جستجوی مقالات مرتبط با کلیدواژه « تغییر اقلیم » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Limiting bioclimatic conditions, destructing natural tourist attractions, displacing the tourism calendar, climate change, and global warming may also displace tourism territories to different degrees (Xiu Xia et al., 2023). The findings of various studies assert that due to the increase in temperature, heat stress, and the deterioration of the bioclimatic conditions in the Mediterranean, Malaysia, and America, favorable areas for tourism have been moved to northern latitudes (Jong et al., 2023; Matzarakis & Amelung, 2008). The assessment of the effects of climate change on Iran's tourism also indicates an increase in the locational changes of favorable tourism sites and the shift of tourism classes from excellent or ideal to desirable and suitable due to the increase in temperature and droughts (Sobhani & Esmaeilzadeh, 2020; Moradjani, 2022; Abedi & Sedaghat, 2022; Farajzadeh & Ahmadabadi, 2010).Located in southwestern Iran, Khuzestan Province is regarded as a region with extensive and concentrated tourism capabilities, whose geographical features have led to the formation of mountain, plain, sea, desert, and even desert landscapes. Moreover,Khuzestan plain is known as the cradle of ancient and historical civilizations that continue to the coast of the Persian Gulf, where the cold months of the year (November to April) make the site a suitable place to visit (Borna, 2018, Rahimi et al., 2019). However, the temperature rate has been increasing by one to three degrees from 2017 on (Mohammadi et al., 2024), placing some restrictions on the tourism calendar. Therefore, climate change (2020–2080) may exert an adverse influence on tourism territories in their golden visiting time. Considering the status of tourism in the prospective development of Khuzestan province and the high sensitivity of such potential to climate change, this study sought to investigate the effect of climate change on tourism in Khuzestan province through a temporal-spatial comparative study using the thermal comfort index coefficient.Study Area: Covering an area of approximately 64236 square meters, Khuzestan province is located in southwestern Iran, whose average annual temperature varies from 15 °C in its northern regions to 26 °C in its southern regions. Moreover, more than 85% of the province’s area measures less than 500 square meters, and 15% of the province falls within the north and northeast mountainous regions. As the province possesses great tourism potential, special tourism development plans have been incorporated into the policy documents of the province to prepare the grounds for its sustainable development. However, increasing chances of climate risks such as heat waves, dust, and floods pose a potential threat to the advancement of the tourism industry in the province. In this regard, the current study set out to investigate the change in the thermal comfort index and its territories until 2050.

Method and Data:

 To investigate the effects of climate change on the tourism calendar and its territories in Khuzestan Province, this study examined the thermal comfort index in terms of two periods: the historic period (1985–2020) and the future period (2021–2050). The index considers such climatic elements as air temperature, precipitation, humidity, radiation, and wind. As for the time periods investigated, the study collected the required data regarding the climate variables recorded in the meteorological stations of Ahvaz, Omidiyeh, Aghajari, Abadan, Behbahan, Dezful, Safiabad, Ramhormoz, Mahshahr, and Masjed Suleiman. Moreover, to calculate the thermal comfort index for the future period, the study used downscaled climate data based on CMIP6 scenarios under the SSP 4.5 scenario.

Climate change and global warming have changed climatic comfort conditions and the displaced territories in Khuzestan province. Comparing the maps prepared for the 1985–2020 period with the ones prepared for the future period (2021–2050), the study found that the thermal comfort index territories have been changed in different classes and months of the year. In other words, it could be said that global warming will create a longer summer in Iranian southern regions, leading to the reduction of the geographical territories’ rank from ideal and excellent to lower classes and the movement of tourism territories. In the historical period (1985–2020), March was found to be the most ideal time for tourism. However, the findings revealed that November would be the best time for tourism in the future (2021–2050) when the province would experience the best thermal comfort index conditions. These findings are consistent with the results reported by Matzarakis & Amelung (2008), Xio Xia et al. (2023), Gössling et al. (2023), Rastegar & Ruhanen (2023), Bakhtiari et al. (2018), Shamsipour et al. (2014), Moradjani (2022), Abedi and Sedaqat (2022), and Farajzadeh and Ahmadabadi (2010).

The findings indicated that Khuzestan province will experience great changes in the future in terms of tourism thermal comfort index. In this regard, the province's thermal comfort index rank will, in the future period (2021–2050), decrease in the months of January, February, March, April, and October compared to the one found for the historic period (1985–2020), suggesting that no part of the province will have ideal conditions to attract tourists. Moreover, the conditions will be quite unfavorable in May, June, July, August, and September. However, compared to the historic period, the conditions will improve in November and December of the future period. Furthermore,  the tourism territories of the province will be limited to northern latitudes and mountainous areas in the future.The prospective changes in the monthly tourism calendar and its territories will exert a great influence on the province's tourism economy. Currently, tourism infrastructure such as accommodation, welfare services, transportation networks, and private sector investment have been prepared and developed based on the historical conditions of the climate. Therefore, the relocation of tourism territories, the weakness of facilities in new areas, and the neocolonialization of previously suitable areas will bring about great economic and social losses. Thus, it is recommended that various aspects of tourism development plans be considered based on uncertainties.

Drought stands as an inevitable natural disaster with no means of prevention. Its detrimental impacts extend across diverse sectors, including water resources, agriculture, and the environment. Drought is typically defined as a substantial scarcity of natural freshwater resources persisting over an extended period due to shifts in precipitation and temperature patterns. Consequently, periods marked by below-average rainfall often result in temporary drought, particularly in arid and semi-arid regions characterized by relatively high temperatures. Globally, drought events are classified into four categories: meteorological, soil moisture (agricultural), hydrological, and socio-economic. Studying drought becomes imperative for implementing optimal solutions and strategies to manage water resources and ensure food security, among other concerns.  This research utilizes the Pedj Drought Index (PDI), a unique and potent tool, to delve into drought analysis. The PDI, grounded in precipitation and temperature data, offers a more accurate and accessible understanding of drought's spatial and temporal characteristics. Based on yearly averages at stations, this innovative approach provides a fresh perspective on drought analysis. We gathered and scrutinized annual rainfall and temperature data from 20 synoptic stations in Northwestern Iran from 1987 to 2021 to conduct this study. The PDI's accuracy and accessibility make it a reliable choice for drought analysis, providing a comprehensive view of the situation.  From 1991 to 1995, most of the studied stations experienced wet conditions, while from 2013 to 2017, they faced a severe drought. The most extended wet period occurred in the south at the Bijar station (9 years), and the most prolonged drought period happened in the east at the Ardabil station (8 years). The maximum wet period based on the PDI index (9 years) was observed at the Bijar station, and the most prolonged drought period was at the Ardabil station (8 years). In the northwest of the country, the average duration of the most extended wet periods is 5.5 years, and the average duration of the most prolonged drought periods is 7.5 years. Additionally, the central station experienced two 5-year drought periods, and the Tabriz station underwent two 4-year wet periods. Based on the precipitation anomaly index, 60% of the stations (12 stations) showed a decreasing trend in precipitation anomalies, with the Sanandaj and Maragheh stations having a significant 95% decrease. In comparison, only 40% (8 stations) exhibited an insignificant upward trend in precipitation. The temperature anomaly index showed an upward trend in all studied stations, with only Bijar, Takab, Sarab, and Miandoab stations (highlighted in red) having a significant 95% increase. According to the drought index PDI, none of the stations experienced extremely dry (ExD) or moderately dry (MiD) conditions. The highest percentage of severe drought was observed in Zanjan (71.5%), moderate drought in Khalkhal (20%), and mild drought in Tabriz, Maragheh, and Piranshahr (42.85%). Severe dryness was observed in the Bijar station (42.85%), moderate dryness in Maku (17%), severe dryness in Takab, Khormabad, and Miandoab (71.5%), and highly severe dryness (71.5%) in Ardabil Province. These findings underscore the urgent need for effective drought management and water resource planning in the northwest region of Iran. These findings underscore the urgent need for effective drought management and water resources planning in the northwest area of Iran. Discussion and This study investigated the effects of annual mean precipitation and temperature changes on drought and long-term dry events using the PDI index in 20 stations in the northwest region of Iran, spanning five provinces over 35 years (1987-2021). The northwest Iran stations experienced wet periods from 1990 to 1994, dry periods from 1998 to 2001, and from 2013 to 2017. The most extended wet period was approximately 5.5 years, and the longest prolonged drought period was around 7.5 years. On an annual scale, the PDI could describe the spatiotemporal variations of drought and wetness in the study area to a considerable extent. Despite its simplicity, the PDI provided highly accurate results compared to common drought indices such as SPI and SPEI, which rely on statistical assumptions and distribution-related parameters. The stronger correlation of PDI with SPEI, as opposed to SPI, demonstrates the capability of PDI to explain the effects of precipitation and temperature changes on drought and wetness in the northwest region of Iran.

Climate change is one of the biggest challenges that humanity has faced in the 21st century, because it can have severe effects on water resources, agriculture, energy, and even human bioclimate. The purpose of this study is to investigate the changes of climatic parameters in four sites of Plour, Rineh, Kangarchal and Poshtkoh in Mazandaran province using general atmospheric circulation models. For this purpose, the data of IPSL-CM5A-MR and CSIRO-MK36 models under RCP4/5 and RCP8/5 scenarios have been scaled with LARS WG statistical micro-rotator to evaluate the impact of climate change. After carrying out calibration, verification and modeling of data in two stations (Abali and Kiasar), the effectiveness of the models in terms of the amount of compliance of the observed data with the simulated value using the RMSE index, R2, MAE was evaluated. The obtained results showed that IPSL-CM5A-MR and CSIRO-MK36 models obtain acceptable results. Considering that in Abali station, the amount of rainfall in the base period is 542.08 mm under RCP4.5 and RCP8.5 scenarios in both IPSL-CM5A-MR and CSIRO-MK36 models in the future between (2021-2060) will increase by 0.91-0.97 percent. The average maximum temperature will increase by 1.7-2.2 centigrade and minimum temperature by 1.56-1.88 centigrade compared to the base period. Accordingly, the amount of rainfall in the base period in Kiasar station is 528.46 mm, which under the scenarios of RCP4.5 and RCP8.5 in both models in the future between the years (2021-2060) is 96. 0-87% will increase. The average maximum temperature will increase by 1.72-18.2 centigrade and the minimum temperature will increase by 1.62-1.82 centigrade compared to the base period.

In the last three decades, land use in the Minab river basin has undergone significant changes and these changes along with climate change in the basin can affect the trend of hydroclimatic variables. Due to the changes that have occurred in the climate and land use of the Minab River Basin in the past decades until now, the investigation of trends and changes in the threshold indicators of precipitation, temperature and flow rate becomes particularly important. Therefore, the aim of the current research is to investigate the trend of land use changes and hydroclimatic variables in the Minab basin.

In the current research, the data of hydrometric, rain gauge and evapotranspiration stations in the Minab watershed were analyzed to extract threshold indices based on the ETCCDI standard including precipitation, temperature and river flow. Trend analysis was done using a non-parametric Mann-Kendall test. Also, land use changes were extracted using TM series Landsat satellite images in 1989, 2004, and 2020 from TM, ETM+, and OLI sensors, respectively, and the accuracy of the extracted images was confirmed with Kappa statistics.

The finding showed that all the temperature limit indices, including tropical nights, hot days, hot nights and the range of day and night temperature, have an increasing trend. The extreme precipitation indices, heavy precipitation of 10, 20, and 30 mm in some stations has an increasing trend, but the annual average precipitation has an increasing trend. The number of wet days is decreasing and the number of dry days is increasing. The investigation of the changes in the rainfall regime of the region showed that the intensity of the rainfall is increasing in the duration of 15 and 45 minutes. According to the changes that have occurred, the change in the precipitation situation in the region is inevitable. The survey of land use changes over the past three decades showed that 22% of the pasture land area has decreased, and residential and agricultural land has increased by 280 and 220%, respectively.

Conclusion and Suggestion:

The findings of this research showed that despite the increase in the average annual rainfall (16 mm per year), and other rainfall indicators that had no significant trend, the trend of 15 and 45 minute rainfall intensity was increasing and significant. Contrary to the trend of precipitation, which cannot be proven with great certainty, the increasing trend of all temperature indices (on average 0.9°C per year) confirmed the climatic changes in the Minab watershed. On the other hand, the trend of minimum and maximum stream flow and maximum discharge was also increasing in the watershed, and it can be one of the reasons for the changes in rainfall, construction of dams, and changes in land use from pasture and war lands to agriculture and residential. Based on the results of this research, it is suggested to investigate the effect of future climate changes on meteorological and hydrological variables and use its findings in natural resources management.

Climate is one of the environmental factors whose changes cause extensive alterations in different parts of the ecosystem and pose a significant threat to sustainable development. Temperature, a main element of climate, can affect the climate structure of any region through sudden, short-term, and long-term changes. In recent decades, the Earth has faced global warming, evidenced by climatic changes worldwide. One important consequence of global warming is the increase in extreme weather phenomena, such as sudden temperature changes, excessive heat, abnormal cold, heavy rains and floods, drought, and dust storms caused by the drying of wetlands. Climate extreme indices not only play an important role in investigating climate events on regional and global scales but also assist in climate modeling and decision-making for various sectors.

In this research, temperature data from the MRI-ESM-2 model under three scenarios—optimistic (SSP1-2.6), average (SSP2-4.5), and pessimistic (SSP5-8.5)—for two periods, the near future (2021-2060) and the distant future (2061-2100), were used. First, historical data for the base period and scenario data for the future period (until 2100) for the studied climate model were obtained from the ESGF database. Then, using the R programming language, the time series of historical data and scenarios were extracted from the model for each desired station, and the statistical downscaling of the data was performed using the bilinear interpolation method at the level of the studied stations. The data were grouped based on the three scenarios in all studied stations, and RClimDex software was used to extract indices based on minimum and maximum daily temperatures. In this research, 16 extreme temperature indices for the studied area were calculated on annual and monthly scales, and trends and breakpoints in these indices were investigated using the Mann-Kendall trend detection test, Sen’s Slope test, and Pettitt mutation detection test.

The results show a decrease in extreme hot events based on the SSP1-2.6 scenario, a decrease in indices related to cold and freezing days, and an increase in extreme warm indices based on the SSP5-8.5 scenario, observed in most areas of the province. Generally, the indices for the number of summer days (with a slope of 40-70%), tropical nights (45-65%), the length of the heat period (30-50%), and the length of the growing season (40-60%) showed significant increases. Conversely, the indices for the number of frost days (-20 to -80), ice days (-10 to -40), and the length of the cold period (-10 to -70) significantly decreased. The results of Pettitt's test indicated change points for increasing and decreasing trends in hot and cold extreme indices, respectively, in the 2040s (near future) and 2080s (far future). Therefore, to control extreme temperatures and their adverse effects on various aspects of human life, especially agriculture and water resources, strategies should be developed and implemented according to the needs of each region.

The results indicated more frequent sudden changes in temperature indices under the pessimistic scenario compared to the other two scenarios, affecting broader areas of Mazandaran province. There is a likelihood of sudden increases in hot extreme indices and decreases in cold and freezing days both in the near future (2030s, 2040s, and 2050s) and far future (2070s and 2080s). Overall, significant changes in temperature extreme indices were observed during the future statistical periods studied, with Mazandaran province expected to experience higher air temperatures and more frequent extreme heat events. These findings align with regional and global studies. The rise in temperature, particularly during hot months when precipitation decreases naturally, significantly impacts agriculture in this region, which is a major area for rice production in the country. These changes also affect the hydrological cycle downstream of the Haraz basin. Additionally, temperature fluctuations during winter and cold months can influence the timing of snow melting in the basin, thereby affecting peak flood flows downstream. Given these factors and the necessity of such research in understanding human activities, water resource management, food security, and human health, it is crucial to investigate the impacts of extreme climate events driven by temperature in future policy-making. Human societies must adapt and adjust based on these anticipated conditions. Therefore, studying the intensity, frequency, and timing of extreme events and raising awareness about them can effectively address environmental challenges and enable rational planning to mitigate and reduce these events.

Watersheds are socio-ecological systems that can be significantly affected by climate change. Changes in the pattern and amount of precipitation and increase in temperature can increase the vulnerability potential of watersheds. Understanding the impacts of climate change on hydrological processes and other aspects at the watershed level is crucial for developing approaches to mitigate or adapt to these effects. Therefore, this research aims to assess the impact of climate change on the health of the Radkan watershed in Razavi Khorasan province. In this study, the health of the watershed was initially evaluated for the historical period of 1990-2014 using the Pressure-State-Response (PSR) framework. Sixteen natural and human criteria were selected and classified into three indices: pressure, state, and response. After calculating the criteria for the 14 studied sub-watersheds, they were standardized and weighted using the Analytic Hierarchy Process (AHP). Finally, the health of the studied watershed was determined using the PSR framework for the 14 sub-watersheds and classified into five categories: healthy, relatively healthy, moderately healthy, relatively unhealthy, and unhealthy. To investigate the impact of climate change on the health of the watershed, data from the climate model MPI-ESM1.2-HR were used under two scenarios: SSP2-4.5 and SSP5-8.5. The predicted climate data in three future time periods of 2026-2050, 2051-2075, and 2076-2100 were investigated. The results indicated that during the historical period, one sub-watershed was classified as healthy, two sub-watersheds were relatively unhealthy, and the remaining sub-watersheds were moderately healthy. Comparing the health index in the future time periods with the historical period revealed a decrease in health under both scenarios. The average decrease in the time periods of 2026-2050, 2051-2075, and 2076-2100 under the SSP2-4.5 scenario was 8.84, 8.49, and 11.11%, respectively. Under the SSP5-8.5 scenario, the average decrease was 7.33, 13.04, and 9.85% for the same time periods.

Considering its pervasive influence on live creatures, climate change is currently considered one of the most crucial challenges facing human societies, the investigation of which is of great significance. Moreover, climate change exerts an adverse influence on biological resources, the natural environment, and water sources, causing various environmental, social, and economic consequences. Therefore, this study sought to investigate and predict the parameters involved in climate change, including maximum temperature, precipitation, and average temperature under the RCP2.6, RCP4.5, and RCP8.5 scenarios. Furthermore, the applicability of the SDSM model was tested on eleven western provinces of Iran. To this end, first, the predictive variables such as temperature and precipitation were downscaled using combined regression techniques and a small-scale stochastic weather generator, and the required data were collected from the site of the monitoring station. Then, variations in maximum temperature, average temperature, and precipitation for the 2021-2036 and 2036-2100 periods were compared to those of the baseline period (1990-2020). The SDSM model was then validated and its accuracy was assessed using metrics such as MSE, MAPE, RMSE, and MAE.

On the other hand, to verify the accuracy, the data collected for the 1990-2020 period were taken as the actual and observed values, followed by the performance of some simulations with the same data to gauge and measure the accuracy of the extracted data for the 2021-2035 and 2036-2100 periods and compare it with the baseline period (1990-2005). Finally, after confirming the collected data against the actual data of the respective years and determining the accuracy of the four validation methods, the verification process was extended to cover the 1990-2020, 2021-2035, and 2036-2100 periods.

The results of the study indicated that the application of the SDSM model led to a reduction in the required accuracy needed for investigating and simulating climate change. Accordingly, the highest MSE, MAPE, RMSE, and MAE values were found under the second scenario (RCP4.5) in Jolfa station (in terms of maximum temperature), Urmia station (in terms of precipitation), Nahavand station (in terms of precipitation), and Zarrineh station (in terms of precipitation), whose reported values were 0.01, 16.90, 0.10, and 0.10, respectively. On the other hand, the lowest values belonged to the second scenario, whose values were reported to be 0.00, 0.41, 0.00, and 0.00, respectively. Moreover, the results obtained from the application of the SDSM downscaling model to the investigation of precipitation under RCP2.6, RCP4.5, and RCP8.5 scenarios revealed that compared to the other two scenarios, the first scenario (RCP2.6) had a higher level of accuracy in predicting precipitation for 2021-2035 and 2036-2100 periods with a small margin of error, simulating precipitation more closely to the observed data in most cases. Furthermore, the model's predictive outcomes suggested that compared to the baseline period, precipitation would undergo changes throughout the 2021-2035 period, ranging from 2.50% to 3.86%. Similarly, it was found that compared to the baseline period the changes would range from 10.02% to 15.73% during the 2036-2100 period. On the other hand, the most significant alterations in precipitation levels were expected to occur in the Iranian Eastern Azerbaijan and Western Azerbaijan provinces. In this regard, the analysis of maximum temperature rates throughout the 2021-2035 period showed that compared to the baseline period, the rise in the maximum temperature across the study region would range from 0.20 to 0.89 degrees. Similarly, the maximum temperature rate would increase from 0.86 to 0.89 degrees during the 2036-2100, compared to that of the baseline data. However, the most substantial changes in the maximum temperature rate were expected to occur in the southern part of the Iranian Fars province and the bordering areas of Kermanshah province.

As for the average temperature rates, the study found that the temperature would increase from 0.05% to 5.07% during the first time period, and from 4.47% to 5.05% throughout the second period. However, while the least significant alterations in the studied parameters belonged to the RCP2.6 scenario, the most notable changes were found under the RCP8.5 scenario. Moreover, precipitation changes were investigated in forty stations across eleven Iranian provinces throughout the 2021-2035 and 2036-2100 periods, proving a reduction in precipitation rate within both periods, with the decrease being more conspicuous in the second period (2036-2100) under all three severity scenarios. Furthermore, climatic assessments at the station level indicated that the study area would experience a considerable rise in the maximum temperature rate throughout both study periods, with the second period (2036-2100) being expected to witness a more substantial temperature rise. In this regard, all three scenarios showed that the range of increase in the maximum temperature rate would vary from 3.99% to 4.16%, indicating a significant temperature rise in the study area over the coming years. The regions investigated in the current study play a crucial role in supplying water to the central and western Iranian provinces. The regions are also known as significant hubs for agricultural production. Therefore, any changes in weather and climatic parameters in these areas may lead to increased uncertainty in future predictions and planning, requiring the identification of the areas susceptible to risks caused by prospective extreme climate changes. Thus, it is necessary to develop and implement management and operational plans to deal with such changes and to devise the required strategies to mitigate the consequences of those changes and ensure adaptability to the new conditions.

The growing global population and increasing demands have significantly intensified the pressure on natural resources and created inherent instability in watershed ecosystems and the complex ecological processes essential for habitat preservation, biodiversity conservation, erosion mitigation, soil formation, water retention, environmental stability, and cultural fulfillment have encountered disruptions. The inter-connected adaptability challenges within human-natural systems, intensified by climate change, bring more uncertainty that complicates the planning and management of ecosystem services in the watersheds. This uncertainty often results in flawed decision-making in water resources utilization and environmental management, contributing to instability and a shift toward critical thresholds in watersheds. This study adopts a systemic approach to analyze the Karoun Basin as a dynamic human-nature system, exploring five systemic analysis patterns and their impacts on water resource management paradigm.

The research progresses through three crucial steps. Firstly, the current status of the Karoun Basin is outlined based on extensive literature studies, characterizing the water system as a complex, adaptive entity. Secondly, leveraging a diverse range of research literature, five recognized structures in dynamic system analysis are explored. These structures offer a detailed understanding of the interconnectedness of human-nature systems, providing insights into the impact of human behavior on water resources management in the Karoun Basin. In the final step, using a systemic thinking approach, the study explores the occurrence of extreme events. This approach enhances the understanding of how extreme events interact with human activities, influencing the sustainability of the Karoun Basin.

The outcomes highlight the inherent risk of a linear approach to water resource management in the Karoun Basin, which is because of the lack of systemic thinking. This approach disrupts various relationships within the water system, leading to instability and worsening extreme events such as floods, droughts, and water disputes. The systemic investigation reveals recurrent behaviors that align with recognized structures in dynamic systems analysis, referred to as ancient patterns. Understanding and addressing these patterns are crucial for achieving sustainability and ensuring the continuous provision of ecosystem services.

Depending solely on short-term solutions, without considering long-term consequences in water resource management worsens water scarcity issues and intensifies social conflicts within watershed units. Solutions for Karoun Basin can be categorized into mitigating (hard) and adaptive (soft) scenarios. Mitigating measures address immediate needs, including advanced water storage and distribution infrastructure, while adaptive solutions incorporating policy measures are crucial for aligning the managed system with fluctuating water availability levels. A comprehensive resolution requires the simultaneous implementation of both strategies, recognizing the interconnected nature of human-nature systems and embracing sustainability principles.

Climate change has a significant impact on water resources and the environment, which is reflected in agriculture, society, and economy. The use of General Circulation Models (GCMs) with downscaling models is a method for assessing climate change. Considering the placement of South Khorasan Province and the city of Birjand in the arid region of Iran, population growth, industrial and mining development, and the pursuit of sustainable agriculture, it is essential to assess the effects of climate change on essential meteorological parameters. The objective of this study is to compare the performance of historical models NCEP and ECMWF in downscaling temperature parameters for the Birjand County and investigate the changes in this parameter until 2030 using the top model and the SSP245 scenario with the CanESM5 model.

In this research, to compare the performance of two GCMs, NCEP and ECMWF, in downscaling temperature parameters, daily temperature data from the Birjand synoptic station for the period from 1990 to 2021 were used as the baseline period. Additionally, to evaluate the performance of these two GCMs, the statistical downscaling model SDSM was utilized. To assess the performance of these two models, evaluation criteria such as NS, KGE, RMSE, and BR2 were employed.

To investigate and compare the performance of two GCMs, NCEP and ECMWF, daily average temperature data from the Birjand synoptic station were used from the January 1990 to the September 2021. The data from 1990 to the January 2008 were considered for calibration, and data from the January 2008 to the September 2015 were used for validation. Both NCEP and ECMWF models had 26 parameters, and for downscaling, the parameters with the highest correlation with observed temperature were selected among these 26 parameters using the R software and the HydroGof package. Additionally, evaluation criteria such as NS, RMSE, KGE, and BR2 were used to assess the models' performance in calibration and validation sections. The closeness of variance and mean values of time series generated by the NCEP and ECMWF models to the variance and mean values of observed time series in the entire simulation period was examined using F and T tests. The results of the calibration section showed that the two models, NCEP and ECMWF, exhibited similar performance since the values of evaluation criteria NS, RMSE, KGE, and BR2 for the ECMWF model were calculated as 0.69, 4.86, 0.85, and 0.7, respectively, and for the NCEP model, they were 0.70, 4.79, 0.85, and 0.7, respectively. Since box plots, mean values, and standard deviations have a high capability in deciding the degree of dispersion and similarity between two time series, box plots, mean values, and standard deviations of the generated time series and observed time series in the calibration and validation periods were used to assess the similarity and closeness of the time series. The results of the evaluation criteria in the validation section showed that the ECMWF model outperformed the NCEP model, with values of evaluation criteria NS, RMSE, KGE, and BR2 for the ECMWF model being calculated as 0.69, 4.9, 0.85, and 0.73, respectively, and for the NCEP model, 0.67, 5.3, 0.83, and 0.7, respectively. Overall, the results indicated that the ECMWF model had a better performance and was selected as the superior model. Therefore, to simulate and predict the average temperature parameter, the parameters mslp, P500, P5-f, P5-u, P850, and P8-u from the ECMWF model were used. Consequently, it is predicted that the average temperature will increase by approximately 3 degrees Celsius compared to the statistical baseline period in the next 8 years.

The results indicate that based on the evaluation criteria, the ECMWF model performs relatively better in estimating the average temperature of Birjand County compared to the NCEP model. Moreover, the analysis of box plots, mean values, and standard deviations of the generated time series in the calibration and validation sections showed that both models produced similar patterns of dispersion, minimum, maximum, and mean values compared to the observed time series. However, the ECMWF model exhibited relatively better performance in terms of mean and variance values of the generated data on a monthly basis in the calibration and validation periods. As a result, the ECMWF model was selected as the superior model for simulating and predicting the average temperature of Birjand County for the years 2022 to 2030 under the SSP245 emission scenario using the CanESM5 model. The predicted results indicate that the average temperature of Birjand County is expected to increase by approximately 3 degrees Celsius compared to the statistical baseline period.

The natural abundance of phytoplankton in the Persian Gulf has changed, creating a constant threat to biodiversity. Monitoring and predicting the spatial-temporal behavior of this phenomenon is crucial to prevent negative social-economic consequences and environmental planning. On this basis, the current research aims to assess the spatial-temporal changes of chlorophyll and analyze its relationship with the sea surface temperature in the Persian Gulf. To this end, we estimated and analyzed changes in the concentration of chlorophyll as an organic compound produced by algae and plants using MODIS sensor images from 2000 to 2021 (monthly and seasonally). Spatial indices of the barycenter were also calculated in addition to accuracy assessment using error evaluation criteria. Global Moran and G-star (G*) statistics were used to identify the degree of spatial autocorrelation and the magnitude of spatial patterns. The results showed that the intensity of this phenomenon on average varies between 0.05 and 16.38 mg m-2. The northwest and southwest have the maximum values and the central parts of the Persian Gulf have the minimum values. The northern coast of the Persian Gulf is located in Iran, and the northern parts, i.e., areas with less population and industries, have a low amount of this phenomenon. The ellipse line shows three standard deviations in the northwest and southeast directions following the cities and industries. The time series of changes showed that as the temperature decreases in the cold season, especially in January, the concentration increases, and as the temperature of the water surface increases in the hot season the amount of chlorophyll in the water decrease. Statistical analysis of the spatial distribution also indicates a significant cluster spatial pattern. Slope changes verified that the southern regions of the Persian Gulf, especially in the countries of Bahrain, UAE, and Saudi Arabia, have the most positive slope of changes. In the main part of the area, including the northern part of the Persian Gulf, whose coastline is entirely owned by Iran, except for the northwest, which includes the Mahshahr port and Asaluyeh, the slope of changes has been very low or decreasing. This research could identify areas exposed to adverse changes in phytoplankton concentrations and can be used in environmental planning.

Coping with climate change (CC) is part of the way to face this phenomenon. This depends on the understanding of CC and the degree of adaptability to it.

The research was conducted with the aim of measuring the level of nomads' understanding (NU) of CCe and their adaptation strategies (AS) in the face of CC.

The research was descriptive and the data collection tool was a questionnaire whose items were obtained based on interviews.

NU of the effects of CC is not the same. According to them, CC has had the most obvious impact on vegetation. The priority of AS is also different in the face of CC. A positive relationship was observed between understanding the effects of CC and the degree of adaptability in facing CC. Nomads who had a better understanding of the effects of CC have used livestock management strategies to adapt to it. Age and history of animal husbandry had a positive relationship with the level of understanding of the effects of CC. A negative and significant relationship was also observed between the number of animals and the degree of compatibility.

The NU of CC and its effects on the ecology of the environment is an important starting point in dealing with the negative effects of CC and choosing appropriate strategies to adapt or deal with it. So that the selection of suitable adaptation methods by the nomads reduces the vulnerability of CC on the condition of livestock and rangeland.

One of the most important challenges in sustainable development is the increase in the emission of greenhouse gases, especially carbon dioxide (CO2) in the atmosphere, and the subsequent increase in the earth's temperature and climate change. Carbon sequestration by plants is a practical solution to reduce the concentration of CO2 in the atmosphere. During the process of photosynthesis, atmospheric CO2 is absorbed and stored as organic carbon. This study was conducted to measuring the amount of CO2 sequestration in six plant species (Date palm, olive, oleander, eucalyptus, pine and haloxylon) that were cultivated in Chadormalu Mining and Industrial Company. In the soil section, a study was conducted as a factorial experiment based on randomized complete block design (RCBD) and the amount of carbon sequestration was measured in seven uses (control area and six plants) and two depths (0-25 and 25-50 cm). The results of analysis of variance showed that the effect of land use, depth and their interaction was significant on the amount of sequestration (P< 0.05). The results showed that the carbon sequestration at 0-25 cm was significantly more than 25-50 cm and all plants had more carbon sequestration than the control area, significantly. Pine had the highest amount of sequestration in the soil (24.59 ton.h-1 of CO2), which was significantly more than other plants. The amount of carbon sequestration in the leaves, roots and shoots of six plants was investigated based on a RCBD. The highest amount of sequestration was observed in litter, root and shoot of oleander, pine and date (31, 23.32, and 78.75 ton.h-1 of CO2), respectively. The lowest amount of sequestration was observed in the litter, root and shoot of haloxylon (1.23, 1.94 and 19.6 ton.h-1 of CO2, respectively). The results showed that the maximum amount of total carbon sequestration in the parts of the soil, litter, root and shoot observed in pine (161.39 ton.h-1) that was significantly higher than other plants. The lowest amount of total carbon sequestration observed in the haloxylon (52.3 ton.h-1). According to the results of this study, pine has the highest carbon dioxide sequestration capacity among the investigated plants and is recommended as a suitable plant for cultivation in the green space of similar areas. It seems that by choosing plants compatible with the climate, soil and water conditions of the region, while creating a suitable green space, it is possible to reduce the concentration of CO2 in the atmosphere and improve the air quality.

Failure to meet needs, climate change and resulting social and economic conflicts have faced many challenges in the life of nomadic communities. So that they are forced to leave animal husbandry temporarily or permanently. In this regard, the present research has identified and analyzed the reasons for the abandonment of livestock by nomadic in the summery rangeland of northeastern Iran.

The current research purpose is applied and descriptive information was collected by survey method and using a mixed approach. The statistical population is consists of 175 Kurdish nomadic households of Kurmanj, North Khorasan, from four traditional communities in the rangelands of Ajisu area in Marve-tape city, Golestan province. According to Yamane's (1967) formula, 122 beneficiaries were selected as a sample using a stratified random method with proportional assignment within the studied customary systems. The measurement tool was a researcher-made questionnaire, the items and components of which were finalized based on 15 interviews with experts who were identified based on the snowball technique. So that the questionnaire was prepared based on 46 items in the form of 11 components. Each of the items through a five-point Likert scale including very little (with a numerical value of 1), little (with a numerical value of 2), to some extent (with a numerical value of 3), a lot (with a numerical value of 4) and A lot (with a numerical value of 5) were measured. The content and face validity of the questionnaire was confirmed through experts' opinions and based on the convergent validity criterion (AVE), which has a minimum value of 0.5. To determine the reliability of the measurement tool, two criteria, Cronbach's alpha coefficient and composite reliability (CR) were calculated, for which a value greater than 0.7 is acceptable. After collecting data, statistical calculations in this research were done based on SPSS25 and Smart PLS3 software in two descriptive and inferential sections.

The results obtained from the average rank comparison showed that the five components of "livestock and rangeland management challenges", "climate changes and the resulting threats", "incompatibility of the market with the new needs of grazing", "lack of economic The presence of animal husbandry and the reduction of saving power" and "life experiences outside of animal husbandry and changes in local customs" respectively, with the highest mean values of 10.09, 9.87, 8.27, 8.14 and 7.93 are the highest. It has had a degree of importance and influence in the abandonment of livestock by nomadic in the studied area. The results obtained from the relationships between the components of the model of the reasons for the abandonment of livestock by nomadic showed that all the relationships were positive and significant at the 95% confidence level. So that the strongest relationships are assigned to "life experience outside ranching → conflict" and "reduction in family ability → life experience outside ranching" with path coefficients of 0.687 and 0.547. The weakest of them also belong to the two relations "weakness of government services → reduction of savings" and "weakness of government services → market mismatch". As the results showed, climate change and its consequences on the two factors of production reduction and poor access to financial resources (ρ=0.010, β=0.363 and t=2.585) and the weakness of government services and lack of Infrastructures (ρ=0.000, β=0.301 and t=4.364) had a positive and significant effect at the 99% confidence level.

According to the obtained results, the development of livelihood support of the government, including educational facilities and health and medical infrastructures, providing facilities and cheap bank loans for the development of businesses with the aim of reducing the dependence of users on rangeland, development of veterinary services free in the affairs of the nomads of the province, increasing government subsidies in the distribution of supplementary animal feed, flour and fuel, laying the groundwork for providing drinking water for animals and families, creating job opportunities through the holding of professional and skill training courses Businesses such as handicrafts and the production of targeted dairy products, the creation of livestock markets and the sale of live livestock are suggested in order to prevent nomadic communities from abandoning traditional animal husbandry.

Extreme precipitation has a significant impact on the frequency, severity, and duration of natural hazards, such as floods, droughts, and landslides. This has a significant impact on human life, the economy, natural ecosystems, and agriculture (Song et al, 2015: 34). Between 1880 and 2012, there was a 0.85 °C increase in the average global temperature, with a general increase in precipitation in the mid-latitudes of the Northern Hemisphere (IPCC, 2013: 2; Lio et al, 2017: 822). In addition, there is a possibility of a rise in extreme precipitation in the future (Klein Tank et al, 2006: 1), and so far, the reason for these changes and their relationship with the general circulation of the atmosphere have not been considered. The aim of this study is to analyze the trend of changes in extreme precipitation indices in northwestern Iran and its association with the general circulation of the atmosphere.

In order to analyze the changes in extreme precipitation events in northwestern Iran, daily precipitation data was collected from 20 synoptic stations in the region between 1986 and 2010. The region that is being studied encompasses West Azerbaijan, East Azerbaijan, Ardabil, Zanjan, and Kurdistan. In assessing limit events, high quality and reliable long-term climate data with daily (or higher) resolution is required (Clintanak et al., 2009: 9). The first step was to examine the quality control and homogeneity of data. The RClimDex software package, introduced as a standard tool by ETCCDI, was used to perform quality control and evaluate data homogeneity in this research. The Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) introduced 11 indexes to examine changes in precipitation level indices in northwest Iran. RClimDex software calculates these indicators with a significance level of 0.05. This process seeks to establish a standard set of indicators to examine and compare the characteristics of different regions. The software was used to calculate precipitation indices and display the trend and rate of change on a map.

The extreme precipitation indices were calculated to determine the regional trend and percentage of stations with positive and negative trends for the studied stations in northwestern Iran. Afterward, a map was created showing the spatial distribution of the slope for each of the indices. All precipitation indexes, except for the maximum growth period index (CDD), are declining according to the results. The probability of precipitation has decreased due to the more stable winter atmosphere in the region from the point of view of general atmospheric circulation. The region's spring atmosphere, similar to that of winter, shows an increase in stability, which will result in less rainfall. In summer, except for the coastal provinces of the Caspian Sea and the coasts of the Oman Sea, the rest of the country has recorded a decrease in rainfall of 1 mm per day. Most parts of the country experienced an increase in atmosphere thickness to 6 meters in autumn in the study area. Autumn in the region is typically stable and barotropic, but the study area is experiencing less rainfall. This study examines the trend of changes in extreme precipitation indices in northwestern Iran and its relation to a large-scale general circulation of the atmosphere. According to the results, 75% of stations in the region are experiencing a decrease in the maximum daily rainfall (RX1day) and 80% are experiencing a decrease in the maximum five-day rainfall (RX5day). While both the very wet (R95P) and ultra-wet (R99P) day indices are experiencing a downward trend, the R95P index is experiencing a more pronounced downward trend. All three indices R10, R20, and R25 have been declining for the past 25 years, but the R10 index has fallen more rapidly than the other two indices. Sarab station has a positive CWD trend alone, while other stations have a negative and decreasing trend of this index. In most stations throughout the region, the CDD index is increasing. In 85% of stations in the region, the PRCPTOT index is decreasing and there is a noticeable increase in rainfall. The SDII index is experiencing a decrease in 60% of the stations in northwestern Iran, while an increase is being observed in 40%. All precipitation indices, except for the CDD index, have a decreasing trend in general. Drawing and analyzing combined difference maps for geopotential height parameters of 500 hPa, relative rotation of 500 hPa, vertical velocity (omega), rainwater and precipitation rate to study the general atmospheric circulation of the region indicates an increase in altitude has led to a 500 hPa increase in climate stability in the study area (northwest of Iran). The study of omega and relative rotation shows that the region is experiencing a decrease in upward currents and positive rotation. The lack of atmospheric moisture load and rainfall in all seasons can be seen in rainfall water difference maps and rates. Precipitation indices and the general circulation model of the region's atmosphere are compared, indicating that the moisture load of the region's atmosphere has decreased, resulting in drought.

GCM models are widely used to assess climate change on a global scale, but outputs of these models are not sufficient and accurate to assess climate change at local and regional levels. Therefore, in this study, SDSM model was used for micro-scaling of CanESM2 model data and climate conditions of Semirom region based on three scenarios of RCP2.6, RCP4.5 and RCP8.5 in the period 2020 to 2100. The results of model evaluation based on NCEP database showed that the model was more accurate in estimating and predicting temperature data especially mean temperature. Comparison of observation and simulated data of temperature and precipitation of GCMs in the baseline period (1980 to 2005) based on NCEP predictor variables showed the mean correlation of precipitation data of 0.52, mean temperature of 0.88, maximum temperature of 0.80 and minimum temperature of 0.70 for validation and verification periods. The results of the estimation of precipitation variations in different scenarios also predicted a decrease of at least 7.24% and a maximum of 18.55% for the time period of 2020 to 2100 compared to the baseline period (1980-2005). The results of precipitation prediction also show the changes of precipitation pattern. The comparison of the scenarios also shows that the RCP2.6 scenario as the most optimistic scenario has the least rainfall while the RCP8.5 scenario predicts the highest rainfall reduction. Examination of the predicted changes in temperature also shows an increase for the mean, minimum and maximum temperatures,

A spatial survey of the degree of vulnerability is an important step in order to identify vulnerable areas and make decisions to protect natural resources and achieve sustainable development. Therefore. the present research the present study was conducted with the aim of spatially investigating the degree of vulnerability of Dorodzan Dam watershed in Fars’s province with a climatic approach.

Climatic data of 9 climatology station in the period 1991-2020 was used. indicators affecting the vulnerability of the watershed were extracted. including wet season. dry season. cold season temperature. hot season temperature. maximum wind speed. and height above sea level. After determining the aforementioned indicators. the overall vulnerability index (OVP) was calculated.

In this method. vulnerability indicates the performance. nature. rate of climate change in the watershed.  The results showed that the dry season index ranged from 6.40 to 66.19 mm for Chamriz and Emamzadeh Ismail stations and the wet season index ranged from 70.67 to 538.03 mm for Eghlid and Chamriz is variable. Also. the cold period index was 1.77 to 2.65 °C for Dorodzan and Zarghan stations. respectively. and the warm period index was 1.52 to 2.21 °C for Chamriz and Zarghan stations. respectively. The maximum wind speed index was calculated between 0.08 and 0.85 km/h for Eghlid and Dorodzan stations. respectively.  Altitude was also assessed.  The results based on the analysis of climatic indicators show that the total vulnerability index in the Dorodzan dam reservoir is between 25.7 to 34.2 and fall within reversible class.

Given the results obtained and considering the good Potential of this watershed. the need to consider management and preventive measures against drought and flood threats caused by climate change and human change is important. Considering that according to the standard of the mentioned method. the index is less than 40 in the reversible range. therefore. according to the location of the vulnerability index of the studied area with the climatic approach. it can be concluded that although the region has undergone many changes especially in recent years however. due to the slight changes in climatic indicators in the long term. this watershed is still far from the limits of resilience from physical aspects. and with the adoption of suitable management patterns. it has the ability to return to ideal conditions.The results of the research. in addition to expressing the importance of the effects of climate change. will help their application in the application of correct and compatible management with climate change in future watershed management policies.

Urban watersheds have become important due to the problems related to water resources management, including floods and pollution control. Therefore, the approach of engineers in recent years is toward computer software for estimating and simulating runoff. So far, many rainfall-runoff software with different capabilities and complexity have been developed and used for flood forecasting. The approach of the current research is to simulate the surface runoff and to identify the flood and critical nodes under the influence of climate change and to determine the efficiency of the surface drainage network in the west of six districts of Tehran municipality.

This research includes two parts of hydrology and water flow. In the hydrological department, after calculating the concentration time, in order to extract the rainfall intensity of the plan and to analyze the maximum rainfall for different durations of the base period (1980-2020), the near future period (2021-2050) and the far future (2051-2100) and preparing the IDF curve at different times and durations, based on the RCP2.6 and RCP8.5 scenarios, the Abkhezer-Qahraman method was used. In the water flow section, the assessment of the drainage network and flood control nodes in the western part of the six municipalities of Tehran in the base period (1980-2020) and under the influence of climate change (2021-2050), (2051-2100) in the RCP 2.6 and RCP8.5 scenarios for the period 25 and 50 year returns were made.

The results showed that the values ​​of rainfall intensity in the duration of rainfall and different return periods in all three scenarios have increased compared to the frequency intensity curve of the base period, and the maximum rainfall intensity has increased in the short-term time base, and with the passage of time, the maximum rainfall intensity has decreased. and IDF curves are affected by short-term rainfall. The results of the model calibration showed that there is a good agreement between the observed and simulated data in the simulation of water runoff in the five investigated rainfall events. The results of the sensitivity analysis showed that the impervious areas have the greatest impact on the change of peak water discharge. The results of the evaluation of flood nodes showed that, for example, the number of flood nodes in the base period (1980-2020) and under the influence of climate change in the RCP2.6 and RCP8.5 scenarios (2021-2050) for the 25-year return period are 7, 10, and 12, respectively. The number of flood nodes in the base period (1980-2020) and under the influence of climate change scenario RCP2.6 and RCP8.5 (2051-2100) for the return period of 50 years are 9, 14 and 17, respectively.

Conclusion and Suggestions :

The results of this research showed that the number of flood catchment nodes in the basin increased in the 25-year return period compared to the 50-year return period in each period. According to the identification of critical nodes in the research area, by applying modern methods of urban runoff control, such as creating permeable surfaces and gardens, absorption wells and reservoirs and storage ponds, the runoff can be controlled at the source to reduce the volume and peak water in the downstream, so that the probability of occurrence Minimize flooding and flooding. Since the percentage of impervious areas in the study area is high, it is recommended to increase the dimensions of the channels in terms of the passage of flood during rains and to have the ability to direct more amount of runoff and reduce the amount of peak flow of runoff in the location of super critical nodes.

According to the Global Risks Report, climate change was the fifth global risk in 2018 in terms of probability of occurrence. According to the same report, this phenomenon ranked second among other risks in terms of its effects on the world and the environment. It emphasizes the importance of investigating the effects of climate change. In this research, the economic analysis of the effects of climate change on the income of wheat farmers in the country during (2052-2022) has been done. DSSAT simulation program has been used to simulate the yield of irrigated wheat and economic analysis under climate change scenarios. The meteorological data required under different scenarios are taken from MarkSim software. The results show that the income of irrigated wheat farmers will decrease under climate change scenarios. Also, in some regions of Iran, irrigated wheat planting is dominant in conditions without climate change and under climate change scenarios, and it seems that these areas can be considered as favorable areas for creating irrigated wheat cultivation ports.

The warming of the earth also affects the condition of other parts of the climate system and causes the phenomenon of climate change. Global warming increases the moisture capacity of the air and decreases the relative humidity of the air. The most common method of simulating climate parameters is to use the output data of atmospheric general circulation models. When the results of the models are evaluated on a much smaller scale, significant differences are created. But how can these differences be eliminated and models can be chosen that can provide a suitable background for the future of climate change. In this research, these differences are investigated and the accuracy of the results of climate models in predicting the relative humidity of the air is investigated using the validation method. In this regard, the relative humidity data of Dasht Naz synoptic station in Neka city was used on a daily basis with a statistical length of 25 years (1990-2014). Next, the relative humidity parameter data of the closest cell to the synoptic station was downloaded from the data of general atmospheric circulation models, the sixth report of the Intergovernmental Panel on Climate Change, in the common period (1990-2014) and extracted in Arc GIS 10.8 software. According to the results, ACCESS-ESM1-5, CNRM-CM6-1, CanESM5, IPSL-CM6A-LR, GISS-E2-1-G and MIROC6 models showed the highest correlation with observational data, but Except for two ACCESS-ESM1-5 and CanESM5 models, the other mentioned models had a high bias compared to the observational data. Therefore, ACCESS-ESM1-5 and CanESM5_ESM models are recommended among climate models to predict relative humidity in the future in the Neka region.

Applying the raw data of regional climate models in assessing the impact of climate change is not advisable due to possible biases. Therefore, correcting the bias of these data is necessary before using them for climate scenarios of the future. The aim of this study is to evaluate the performance and introduce the best combination of bias correction methods for precipitation and minimum and maximum temperature simulated by three CMIP6 climate models.

Five bias correction methods including linear scaling, variance scaling, local intensity scaling, power transformation and distribution mapping were investigated using root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (r) and Student's t test for the historical period (1990-2014). Afterward, the combination of the best bias correction methods was used to project precipitation and temperature under SSP2-4.5 and SSP5-8.5 scenarios in the future period (2051-2075).

Based on the results, three methods of variance scaling, local intensity scaling and power transformation for correcting the bias of the investigated data had weaker performances compared to the other methods. Two methods of linear scaling and distribution mapping had the lowest RMSE and highest r and NSE. Projecting the future climate using the combination of these two selected methods showed that the average annual precipitation in the Hamedan-Bahar region will decrease by 28 and 37 percent under scenarios of SSP2-4.5 and SSP5-8.5, respectively. Furthermore, the annual average of the maximum and minimum temperature will increase by 0.7, 0.9 under scenarios of SSP2-4.5 and 1.4 and 1.5 °C, under scenarios of SSP5-8.5, respectively. In addition, the highest seasonal decrease in precipitation (19.8 mm) compared to the baseline period will occur in the spring under the SSP5-8.5 scenario. Moreover, the highest seasonal increase of maximum and minimum temperature compared to the baseline period was projected in winter (1.6°C) and spring (1.7°C), respectively, under the SSP5-8.5 scenario.

Two methods of linear scaling and distribution mapping are suitable for reducing the bias of the CMIP6 models in the Hamedan-Bahar plain. Also, considering the projected increase in temperature and decrease in precipitation in this region, this study can provide useful information for policy-makers of water resources and agriculture to decide about the rainwater harvesting, recharging of aquifers, crop selection, cultivating period, crop rotation and management methods to reduce the impact of future climate change.