نشریه پژوهشهای تغییرات آب و هوایی
پیاپی 14 (تابستان 1402)

The aim of this study was to predict the flood zone in the context of climate change based on the scenarios of the Fifth International Climate Change Board's assessment report in Qarahsu Basin (Golestan Province). This research consists of two phases: climatic and hydrological. In the climatic phase, daily data of minimum, maximum temperature, precipitation and sunny hours of Gorgan Hashemabad Synoptic Station in the period 2001-2020 were simulated using LARS-WG statistical model and after ensuring the efficiency of this model in simulation Construction of meteorological parameters in Gharasoo watershed of Golestan province, to investigate the impact of climate change on runoff in the study area, data of three scenarios A2 (maximum scenario), A1B (medium scenario) and B1 (minimum scenario) of the model HadCM3 was scaled in the period 2021-2050 with a small LARS-WG statistical model. In the hydrological phase, precipitation-runoff is simulated using the SWAT hydrological model and after calibration (from January 1, 2012 to December 29, 2016) and validation of this model (December 30, 2016 to December 29, 2018), temperature and precipitation data. The output of LARS-WG model was entered into SWAT model and the changes of runoff due to climate change in future periods compared to the base period were calculated. The results showed that according to the estimation of LARS-WG model for the studied scenarios in the future periods, the average temperature of Qarahsu watershed will increase by 0.56 to 4 degrees Celsius. The amount of precipitation also decreases by 10 to 24% compared to the base period. Finally, the flood of the study basin is zoned in GIS environment by combining the average runoff map under scenario A2, distance from the river and the shape coefficient of the basin using Fuzzy-AHP method. According to zoning, 44% is at high and very high risk.

The characteristics of drought, as one of the environmental events, may change under the effect of climate change in future periods. The current study was conducted with the aim of predicting meteorological drought in Gorganroud watershed. For this purpose, the output of temperature and precipitation of 8 General Circulation Models (GCMs), were investigated under two scenarios (SSP2-4.5) and (SSP5-8.5) during two periods of 2025-2054 and 2071-2100. To downscale the GCMs, quintile mapping bias correction method was used. After projecting the future precipitation and temperature, the SPEI index was calculated at the annual scale for the baseline and future periods. Thereafter, the drought characteristics (duration, intensity and Severity) were computed using the median results of SPEI bands based on the Run theory method. With the aim of improving the process of evaluating and quantifying the effects of climate change on future droughts in the region and showing the uncertainties caused by climate models, the prediction bands of climate variables have been used to quantify the SPEI index. According to the simulated results of performance of multi model ensemble, that in the future climate conditions, the average values of mean temperature and precipitation will increase in both scenarios; So that the temperature increases from 1.2 to 4.4 Celsius and precipitation increases by 2 to 11.9 percent. The results of this study show that the conditions of the region in the coming decades likely the normal or wet climate conditions in the near decades, but these conditions would change in the far future as the more we move towards the last decades of the century, the more occurrences of droughts would happen. The results obtained from of the Run theory indicate that the duration and magnitude intensity profiles will decrease in the SSP2-4.5 scenario and the near future period in the SSP5-8.5 scenario.

Temperature and precipitation are two important meteorological parameters, especially in arid and semi-arid regions. As a result, it is necessary to know the amount of these parameters, their changes and predict these phenomena, in order to have more accurate planning in the management of agricultural, economic and etc. The aim of this study is numerical simulation and forecasting of Iran's weather changes with emphasis on two climatic parameters of temperature and precipitation. The method used in this research is artificial neural network algorithm for simulating 24-hour temperature and precipitation variables by month during a 31-year period compared to the base period. Factorial variance analysis has been used to compare the average changes in temperature and precipitation during the observed period of 1990-2020 and the simulated period of 2020-2050. The comparison results show that there is no significant difference between temperature changes and precipitation in 24-hour selected meteorological stations in Iran. The computed Partial Eta Squared is 10.6 percent for temperature and 5.7 percent for precipitation. The highest and lowest precipitation observed occurred in the months of March and July. The simulations have predicted the highest and lowest precipitation for the months of March and August. Also, the highest and lowest observed temperatures occurred in the months of July and January, respectively, and the simulations predicted the same values for the same months. The highest and lowest observed precipitation values were recorded in Rasht 109.95 and Yazd 4.36 mm, respectively. The simulations predicted the highest and lowest amount of precipitation for the same stations as 112.46 and 5.63 mm. Also, the highest and lowest observed temperatures were recorded at Bandar Abbas 26.99 and Ardabil 9.36 Celsius degrees, respectively. The simulations have predicted these values 27.10 and 9.45 Celsius degrees, for the same stations, respectively.

In this research, the effects of climate change on the amount and distribution of precipitation and temperature in the Kerman synoptic station were investigated. For this purpose, the output of global climate models of CMIP6 for the historical period (1965 to 2014) for precipitation and temperature were evaluated in comparison with the station data. To evaluate the models, evaluation metrics including correlation coefficient (r), root mean square error (RMSE), mean error (ME) and KGE index were used. Then the best-performed models were selected to predict these two variables in the future period (2051 to 2100) based on different climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Finally, changes in the distribution of precipitation and temperature in the future period compared to the base period were investigated. The results showed that after bias correction, the ACCESS-CM2 and MRI-ESM2-0 models performed more accurately for temperature (ME=0 °C, RMSE=1.87 °C, r=1, KGE=0.998) and precipitation (ME=-0.002 mm/month, RMSE=17 mm/month, r=0.484, KGE=0.468) estimation, respectively. The results of trend analysis indicated the trends in the amount of precipitation were not significant (P-value>0.1). Comparison between the average and variance of precipitation also was not significant (P-value<0.05), and therefore the possibility of increasing the occurrence of extreme precipitation cannot be statistically expected. However, trends in temperature and its average and variance are statistically significant and the possibility of heat stress will increase in the future. A significant increase in temperature in the future can affect the quantity and quality of Kerman's water resources, requiring more attention to the proper management of water resources.

In recent decades people around the world have embraced "smartphones" and their technologies. The application softwares (Apps) in these devices have become efficient and easy to use. Citizen science (CS) projects that attempt to collect data affordably from people of diverse backgrounds require the design and delivery of a user-friendly technology. In this research, an attempt was made, through reviewing the steps of designing a CS App to address the considerations and necessity of designing and disseminating a CS App with the subject of meteorology and hydrology. Payeshyar App that has been designed by the authors aimed at monitoring three parameters of air temperature, precipitation and water level in border and remote villages of Golestan. In order to gain sufficient experience, after placing simple and inexpensive measurement tools at the disposal of volunteers, the necessary data were collected for a period five months (December 2021-May 2022) via various communication methods including text messages, phone calls, paper and pencil forms and social media Apps. After that, necessary feedbacks and tips were recorded and according to the experiences gained, the initial version of Payeshyar was designed. In addition, in this research, by comparing the data collected through this App during eight months (June 2022 -January 2023) with the conventionally recorded data at formally established monitoring stations, its weaknesses and strengths were listed and its efficiency was investigated. Finally, the review of the effectiveness, advantages, disadvantages, limitations and challenges of Payeshyar showed that a well-designed Apps that can be installed and run on smartphones facilitates the communication of information from users to relevant databases and researches and seems that they are necessary to promote and expand long-term monitoring and surveillance systems based on CS. However, methods of sending meteorological and hydrological data to CS systems is a new field that needs more research.

The surface temperature of the earth is one of the key parameters in the studies of cities.Air pollution is also one of the causes of the creation of heat island in the city, which has been increasing in recent years due to high population density and pollution.The aim was to investigate the temporal changes of urban heat islands and its relationship with air pollution in Kerman in a period of 16 years(2007-2022)And to investigate the trend of temperature changes, the temperature data of Kerman station and the Mann-Kendall test were used, and the data of Kerman air pollution station were used to investigate air pollution.Although the number of polluted days was high, but due to the limited passage of the Badoureh satellite for 16 days from Kerman, the limited number of days with high pollution intensity in Kerman, and the non-compliance of polluted days with the time of satellite passage, finally, seasonal images of five years were selected. The results showed that the temperature of the earth's surface had an upward trend in both seasons, but the heat island occurred only in the summer season.So that in the summer of 2009, the density of very hot temperature was less than 30%, but with the passage of time and in 2022, in the summer season, more than 60% of the urban area has very hot temperature.And parts from the west to the south of the city have formed the center of thermal islands due to the presence of barren lands and also due to the lack of benefit from evaporation and transpiration cooling mechanisms.Also, the correlation between the temperature of the earth's surface and the air pollutants was confirmed at a significant level of 0.01 and 0.05, that in the summer the correlation coefficients are stronger and pollution has also increased.