فهرست مطالب elham kadkhoda

It is challenging to estimate how the climate of a region will change under global warming in the future. Among the essential climate variables, temperature, relative humidity and wind speed are very important, and their changes in relation to each other can have many consequences, such as increasing heat stress and evapotranspiration.    In this research, we investigated future temperature, wind speed, and relative humidity in Iran. Five models, namely GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have been evaluated versus observations for 1990-2014. A multi-model ensemble was generated with the Integrated Weighted Mean (IWM) method from selected CMIP6 models, and the performances of individual CMIP6 and CMIP6-MME models in estimating temperature, wind speed, and relative humidity were investigated. The results showed that all five selected models are capable to reproduce the variables; however, the CMIP6-MME significantly improved the results. The CMIP6-MME showed good agreement with observational data both in terms of climatology and spatial distribution of each variable, and its performance is higher compared to individual models.   We have used two Shared Socioeconomic Pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, and three-time periods, namely near-term (2026-2050), midterm (2051-2075), and long-term (2076-2100) relative to the historical period (1990-2014).The results of this study showed that Iran will undergo changes in temperature, wind speed, and relative humidity spatial distribution in the future. The decrease in relative humidity and dryness of the air in the future can have important consequences for agriculture, food security, and water resources management. The findings of this study, in agreement with previous studies, emphasize the significant increase in temperature throughout Iran. Under the SSP2-4.5 (SSP5-8.5) scenario, the average annual temperature of the country increases by 1.40 (1.83), 2.34 (3.58), and 2.99 (5.58) degrees Celsius in the near (2026-2050), middle (2051-2075), and far (2076-2100) future, respectively.    Along with the increasing trend of temperature in Iran, wind speed will decrease in most regions of the country in the middle and end of the 21st century under two SSP scenarios. This decreasing trend can be a result of decreasing atmospheric instability and increasing potential temperature. The northwest of Iran has shown the maximum increasing temperature and the maximum decreasing wind speed. The decrease in relative humidity in Iran has been evident since the 1990s, and the projection results indicate that it will decrease in large parts of Iran in the future. However, the relative humidity in the southeast of Iran shows an increasing rate in the future. Results of this study show that the heat stress will be significantly higher through SSP5-8.5 than SSP2-4.5 in the 21st century due to the decrease in wind speed and increase in temperature throughout Iran. Therefore, Iran should quickly move on to formulate and implement long-term adaptation plans for resilience against climate change.

To assess thermal comfort of residences, several different factors as meteorological, physiological, and socio-cultural, should be considered. The integrated effect of these variables on thermal stress can be obtained and evaluated using thermal comfort indices. Thermal comfort as a bio-meteorological index is of special importance. The purpose of this research is to analyze the seasonality and trend of heat stress in Iran in the period from 1981 to 2020. In this research, the Universal Thermal Climate Index (UTCI) index as a common thermal index was evaluated for outdoor thermal comfort, using ERA5 dataset. ERA5 is the fifth generation ECMWF reanalysis data for the global climate and weather for the past 4 to 7 decades. The ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave, and land-surface quantities. An uncertainty estimate is evaluated by an underlying 10-member ensemble at three-hour intervals. Such uncertainty estimates are closely related to the information content of the available observing system which has improved considerably over time. They also indicate flow-dependent sensitive areas (Hersbach et al., 2020). Also, root mean square error (RMSE), Percent bias (PBIAS), Nash–Sutcliffe model efficiency coefficient (NSE), and Root Mean Standard Deviation Ratio (RSR) metrics were used to evaluate the quality of ERA5 dataset.The seasonal variability of the UTCI index shows that this index has significant regional heterogeneity in Iran. The increase in UTCI from the north to the south of Iran leads to an increase in thermal stress. The spatial distribution of areas that do not have thermal stress during the hot period of the year are mainly consistent with the altitudes. During the cold seasons of the year, areas with elevations of more than 3,000 meters in Iran have moderate cold stress. The investigation of the trend of thermal stress during the last four decades, which was analyzed with the modified Mann-Kendall test, shows that the UTCI in Iran has a dominant increasing trend. The UTCI index shows an increasing trend in the spring and autumn seasons by 100%, and in the winter and summer seasons at 99.83 and 99.75% of the country, respectively. The maximum significant increasing trend of the index at the level of 0.05 was achieved in the spring. In the same way, the highest value of Sen's slope estimator test of the area-averaged trend is also seen with the value of 0.52 oC in this season in the study period. The results of this study for climatology and the trend of the UTCI index in Iran show that: 1- There is a close relationship between heat and cold stresses in Iran and topography, but this relationship is not a linear; 2- Along with global warming, the UTCI index in Iran during the years 1981-2020 has shown an increasing trend; 3- In general, areas with UTCI cold stress in the country are decreasing and areas with heat stress are increasing; 4- One of the key findings in this study is the significant increase in trend of the UTCI index in the spring season.

Climate change has significantly increased the frequency and intensity of heat stress and has more effects than increasing average temperature. This study has investigated the spatial distribution of the universal thermal climate index (UTCI) during historical and future periods in Iran. The UTCI (°C) refers to “the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model” (Jendritzky et al., 2012). In this way, the UTCI is an equivalent temperature, similar to PT. The thermal impact of the meteorological conditions is compared to the one of a standardized reference “indoor” environment with RH = 50% (Ta < 29 °C), WS = 0.5 m s−1, pa = 20 hPa (Ta < 29 °C), and Tmrt = Ta (Shin et al. 2022). Three variables of daily temperature, relative humidity, and wind speed from two sets of data, including 124 meteorological stations and five models from the Coupled Model Intercomparison Project phase 6 (CMIP6) model, including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL were investigated with a horizontal resolution of 0.5o. Then, an ensemble model (CMIP6-MME) was generated from these five models using the independent weighted mean (IWM) method. The performances of individual models and the generated ensemble model were examined by Taylor's diagram. The results showed that the multi-model ensemble has higher performance than individual models for all three variables. The results revealed that the spatial distribution of the seasonal averages of the UTCI index has significant variability in Iran, and the variability of this index is affected by the latitude, complex topography, and distance to water resources in Iran. In general, heat stress will increase significantly in Iran by the end of the century. So, we will witness a significant decrease in areas with no heat stress until the end of this century. On the contrary, strong to very strong heat stress events will increase significantly in the country at the end of the century. While the areas with no thermal stress show a spatial displacement to mountainous regions and higher latitudes. These results show that effective adaptation methods should be taken to adapt to global warming and reduce its consequences to avoid the adverse effect of increasing heat stress events in Iran. The results show the overall increasing trend of Iran's heat stress in the near and far future. The highest increase in heat stress anomalies (13.3 degrees Celsius in winter during the far future period under the SSP5-8.5 scenario) can be found in the northwest and west of the country. The increasing intensity of heat stress in the western and northwestern parts of Iran may be related to elevation-dependent warming (EDW).

The Urmia Lake basin is one of the most vulnerable areas to frequent high-intensity droughts in Iran. The aim of this study is to project meteorological drought in the Urmia Lake basin through the 21st century. For this purpose, the standardized precipitation-evapotranspiration index (SPEI-1) was investigated using the bias-corrected CMIP6 models under SSP1-2.6 and SSP5-8.5 scenarios during the period 2026-2100. The performance of individual CMIP6 models and multi-model ensemble (MME) generated by the independent weighted mean (IWM) method with three metrics including NRMSE, MBE, and PCC were evaluated. Overall, all individual CMIP6 models showed a good performance in the Lake Urmia basin, despite some overestimations of precipitation. However, the generated CMIP6-MME has increased the PCC values in all stations to 0.99. The CMIP6 MME showed a good performance of the SPEI-1 index in autumn, winter, and spring against observation from ground stations in the historical period. The result indicates a significant increase in drought events mainly in the west and north of the Urmia Lake basin in the warm period of the year during the 21st century. The severity and the percentage of below-normal years for the basin-averaged drought in the middle 21st century (2051-2075) is more than the ones in the far future (2076-2100), especially for the SSP5-8.5 scenario. These results can provide a basis for the development of drought adaptation plans in the Urmia Lake basin.

Precipitation is one of the most variable climatic parameters. These changes occur both in terms of location and time in terms of the region's climate. This study was conducted to model the spatial relationships of seasonal rainfall in the northeast of the country with a joint monthly statistical period of 30 years (1980-2010).

In order to achieve spatial variation of rainfall, new methods of spatial statistics such as spatial autocorrelation, global Moran, spatial dispersion index and geographic weight regression model (GWR) were used in GIS software.

The results of this study showed that rainfall changes in northeastern Iran have a high cluster pattern or positive. The Global Moran Index for each of the four seasons and the annual sum is above 0.93, the highest Global Moran index with the value of 0032191 is for the summer season.

The results of the GWR model showed that rainfall in the northern parts of the study area had positive spatial auto-correlation and in the southern parts, which are mostly desert areas had negative spatial auto-correlation. Also, the results of dispersion data were the result of cluster pattern of precipitation in the northeast of the country. Based on the frequency index of clusters or the ICF, the winter season is the largest cluster with a numerical value of 2646.26 in Northeast of the country.

Although nature has the ability to deal with change, it cannot tolerate the growth of industries, deforestation and degradation of the environment. These huge and abrupt changes have caused to increasing the destruction of nature in recent decades, and increasing greenhouse gases. Ground water plays an important role in sustaining ecosystems and enabling human adaptation to climate variability and climate change. Groundwater resources are the largest store of fresh water in arid and semi-arid regions. In the areas where surface water resources are limited and inaccessible for people, groundwater can be considered as a secure resource. Climate change caused fluctuations in the ground water. Many wells dried up and some of them have lost their advantage due to the lack of available water. Climate change through changing climatic variables (including temperature, precipitation and evapotranspiration) directly affects surface water resources in many parts of the country. Besides, there have been many related problems such as drying up wells, reducing river flow, decreasing water quality, subsiding in lands, diminishing fresh water supplies and increasing salts in water. Climate change and its associated problems appear to be a serious challenge in the regions as Khorasan Razavi that 32 of 34 desert plains were declared prohibited. Due to cold and dry weather conditions and use of aquifers and wells as the main sources of water, Mashhad relies on groundwater as well as underground resources. The water resources of this plain are affected and threatened by changes in groundwater levels, temperature, rainfall, and frequent droughts. This research aims to study the groundwater level decline in Mashhad plain which is located in Khorasan Razavi province, northeastern of Iran. The study tries to conduct a comprehensive study through the analysis of monthly precipitations data acquired from 34 synoptic, climatology and rain-gauge stations, monthly discharge of 13 hydrometric stations, and 60 groundwater piezometric wells. It confirmed the normality of climatic data, using the Kolmogorov-Smirnov test. The research also used multivariable regression methods as well as HadCM3 climate model scenarios to model groundwater perspective of Mashhad’s plains and to predict future climate variables, respectively. To compare the output scale of the mentioned model with the required scale of climate change studies, the data of temperature and precipitation were downscaled for the two periods of 2015-2030 and 2046-2065, under three scenarios of A1B, A2 and B1 using the LARS-WG5.5 model. It later employs geographical weighted regression (GWR) to model the ground water level (as the dependent variable) with climatic parameters of temperature, precipitation, and evapotranspiration (as independent variables). Mashhad’s prospect of precipitation and temperature revealed a rainfall reduction for the cold season, its increase in the warm season, and at least one-degree increase compared to the base year. Modelling of groundwater level changes (using the parameters of rainfall, temperature and evapotranspiration) showed decline in groundwater levels in the range of at least 0.2 meters and maximum 0.7 meters. The research provides an intelligent simulation for fluctuations of groundwater using multiple regression models and the historical data of rainfall, temperature and evapotranspiration, which helps to estimate underground water level in the coming years. The results indicate a drop in the water level of all studied piezometric wells, under three climate change scenarios for future periods. The study concluded that the most important reason of water loss in Mashhad’s plains is the human overuse of groundwater. Fluctuations in groundwater assessment have shown that the highest annual fluctuations of groundwater were occurred in the southern part of Mashhad plain. Fluctuation assessments of surface water showed negative fluctuation in upstream and positive in downstream. These issues reflect the role of human factors in increasing the groundwater level in downstream and reducing the level in upstream. Modeling of groundwater level fluctuations with climate parameters (temperature, precipitation and evapotranspiration), using GWR and OLS models, represents superior of geographical weighted regression model compared with ordinary least squares models. Fitting weighting matrices to GWR model showed high accuracy of Gauss method than the other studied methods. The important issue that was obtained from simulations of Mashhad station is reducing the variance of rainfall in A1B emission scenario, indicating the absence of stable conditions in the future. The results of this research showed that the most important reason for the drop in Mashhad plain is overuse of groundwater.

IntroductionThe meaningful, complex, and ongoing connection between the rainfall and other climatic elements causes diversity in space and time. A new approach in climatology is to describe the spatial variability of the rainfall based on the spatial statistics. Unlike the classical statistics, the spatial statistics shows the statistical data on a map. Therefore, the attention and emphasis on the spatial differences and the identification of the specific and unique points or homogeneous regions will be provided. Modeling of the rainfall behavior is one of the main foundations in any climate research. In this regard, two major efforts are of interest to climatologists. One of them is the precipitation zoning. The other one is the analysis of the spatial temporal variations of the precipitation. This analysis is important for weather forecasting and a wide range of decision makers, including hydrologists, farmers, and industrialists.
MethodologyUsing statistical methods, the present study aimed to introduce the fundamentals of the spatial data and the general precipitation behavior of Mashhad’s plain along the space. In this regard, the study used the daily precipitation data of 34 synoptic stations, climatology, and rain gauge during the survey period, 1963-2013. The study initially analyzed the spatial and temporal distributions of the precipitation based on the classical statistical methods. Then, it focused on the central average, standard distance, and directional distribution. In this research, the universal Moran method was used to calculate the spatial autocorrelation data. In addition, the central mean method was used to calculate the basin rainfall gradient. Finally, directional distribution was used to calculate the trend and direction of the precipitation distribution.
DiscussionThe results showed that the gravity center, the centroid, of the annual rainfall during the last half-century sustains a displacement of 3.83 km where the distribution arrow demonstrates the magnitude of the tilt and orientation on the amount of the precipitation.
Also, the standard distance of the precipitation in Mashhad, in the fifth decade (2003-2013) compared to the first decade (1963-1973), changed to 1254.57. This change can be one of the reasons of the instability of the linear relationships of the spatial factors and the rainfall in the plains of Mashhad.
ConclusionThe results showed that the center of the rainfall gravity of Mashhad plain was displaced over 3.3 km during the 50-year period. In addition, there was a change of 0.269 degrees in the direction of the distribution of the precipitation in the fifth period compared to the first period. Since, this shift was negative to areas with spatial dependence, it indicated a general drop in the rainfall in the Mashhad plain. The results also showed that the roughness and height might be two important factors affecting the spatial patterns of the rainfall in Mashhad Plain.