For this purpose, rainfall and temperature data from 45 climate data stations and 20 hydrometric stations from 1983 to 2013 were used. The average precipitation and temperature at basin level were calculated by drawing both Isohyet and Isothermal lines by usage Kriging method. Mann-Kendall and Sen’s slope estimator tests were used to determine the significance of the data trends and their slope, respectively. The results showed that temperature has increased in all catchments during the study period and this trend was significant in most of them but no significant trend was observed for precipitation. Discharge has also decreased in most basins and this trend was significant in Shafarood, Navrood and Chafrood basins. However, for future periods, precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario. Rivers discharge in the RCP2.6 scenario is not significant in any of the basins, but in the RCP4.5 scenario the Shafarood and Ghasht-Roodkan catchments have a significant reduction in the 95% confidence level. In the RCP8.5 scenario, the Chafrood and Shafarood basins have a 99% confidence reduction trend.
Population and technology growth, increased water consumption and climate change have led many researchers to study and model water resources in the present and future periods. Especially in areas like Iran that are facing a lot of water stresses. The purpose of the present study, which was carried out in the Guilan province, is to provide information on the present and future status of surface water resources, and to prepare them for facing the problems of potential water resources exploitation.
In this study 45 synoptic, evaporative and rain gauge stations and 20 hydrometric stations data with sufficient statistics were used. The period of study is also between 1983 and 2013. In this regard, after calculating the average precipitation and temperature values of each basin using Kriging model, first, the annual average of precipitation and temperature values of each basin were calculated. Then, multivariate regression was used to obtain the regression equations between precipitation, temperature and discharge data, then by using SDSM model and climate scenarios (RCP2.6, RCP4.5, RCP8.5) future temperature and precipitation data were generated. By placing these generated data in the Created regression equations, the discharge of the rivers was calculated for the period 2020 to 2050. The trend of time series and their slope were analyzed respectively by Mann-Kendall and Sense tests.
The study of the annual average precipitation trend of the selected catchments during the study period showed that all the basins had no significant trend at any of the confidence levels (95% and 99%). However, for the temperature there is an increasing trend. In Chafrood, Zilaki, Chalvand, Lavandevil, Tutkabon, Chubar, Lamir, Hawigh, Dissam, Shirabad, Ponel, Samoosh, and Polrood basins there is significant trend at 95% confidence level. For the Hawigh River basin there is significant trend at 99% confidence level. Also in most of the basins there is a downward trend of rivers discharge. In addition, in the three basins of Chafrood, Navrood and Shafarood, there is a significant decreasing trend at 95% confidence level, which is also significant at 99% confidence level for Navrood and Shafarood rivers.
Analysis of future data showed that precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario in RCP2.6 scenario. For rivers discharge there was no significant trend in any of the basins, but in RCP4.5 scenario there is a significant decrease in 95% confidence level in Shafarood and Ghasht-Roodkan. Also in the RCP8.5 scenario, a significant decreasing trend of flow discharge at 99% confidence level is observed for Chafrood and Shafarood basins. Finally, the catchments were grouped according to the level of risk involved with decreasing discharge. The results of grouping showed that most of the basins in the three scenarios were in the medium risk group but Shafarood, Chafrood and Ghasht-roodkhan watersheds have higher risk than the other watersheds, respectively.
Investigation of river discharge trends for the period 2020 to 2050 in different scenarios showed that the basins of Ghasht-roodkhan, Chafrood and Shafarood are more sensitive to climate change than other basins. Overall, escalating temperature trends in future and precipitation irregularities can create very difficult conditions in future to use these resources. Especially, this study's concordance with other studies in Iran and the study area confirms that such crises are more likely to occur..
Keywords: Climate Change Scenarios, Rivers Discharge, Man-Kendall, Sen’s Slope estimator, Guilan Province
Abstract
In this essay, we investigated the effects of climate change on the rivers of selected basins of Guilan province, one of the northern provinces of Iran for the period 2020 to 2050 under three climate scenarios: RCP2.6, RCP4.5, RCP8.5. For this purpose, rainfall and temperature data from 45 climate data stations and 20 hydrometric stations from 1983 to 2013 were used. The average precipitation and temperature at basin level were calculated by drawing both Isohyet and Isothermal lines by usage Kriging method. Mann-Kendall and Sen’s slope estimator tests were used to determine the significance of the data trends and their slope, respectively. The results showed that temperature has increased in all catchments during the study period and this trend was significant in most of them but no significant trend was observed for precipitation. Discharge has also decreased in most basins and this trend was significant in Shafarood, Navrood and Chafrood basins. However, for future periods, precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario. Rivers discharge in the RCP2.6 scenario is not significant in any of the basins, but in the RCP4.5 scenario the Shafarood and Ghasht-Roodkan catchments have a significant reduction in the 95% confidence level. In the RCP8.5 scenario, the Chafrood and Shafarood basins have a 99% confidence reduction trend.
Population and technology growth, increased water consumption and climate change have led many researchers to study and model water resources in the present and future periods. Especially in areas like Iran that are facing a lot of water stresses. The purpose of the present study, which was carried out in the Guilan province, is to provide information on the present and future status of surface water resources, and to prepare them for facing the problems of potential water resources exploitation.
In this study 45 synoptic, evaporative and rain gauge stations and 20 hydrometric stations data with sufficient statistics were used. The period of study is also between 1983 and 2013. In this regard, after calculating the average precipitation and temperature values of each basin using Kriging model, first, the annual average of precipitation and temperature values of each basin were calculated. Then, multivariate regression was used to obtain the regression equations between precipitation, temperature and discharge data, then by using SDSM model and climate scenarios (RCP2.6, RCP4.5, RCP8.5) future temperature and precipitation data were generated. By placing these generated data in the Created regression equations, the discharge of the rivers was calculated for the period 2020 to 2050. The trend of time series and their slope were analyzed respectively by Mann-Kendall and Sense tests.
The study of the annual average precipitation trend of the selected catchments during the study period showed that all the basins had no significant trend at any of the confidence levels (95% and 99%). However, for the temperature there is an increasing trend. In Chafrood, Zilaki, Chalvand, Lavandevil, Tutkabon, Chubar, Lamir, Hawigh, Dissam, Shirabad, Ponel, Samoosh, and Polrood basins there is significant trend at 95% confidence level. For the Hawigh River basin there is significant trend at 99% confidence level. Also in most of the basins there is a downward trend of rivers discharge. In addition, in the three basins of Chafrood, Navrood and Shafarood, there is a significant decreasing trend at 95% confidence level, which is also significant at 99% confidence level for Navrood and Shafarood rivers.
Analysis of future data showed that precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario in RCP2.6 scenario. For rivers discharge there was no significant trend in any of the basins, but in RCP4.5 scenario there is a significant decrease in 95% confidence level in Shafarood and Ghasht-Roodkan. Also in the RCP8.5 scenario, a significant decreasing trend of flow discharge at 99% confidence level is observed for Chafrood and Shafarood basins. Finally, the catchments were grouped according to the level of risk involved with decreasing discharge. The results of grouping showed that most of the basins in the three scenarios were in the medium risk group but Shafarood, Chafrood and Ghasht-roodkhan watersheds have higher risk than the other watersheds, respectively.
Investigation of river discharge trends for the period 2020 to 2050 in different scenarios showed that the basins of Ghasht-roodkhan, Chafrood and Shafarood are more sensitive to climate change than other basins. Overall, escalating temperature trends in future and precipitation irregularities can create very difficult conditions in future to use these resources. Especially, this study's concordance with other studies in Iran and the study area confirms that such crises are more likely to occur..
Keywords: Climate Change Scenarios, Rivers Discharge, Man-Kendall, Sen’s Slope estimator, Guilan Province
Valiollah Sheikhy, Hossein Malakooti, Sarmad Ghader,
Volume 7, Issue 4 (2-2021)
Volume 7, Issue 4 (2-2021)
Abstract
Abstract
Increasing population growth and consequently the development of urban areas can profoundly affect climate events and thus intensify phenomena such as heat stress. Given the expected effects of this phenomenon on human health, it is very important to provide mitigating operational solutions to control future conditions. Therefore, the present study was conducted with the aim of simulating the effect of urban planning solutions on dynamic processes in the urban environment and at the local scale in Tehran city using the WRF mid-scale numerical model. Simulations were performed using 4 nested domains with a two-way interactive nesting procedure. The study used a simple Single-Layer Urban Canopy Model and a more advanced multi-layered approach called Multi‐layer urban canopy (BEP). The results of the simulations, after comparing the two urban schemes with a sensitivity measurement for different strategies, showed that the surface reflectance change scenario has the greatest impact on the land surface compared to the two scenarios of increasing urban green areas and reducing building density. Due to Tehran's specific topographic location and high overall temperature in this region, Tehran is relatively vulnerable to heat stress. Compared to the intensity of 5.5 °C for base mode, applying control measures can reduce the intensity of UHI up to 3 °C when using bright colors with high reflectivity for the ceiling and 1 ° C by replacing impermeable surfaces with natural vegetation in urban areas of Tehran.
Increasing population growth and consequently the development of urban areas can profoundly affect climate events and thus intensify phenomena such as heat stress. Given the expected effects of this phenomenon on human health, it is very important to provide mitigating operational solutions to control future conditions. Therefore, the present study was conducted with the aim of simulating the effect of urban planning solutions on dynamic processes in the urban environment and at the local scale in Tehran city using the WRF mid-scale numerical model. Simulations were performed using 4 nested domains with a two-way interactive nesting procedure. The study used a simple Single-Layer Urban Canopy Model and a more advanced multi-layered approach called Multi‐layer urban canopy (BEP). The results of the simulations, after comparing the two urban schemes with a sensitivity measurement for different strategies, showed that the surface reflectance change scenario has the greatest impact on the land surface compared to the two scenarios of increasing urban green areas and reducing building density. Due to Tehran's specific topographic location and high overall temperature in this region, Tehran is relatively vulnerable to heat stress. Compared to the intensity of 5.5 °C for base mode, applying control measures can reduce the intensity of UHI up to 3 °C when using bright colors with high reflectivity for the ceiling and 1 ° C by replacing impermeable surfaces with natural vegetation in urban areas of Tehran.
Mr. Erfan Naseri, Mr. Alireza Massah Bavani, Mr. Tofigh Sadi,
Volume 8, Issue 1 (5-2021)
Volume 8, Issue 1 (5-2021)
Abstract
Detection and Attribution of Changing in Seasonal variability cause of climate change (Case study: Hillsides of Central Southern Alborz Mountains)
Abstract
One of the most important challenges for the human communities is Global Warming. This vital problem affected by Climate Change and corresponding effects. Thus this article attempted to assess the trend of real climate variables from synoptic stations. Daily precipitation, Daily Maximum Temperature and Daily Minimum Temperature have been selected for the Hillsides of Southern Central Alborz Mountains and have been tried to prove climate change and attribute the related forcing such as Greenhouse Gases. The Capital of Iran located in this region and this region has a special occasion, because at least a quarter of Iranian population live in these provinces (Tehran and Alborz) and four big dams located in this region. The Intergovernmental Panel on Climate Change’s defines ‘‘detection’’ of climate change as ‘‘the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change,’’ while ‘‘attribution’’ is defined as the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. Regional D&A studies provide an insight to local changes in natural systems and may help in planning and developing robust adaptation strategies. Previously, formal detection and attribution have been used to investigate the nature of changes in various climatological variables such as air temperature, surface specific humidity, ocean heat, sea level pressure, continental river runoff, global land precipitation and precipitation extremes. However, almost all of these studies deal with climatological or meteorological variables at the global or continental scale. Studies which have attempted to formally detect and attribute regional hydrometeorological changes to anthropogenic effects are rare. Regional-scale D&A analysis is more difficult because the detection of anthropogenic ‘‘signal’’ in natural internal climate variability ‘‘noise’’ is determined by the signal-to-noise ratio which is proportional to the spatial scale of analysis, especially for real observation data. For overcoming this issue interpolation method (IDW) has been applied to transfer point data to area (gridded) data. The point data gathered from 3 synoptic stations (Mehrabad, Karaj and Abali). Then transferred data have been Standard and Averaged for 3 years. Standard values of annual and seasonal amounts have been computed for individual stations as the average of the standard values of annual and seasonal amounts available 3 years anomaly values. Estimates of annual or seasonal variables anomalies were obtained by averaging the annual or seasonal by 12 or 3 respectively. For detecting and attributing 3 simulation signals (ALL, GHG and NAT) selected from Canadian General Circulation Model (CanESM2.0) of CMIP5 archive subcategories. Space–time series of observations and model simulated variables responses to external forcings (the “signals”) first have been compared qualitatively by computing correlation coefficients between observations and simulations. This simple method does not optimize the signal-to-noise ratio nor provide a quantitative measure of the magnitude of model simulated response relative to that in the observations. Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Optimal detection and attribution analysis very often requires a reduction of dimensionality. This is typically done by projecting both observations and simulations onto leading empirical orthogonal functions (EOFs) of internal variability and using the residual consistency check to determine the number of EOFs to be retained in the analysis. To produce internal variability for residual test and consistency, Pi-Ctrl Runs have been used. The Preindustrial simulations have high volume, this subject complicates calculation therefore Experimental Orthogonal Functions (EOFs) have been used to reduce the Pi-Ctrl simulations volume and provide situations for Optimal Fingerprint. Optimal Fingerprint method is the best method for Detection and Attribution. Results have been obtained by this manner indicated Global Warming affected the study region by affecting on mean cumulative winter precipitation (0.88), mean spring minimum temperature (0.78) and mean summer maximum temperature (0.76). These numbers are the beta coefficient that named scaling factor. Although the scaling factor for the mean spring minimum temperature affected from GHG signal obtained (0.73), but the GHG forcing alone didn’t have a significant effect on the precipitation and maximum temperature. Also, NAT signal didn’t have significant effect on the region alone, too. The obtained results of this study indicate the earlier studies, such as Wan et al, 2014.
Key words: Climate change, Detection, Attribution, Optimal Fingerprint, Hillsides of Central Southern Alborz Mountains
Mr Mohammad Hossein Aalinejad, Pro Saeed Jahanbakhsh Asl, Pro Ali Mohammad Khorshiddoust,
Volume 8, Issue 3 (12-2021)
Volume 8, Issue 3 (12-2021)
Abstract
Investigation of Temperature and Precipitation Changes in the Seymarreh Basin by Using CMIP5 Series Climate Models
Abstract
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
Abstract
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
- : Climate Change, Climate Scenarios, Uncertainty, LARS-WG, Seymareh.
Mr Alireza Sadeghinia, Mrs Somayeh Rafati, Mr Mehdi Sedaghat,
Volume 8, Issue 4 (3-2022)
Volume 8, Issue 4 (3-2022)
Abstract
Introduction
Climate change is the greatest price society is paying for decades of environmental neglect. The impact of global warming is most visible in the rising threat of climate-related natural disasters. Globally, meteorological disasters more than doubled, from an average of forty-five events a year to almost 120 events a year (Vinod, 2017). Climate change refers to changes in the distributional properties of climate characteristics like temperature and precipitation that persist across decades (Field et al., 2014). Because precipitation is related to temperature, scientists often focus on changes in global temperature as an indicator of climate change. Valipour et al. (2021) reported the mean of monthly the global mean surface temperature (GMST) anomalies in 2000–2019 is 0.54 C higher than that in 1961–1990. Many studies have been done on climate change in Iran. These studies have mostly studied the mean and extreme temperature trends (Alijani et al., 2011; Masoudian and Darand, 2012). In general, the results of previous studies showed that the statistics of mean, maximum and minimum air temperature in most parts of the Iranian plateau have increased in recent decades. Also, the increase of minimum temperature is greater than maximum temperature.
A review of the research background shows that we need to understand more about regional climate change in Iran. Therefore, present study performs the climate change of 14 extreme temperature indices using multivariate statistical methods at the regional scale.
Data and methodology
Historical climate observations including daily maximum and minimum temperature were obtained from the Iranian Meteorology Organization for the period 1968 to 2017 at 39 stations. In this paper, 14 extreme temperature indices defined by ETCCDI were analyzed. The indices are as follows: (1) Annual maxima of daily maximum temperature (TXx); (2) Annual maxima of daily minimum temperature (TNx); (3) Annual minima of daily maximum temperature (TXn); (4) Annual minima of daily minimum temperature (TNn); (5) Cold nights (TN10p); (6) Cold days (TX10p); (7) Warm night (TN90p); (8) Warm day (TX90p); (9) Frost days (FD); (10) Icing days (ID); (11) Summer day (SU); (12) Tropical nights (TR); (13) The warm spell duration index (WSDI) and (14) the cold spell duration index (CSDI). The extreme temperature indices were extracted using R software environment, RclimDex extension. The Mann–Kendall Test and Sen’s Slope Method was employed to assess the trends in 14 extreme temperature indices. To identify homogeneous groups of stations with similar annual thermal regimes, Principal Component analysis (PCA) and Clustering (CL) was applied. Pearson correlation coefficient was used to investigate the relationship between height and trend slope.
Result
All the extreme temperature intensity indices (TXx, TNx, TXn, and TNn) showed increasing trends during 1968 to 2017. The increasing trends of TXx, TNx, TXn, and TNn were 0.2, 0.3, 0.44, and 0.5 ° C per decade, respectively. These results indicated that the extreme warm events increased and the extreme cold events decreased. The average of the extreme temperature frequency indices over Iran showed that the frequency of warm night (TN90p) and warm day (TX90p) significantly increased with a rate of 6.9 and 4.2 day per decade, respectively. Also, the frequency of cold night (TN10p) and cold day (TX10p) significantly fell with a decrease rate of 3.8 and 3.8 day per decade, respectively. The frequency of warm nights (TN90p) was higher than that of warm days (TX90p). The result indicated that the trend of nighttime extremes were stronger than those for daytime extremes. The average of frost days (FD) and icing days (ID) indices over Iran showed decreasing trends during 1968 to 2017 with rates of 3 and 1.1 d per decade, respectively. While, the averaged of summer days (SU) and tropical days (TR) indices over Iran showed increasing trends with rates of 4.4 and 6.4 day per decade, respectively. The warm spell duration index (WSDI) indices showed a clear increase, with a rate of 2.1 per decade. In contrast the cold spell duration index (CSDI) showed a significant decrease, with a rate of 1.7 per decade. In general, the cold indices displayed decreasing trends, whereas the warm indices displayed increasing trends over most of Iran. Pearson correlation coefficient between height and Sen’s Slope was estimated to be equal to -0.62 (p < 0.01). In general, the results of this study showed that there is a negative correlation between the elevation factor and the Sen’s Slope of warm extreme indices. That is, as the altitude decreases, the Sen’s Slope increases. Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Approximately 38% of the studied stations were located in cluster 1. Cluster 2 showed a moderate heating trends. 33% of the stations were located in cluster 2. Most of the stations of cluster 2 are located in the northwest and west of Iran. Cluster 3 showed a weak increasing trends compared to clusters 1 and 2. The stations of cluster 3 did not show a special geographical concentration and were scattered in all parts of Iran. 18% of the studied stations are located in cluster 3. The stations of Cluster 4, have experienced weak decreasing trends, which was different from the other three clusters
Conclusion
In this study we analyzed the climate change of extreme temperature indices in Iran. The result showed that the frequency of warm nights, warm days, summer days and tropical days increased. Also, the frequency of cold nights, cold days, Frost days and icing days decreased. The warm spell duration index showed a clear increase. In contrast the cold spell duration index showed a significant decrease. In general, the extreme warm events increased and the extreme cold events decreased over most of Iran. There is a negative correlation between the elevation factor and the Sen’s Slope of extreme warm indices (R = -0.62). Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Therefore, the most heating have occurred in Low-lying areas of Iran. Cluster 2 and Cluster 3 showed a moderate and weak heating trends, respectively. The stations of Cluster 4, have not experienced clear trends.
Key words: climate change; Extreme temperature; clustering; Iran
Mrs Fatemeh Falahati, Dr Bohlol Alijani, Dr Mohammad Saligheh,
Volume 8, Issue 4 (1-2021)
Volume 8, Issue 4 (1-2021)
Abstract
In many areas, snow cover in the mountains is a major source of surface and underground water supply. Due to climate change and its effect on the time of melting ,it is very important for environmental planning to predict the arrival time of water from snow melt to water consumption cycle. The purpose of this study is to investigate the volumetric changes and time distribution of snow flood flows in future by integrating remote sensing , GIS and climatic models.The studied area is the Upper Basin of Amir Kabir Dam, which is located on the southern slopes of Alborz Mountains. In this study, digital elevation maps (DEM) and GIS software were used to estimate parameters such as area, environment, main length, highest and lowest elevation points. In order to complete the snow cover data, MODIS products (MOD10A100) were extracted and the snow cover was extracted in the Upper Basin of Amir Kabir Dam. Next, runoff and snow melting models were simulated using SRM software. Calibration and validation of the model's acceptable performance were estimated. Then, in order to investigate the effects of climate change on the future of snowmelt runoff production in the basin of Amir Kabir Dam, the latest CMIP5 climatic models were used under four scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5. A survey on the relationship between snow cover area , temperature and precipitation was used to predict snow cover in the future. The increase in temperature in the autumn and winter season has led to a reduction in the shape of precipitation in the form of snow, and as a result, the amount of snow storm is reduced. The results show that the amount of runoff in the autumn and winter increases due to increased rainfall in the form of rain, and it will be increased late winter and spring due to the increase in the amount of water resulting from snow melting. The results of this study are based on the increase of snow melt as a result of increased runoff volume, reduction of snow reserves and maximum flow transmission to earlier than normal conditions due to early snow melting due to temperature rise. Generally, in the future, the average annual runoff will be decreased about 1.1 cubic meters per second, and the average annual melting share will be about 13.9%
Ali Mohammad Khorshid Doust, Ali Panahi, Farahnaz Khorramabadi, Hossein Imanipour,
Volume 9, Issue 2 (9-2022)
Volume 9, Issue 2 (9-2022)
Abstract
Introduction
Climate or climate reflects the daily weather conditions in a particular place for a long time. Most climatic elements are closely related to ecological factors, which is why the analysis of the relationship between climate and plant distribution patterns has been discussed in scientific and research circles for many years. And in recent years, scientists have been using a combination of climatic characteristics with other environmental factors to describe vegetation around the world. Climate change and atmosphere condition will change the content and composition of many plant communities.
The Study Area
The geographic coordinates of the studied area are between latitudes 29°32’ to 33°59’ and 51°27’ to 55°5’. The position of the selected provinces of central Iran compared to the neighboring provinces are shown in Figure 1 The annual data of 8 stations have been analyzed during the stations period determined by the National Meteorological Organization. The stations characteristics including latitude, longitude, elevation and specific statistical period are shown in Table 3.
Data and research methods
In this study, the role of temperature changes and relative humidity on vegetation in Central Iran has been investigated using statistical models of analysis of the main components and hierarchical clustering. This research is applied and its method is slightly analytical. In order to investigate the climatic fluctuations of the center of Iran with respect to urban green space, statistical data related to average temperature and relative humidity during the 32-year period (1986 to 2018) selected central stations of Iran to come and statistical deficiencies such as Data loss was performed by reconstructing differential equations using SPSS software. The criterion for selecting stations is the availability of long-term statistics. Using statistical methods and Geographic Information System (GIS), vegetation classification was performed for Central Iran. ArcGIS, Minitab, SPSS and EXCEL software are used in this research. After identifying the stations, climatic variables including temperature and relative humidity were selected from the data of 8 meteorological stations and were analyzed using the techniques mentioned above. Then, using statistical regression analysis, the impact (topography, average temperature and average relative humidity) on how to distribute and distribute vegetation was investigated. Kendall-man non parametric test was used to investigate changes in the vegetation index trend.
Results and discussion
Analysis of temporal changes in climatic parameters and NDVI index
The results show that the distribution of relative humidity in Abadeh and Kerman stations has decreased by 3% and the temperature distribution in these stations has increased by more than one percent. Relative humidity changes in Kashan and Sirjan stations have a weak decreasing trend, while the relative humidity distribution in Isfahan station has decreased by about 2%.The temperature distribution of Shiraz and Yazd stations increased by 3%, Abadeh station increased by 2% and also Isfahan and Kerman stations increased by 1%. The distribution of vegetation in Yazd and Khor Biyabank stations has decreased by one percent, while the growth of vegetation in Isfahan, Abadeh and Sirjan stations is increasing by less than one percent.
Distribution of NDVI vegetation index in Central Iran using cluster analysis
The stations are located in three distinct areas in terms of distribution of vegetation, each group having the same climatic characteristics in the distribution of similar vegetation. Based on this, three climatic zones in the study area can be identified.
Conclusion
The aim of this study was to investigate the effect of climatic parameters (average temperature and relative humidity) on the distribution of vegetation in Central Iran using comparison of statistical models; by examining the distribution and density of vegetation, eight factors were identified. Among the factors, the first and second factors, with 81.57% of the total vegetation variance, have played the most important role in determining the climatic diversity of Central Iran. In total, these eight factors have justified about 100% of the vegetation behavior in the area Also, according to the analysis of images of Modis satellite measuring satellites from the vegetation situation in the last 5 years, Central Iran, the value of NDVI index in Central Iran varies between 0.2 to 0.64, the northwestern parts of Fars province have the highest vegetation density and The central parts of Isfahan, especially Yazd, lack vegetation. Based on the results, altitude has a direct and significant relationship with temperature distribution in plants, especially in the study area. However, the height of Iran's central regions has affected the distribution of vegetation.
Keywords: climatic parameters, vegetation distribution, central Iran
Dr. Homayoun Motiee, Mrs. Saba Ahrari,
Volume 9, Issue 2 (9-2022)
Volume 9, Issue 2 (9-2022)
Abstract
Glaciers are one of the most important water resources in the world, which are heavily affected by global warming and climate change. This paper investigates the effects of global warming on the changes in the snow cover level of the Takht Suleiman region located in Mazandaran province during the warm months of the year through the past three decades using remote sensing. For this purpose, the images from June to August of the Landsat-5 and 8 satellites in the period of 1990 to 2021, as well as the data of the air temperature product of the ERA5 sensor were processed on the Google Earth Engine. In this research, NDSI index (Normalized Snow Cover Surface Index) was used to detect snow covered surfaces and the Mann-Kendall test was used to evaluate the trend of the data. The results of the overall accuracy and Kappa coefficient in the Google Earth Engine system show an overall accuracy of 94% and a Kappa coefficient of 89% in 2021, which shows the high compatibility of this method with real data.
The results obtained during the investigated period show an increase of about 1.5 degrees in temperature during the last three decades at a significant level of 95%. The snow and ice cover of the Takht Suleiman region in June month decreased from 127 square kilometers( in 1990) with a decrease of 82% to 22 square kilometers( in 2021). The trend of changes in the level of snow cover in June was analyzed with the Mann-Kendall test, which shows a decreasing trend at a significance level between 80 and 90%. In general, these results indicate an increase in temperature and a decrease in the level of this glacier during the statistical period studied, and the continuation of the gradual depletion of the glaciers of this region in the future is a serious threat to the downstream water source and the surrounding environment.
Mr Loghman Khodakarami, Dr Saeid Pourmanafi, Dr Alireza Soffianian, Dr Ali Lotfi,
Volume 9, Issue 2 (9-2022)
Volume 9, Issue 2 (9-2022)
Abstract
Space-based quantification of anthropogenic CO2 emissions in an urban area using “bottom-up” method
(Case study: Isfahan Metropolitan)
Abstract
Increasing consumption of fossil fuels in urban areas emits enormous amounts of greenhouse gases into the atmosphere. Therefore, the study of carbon dioxide (CO2) emissions from urban areas has become an important research topic. The main purpose of this study is space-based quantification of carbon dioxide emissions driving from fossil fuel combustion in different source sectors in Isfahan. To achieve it, in the present study, the "bottom-up" method was used to quantify the carbon dioxide gas emission based on its production sources sectors. In this method, the amount of emission was measured distinctly for different sources of energy consumption and consequently the spatial distribution map the CO2 emission was generated. The results of this study revealed that the total amount of carbon dioxide emissions driving from fossil fuels is 13855525 tons per year in Isfahan. Separately stationary sectors of power plant, housing and commercial and mobile sources including road and railroad and existing agricultural machinery were responsible for emitting 50.61, 21.78, 17.18, 4.92, 4.37, and 1.14% of CO2, respectively. In conclusion, through applying the bottom-up method and CO2 emission distribution mapping based on different source sectors, mitigation measures can be applied more efficiently in urban planning.
Key words: Greenhouse gas (GHG), Fossil fuel combustion, Mobile and stationary source of energy consumption, climate change, Mitigation strategies
Mr Sayyed Mahmoud Hosseini Seddigh, Mr Masoud Jalali, Mr Hossein Asakereh,
Volume 9, Issue 3 (12-2022)
Volume 9, Issue 3 (12-2022)
Abstract
The expansion of the pole toward the tropical belt is thought to be due to climate change caused by human activities, in particular the increase in greenhouse gases and land use change. The variability of the tropical belt width to higher latitudes indicates the expansion of the subtropical arid region, which indicates an increase in the frequency of drought in each hemisphere. In order to change the width of the tropical belt of the Northern Hemisphere in the middle offerings, indices of precipitation minus evaporation, wind vector orbital component, stream function, tropopause surface temperature, OLR, and SLP have been used. Findings showed that the expansion of tropical belt latitude with stream function to higher latitudes with 1° to 3° latitude and the effect of Hadley circulation subsidence has increased the amplitude of evaporation minus precipitation has shown that the fraction of precipitation minus evaporation 1° to 3° latitude geographically increased. The subtropical jet has increased the movement of the upper branches of troposphere from the Hadley circulation by 2° to 4° latitude, which can have a negative effect on transient humidification systems as well as on the amount of precipitation. The extension of the pole towards the tropical belt, which is a consequence of climate change and hazards, will lead to the displacement of the pole towards the tropical side of the river, thus providing dry tropical belts to the pole; Also, the long-wave radiation of the earth's output has increased by 1° to 2° latitude and has caused an increase in heat in the upper troposphere, which has increased the dryness and slightly reduced the clouds in the upper troposphere and also caused the tropical belt to expand to higher latitudes. Has been. In general, the research findings showed that most tropical belt indicators have been increasing since 1979.
Seyyed Mohammad Khademi Nosh Abadi, Dr Maryam Omidi Najaf Abadi, Dr Seyyed Mehdi Mirdamadi,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
Industrial and agricultural activities in the world have led to an increase in the concentration of greenhouse gases such as carbon dioxide, methane and nitrogen oxide and have caused the earth's climate to become warmer. This phenomenon has caused climate change and has changed the thermal and rainfall patterns. Climate change in Iran in recent years has caused a decrease in rainfall and an increase in temperature and continuous droughts. Agricultural production in Iran has been affected by climate change and has faced a decrease in the production of crops such as wheat. Therefore, according to the government's policy of self-sufficiency in wheat production and the establishment of sustainable food security in the country, it is necessary to use climate smart agricultural technologies to sustainably increase agricultural productivity, Adapting and resilience of agriculture to climate change and reduction greenhouse gases emission from agriculture. The purpose of this study was to design a behavioral model for the use of climate smart agricultural technologies with an emphasis on motivation. The research method was quantitative, in terms of practical purpose, and research data was collected through a cross-sectional survey.The conceptual model was designed using the theory of planned behavior and the theory of norm activation. Bayesian structural equation modeling was used to test the model and hypotheses. The statistical population of this research was 800 wheat farmers of Nazarabad city, Alborz province. The sample size was calculated using Cochran formula 260 people, and stratified random sampling method with proportional assignment was determined as the sampling method. A researcher-made questionnaire was used to collect research data. The validity of the questionnaire was confirmed through agricultural extension and education experts, and its reliability was also confirmed through the pre-test and calculation of Cronbach's alpha coefficient. The findings of the research show that subjective norms, personal norms and perceived behavioral control related to the use of climate smart agricultural technologies have a significant effect on the intention to use these technologies. While the attitude towards the use of climate smart agricultural technologies do not have a significant effect on the intention to use these technologies. The variable of intention to use climate smart agricultural technologies also has a significant effect on the behavior of using these technologies.
Dr Seyed Keramat Hashemi Ana,
Volume 10, Issue 1 (5-2023)
Volume 10, Issue 1 (5-2023)
Abstract
Abstract
Introduction and issue: In today's century when the effects of climate change on different sectors are undeniable, investigating and analyzing the behavior during dry spells is always of special importance and basic priority. On the other hand, the occurrence of extreme events such as precipitation can accelerate the occurrence of climate change. In Iran, rainfall is one of the basic variables for evaluating the potential availability of water resources, but its temporal and spatial distribution is very uneven. The change of dry Spells depending on precipitation always have different fluctuations in different seasons of the year. It seems that this is due to the inherent behavior of precipitation, which generally shows itself as an unstable and unruly variable. This feature causes changes and differences in the temporal and spatial distribution of precipitation in arid and semi-arid regions such as Iran. This inconsistency will face fundamental challenges to regularize dry spells on a seasonal and monthly scale. With a detailed understanding of the behavioral mechanism of dry spells, it is possible to know more precisely the climatic condition of different regions in order to plan in sectors such as; Water resources, agriculture, health, transportation and etc we able to do basic and preventive measures compatible with climate change. It is hoped that this research and related studies will be a positive step towards a more accurate understanding of the climate and its behavior in different seasons of the year.
Data and method: In order to investigate the seasonal behavior of the duration of dry spells, we used daily precipitation data for 44 synoptic stations of Iran and a 30-year statistical period (1988-2018). To reveal the behavior of dry spells, the precipitation data after validation and temporal integration were classified on a seasonal scale.
After the statistical integration of the data, dry spells related to precepitation were extracted and long-term periods lasting more than 20 days were the basis of the study. In the next step, to determine the seasonal weight of courses was used, the step-by-step evaluation method of Swara's fuzzy-numerical logic (SWARA). Thus, in the first step, the longest and most frequent periods are sorted based on relative importance. In the second step, the initial weights of the courses are determined, and in the third and fourth steps, the final and normalized weights of the courses in different seasons are determined, and unrealistic results are removed from the final analysis for proper explanation.
Findings and Results: The effectiveness and weight of each of the criteria with the Swara method in the fuzzy environment showed that in the western and northern regions of the country, winter and spring seasons and criteria such as reversibility and percentage of probability of occurrence have the most initial weight in explaining the periods. In the final explanation, these two season,s had a high weight. These two seasons explain more than 65% of the weight of courses in these regions. In the southern regions and parts of the center (Isfahan, East Fars and West Kerman), winter and autumn explain more than 71% of the weight of periods. Among the criteria explaining the weight of the courses, the reversibility criterion and the probability of occurrence have taken more than 55% of the weight. The northern and humid regions of the country vary in criteria from periods such as; Reversibility, continuity and probability of occurrence are more apparent and this indicates that the border of dry areas in the future of Iran's climate will move towards northern areas. It can be acknowledged that the behavior of long-term dry periods is more a function of two criteria of reversibility and probability of their occurrence. The weighting of the criteria affecting dry periods showed that the return period and the continuation of periods in the cold seasons of the year in dry areas have a more irregular behavior than in wet areas and have more weight in explaining the periods. By determining the weight of seasons in explaining dry periods, we can have better planning and management in related sectors such as water and agriculture.
Key words: dry spells, weighing, precipitation, climate, Swara method, Iran.
Mrs Halimeh Shahzaei, Dr Mohsen Hamidianpour, Dr Mahsa Farzaneh,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Spatial analysis of Iran's climate change from the point of view of sensible heat flux and latent heat flux by Bowen method
Halimeh Shahzaei; Ms.c student of Climatology, Departement of Physical Geography, University of Sistan and Baluchistan, Zahedan, Iran.
Mohsen Hamidianpour[1]; Associate Professor, Departement of Physical Geography, University of Sistan and Baluchistan, Zahedan, Iran.
Mahsa Farzaneh; Ph.D Graduated. Climatology.
Abstract
Sensible heat flux and latent heat flux are among the variables that are closely related to temperature and humidity and show heat transfer on a surface. So, their changes can be considered related to changes in temperature and humidity. In this regard, the current research aims to analyze and reveal the climatic changes of Iran by examining the course of changes in sensible heat flux and latent heat and the ratio between the two. For this purpose, NCEP/NCAR reanalysis data including sensible and latent heat flux during the period 1948-2020 was used in Iran. Bowen coefficient was calculated from the ratio of these two heat fluxes. Interpolation methods were used for their spatio-temporal analysis. In addition, by using the non-parametric methods of Mann-Kendall and Shibsen, spatial and temporal changes were also investigated. The first part of the results showed that, spatially, the Bowen coefficient is a function of latitude and roughness. And in terms of time, the lowest value corresponds to the month of January and the highest value corresponds to the month of July. The results of the second part show that the Bowen coefficient has a positive trend over time. Its upward trend indicates an increase in the dryness coefficient of the country. So that this situation can be seen in the positive trend and increase in temperature.
Keywords: climate change, Bowen coefficient, global warming, spatio-temporal analysis.
[1]. Autehr corespound:Email: mhamidianpour@gep.usb.ac.ir
Seddigheh Farhood, Asadollah Khoorani, Abbas Eftekharian,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
Dr Sara Kiani, Dr Morad Kavyani, Dr Amirali Tavasoli,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
The Namak Lake is situated between three provinces: Isfahan, Qom, and Semnan. However, the functioning of Namak Lake and its susceptibility to environmental, ecological, economic, and social influences not only affect the immediate surroundings but also impact other provinces. Naturally, a crisis in this lake can have negative effects on human communities and the residents of the surrounding areas in terms of environmental, economic, and social aspects. Therefore, the aim of this research is to identify the temporal-spatial changes in the salinity of Namak Lake and, subsequently, to investigate and analyze the effects of these changes on the environmental security of the surrounding regions. To achieve this goal, salt zones were identified using soil salinity indices, including the Normalized Difference Salinity Index (NDSI), Salinity Index 1 (SI1), Salinity Index 2 (SI2), and Brightness Index (BI), over a 30-year period (1992-2021) with five-year intervals. Then, using the maximum likelihood method, the salt zones were classified into four land cover types, including water zone, moist zone, salt zone, and other uses. The results of this study indicate that due to the reduction in water inflow into the lake as a result of dam construction in the upstream basin and the effects of climate change, the water zone, or seasonal lake, of Namak Lake has disappeared and the salt zone has expanded in this area. The most significant changes in the lake are related to the northwestern part of the lake, where major rivers such as Jajrood, Shur, Qarechai, and Qamaroud flow into this part of the lake, contributing to its drainage. Therefore, dam construction on these rivers has led to a downward trend in water flow into the lake. Furthermore, the results suggest that due to the absence of settlements and human communities near Namak Lake and the natural and climatic conditions of the region, it is not expected that environmental incidents that could have security and political implications will occur in the short term.
Sahar Afiati, Bohloul Alijani, Sayyed Mohammad Hosseini,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Cold and frost are one of the climatic hazards that cause damage to various activities every year. Climate change, on the other hand, causes spatial and temporal changes in glaciation. The purpose of this study is to analyze the temporal-spatial changes and predict the future of glaciers in Hamadan province. CanESM2 model was used to predict the minimum daily temperature in the province. Data mining of general circulation models was Downscaling using LARS-WG model. The above parameters were simulated for a period of 30 years (2050-2021) under three scenarios RCP2.6, RCP4.5 and RCP8.5 for selected stations. The results of the monthly minimum temperature survey in the study stations of the province showed that the minimum temperature in the period (2050-2021) in all studied stations according to all three scenarios will increase in all months of the year compared to the base period. The average minimum temperature of the province is equal to 2.5 degrees Celsius, which in the coming decades based on the scenarios of RCP2.6, RCP4.5 and RCP8.5 will reach 6, 6.2 and 6.3 degrees Celsius, respectively, which is the highest The changes are related to Nojeh station and the lowest is related to Hamedan. The spatial distribution of the beginning and end of freezing in the future period indicates that freezing in the northeastern and northern parts of the province starts earlier and ends later than in other parts of the province, while in the southern parts of the province it starts later and ends earlier. The results of examining the changes in the onset of frost in the next decade compared to the base period showed that in all stations studied the onset of frost will decrease between 3 to 11 days.
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