Yousef Zarei; Ali Mohammad Khorshiddoust; Majid Rezaeebanafshe; Hashem Rostamzadeh
Abstract
Climate change is one of the main problems on Earth today, so predicting these changes in the future and their impacts on water resources, the natural environment, agriculture, and environmental, economic and social impacts is of particular importance. Therefore, in the present study, the effects of ...
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Climate change is one of the main problems on Earth today, so predicting these changes in the future and their impacts on water resources, the natural environment, agriculture, and environmental, economic and social impacts is of particular importance. Therefore, in the present study, the effects of global climate change on different climatic regions of the country were studied in 12 climatic regions. In this study, NCEP data and climatic elements of precipitation, maximum and minimum temperature were used for statistical downscaling with SDSM model. And using the CanEMS2 model output under RCP scenarios for the three statistical periods of 2011-2040, 2041-2070, and 2071-2099 annual climate change data were obtained. Correlation coefficient, determination coefficient and error indexes of RMSE, MSE and MAD were used to evaluate the performance of the model. However, the results showed that the accuracy of the model was different at different stations. In this way, each model performs better than rainfall in simulating minimum and maximum temperatures. The annual long-run results also show that precipitation will decrease in all climates studied in the coming decades, with the largest decrease occurring in semi-warm (35%) and very humid and temperate (32%) desert areas. But minimum and maximum temperature variations will be different in different climatic regions so that under RCP scenarios during all statistical periods at Sabzevar and Tabas stations minimum temperature changes will decrease but in other climatic regions the trend of minimum and maximum temperatures will be incremental. The highest minimum and maximum temperature increases based on RCP scenarios under RCP8.5 scenario during the period 2071-2099 in the cold mountain climatic region will be 3.03, 4.27 ° C, respectively.
Climatology
Ali Mohammad khorshiddoust; Kaveh Mohammadpour; Seyed Asaad Hosseini
Abstract
Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact ...
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Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective. Methodology Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression models as an effective tool in the prediction of nonlinear processes to predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively. Result and Discussion The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuation of the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital. Conclusion The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01 coefficient and 0.99 confidence. Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmic ratio of each of cited parameters is significant with β-coefficients (-0.517), (-0.734) and (-0.977), respectively, that throughout of studied parameters is wind element of effective in asthma admissions then others to the hospital. In general, Artificial Neural Network model showed more sufficiency and accuracy than Logit model. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies. Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective. Methodology Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression modelsasan effective toolinthe prediction ofnonlinearprocessesto predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively. Result and Discussion The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuationof the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital. Conclusion The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01coefficient and0.99 confidence.Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmicratio ofeach of citedparametersissignificant with β-coefficients (-0.517), (-0.734)and(-0.977), respectively, thatthroughoutofstudied parametersis windelement of effective in asthma admissionsthen others to thehospital. In general, ArtificialNeural Networkmodelshowed more sufficiencyandaccuracy than Logitmodel. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies.
Geotourism
M. Taghvaei; fatemeh jalalian
Abstract
Evaluation of climate comfort and the suitable days for recreational walking in urban areas is a significant aspect of successful planning aimed at promoting the urban tourism industry. Khuzestan is a vast province in Iran with very high tourism potentials as well as climatic variety, and can be regarded ...
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Evaluation of climate comfort and the suitable days for recreational walking in urban areas is a significant aspect of successful planning aimed at promoting the urban tourism industry. Khuzestan is a vast province in Iran with very high tourism potentials as well as climatic variety, and can be regarded as a tourism pole in Iran. This study aims to use the Equivalent Physiological Model for determining the number of climate comfort days in Khuzestan Province. To this end, the mean values of the daily data provided by eight synoptic meteorological stations during a ten year period (2000-2009) were used. Four meteorological factors were considered: dry air temperature, relative humidity, cloudiness, and wind velocity. The following cities were studied: Abadan, Ahvaz, Behbahan, Dezful, Iezeh, Masjed-e Soleiman, Ramhormoz, and Shustar. Upon feeding the mean data obtained for these cities to Rayman, the number of climatically suitable days for recreational walking in each city were determined. Upon taking into account the periods with negligible high- and low-temperature stresses, the output obtained from the computer model indicated that Iezeh with 168 climate comfort days was the most suitable city for accomodating tourists; followed by Masjed Soleiman (139 days), Ahvaz (130 days), Shushtar and Abadan (128 days), Behbahan (124 days), Ramhormoz (119 days), and Dezful (116 days). The results also showed that between late November and late March, Khuzestan Province would generally provide climatically favorable conditions for the visiting tourists.
Climatology
ali Mohammad Khorshiddoust; Behrooz Sobhani; Kamel Azarm; Jamal Amini
Volume 19, Issue 52 , June 2015, , Pages 141-161
Abstract
Canola is the world third leading oil seed after soybean and oil palm by producing 15 percent of the global plant oil. Climate, topography and lands capability are the most important environmental factors on which the crop production capability in a region depends. Therefore by evaluating these factors ...
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Canola is the world third leading oil seed after soybean and oil palm by producing 15 percent of the global plant oil. Climate, topography and lands capability are the most important environmental factors on which the crop production capability in a region depends. Therefore by evaluating these factors one can identify suitable lands for canola cultivation. In the present study, the climatological data such as temperature, precipitation, growth degree day, relative humidity, freezing days, and sunshine hours were collected from the West Azerbaijan province synoptic and rain gauge stations (since their installation untill 1388) which were associated with the phenologic stages of canola growth. In addition to the climatological data, earth resources like topographic layers, lands capability, soil depth and land-use were analyzed focusing on the climatological and ecological requirements of canola. After generalizing the data and processing by using ArcGIS, their corresponding information layers were derived. In order to prioritize and assess the criteria and information layers in relation to each other, the multi criteria decision method was employed based on analytical hierarchy process. Then, combination and spatial analysis of the information layers using TOPSIS model and GIS capabilities were done and the final capability ecological evaluation layer for canola cultivation was produced. Based on the obtained results, the province lands were divided into four categories of highly suitable (%18.6), suitable (%34.4), moderate (%32.1) and weak (%14.7) lands on the basis of the environmental and climatologocal potentials for canola cultivation.
Abdollah Seif; Seaed mortaza Abtahi
Volume 17, Issue 46 , February 2014, , Pages 91-111
Abstract
Two important characteristics, alternate climatic fluctuations and human being appearance, distinguish Quaternary from other geology periods. These climatic changes have shown increase and decrease of glacialical scope in high latitudes, but there are different and opposite theories about climatic situation ...
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Two important characteristics, alternate climatic fluctuations and human being appearance, distinguish Quaternary from other geology periods. These climatic changes have shown increase and decrease of glacialical scope in high latitudes, but there are different and opposite theories about climatic situation of low latitudes in Iran during glacial periods. In this study the climate of the last glacial era of Namak Lake basin, located in North central Iran was investigated by using the past geomorphic evidence and statistic analyzes. Present temperature and rainfall of this basin was studied and its related displacements were plotted. Glacial cirques and lake terraces as geomorphic evidences were also studied. Regarding the snowline in different points of basin, the temperature and rainfall of the basin in Wurm glacial period was rebuilt by Wright method and the changes related to the present time was studied. Morphogenetic plans of the basin in two periods were prepared by using annual rainfall and temperature and Politer method. Results show %48 (180 mm) increase in annual rain and a 5.6oC decrease in Wurm glacial era comparable to present. Reviewing geomorphic evidence out of climatic changes including NamakLake terraces, travertine mines, pediment vast and human civilization have proven the results.
