نشریه پژوهشهای تغییرات آب و هوایی
پیاپی 12 (زمستان 1401)

Therefore, investigating the relationship between the spatial changes of soil carbon deposition with the climatic elements of temperature and precipitation in order to study and apply practical methods to calculate the amount of carbon deposition, study models such as RothC are very important. In the present study, the efficiency of the RothC model to estimate the organic carbon stored in the mountain pastures of Mereg Serfirouzabad, Kermanshah province has been investigated. Sampling was done in the form of a managed random design using 45 points with a radius of 20 km in the surface soil. Model inputs include independent variables (climate with 25-year statistics; monthly rainfall, monthly evaporation and transpiration, average air temperature) and soil texture and vegetation of the region. The output of the model included soil organic carbon with four active parts (IOM), (DPM), (RPM), (BIO) and a separate part (HUM), each part is decomposed depending on its characteristics. In this research, three time periods were sampled, which were collected in the years 1380, 1389 and the last statistics in the fall of 1396 respectively. Finally, after calibration, the RothC model was implemented in 1380 to 1389 harvesting cycles for 1396 and it was found that the climate change parameter has a strong effect on organic carbon storage. Therefore, the RMSE error for the model in question is less than 0.11 and MBI has reached 0.05, which has caused the model performance factor to be -0.96 and the high accuracy of the RothC model is emphasized. The results show that the amount of total carbon stock for pastures is 4.48 tons per hectare, and this value is lower in agriculture and reaches 4.36 tons per hectare. In general, all indicators show the effect of agriculture on the weakening of carbon storage in all stages.

One of the direct and evident impacts of climate change is the change in the amount of snowfall in different geographical areas. It is worth mentioning that snowfall in mountain basins is always taken into account as the most important source of water supply in dry seasons. Due to some restrictions, data collection, particularly on a large scale, is difficult and at sometimes impossible. Consequently, using indirect methods is recommended. In this study, the efficiency of least squares support vector machine in modeling the depth of snow and the impact of feature reduction with genetic algorithms model in Chelgerd , Iran was investigated. At first, with using the Hypercube model, the locations of 100 points were specified, and the data of snow depth at certain points as well as other 195 points were randomly collected. Afterwards, with using DEM,, 25 Geomorphomety parameters were extracted, and these parameters with six bands, eight Landsat satellite images and the difference index of normalized snow were chosen as the inputs of models. In this study, genetic algorithm is used to increase the speed of support vector machine which is considered as a classifier and make it ready. Also, genetic algorithm is utilized to choose the variants having the most coherence with the snow depth. Since the reduction of ineffective features can increase the accuracy of learning, genetic algorithm was used in this study for the optimization process. The results showed that the least squares method of the S.V.M with the coefficient of determination of 0.36 and the SMSE of 17.8 has modeled the snow depth. However, the genetic algorithm by selecting the effective features was able to model snow depth changes better with a determination coefficient of 0.95 and RMSE equal to 3.97 cm which is more accurate than using all features.

The impact of climate change on hydrology and water cycle in nature is very serious and a little knowledge of these effects to deal with its consequences. In order to model the two watersheds of Telangu and Hosseinabad in Kerman province,. Also, by using the LARS-WG model, variables were predicted for the period from 2021 to 2060. In the other part of this study, to run from the SWAT model, sensitivity analysis was performed using the global sensitivity analysis method using 29 parameters. The results showed that in general, the temperature has increased in the next period compared to the previous period in the whole month, in all three stations this increase was less in the warm months of the year than in the cold months. Precipitation has generally increased and the highest monthly precipitation increase for all three cases is April, March and March, respectively Sutcliffe (ENS) is greater than 0.65. The results of evaluating the effect of climate change on the discharge of the basin show that in general the discharge of the Telango Bam watershed has been much lower than the Hosseinabad Rain watershed in the current period and in the future. In the next 60 years, the discharge of the Telango Bam watershed will increase compared to the current period, the discharges of January, May and July will increase and will decrease in the other months except September, which is unchanged. The general results of the present research indicate that in the near future in dry ecosystems similar to the study area, following the change in the rainfall regime resulting from global warming, the hydrological behavior of the basins will change, especially in the winter and spring seasons, and the occurrence of extreme rainfall and flood events in These seasons are more likely than before.

Climate change is a condition that refers to any change in climate that occurs over time. These changes may not be seen in the average of the data series but occur in quantiles of the series with different intensities that can be examined by quantile regression. In this research, to investigate Spatio-temporal changes of maximum and minimum temperature in different seasons in Iran, after fitting quantile regression on 102 meteorological stations with the statistical period of 2016-1986, the slope of the trend was calculated in the different quantiles, and by comparing its results with the results of the ordinary linear regression, they were spatially zoned. The results showed that in the spring season, the highest slope of the increasing trend is in the lower quantiles and the eastern half of Iran, but in the winter season, it was in the middle and upper quantiles and the northwest and west of Iran. In the summer season, the lower quantiles had an increasing trend, but in the autumn season, they had a decreasing trend. However, in spring and summer, the lower quantiles of daily temperature and in autumn and winter, the upper quantiles increased more strongly. Also, the comparison of the two regression methods shows that most of the quantiles had a slope different from the slope of the least square regression, and it’s not correct to assign the slope of the ordinary square regression line for the trend of temperature changes in the whole series. Finally, it can be stated that climate change has occurred in the maximum and minimum daily temperatures in Iran, but the intensity of these changes varies according to different seasons, quantiles, and locations.

The role of atmospheric boundary layer height in urban microclimate is one of the most important topics. As a consequence of planetary climate change and human intervention and activities in urban settings, this part of the climate is affected. When its height increases or decreases, some atmospheric polluting variables become more relevant, including nitrogen dioxide (NO2), sulfur dioxide (SO2), and aerosol index (AI). In this study, we evaluated the daily monitoring of Sentinel-5P for a period of four years in Isfahan's cities. After comparing the relative frequency of the variables above and below the boundary layer threshold of 750 meters, we identified the effect of the height of the boundary layer on the variables. Results indicate that the highest concentrations of aerosol index (AI) are observed in urban areas of Isfahan province above the threshold of 750 meters. In this case, nitrogen dioxide (NO2) and sulfur dioxide (SO2) gases are completely different, but the decrease in height of the boundary layer indicates their frequency. Statistically, the Kappa coefficient reveals a high number of internal inconsistencies for all three variables above and below 750 meters of the boundary layer. Also, the weighting done in these two floors also completely reveals the difference in weights.

Climate change and the increase in the population of cities have caused many environmental problems. The decrease in the function of urban agriculture has also made the efficiency of green infrastructures and their ecosystem services naturally inefficient. The purpose of this research is to provide a model of sustainable cultivation of productive plants in cities to moderate the effects of climate change and land use in District 22 of Tehran Municipality. In this study, the land use map of 2000 and 2020 was made using Landsat satellite images. Then, the LCM model was used to predict land use changes in 2050. RCP scenarios were simulated to investigate climate change. 4 criteria and 21 sub-criteria and the best-worst method (BWM) and the cumulative ratio evaluation method (ARAS) were used to obtain the weight of the selection criteria of fruitful plants. Based on the obtained results, it was determined that green and agricultural areas will change by 12% and -4.5% from 2000 to 2050, respectively. The risk of climate change also predicted a warming of 1.32 to 2.27 degrees. Based on this, the suitability map of the region for urban agriculture and the area of suitability classes according to the criterion of compatibility with the environmental conditions of the region (with a weight of 0.472), ecological conditions (0.268), compatibility with the urban environment (with a weight of 0.179) and aesthetic-cognitive (with a weight of 0.081) was prepared. According to the opinion of experts, fruitful plant species such as Wild Pistachio, Christs thorn, Wild almond, Pistachio, Olive have found the highest priority for cultivation in large and medium urban scale. About 40% of the area has good and high suitability for planting these plants.