: climate change

Runoff is an important hydrological component in the assessment of water resources. Most water resource applications rely on runoff as an essential hydrologic variable. The hydrology of basins is influenced by many factors, including climate change. Basin discharge estimation is an important step in planning and managing surface water resources, especially in basins that lack reliable flow data. In this study, due to the inappropriate spatial distribution of meteorological stations in the study area of Takab, satellite images and products were used to evaluate the possible effects of climatic factors including rainfall and temperature on runoff. For this purpose, in order to investigate the changes in rainfall and temperature from 1998 to 2020, TRMM and FLDAS satellite products, respectively, were evaluated using different statistical criteria with Takab synoptic station data. The evaluation results indicate the appropriate accuracy of these satellite products compared to the observed values. Choosing a suitable rainfall-runoff model for the catchment area is important for the efficiency of planning and management of water resources. Also, choosing a model requires recognizing the capabilities and limitations of hydrological models of the catchment area, which requires access to meteorological parameters such as rainfall and temperature. According to the studies, the IHACRES hydrological model has been used to estimate the amount of changes in discharge and runoff in many basins. Its purpose is to help water resources engineers describe the relationship between basin runoff and precipitation.

Finally, the trend of changes in rainfall and temperature was investigated with the non-parametric Mann-Kendall and Sense's slope tests. Examining the rainfall data of the study area of Takab indicates that the highest amount of rainfall occurs in the 3 months of April, March and November respectively. which is approximately equivalent to 45% of the total annual rainfall of the study area and its values are estimated as 53.1, 40.4 and 39.6 mm per month respectively. Also, the highest and lowest average temperatures of the range are in the months of July and January, respectively, which are estimated at 24.2 and -3.4 degrees Celsius, respectively. In the following, the IHACRES model was used to simulate the river discharge using temperature and rainfall data from satellite products. Also, in this study, the IHACRES model was used to predict production runoff under the influence of climate change and evaluate different climate scenarios. For this purpose this study focused on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) for the coming years until the year 2100 were used.

The flow simulation results from the RCP2.6 scenario indicate that the greatest increase in discharge in the coming period for the Takab study area was estimated for the months of December, November and January. Also, according to the RCP8.5 scenario, the largest flow increase in the future period was calculated in the months of August, July and January, respectively. In addition, the maximum decrease in discharge in both scenarios was simulated in the months of April and May, respectively. According to the obtained results, according to the RCP2.6 and RCP8.5 climate scenarios, the average annual discharge was predicted to be equal to 8.3 and 1.5 cubic meters per second, respectively. In order to evaluate the IHACRES model, the determination coefficient (R2), Nash-Sutcliffe efficiency coefficient (ENS), root mean square error (RMSE) and bias error (Bias) were used in the calibration and validation period. According to the obtained results, these values for the 14-year period of model calibration are 0.82 and 0.80 (-), 1.4 (cubic meters per second) and 0.42, respectively. Also, these values for the validation period were calculated as 0.71 and 0.68 (-), 4.7 (cubic meter per second) and 0.1, respectively.

In this study, to investigate the trend of rainfall and temperature changes from 1998 to 2020, TRMM and FLDAS satellite products, respectively, were evaluated using different statistical criteria with Takab synoptic station data. According to the obtained results, the annual changes of rainfall in the entire study area of Takab are incrementally insignificant. Also, in general, the percentage of annual rainfall changes in the highlands is higher than the average annual rainfall in the plains. After examining the trend of temperature and rainfall data, the IHACRES rainfall-runoff model was used to simulate the discharge. After simulating discharge for the statistical period, different climate scenarios were used to predict production runoff. According to the obtained results, according to the RCP2.6 and RCP8.5 climate scenarios, the average annual discharge was predicted to be equal to 8.3 and 1.5 cubic meters per second, respectively. According to the RCP2.6 scenarios, the predicted discharge has an insignificant upward slope. Also, the percentage of annual changes in river flow according to this scenario was calculated as 19.4%. Also, in the examination of the output of the IHACRES model resulting from the RCP8.5 scenarios, it was observed that the future trend of the river is a significant downward trend. In other words, the percentage of annual changes in the simulated discharge according to this scenario was estimated as -68.1%.

IDF (Intensity-Duration-Frequency) curves play a crucial role in hydrological modeling, infrastructure design, and flood risk management. Traditional methods, relying on ground-based observations, face challenges such as limited spatial coverage, short temporal records, and the stationary assumption, particularly under climate change. This study addresses these issues by utilizing ERA5 reanalysis data to develop basin-scale IDF curves for the Karkheh River Basin (KRB) in Iran. Annual Maximum Precipitation (AMP) series for 6-, 12-, 18-, and 24-hour durations were extracted from ERA5 data and corrected for bias using observations from seven synoptic stations. Bias correction significantly improved ERA5 estimates, particularly in high-altitude regions prone to systematic errors. An elevation-bias relationship was established to extend corrections basin-wide. The corrected AMP data were modeled with the Generalized Extreme Value (GEV) distribution under stationary and non-stationary conditions to construct spatially distributed IDF curves. Based on 82 grid points, these curves provide detailed rainfall intensity estimates, overcoming limitations of station-based methods. The findings underscore ERA5 data's potential, combined with bias correction, to enhance hydrological analyses in data-scarce regions by better capturing spatial variability and extreme precipitation. This work supports improved flood management and infrastructure planning. However, future research must address uncertainties in bias correction and parameter estimation while extending data records. High-resolution reanalysis datasets are pivotal for adapting to evolving climatic conditions, extreme weather, and prolonged droughts.

One of the most critical meteorological variables influenced by climate change is the Probable Maximum Precipitation (PMP), a key parameter in the design of high-risk water infrastructures such as dams. This study assesses the impacts of climate change on PMP across the coastal provinces of the Caspian Sea (including synoptic stations in Anzali, Rasht, Ramsar, Babolsar, Qaemshahr, and Gorgan). To generate climate change scenarios for two periods (2025-2054 and 2055-2084), the outputs of 18 AOGCM models and three greenhouse gas emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) were used. To reduce uncertainties in the AOGCM outputs, two methods-weighted averaging and probabilistic analysis-were employed. In the weighted averaging method, AOGCM models were ranked and weighted based on their accuracy in simulating precipitation of baseline period. In the probabilistic method, probability distributions were applied to generate precipitation scenarios at probability levels of 0.5, 0.75, and 0.90. The findings revealed significant uncertainty among AOGCM outputs and emission scenarios in estimating PMP, indicating that PMP changes in the studied stations do not follow a consistent pattern. However, in the weighted averaging approach, PMP is generally expected to decrease in the first period and show a slight increase in the second period. For designing high-risk structures, it is recommended to utilize the results from the critical scenario with a 0.90 probability level. In this case, the increase in PMP due to climate change varies from approximately 12% at Qaemshahr station to a maximum of 48% at Gorgan station.

After wheat, rice and corn, potato is the fourth product in the food basket of human societies, which shows the importance of its role in providing protein and food needs of people. Potatoes contain large amounts of vitamins A, B1, B2, and C along with potassium, phosphorus, and calcium, and consumption of 300 grams of potatoes tuber per day provides more than 50% of the human need for vitamin C and potassium. The World Food and Agriculture Organization (FAO) has introduced potatoes as a product that provides future food security in the world, and the country of Iran, having a diverse climate and with an annual production of five million tons of potatoes, ranks thirteenth in the production of this product in the world. So, in this research, the consequences of climate change on the yield of potato tuber, the statistics of the production values, and the area under cultivation of potatoes were evaluated.

The dada of Ardabil, Tabriz, Shahrekord, Hamadan, Jiroft, Kohnouj, Manojan, Isfahan, Sanandaj, and Shiraz stations in the statistical period of 1982–2015 were used. The mentioned regions are among the main centers of potato production in the Iran and account for 62.7% of the cultivated area and 68.3% of the country's potato production. In this research, the production and performance data of 10 meteorological stations inside Iran were used and the data of the fifth report of the Representative Concentration Pathway scenarios (RCPs) have been used to evaluate the consequences of climate change on potato tuber yield.In order to prepare field data for calibration and determining the validity of the WOFOST model in potential conditions (conditions without water and nutrients restrictions, disease, and weed control) the data of the Agricultural Research Stations of Hamedan, Isfahan, Ardabil, Shiraz, Tabriz, Shahrekord, Jiroft, Kohnouj, Manojan and Sanandaj were collected during 2010-2014 and used as a base or monitoring period.This information includes planting date, phenological stages from planting to germination, germination to flowering, and germination to physiological maturity of the potato plant in each of the regions.The SDSM statistical model was used for downscalling of CanESM2 model data, and the WOFOST model was used to simulate the performance of the potato tuber. In order to calibrate the WOFOST model using field data, the phenological and performance data in the years of 2010, 2011, and 2013 were used to calibration of the model, and from phenological and performance data in the years of 2012 and 2014 were used to determine the validity of the model. In order to calibrate and determine the validity of the WOFOST model in the study areas, it is first necessary to adjust the potato plant parameters (coefficients) for different climatic conditions. Therefore, the creation of the plant file, which is the most sensitive part of WOFOST model calibration, was done for tuber performance. In this study, WOFOST version 7.1 was used. This model uses parameters and functions to describe the effects of temperature, radiation and water stress on the main processes of crop growth. The outputs of the model include the final yield, leaf area index, crop growth rate, dry matter accumulation rate in each organ, and development stages with one-day intervals. The calibration of the model and the determination of the coefficients of the model were done in such a way that the expected performance of the tuber was simulated by the WOFOST model. Parameters of specific leaf area as a function of development stage (SLATB), maximum leaf CO2 assimilation rate as a function of development stage of the crop (AMAXTB), efficiency of conversion into leaves (CVL), efficiency of conversion into roots (CVR), efficiency of conversion into stems (CVS) and Lower threshold temperature for emergence (TBASEM), which were necessary for the simulation of gland function, were calibrated and determined based on the permissible range of model coefficients changes and using resources. Then, in order to simulate the potential performance of the potato plant for all 10 study areas and every five years, with conventional planting dates and also with a range of planting dates (three dates) a total of 150 loads were executed.

According to the obtained results, the average yield for the 10 studied stations was 46190 kg/ha for the calibration stage and 44434 kg/ha for the validation data. In the model implementation stage, the average yield during five years for the 10 studied stations was 45919 kg/ha for calibration and 43813 kg/ha for the validation stage by the model. Therefore, by evaluating the statistical indicators, the WOFOST model has high accuracy for simulating performance in all regions.

The results of the simulation of the yield of potato tuber under the future climate conditions indicated a decrease in yield in the studied stations. Thus, the greatest decline in performance was observed in the period of 2071-2099 and under the RCP8.5 scenario. In the studied areas, on average, the highest yield decrease was related to Ardebil station with 2397 kg/ha and Hamedan with 1817 kg/ha, and the lowest decrease in the yield of potato belonged to Sanandaj station with 813 kg/ha and Isfahan station with 982 kg/ha. On average, in the 10 studied areas, a decrease in potato tuber yield was observed by 1436 kg/ha. Reducing the length of the potato ripening period has the biggest contribution in reducing the potato tuber yield. The results obtained from the simulation of the yield of potato tuber showed that in parallel with the increase in temperature, the yield of potato also decreases, so that for one degree Celsius increase in the average annual temperature, the yield of potato will decrease by 3.77%.

Adaptive planning and water allocation in the conditions of climate change is facing deep challenges due to various uncertainties. In this situation, decisions should be made that are more stable and maintain their function in different climatic scenarios. This study was carried out with the aim of planning agricultural adaptation measures in the conditions of climatic uncertainty in the Karun 3 watershed using the robust decision-making (RDM) approach. To achieve this goal, GCM models under two emission scenarios (RCP4.5 and RCP8.5) and WEAP model were used to simulate the current situation of water resources and uses in the basin. Runoff and cultivated area were considered as parameters with uncertainty.

To realize the research goal, the WEAP model was used to simulate the current situation of the basin's water resources and uses. Because under the effect of climate change, the precipitation and temperature parameters of the area will undergo changes in the future from three models - EC-EARTH, CNRM-CM5 and GFDL-ESM2M under two scenarios RCP4.5 and RCP8.5 to investigate the amount of temperature changes and future period precipitation was used. Also, in this research, the IHACRES rainfall-runoff model was used to simulate the runoff of the Karun River in the future. Measures such as 10% increase in irrigation efficiency (S1) and changing the cultivation pattern (S2) were considered as effective adaptation measures in the agricultural sector. Finally, by using the robust decision-making approach (RDM), the strength of the presented strategies under the existing uncertainties for the future period was examined and evaluated. Based on two approaches, RDM minimizes the effect of uncertainty. The Regret Method, which is obtained by minimizing the amount of deviations in performance caused by uncertainty compared to the ideal state, and the robustness approach or the satisfactory boundary, which calculates the fraction of scenarios that meet the designed criteria.

The results of runoff simulation for the future period (2055-2030) show that the amount of Karun river runoff will decrease by 22-8% in different months compared to the base period. Evaluation of the simulation results of the WEAP model in the current situation, the drinking water needs are almost completely provided in most of the sectors and only in the A8 sectors, which is the study area of 2301 (Khorramshahr) and A6 (the study areas of Lali and Indica). ) are facing water shortage. Also, the results showed that there is no shortage in the industry sector. Meanwhile, sectors A1, A2 and A3 are facing a shortage of water resources in the agricultural sector. The results show that due to the deficiencies in the existing conditions, the Behesht Abad watershed will not have the necessary potential to transfer water to the volume of 580 million cubic meters per year through the Behesht Abad tunnel to the Zayandeh Rood watershed. Also, the results showed that the satisfaction index of strategy 2 and strategy 1 is 0.78 and 0.65, respectively. Also, the comparison of the results of the regret index shows that strategy 2 is stronger than strategy 1.

Finally, based on the research approach and the definition of stable decision-making, the results showed that the strategy of changing the cultivation pattern can be put on the agenda as a stable strategy, so that it can work in a wide range of uncertainty and climate scenarios. Maintain within the current status of the basin. The main strength of this study is its systematic methodology, which helps to make more informed decisions and design comprehensive adaptive programs. It can also be applicable to other basins by considering different goals and indicators. However, future studies on climate change uncertainties and socio-economic evaluation of adaptation measures are other areas that could improve the analyses.

Due to the global warming and climate change and its outcomes are among the most significant environmental challenges today. Iran, as a country with a semi-arid and arid climate, has always faced issues such as water scarcity and drought, climate change can exacerbate these problems and have destructive effects on the environment, economy, and human societies. Thus, a precise understanding of the impacts of climate on the land is crucial for reducing societal vulnerability, enhancing resilience against climate changes, and preserving the country's natural resources. In this context, this study aims to examine the effects of climate change on the aridity index on seasonal and annual scales under the climate scenarios of the latest climate report (CMIP6). This study aims to offer a comprehensive outlook on the number of industries likely to be affected by varying intensities of climatic drought across Iran by providing long-term drought forecasts under the SSP climate scenarios.

In this study, data from 31 synoptic stations distributed across the country were utilized. Precipitation and temperature data from the statistical period 1997 to 2014 served as observational data, while high-resolution climate data from NEX-GDDP provided the basis for projections for the three future periods of 2025-2049, 2050-2074, and 2075-2099. These data were analyzed under the SSP2-4.5 and SSP5-8.5 scenarios. The climate models used in this research included CNRM-CM6-1, CanESM5, GFDL-ESM4, HadGEM3-GC31-LL, and MIROC6. The monitoring of changes in the aridity index was performed using the De Martonne aridity index. An evolutionary algorithm was employed to optimize the coefficients of the climate models and their integration. The statistical indices RMSE, MAE, and MBE were used to evaluate the performance of the climate outputs compared to the observed values in the base period.

The climatic classification of the studied stations, based on the De Martonne aridity index for the period from 1997 to 2014, indicates that 39% of the stations are situated in semi-arid climates, while 23% are in dry climates. The findings reveal that the stations in Sistan and Baluchistan, Yazd, Khuzestan, and Hormozgan are classified as very dry climates. The evaluation of the climatic output accuracy, using statistical indices, demonstrated that there is no significant bias in precipitation and temperature estimations for 81% and 90% of the stations, respectively. An analysis of changes in the De Martonne aridity index for the upcoming three periods, relative to the base period, shows a trend toward increased dryness in the stations of Isfahan, Qom, Semnan, Kerman, Hormozgan, Mazandaran, Golestan, Ilam, Chaharmahal and Bakhtiari, Fars, and Tehran. Furthermore, a review of the distribution of industrial facilities and their water consumption reveals that provinces such as Isfahan, Fars, Tehran, Alborz, East Azarbaijan, and Razavi Khorasan each host over 1,250 industrial workshops. The industrial water usage in East Azarbaijan, Tehran, Isfahan, Khuzestan, Bushehr, and Razavi Khorasan exceeds 62,217,790 m3.

The results indicate that, based on observational data from the base period, a significant portion of the country falls within semi-arid to very arid climate classes. The base period’s results suggest that precipitation and temperature data from the sixth climate change report are valuable resources for monitoring future drought conditions under various climate scenarios. The findings reveal that the decrease in the DMI is more pronounced during the summer and autumn seasons compared to spring and winter. Overall, the results demonstrate that, under both SSP2-4.5 and SSP5-8.5 scenarios, many provinces in the country will experience a shift towards semi-arid, dry, and very dry climate conditions. Given the concentration of industrial workshops in these provinces, it is imperative to develop and implement strategies for water resource management in these areas. The outcomes of this research can significantly contribute to the sustainable management of water resources in the face of climate change.

Evaluation of the climate change phenomenon and its probable consequences on basin hydrological processes will effectively help managers and planners of water resources in upcoming periods. The impact of climate change is evaluated through the simulation of hydrological processes via the runoff-precipitation physical model. Hydrological models provide a framework for evaluating the relationship between meteorology and water resources. The objective of this study is to simulate the runoff production in climate change condition based on climate scenarios and the SWAT model.

Climate change and its consequences are one of the basic problems in water resources management, and it is necessary to estimate its effects in the future period. The area studied in this research is the Po-e-Shah catchment area with an area of 721 square kilometers, which is one of the sub-basins of the Alvand basin in Kermanshah province. This basin has many floods every year, which sometimes causes damages and flooding of agricultural lands. The Dirah River located in this basin is the source of water supply for part of the agricultural lands nearby and downstream of the river. Therefore, investigating the effect of climate change on the yield of this river is very important. In this research, AOGCM general circulation models are used to estimate monthly temperature and precipitation in the future period. The RMSE, MAE and NS indices are used to validate and evaluate the accuracy of estimation of general circulation models and data fitting. In this research, the runoff at the Pol-e-Shah hydrometry station is investigated first. Using SWAT CUP software, based on hydrometric station statistics and using SUFI2 optimization algorithm, parameters affecting flow rate are recalibrated for the period 1994 to 2011 and validated for the period 2012-2015. Then, in order to investigate the statistical indices of precipitation and temperature under the influence of climate change, using the LARS-WG6 software and using HADGEM2 and MIROC5 climate models under RCP2.6, RCP4.5 and RCP8.5 emission scenarios, the micro-scaling and extraction of precipitation data and temperatures are performed for the statistical period from 2020 to 2080. Finally, to simulate the impact of climate change on runoff, the SWAT software is implemented under each of the climate scenarios in different statistical periods. Then, the results of monthly runoff simulation under climate scenarios are compared with recorded observational data.

The results of the SWAT model efficiency evaluation indicate the proper performance of this model in the validation and verification period, so that the values of correlation coefficient and Nash-Sutcliffe coefficient are obtained for the calibration stage 0.75 and 0.79 respectively and for the validation stage 0.71 and 0.61 respectively. The results of the SWAT model implementation show that in all climate scenarios, the pattern of monthly runoff production is consistent with the pattern of precipitation changes in different months in these scenarios. Therefore, in all future scenarios, the distribution of monthly runoff in different months is messed up compared to the base scenario so that in some months there is a decrease in runoff and in some months an increase in runoff. This shows the need to use dams and flow control structures to store water in high water months such as winter and spring and use it in low water months. The results indicate that the changes in the volume of annual production runoff under the RCP2.6, RCP4.5 and RCP8.5 climate scenarios for the periods of 2045-2018 and 2072-2046 compared to the period of 2018-1991 vary between 60 and 87 million cubic meters on average. The amount of annual runoff volume changes in the period 2045-2018 is insignificant in most scenarios. In the period of 2072-2046, the volume of annual runoff is decreased between 3 and 10 percent in most scenarios.

The results showed that the decrease in precipitation and increase in temperature and thereby increase in evaporation cause changes in the current climate cycle, which leads to a decrease in runoff. Therefore, it is necessary to adapt and reduce the negative consequences of climate change on the water resources of the region to avoid from adverse effects of climate change on the water resources of the region by using the correct management of water resources and considering the needs of agriculture, drinking, industry and the environment in the coming years to lead to the best conservation of these resources. The results showed that in most of the climate scenarios, the precipitation displacement has happened. Therefore, it is necessary to change the cultivation pattern or change the date of cultivation of different crops according to the changes in precipitation and temperature changes in different months.

The most important effect of global warming is the increase in extreme weather events. According to AR5 reports, between 1951 and 2010, the number of warm days and nights increased and the number of cold days and nights has declined globally. In addition, the duration and frequency of hot periods, including thermal waves, have increased since the middle of the twentieth century. The trend analysis of temperature extreme indices is important in estimating the trend of global warming. Temperature Changes are affected by many complex factors. A significant part of these changes is due to the elements of the general circulation of the atmosphere and the sea surface temperature. Given that extreme weather events are one of the most devastating natural hazards and have harmful effects on different parts of society, therefore, many researchers have studied the changes in the past and future of extreme events and the mechanisms that trigger these changes. This research attempts to study the trend of changes in extreme temperature indices in North-West of Iran, and also their relation with general circulation of atmosphere.

At first, diurnal data of minimum and maximum temperature of 20 synoptic stations of the Northwest of Iran, which have long-term and reliable statistics, extracted for the period of 1986-2010 and quality control and data homogeneity of them were investigated. afterwards, 16 Extreme temperature indices introduced by ETCCDMI were applied. In general, these indices are categorized into five categories of absolute indices, based on percentiles, based on thresholds, periodic, and amplitudes that measure the frequency, severity and duration of the temperature. These indices are estimated by RClimDex software and the trend rate of the changes in indices was shown through maps. To measure the changes in the general circulation of atmosphere the annual mean circulation composites extracted for the periods of 1961-1985 and 1986 -2016 based on the reanalysis data of the NCEP / NCAR. Then the difference maps plotted using grads software.

The regional trend of extreme indices and the percentage of stations with a positive and negative trend were identified and the spatial distribution of the gradient of each of the indices was mapped. The results show that all absolute temperature indices have an increasing trend. On average, the maximum temperature (TXx and TXn) has increased by about 0.04 degrees over the decade. The increase rate of TNx is about 0.03 degrees, while the TNn increased by about 0.1 degrees Celsius per decade during the study period. Therefore, in the north-west of Iran, temperature increase has mainly occurred at night. The values of cold days (TX10) and cold nights (TN10) decreased with a gradient of -0.46 and -0.42 days in the decade. The warm days (TX90) and warm nights (TN90) have an increasing trend in 95% of the stations in the area. Frost days (FD) and icing days (IDs) have a decreasing trend, whereas, summer days (SU25) and tropical nights (TR20) have an increasing trend. The number of frost days with a gradient of -0.95 and the number of icing days with a gradient of -0.63 days in decade are decreasing. While, the number of summer days with a gradient of 0.81 and the number of tropical nights with gradient of 0.31 days in decade are increasing. In the northwest of Iran, all stations have been experiencing the increasing trend in Warm Spell Duration Index (WSDI), but the Cold Spell Duration Index (CSDI) in 70% of the stations in the region has decreased. Growing season length, as an effective index especially in agriculture, is increasing by an average of 1.1 days per decade. Based on the results of research carried out globally and at Iran, the trend of Daily Temperature Range (DTR) is negative, while this index has a positive and increasing trend in 65% of North-West stations in Iran. Except TNx and TNn indices that have positive trend in most stations in the region, Comparison of warm and cold extreme indices indicates that warm indices have a positive and incremental trend, while cold indicators show a decreasing trend. The positive gradient of these indices also corresponds to the decreasing trend of cold day and night indices, which indicates an increase in temperature and a decrease in cold days and nights. The study of large-scale changes in atmospheric circulation shows that the study area has got warmer in the spring and summer and colder in autumn and winter.

In this study, the trend of temperature extreme indices in North-West of Iran and its relation with the large-scale general circulation of the atmosphere have been investigated. The results show that all absolute temperature indices (TXx, TXn, TNx and TNn) are incremental. The indices of cold days (TX10) and nights (TN10) decreased with a gradient of -0.46 and -0.42 days in the decade and the indices of warm days (TX90) and warm nights (TN90) are increasing in 95% of the stations in the area. Frost days and icing days (IDs) show declining trend and summer days (SU25) and tropical nights (TR20) have an increasing trend. In the north-west of Iran, all stations have experienced an increasing trend in warm spell duration index (WSDI), but the cold spell duration index (CSDI) has been decreasing in 70% of the stations in the area. Growing season length (GSL) is increasing by an average of 1.1 days in every decade. Daily temperature range (DTR) has a positive and increasing trend in 65% of stations in north-west Iran. Comparison of warm and cold extreme indices indicates that warming indices have a positive and incremental trend, while cold indices show a decreasing trend. Study of the general circulation of atmosphere of the region by drawing and analyzing difference maps indicates that the study area has been warmer in spring and summer and colder in autumn and winter.

Forest soils are recognized as one of the most important carbon sinks on Earth, playing a critical role in climate balance and greenhouse gas mitigation. This study investigated the effects of altitude, climate change, grazing, and manure application on soil organic carbon (SOC) stock in forest soils of Talesh County, Iran. The Century model was used to investigate the effects of climate and management factors on soil organic carbon storage. The results showed that SOC stock increased with increasing altitude due to higher precipitation and lower temperature. The Century model estimated SOC stock with high accuracy. The defined scenarios for the Century model showed that climate change with reduced precipitation and increased temperature significantly decreased SOC stock. The negative effect of climate change was more pronounced at higher altitudes. Grazing also reduced SOC stock, especially at higher altitudes. In contrast, manure application increased SOC stock, and its positive effect was more pronounced at higher altitudes. In the climate change scenario with manure application, manure application largely compensated for the negative effect of climate change, but did not completely neutralize it. The results of this study indicate that soil organic carbon simulation models are accurate tools for predicting the effects of climate change, grazing, and manure application. In addition, the conservation of high- altitude forests and optimal forest management are of particular importance to prevent the loss of SOC stock and to combat climate change.

In order to adapt the agricultural systems to increased climate variability, meteorological information needs to be combined with management recommendations before the start of the cropping season. There are solutions to reduce the vulnerability of agriculture to increasing climate change through agro-meteorological advisories based on weather forecasts. Therefore, the purpose of this study is to review the services provided by agricultural meteorology to reduce the vulnerability of agriculture in climate change conditions. For this purpose, we attempt to use successful experiences from different countries, from developed countries with advanced technologies to less developed countries with local knowledge, in adapting to climate change. This includes various types of agricultural meteorological services, major obstacles, and gaps in providing them. Climatic services provide solutions to offer better climatic services to farmers, climatic services used in agricultural meteorology, as well as approaches to accelerate the adoption of agricultural meteorological technologies and consulting services by farmer.

To be resilient against the negative effects of natural disasters and events, there is a need for resilience programs and policies to be accompanied by civic participation, the development of indigenous knowledge, and the use of technology in line with indigenous knowledge and regional infrastructures, according to the geographical, social, and cultural characteristics of each region. Additionally, the decisions of governments and policymakers, in line with the establishment of laws and regulations and resilience policies, can strengthen or limit the ability of other actors to adapt to the effects of climate change. In the same vein, the results of the review of sources include a list of existing barriers and gaps, such as the lack of real climate forecasts, lack of information on crop growth and development, low participation of agricultural producers in the production and use of services, unfair access to communication channels, and insufficient adaptation of agricultural meteorological services to needs. It also highlights the need to move towards the transformation of digital agriculture, targeted training in Agro-meteorology, strengthening the role of information and communication technology in providing accurate information, increasing investment in installing automatic weather stations and radar, and holding seminars and training workshops to overcome obstacles and improve the quality of climate services in the agricultural sector.

The Persian Gulf and Gulf of Oman basin, due to its specific geographic-climatic position and location in the arid and semi-arid regions of the globe, is highly vulnerable to climatic anomalies, such that one of its most important climatic elements, precipitation, exhibits severe variability. This leads to prolonged droughts in some areas and causes floods and river overflows in other locations. Therefore, the study and prediction of climatic changes in this basin is crucial for reducing potential hazards and damages at various social, economic, and environmental levels.

In this research, daily precipitation data from 47 meteorological stations over 30 years (1993-2022) were obtained from the Iranian Meteorological Organization. After acquiring the data and creating a database, the daily Precipitation Concentration Index (PCI) was calculated for all studied stations. The PCI value is a number between zero and one. The closer the PCI is to one, the higher the concentration of precipitation within a limited number of days, increasing the likelihood of floods and heavy rainfall events in those areas. Ultimately, the long-term trends of the PCI were analyzed using Sen's slope estimator.

The long-term average of the daily PCI values for the Persian Gulf and Gulf of Oman basin indicates a high level of this index in the studied basin. The highest values of this index are observed along the southern to southeastern coastal strip from Bushehr to Chabahar, while the lowest values are found in the western and southwestern parts of the basin from Abadan to Piranshahr. The trend analysis results showed that, except for a few stations) Brujen, Jusk, Masjed Soleiman) with negative trends, the entire basin has been dominated by increasing trends. The increasing trend slope indicates that precipitation is concentrated within fewer rainy days, which could lead to an increase in heavy rainfall and flood events within this basin.

The PCI trend analysis results showed that most of the studied basin area has experienced increasing trends, meaning that precipitation has become concentrated within fewer rainy days. This increasing trend, which could be due to an increase in droughts in this basin, may exacerbate the occurrence of flood-like rainfall events within the basin. Therefore, the changes of this index in the sub-basins of Bandar Abbas-Sadij, South Baluchistan, Karun and Western Marzi have been statistically significant. This trend highlights the need for serious attention to flood, drought, and other hydroclimatological hazard management in this basin.

Astragalus is the vegetation of many mountains of Iran's plateau and plays a major role in providing ecosystem services due to its pillow shape and deep rooting system, they facilitate the control and penetration of precipitation into the soil. The correlation of Astragalus ecosystems with arid and semi-arid climates has made them vulnerable to climate change. In this study, a runoff yield map based on the Budyco curve under current and future conditions of climate change (2050) was prepared using climate and temperature data from the Chelsea site (CanESM2 GCM) in TerrSet software and by using maps of sub-watersheds, annual precipitation, annual potential evapotranspiration, soil depth, plant accessible water and the current and future "Land Cover - Land Use" map, with a combination of field methods and species distribution models at the local scale of the Shur River watershed of Dehaghan (Central Zagros). Finally, the excess runoff damage produced due to climate change was estimated using the replacement cost method. The results indicated an increase in the annual runoff volume of the watershed from 70 million cubic meters to 105 million cubic meters under climate change conditions for the RCP26 scenario in 2050. Taking into account the cost of 10 million Rials for controlling 530 cubic meters of runoff through various watershed management projects, preventing the damages of excess runoff produced requires a credit amounting to 660 billion Rials based on the present value. This study proved the ability of TerrSet software to predict and produce an ecosystem service map of runoff yield under climate changes or land use changes and with the purpose of valuation on a local scale. Also, the above valuation can be the basis for planning and providing credit for the study and implementation of watershed management projects to deal with the threats of climate change.

In the present study, the impact of climate change on maximum temperature and daily precipitation in 16 weather stations was investigated in the Sefidrood Basin from 2023 to 2052. 10 AOGCM models related to the sixth IPCC Assessment Report (CMIP6) were ranked based on their ability to simulate temperature and precipitation in the historical period (1980 to 2014). Then, the maximum temperature and daily precipitation outputs of the best model at each weather station were extracted using the LARS-WG downscaling model under three emission scenarios SSP126, SSP245, and SSP585 from 2023 to 2052. The Mann-Kendall test (95% confidence level) was also used to investigate the trend of changes in the average maximum temperature and maximum daily precipitation. The results showed that different AOGCMs have different accuracies in simulating temperature and precipitation in different regions of the basin, and their accuracies in simulating temperature were better than simulating precipitation. In general, the IPSL-CM6A-LR and HadGEM3-GC31-LL models had the best performance in simulating maximum temperature and precipitation, respectively. Results also indicated that the mean maximum temperature will increase between 0.9 and 2.8 °C in different emission scenarios. Also, the mean maximum daily precipitation will change between -8.6 and 7.17 mm in different emission scenarios.

The main goal of this research is to simulate the interaction of surface water and groundwater by creating a connection between surface water and groundwater models in the Lor plain under climate change conditions. In this regard, the effects of climate change on surface water and groundwater sources were investigated based on the sixth report of the inter-state commission using a WEAP-MODFLOW coupled integrated model. The changes in the water level of the aquifer and the amount of the dropdown in the groundwater level were evaluated under the reference scenario assuming the continuation of the current situation and climate change scenarios, and the number of fluctuations in the entire plain for the 27-year period of 2050-2023(September 2050) in all climate change scenarios based on a model. A hybrid model, composed of different models, was predicted. The results showed that the average dropdown in the groundwater level at the end of 27-year period of 2023-2050 will be about 11 meters if the current situation (observational scenario) continues. In this scenario, the maximum dropdown in the groundwater level will be 38.7 meters in a part of the central and southwestern areas of the plain. If the climatic parameters predicted by the hybrid model are used in the coupled model of surface water and groundwater, the average dropdown in the groundwater level in the scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP4-8.5 will be 9.8, 10, 10.18 and 10.83 meters, respectively. The maximum dropdown in these scenarios will be 34.5, 35.2, 35.5 and 38.2 meters, respectively.

Given the complexity of the climate system and the non-linear relationships between the ocean and atmosphere within this system, it is imperative to comprehend and consider the uncertainties that stem from different sources. Understanding and accounting for uncertainties play a crucial role in predicting climatic variables and facilitating a comprehensive evaluation of greenhouse gas mitigation and adaptation policies. The objective of this study is to quantify the uncertainties in historical and future average monthly precipitation by employing various General Circulation Models (GCMs), bias correction methods, Shared Socioeconomic Pathways (SSPs) scenarios, and seven projection periods. To achieve this, the outputs of ten GCMs were adjusted using nine quantile mapping bias correction methods for the Rafsanjan study area, and a suitable method was chosen to analyze the uncertainties of SSPs and projection periods. Two statistical criteria, namely the standard deviation and interquartile range, were utilized to measure the uncertainties. The results revealed that the standard deviation and interquartile range of average monthly precipitation were lower during the historical period compared to the projection period. This difference was determined based on the selection of bias correction methods and GCMs. Furthermore, for both the historical and future periods, the STDEVs and IQRs of average monthly precipitation were lower depending on the type of bias correction methods rather than the type of GCMs. In general, the uncertainties associated with projection periods and the type of GCMs are higher during future periods compared to other sources of uncertainties such as bias correction methods and SSP scenarios. This highlights the necessity for a more accurate analysis. This study contributes to an enhanced understanding of the inherent uncertainties in climate change projections that arise from various sources.

In this study, the combination of the Improved Water Optimization (IWO) algorithm and the Water Evaluation and Planning System (WEAP) simulation model was employed to investigate the potential increase in the economic optimum height of the Zarineh Rood Reservoir Dam. The WEAP model's results indicated deficiencies in meeting the drinking, industrial, agricultural, and environmental needs of the study area under current conditions. Furthermore, the WEAP-IWO modeling results revealed an economically optimal increase in the height of the Zarineh Rood dam by 6.3 meters, resulting in a new reservoir volume estimated at 913.4 million cubic meters. By incorporating this increased reservoir volume into the WEAP model, there was an average 17.76 (precent ) enhancement in demand coverage and water supply system reliability across the study area. Additionally, the study assessed the impact of climate change on inflows to the Zarineh Rood reservoir for the future period (2022-2040), indicating an overall decreasing trend in the average annual river discharge compared to the baseline period. Furthermore, under both the SSP1-2.6 (optimistic) and SSP5-8.5 (pessimistic) scenarios, water scarcity for meeting agricultural demands in the study area is projected to worsen relative to current conditions. Reductions in demand coverage and reliability index results for the study area were observed under both SSP1-2.6 and SSP5-8.5 scenarios compared to current conditions. Therefore, increasing the dam's height to mitigate the effects of climate change appears necessary.

Climate change is one of the fundamental challenges of mankind in the last century and the coming years, which affects the status of water resources at the global level, especially at the scale of the watershed. In addition, climate change affects the hydrological cycle at the basin level and thus the management of water supply and demand. The Intergovernmental Panel on Climate Change (IPCC) emphasized that climate change is an inevitable phenomenon. Therefore, it is necessary to estimate water demand sites under the influence of climate change and evaluate different climate scenarios. Several software and models have been developed to estimate the effects of climate change and integrated management of water resources at the watershed level, among which we can mention the Water Resources Planning and Management System (WEAP).By using the WEAP model, it is possible to investigate, manage, and allocate different policies for the optimal use of water resources under the influence of different management scenarios, including investigating the impact of climate change on the amount of water allocated to different supply and demand sites in Therefore, according to the importance of the topic in this research, the evaluation of different climatic scenarios of the Mahabad watershed was done.

In this research, IPCC data were extracted under two scenarios RCP 4.5 and RCP 8.5, and then in order to use the data extracted in the study area, these data were used using the LARS_WG model with a smaller scale. In the following, the IHACRES model has been used to simulate rainfall-runoff in the future period in the study area. The outputs of the IHACRES model were introduced as the input of the Weap model, which is a comprehensive and integrated model in water resources. Then, the impact of climate change on supply and demand sites under different climate scenarios was simulated using the Weap model.

In this study focused on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) for 2020–2039 were used. The results obtained in the mentioned scenarios for the statistical period of 2020-2039 show a decrease in precipitation and an increase in temperature. In order to estimate the productive runoff of the basin under the influence of climate change, the IHACRES rainfall-runoff model was used and the performance of the model in the study area was evaluated with correlation coefficient (R) and bias error. It was found that in the calibration and validation period, the correlation coefficient and bias error were 72%, 0.08, 69%, and 0.59, respectively, which shows the acceptable performance of the model in the region. In the following, the outputs of the IHACRES model were introduced as input to the WEAP model and it was determined that with the priority of allocating one for the drinking site and two for the agricultural site of the MIROC 8.5 scenario, the highest amount of unmet demand with the amount of 121.4 million cubic meters, especially for the year 2039 will have. On the other hand, the GFDL 4.5 scenario will have the highest reliability with 72% volume reliability and 42% time reliability.

By examining the results, it can be concluded that according to the MIROC 8.5 scenario, the amount of rainfall will decrease by 4.8% compared to the observation period. This reduction trend was also observed in three other scenarios. Then, using the IHACRES model, the monthly average amount of flow for the periods of 2020 to 2039 was produced and it was observed that the amount of runoff produced by the model will decrease from 2036 onwards. In the following, the runoff produced by the IHACRES model as the input of the simulator-optimizer modelWEAP entered. Then, the information related to climate change was checked under four scenarios and it was found that the highest unsupplied water needs for drinking sites considering allocation priority one and agriculture with priority allocation two are related to the MIROC 8.5 scenario with the amount of unsupplied demand of 121.4 million. Is cubic meters. Of course, it is worth mentioning that in this study, underground water supply and water allocation to the industrial sector have not been modeled.Keywords: Integrated water resources management, Climate Change, IHACRES, WEAP, Mahabad river basin.

Using the most accurate methods and models to simulate the impact of climate change on meteorological variables in different regions of the world is of utmost importance. In this study, the accuracy of 10 AOGCM models related to the sixth assessment report of the IPCC (CMIP6) was investigated for simulating temperature and precipitation in the Sefidrood Basin. For this purpose, observational data of temperature and precipitation from 16 weather stations located in the basin during the time period from 1980 to 2014 were compared with the output of AOGCMs. The Kling-Gupta Efficiency (KGE) index was utilized for this comparison. The comparison was conducted on both annual and monthly time scales, and the more accurate models were identified for each time period. The results indicated that the accuracy of AOGCM models in estimating temperature in the study area was higher than their accuracy in estimating precipitation. Additionally, different models exhibited varying capabilities in simulating these variables across different months. Based on the results obtained, the MIROC6 and MRI-EMS2-0 models performed better than other models in estimating the temperature of different months. Furthermore, the HadGEM3-GC31-LL model showed a better performance than other models in estimating historical precipitation for most months of the year. Based on the results obtained, it is necessary to select and use the best AOGCM models for each month before conducting climate change simulation studies in the study area.