جستجوی مقالات مرتبط با کلیدواژه « بارش » در نشریات گروه « اکولوژی »

In recent years, the heightened concentration of greenhouse gases has brought increased attention to the pressing issue of climate change. Therefore, monitoring climatic variables to prevent the adverse effects of climate change is more important than ever. In the pursuit of long-term climatic forecasting and the assessment of their evolving patterns, various international scientific societies have concentrated their efforts on understanding the extent of climate change and devising measures to counter its adverse effects. The development of general circulation models of the atmosphere (GCM) has been a significant stride in this direction. However, GCMs may lack precision in predicting minor changes at a local scale. To address this limitation, the utilization of downscaling models such as SDSM and Lars_WG (that were used here) becomes imperative. These models serve as essential tools for simulating the viability of cultivating agricultural species in the future, especially when considering localized impacts. Because climate change will probably change the conditions for growing canola, as one of the strategic and prominent crops in Iran, studying the effects of this worldwide event on the canola-grown fields in the future is needed. 

In this research, using temperature and precipitation forecasting models along with GIS functions and hierarchical analysis process (AHP), canola suitability classes for 2050 were determined in Mazandaran Province. For this, 37 meteorological and synoptic stations were involved, and climatic data (including temperatures and precipitation) were generated under three RCP scenarios (2.6, 4.5, 8.5). In this study, we utilized two general circulation models (Can-ESM2 and HadGEM2-ES) that had been recommended for application in the study area.

A comparison of the involved models showed that the SDSM model was superior in predicting temperature, while the Lars-WG model performed better in predicting precipitation. The results for the land suitability revealed that the changes in climatic variables in the future would lead to changing the suitability of agricultural lands for growing canola. The examination of temperature change maps in the investigated region revealed that both minimum and maximum temperature variables are poised to rise under climate change scenarios, with a more pronounced increase anticipated in maximum temperatures. The findings indicate that the projected temperature increase in the future will establish more conducive conditions for canola cultivation. Additionally, precipitation patterns exhibit an increase in both RCP 2.6 and 4 scenarios, with a more substantial rise in the RCP 2.6 scenario. Conversely, the RCP 8.5 scenario predicts a decline in precipitation levels. Top of Form
Also, considering the climate change scenarios, the spatial distribution and the area of each suitability class changed slightly, so the high-suitable class will extend under RCP2.6, especially toward the center parts of the study area. Under RCP 8.5 and RCP 4.5 scenarios, not only suitable lands were not considerable, but also the less suitable land extended to the southern and western parts of the study area.

The output of the land suitability maps showed that climate change would change the suitability of the studied agricultural lands in the future. Also, with the implementation of the climate change scenarios, the area and geographical distribution of detected classes will change. In general, in the optimistic scenario, the lands will be more appropriate for canola cultivation and will cover a wider area. In other scenarios, the conditions for canola cultivation in the lands of Mazandaran will be more unsuitable compared to the present, and the decrease in the level of suitability will be more evident in the western and southern lands of the province. Therefore, solutions such as the use of more compatible cultivars and changes in agricultural management in facing new conditions in these areas should be considered. The outcomes of these studies offer practical insights for shaping regional planting strategies, aiding decisions on crop inclusion or exclusion, and informing the overall design of agricultural patterns in the area

In order to carry out the appropriate program for agricultural operations and to know the appropriate planting time of dry wheat crops, the length of the growth period and its beginning and end are special. The studies carried out during the growing period of dry wheat in the country are based on the growth degree day method, which does not exist for dry or semi-arid regions. In this study, the length of the dry season wheat growth period for a statistical length of 30 years (1371 to 1400) for 38 sinopic stations of the country by taking meteorological temperatures and precipitation, with the help of the proposed method of the ecological zoning guidelines of the Food and Agriculture Organization of the United Nations (FAO) and By finding the intersection points of the graphs of precipitation and 50% potential evaporation and transpiration, the beginning and the end, the length of the growth period has been estimated, which corresponds to the results presented for the length of the growth period in the ecological classification publication of the FAO organization. Results of the obtained results, Astara station with 301 days of growth period and Isfahan, Saravan, Tabas, Semnan, Zahedan and Kerman stations with 0 days of growth period are respectively the most and the least. to give have given. Also, the results showed that the use of the FAO method to estimate the length of the growth period in arid regions obtains more realistic estimates than the growth degree day method.

The main challenge to compromising with climate change from a management approach is dealing with the uncertainty of future climate changes. Analyzing the effect of climate change on tree species used for afforestation in Robat Karim as a city in the central arid regions of Iran was the purpose of this study. Afforestation with two-year seedlings of Mulberry (Morus alba), Tehran pine (Pinus eldarica), Tree of heaven (Ailanthus altissima), Ash (Fraxinus rotundifolia), Blackberry (Acer negundo), and, Acacia (Robinia pseudoacacia) took place after preparing the planting substrate in 2016. The diameter of the trunk, the height of the seedlings, the freshness of the leaves (good, medium, and pale), and the health of the seedlings (healthy, semi-healthy, infested) were measured during the planting. These parameters were measured again in 2022 after five years. Furthermore, to analyze the trend of seven climatic factors such as mean temperature (°C), mean maximum temperature (°C), the mean minimum temperature (°C), mean wind speed (m/s), the mean number of daily sunny hours from 1951 to 2021 and average reference evapotranspiration (mm) from 1991-2021, was carried out. The analysis results of climate elements showed that climate changes are taking place in the form of an increase in the mean annual temperature, an increase in the maximum and minimum temperature, a reduction in the total annual rainfall, a decrease in the wind speed, and an increase in the reference evaporation and transpiration. The number of Boxelder maple reached 80 trees, while the number of Tehran pine and Acacia seedlings did not change much and were equal to 145 individuals in 2022. The highest annual diameter growth rate includes Acacia and Tehran pine with 1.1 and 1.05 cm, respectively, while the lowest annual diameter growth rate involves black maple with 0.78 cm. The ANOVA test results to compare the condition of the trees in terms of the decay degree of the studied species in 2022 indicate a significant difference (p=0.05) between the studied groups. The priority is to determine native species and species such as Tehran pine and Acacia that can adapt to occurring tensions.

Climate change is one of the ongoing challenges. This phenomenon will affect all parts of the world, including Iran. Therefore, understanding and projecting climate change can be a way forward for future planning in different areas. To do this, the present study applied six general circulation models (GCM) to assess climate change in some stations in northwest, west and southwest of Iran under three different scenarios: RCP2.6 (optimistic), RCP4.5 (middle) and RCP8.5 (Pessimistic) by using LARS-WG during 2021-2100. Based on the results, the highest values were observed under the RCP8.5 scenario and in NorESM1.1, CanESM2 and HadGEM2-ES GCMs for sunhours, and minimum and maximum temperatures, respectively. Compared to the base period: The highest increase in minimum and maximum temperatures have been observed in Aligudarz station during 2081-2100 with 69% and 25%, respectively. Daily average rainfall and Sun hours might enhance 35.4% in Ahvaz during the years 2061-2080 and 9.8% in Parsabad during the years 2041-2060. On the other hand, the lowest daily values compared to the base period were observed in the RCP2.6 and RCP4.5 scenarios in Ahvaz, with a 0.23% decrease in minimum temperature and a 2.93% increase in maximum temperature. Also, compared to the base periods, the daily average precipitation in Parsabad has decreased by 8.63 % and sun hours in Mianeh have reduced to 14.54%. In general, the southwest may experience more precipitation compared to the northwest in the future.

Drought monitoring using appropriate drought indices is of importance in water resources management, especially in arid and semi-arid regions. Therefore, choosing the suitable drought index and calculating the desired drought index appropriately is of considerable importance in the study of drought. This study is aimed to determine the appropriate statistical distribution to calculate RDI drought index in arid and semi-arid regions of Central Iran. For this purpose, 16 synoptic stations in Central Iran were selected. To calculate RDI, precipitation and potential evapotranspiration values are required. The FAO-Penman-Monteith method was used to calculate potential evapotranspiration. To select the most appropriate statistical distribution, 17 statistical distributions were used. RDI for each synoptic station was calculated annually by fitting to each of the 17 distributions, separately. Then, based on the AIC and BIC criteria, the best statistical distribution was selected to calculate RDI for each station. While based on the literature, it is recommended to calculate RDI by fitting the data to one of the Gamma or log Normal distributions, the results showed that in most of the studied stations, the log Normal and Gamma distributions are not the most appropriate distribution. However, according to the results, Gamma distribution was one of the top 6 distributions in all the studied stations. The results also showed that the difference of RDI values ​​calculated based on different distributions in dry and wet years are relatively significant, which shows the importance of choosing the appropriate statistical distribution. The fit of the studied distributions to the precipitation data at different stations showed that the Nakagami distribution presents the best performance. In case of potential evapotranspiration, different distributions provided the best fit at different stations.

Stochastical analysis can increase decision-making reliability, meanwhile increasing the flexibility of decisions. The critical role of climatic factors in land zoning on the one hand and the stochastical nature of climatic variables, on the other hand, causes the uncertainty of land suitability grading for plant cultivation, especially in dryland conditions. Golestan province has the third rank among the Iran provinces with about 400,000 hectares of cultivated land and production of more than one million tons of wheat, of which about 57 and 50 percent are allocated to rainfed cultivation, respectively. The present study was conducted to examine changes in various climate variables affecting wheat growth under the influence of different occurrence probabilities and zoning agricultural land in Golestan province for the rainfed wheat cultivation under these conditions.

 First, dates of planting and occurrence of each phenological stage of rainfed wheat were determined by daily data of precipitation and minimum and maximum temperatures for 33 weather stations in Golestan province in a 26-year typical statistical period. Then, by fitting different probability distribution functions and selecting the best distribution, the values of these variables were estimated at five occurrence probability levels of 25, 50, 75, 85, and 95 percent. Next, the average air temperature, the possibility of occurrence of adverse temperatures, precipitation and ratio of effective precipitation to potential evapotranspiration of wheat for each growth stage and total growth period were calculated for whole weather stations at different occurrence probability levels. Finally, by providing zoning maps of these variables and other features including elevation, land slope, land type, soil salinity and overlaying 22 layers for each occurrence probability level, agro-ecological zoning of agricultural lands of Golestan province for rainfed wheat cultivation at different occurrence probability levels was performed.

 Results showed that increasing occurrence probability level from 25 to 95 percent due to delaying suitable planting date from late autumn to early winter and its effect on the climatic-environmental conditions and the length of the plant growth stages improved the degree of land suitability grade for rainfed wheat cultivation in terms of mean air temperature in stages of flowering, maturity and total growth season, the occurrence possibility of temperatures higher than 14 and less than 9 °C in germination and flowering and maturity stages, respectively, and the amount of precipitation in the germination stage. Adversely, it resulted in degradation in the degree of land suitability in term of mean air temperature in the germination stage, the occurrence possibility of temperatures higher than 25 and 30 °C in flowering and maturity stages, respectively, and the amount of precipitation and ratio of effective precipitation to potential evapotranspiration in stages of flowering and maturity and the entire season. The results of agro-ecological zoning showed that the extent of suitable and moderate zones was between 50-56 and 44-50 percent of the agricultural land, respectively, for the occurrence probability levels of 50, 75 and 85 percent and the occurrence probability did not have a significant effect on zoning. In comparison, at the occurrence probability levels of 25 and 95 percent (equal to the early and late planting dates, respectively), about 91 and 78 percent of the province's agricultural land was located in a suitable and moderate zone, respectively. These results are accordant with the findings of Hoseini et al. (2018), who showed that the maximum extent of suitable land for cotton cultivation in Southern Khorasan province occurred in the early planting date. Considering the majority of agricultural land of Golestan province is located in the middle strip of the province, most changes of zoning maps by occurrence probability level have also occurred in this region.

A low reduction in cultivation risk (increasing the occurrence probability from 25 to 50 percent) significantly degraded the land suitability for rainfed wheat in Golestan province while more reduction in the farming risk (increasing the occurrence probability up to 85 percent) led to no significant effect on land suitability. However, if the minimum risk (95% probability) be selected as a management decision for rainfed wheat farming, the lowest land suitability for rainfed wheat cultivation will occur.

Annual climate variation, along with water shortage is an important factor influencing agricultural productivity, especially in rainfed crop production systems. Also, understanding the current relationship between food grain production and rainfall will help in assessing the possible impact of future  climate change on the livelihood of smallholder farmers. In Golestan province due to different climates and extended rainfed lands, amount and spatial and temporal distribution of rainfall is very important. In general, the minimum rainfall for dry farming is about 250-300 mm. In addition, distribution of rainfall during the growing period is also noticeable. The enough moisture in vegetation phase is essential for growth and establishment of plant. In this study, spatial analysis of GIS and some methods of classic interpolation and geostatistical methods were used  to evaluate spatial and temporal changes of rainfall variables in the agricultural lands of Aq-Qala, Kalale and Gonbad Kavous townships in Golestan province.

This research was carried out in Gorgan University of Agricultural Sciences and Natural Resources (GUASRN), during 2015. The studied region was included the agricultural lands of Aq-Qala, Gonbad Kavous and Kalale townships in Golestan province, as three important regions for  dry farming production. First, the data of 10 synoptic stations and 77 rain-gauge stations from Golestan province were used. The thematic maps of rainfall variables such as annual, autumn, spring, November and May precipitations were provided using various methods of geostatistical and classic interpolation such as Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Radial Basis Function (RBF) in GIS (var. 10) media. Before interpolation calculations, common statistical tests were performed on main data. The performance criteria for evaluation were Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE) and Cross Validation. In this research, a semivariogram function was used to show the variation of rainfalls with respect to distance.

The results showed that Kriging-spherical model was the best method to estimate autumn and May precipitations and also, the annual, spring and November precipitations were interpolated by Multi-quadric (RBF) in agricultural lands of Aq-Qala, Kalale and Gonbad Kavous townships. Furthermore, the IDW and Thin Plate Spline (TPS) were introduced as inappropriate methods due to high error  in precipitation estimate. The semivariograms analysis indicated that autumn, spring, November and May precipitation variables were the best fitted by Spherical models, but annual precipitation was fitted by Exponential model. It was found that some areas in north of Aq-Qala and Gonbad Kavous had the limitation in amounts of spring and May precipitations. The spatial distribution of precipitations showed that annual precipitation was increased from north to south, so that, the maximum annual precipitation was observed in Kalale township. Thus, this township can be a region with high suitable degree for dry farming some autumn crops such as wheat and barley. The lowest autumn rainfall amount was observed in the northern parts of Gonbad Kavous township with 31.37 mm, but the southern parts of the Aq-Qala, Gonbad Kavous and especially Kalale had the highest autumn precipitation amounts.

Result of the spatial distribution of precipitation showed that amounts of precipitation variables were increased from north to south of the studied region. Totally, among  the three studied townships, Kalale township had the highest amounts of precipitations and the best spatial distribution of precipitation variables for rainfed farming. Therefore, this township was selected as the high suitable region for  dry farming in Golestan province.

Climatic conditions of each region are the primary factors affecting the formation of settlements. Precipitation is one of the most important climatic parameters that contributing in changes in vegetation, especially in arid and semi-arid ecosystems. This study aimed to investigate the vegetation and precipitation changes using remote sensing data. The vegetation index images captured by X TERRA / MODIS between 2001 and 2013 with 8-day interval in May and June months were used as Vegetation Condition Index (VCI). Consequently, VCI as an index was calculated from the annual average of the two months. To evaluate precipitation data, we used data from 28 meteorological stations. The linear correlation between height and precipitation was used to obtain line equation at each station using DEM (Digital Elevation Model) maps to prepare precipitation maps for each year. The VCI images were normalized between zero and one and were grouped into four classes. The area of each class was then calculated in 2001 and 2013, followed by statistical analysis (i.e. chi-square test) . The results showed that both indices were significant at 95% with Sig = 0.000. The VCI index decreases when the precipitation is low, while it shows an increasing trend where the precipitation is medium to high, which in turn approves the vegetation dynamics.

This study investigates land use suitability to find sustainable production areas in arid and semi-arid regions. Crops perform best in locations where climatic conditions meet their growing requirements. Factors such as elevation, slope percentage, slope aspect, soil type, vegetation cover and other climatic features affecting growth are crucial in identifying the most suitable areas for crops. The trend of land suitability classification includes evaluating particular areas in terms of their aptness for a specific use (Seyedi Shahivandi et al., 2013). The suitability of farms in western Iran were evaluated for rain-fed wheat, while the climate parameters were studied in accordance with the eco-physiological needs of wheat. Finally, the areas were classified into suitable, semi-suitable and non-suitable regions (Kamali, 1996).In this study, geographic information systems (GIS) and the Analytical Hierarchy Process (AHP) were applied to evaluate the feasibility of the agricultural lands of Ilam Province for cultivation of irrigated wheat (Triticum aestivum L.).
In this research, climatic parameters including temperature and rainfall were collected from seven synoptic stations of the region. Also compiled were data on important factors in zoning and environmental parameters including elevation, slope percentage, slope aspect, distance from river, soil texture, organic matter, the probability of the occurrence of appropriate temperatures during planting stage, the probability of the occurrence of the maximum temperature of 25°C during flowering, and the probability of the occurrence of temperatures of 30°C during the grain stage. First, the climatic data were extracted and environmental variables were obtained, then the maps related to these parameters were drawn. Furthermore, the weight of each factor was calculated using AHP with the layers integrated into the GIS environment. Kriging and IDW methods were applied for interpolation of environmental variables. Accordingly, a prospective map of irrigated wheat cultivation of the region was obtained. Lands were zoned into five classes: highly suitable, suitable, semi-suitable, non-suitable and highly non-suitable. Non-suitable lands seem to have extreme limitations and include those which should be used by farmers either rarely or never.
The results of AHP analysis showed that among the factors affecting land suitability, appropriate temperature during planting stage (0.31) had the highest weight and elevation (0.009) had the lowest weight. In the AHP model, the inconsistency ratio is about 0.0346. This showed that the comparisons of factors were absolutely consistent (Bertolini et al., 2006), and the relative weights were adapted for applying land suitability analysis in our study area.The results indicated that about 34.74% of lands were very suitable and 34.74% were suitable. 13.91% of lands had average capabilities of cultivation (semi-suitability). 16.04% of the lands were poor and 8.04% were very poor for irrigated wheat cultivation. The conclusion was that the role of each climatic and land parameter varies in different parts of the region. Moreover, it is possible to determine suitable regions for irrigated wheat using GIS and AHP method.
In this research, the most effective factors determining the suitability of land for wheat were the appropriate temperature during sowing, the probability of the occurrence of the maximum temperature of 25° C during flowering, elevation and slope percentage. The results of this research confirmed the capability of GIS in zoning.

Due to lack of data, using of rainfall-runoff models with high accuracy that do not need to large amounts of data is an important issue. The IHACRES model is a semi- conceptual rainfall- runoff model. This model does not need to large amounts of data and calculates effective rainfall. Then this model simulates the value of runoff. The case study of this research is the Navrood watershed in the Gilan province. For simulation of rainfall and runoff in this research, IHACRES model utilizes daily flow discharge data in the Kharjgil hydrometric station and daily precipitation and temperature data in the Khalian synoptic station. The Kharjgil hydrometric station locates at outlet of watershed and the Khalian synoptic station locates at center of watershed. The model uses from data of time period 1391 to 1394 for calibration and time period 1387 to 1389 for cross validation. Nash- Sutcliffe model efficiency coefficient (E), mean of total error (BIAS) and average relative parameter error (ARPE) criteria are utilized evaluation of results of model. The value of E is 0.55 and 0.46 for calibration and cross validation respectively. These values show that results of this research are acceptable in comparison with previous researches.

In this research, the effect of climate change on rainfall, runoff, temperature and water resource for Ziarat basin of Golestan province, was assessed. General ccirculation model, HadCM3, was used under three scenarios as A1B, A2 and B1 for 3 future time duration as 2011-2030, 2046-2065 and 2080-2099, respectively. In order to down scaling of HadCM3 output, ANFIS model was used. The predicted results showed increasing temperature as 0.32-1.77oC and decreasing precipitation as 1.6-31.46 mm for future durations. Then, the mentioned results, curve number map and physiographic parameters, basin and waterway slope, gained from Arc-GIS, as importing data to calibrated HEC-HMS model was made in order to simulate the discharge of climate future durations. Results presented the runoff volume and peak of discharge have decreased in all three scenarios for horizon 2020, 2055 and 2090 and for all mentioned horizons, the most of decreasing was related to A2 scenario. Percentage decrease in peak and discharge volume was obtained 1.72 and 1.83, 3.06 and 3.07, 4.43 and 4.48 for mentioned periods, respectively. Consequently, based on extracting information from models, no enough storage would happen in dams. Finally, some solution for this problem was presented.

Golestan Province is one of the main areas of wheat production in Iran. In 2015, it ranked third among all the provinces of the country in term of acreage and yield (Anonymous, 2016). Given the strategic importance of wheat, it is necessary to identify suitable areas for its rainfed cultivation based on meteorology, agronomy, soil and land properties. This process is known as agroecological zoning. In addition to analyzing ecological factors affecting rainfed wheat production based on the consolidation of scientific references, this study will also identify suitable zones in Golestan for rainfed wheat cultivation.
Firstly, six factors were determined in 30 weather stations in the concurrent period 1991-2015 including: planting date, dates of each phenological stage of wheat based on growing degree-days (Ahmadi et al., 2016), precipitation, air temperature and the ratio of effective precipitation to crop evapotranspiration in each stage, and total growing season of wheat. These parameters were then estimated by the occurrence probability level of 75% (Sys et al., 1991). The possibility levels of higher and lower temperatures than the tolerable threshold of wheat in various stages of growth were also calculated. Next, a zoning map of the parameters and characteristics of elevation, slope, soil type and salinity were prepared in five classes. Finally, by overlaying these data layer (layer 26), the agroecological zoning of agricultural land of Golestan for the cultivation of wheat was performed.
Average air temperature during germination, flowering and seed filling stages of wheat in Golestan with occurrence probability of 75% were 8 (moderately suitable), 10.9 (moderately suitable) and 22.7 °C (suitable), respectively. Also, the occurrence probability of temperature less than the tolerance threshold in the germination stage of wheat (10 °C) was considerable in the province, but the occurrence probabilities of higher and lower temperatures than the tolerance thresholds of wheat in other growth stages were low in the dominant area of the province. The results showed that in terms of the ratio of effective rainfall to potential evapotranspiration during the total growth period, 92 percent of the province area was located relatively equally in moderately suitable (29%), suitable (9%), and very suitable (20%) classes for rainfed wheat cultivation. Also, soil salinity in 72 percent of the agricultural land was less than the tolerance threshold of wheat (6 dS.m-1). According to the results, total area of very suitable, suitable and moderately suitable zones for rainfed wheat cultivation in the agricultural zones of the province was 602,000 hectares, that is 51% and 117% more than the current acreage of total wheat and rainfed wheat, respectively. Furthermore, the sum of moderately to very suitable zones for rainfed wheat cultivation was calculated at 64.8 of the province and 85 percent of total agricultural land. In the entire country, the highest percentage of very suitable areas were located in Gonbad (24.7%) and Kalaleh (20.9%), suitable areas in Maravehtapeh (18.2%) and Gorgan (16.4%), and moderately suitable areas in Gonbad (22.8%), Aghghalla (19.7%) and Gomishan (17.7%).
Suitable areas for rainfed wheat cultivation in Golestan Province are generally located in the middle strip of east-west. The extents of these zones in the occurrence probability level of 75% are over the current wheat acreage of the province. This result is consistent with the Bidadi et al. (2015) findings.

The physically-distributed models lead to more reliable results for surface runoff process simulation in basins with complicated physical condition. In this study, rainfall-runoff modeling in Ziarat mountainous basin is investigated using GSSHA physically-distributed model. Digital elevation model, soil type and land use maps prepared and three and two events are considered for calibration and validation. Fitness precision criteria including Nash-Sutcliffe (NSE), Percentage Error in Volume (PEV), Percentage Error in Time to Peak (PETP) and Percentage Error in Peak (PEP) beside visual criterion used for results analysis. Median of PEV, PEP and PETP for calibration and validation steps were (25.3 and 61.5), (5.5 and 11.8) and (4.8 and 0) that indicated underestimation for volume, suitable precision for peak and excellent precision for time to peak estimations. Also, evaluation of simulated hydrographs using visual and NSE criteria confirmed model precision for hydrograph simulation. The results show although soil initial moisture selected based on initial estimation in validation step but the overall precision of model in runoff characteristics estimations is suitable.

In a recent decades to increase human knowledge on the Earth's climate and concern for people from climate change in the future, thereby has been identified the precise factors influencing Earth's climate. One of the climate phenomena that changes them cause great anomalies of climate, particularly on temperature and precipitation patterns in many parts of the world that are teleconnection. The reveal relationships between climatic parameters and they towards a better understanding of the volatility and climate variability is very important in every area. In this study, the relationship between large-scale and well-known patterns such as the Southern Oscillation Index (SOI), North Atlantic oscillation (NAO), Pacific North America (PNA), Multivariate ENSO Index (MEI) and (PDO) with 9 temperature and precipitation variables in the Karaj synoptic station as monthly were analyzed over a 26 year period (2010-1985). At first, the normality of the data series based on the Kolmogorov - Smirnov was confirmed. In order to examine the relationship between large-scale patterns with temperature and precipitation variables, was used the Pearson correlation coefficient. Correlations on a monthly were assessed basis without delay and a delay of one month. The results showed that the relationship between the NAO index and the temperature and precipitation variables is observed in autumn and winter months and the impact on the autumn months is higher than the winter months. SOI index further communication with the precipitation variables is paramount; the role of this index is more obvious the autumn and winter months, while the MEI index shows a higher correlation with the temperature variables and was not observed particular relevance with precipitation variables. Role and impact in particular on temperature parameters months of spring, April and May and December in late autumn is stronger. PDO index on the temperature and precipitation variables in May is observed more in the middle of spring. PNA index only, is effective on the temperature variables and in December and February, higher communication is paramount. Results are important for greater understanding of the temperature and precipitation variability.

Given that Qazvin Province located in the seventh drought place in the country and need to manage vast water resources. Water storage estimation methods have developed over the years and observation of changes in the gravitational field by satellite GRACE is one of these methods. This satellite produces a series of water storage changes data with spatial resolution of 1 ° on a regional scale. This study aimed to evaluate data from GRACE satellites in Qazvin province to find a comprehensive solution for quick and easy access to the water resources. To validate the GRACE satellite data, land surface model data (GLDAS) and wells data in the region were used. The results show that the GRACE satellites as a Gravity satellite just produce to estimate changes in Earth's water supply, give a good estimate of the changes in the ground water storage and groundwater level changes. Statistical analysis showed that RMSE =5.54, MAE = 4.16 and MBE = 1.5 the seasonal scale in millimetre between groundwater level data from the GRACE satellites and observation wells, and RMSE =2.92, MAE = 4.24 and MBE =0.184month scale in centimetre between the data changes in land water storage from GRACE satellite and GLDAS model. Also, the correlation between changes in water storage data from the GRACE satellite and GLDAS model and groundwater level changes from GRACE satellite and observational data is significant at the 99% probability level. The results showed that GRACE satellite shows terrestrial water storage changes better than GLDAS model.

Precipitation is considered as one of the most important factures in water cycle. Prediction of monthly Precipitation is important for many purposes such as estimating torrent, drought, run-off, sediment, irrigation programming and also management of drainage basins.In this study we studied and evaluated gene expression programming to predict the Precipitation of the Kakareza river (in lorestan), and the results were compared with results of anfis and artificial neural network model. For this purpose, mean temperature, relative humidity, evaporation, wind speed rate at monthly scale during the period (2005-2015) as input and output parameters were selected as Precipitation . The criteria of correlation coefficient, root mean square error and of mean absolute error were used to evaluate and performance compare of models. The results showed that gene expression programming model has the highest correlation coefficient (0.978), the lowest root mean square error (0.026 mm) and the lowest mean absolute error (0.017mm) became a priority in the validation phase. The results showed that the gene expression programming model to estimate high minimum and maximum values of Precipitation .

Shift changes and heterogeneity analyses of hydro-climatic variables is very important in water resources planning and management. In order to shift changes and heterogeneity analyses of Tmin and Tmax, precipitation and discharge, 2, 7 and 7 stations was used over the 40 years (1972-2011), respectively. The results of annual Tmin and Tmax series showed that both of two stations had the significant increasing trend in 1 significance level. The results of precipitation showed no specific spatio-temporal trend in four last decades and identifying the exact pattern of changes was difficult in compared with other variables. Among 7 hydrometric stations, 5 stations showed the significant increasing trend. However, discharge had the most decreasing trend among the studding variables. Shift changes analyses of hydro-climatic variables by four heterogeneity tests, generally indicated the 50.5 percent of Tmax series, 62.5 percent of Tmin series, 26.25 percent of precipitation series and 45 percent of discharge series had heterogeneity in 95% confidence level in the studding stations. Studding shift changes in Tmin and Tmax series showed that all of the changes were positive/ or increasing. In compare with temperature series, shift changes of precipitation as same as its trend have no specific trend. In general, temporally all of the changes occurred in 1990s.

In this article, new index, the Optimized Synthetized Drought Index (OSDI), is suggested for real-time monitoring of this phenomenon in areas with heterogeneous land covers. The precipitation, one of the important factors in drought, measured by ground stations and improved byTRMM satellite data. Each of the three normalized moisture indices, including Normalized Difference Vegetation Index (NDVI), Visible Short-Infrared Drought Index (VSD), and the Surface Water Capacity Index (SWCI), individually enter to principal component analysis (PCA), with Precipitation Condition Index (PCI) and land surface temperature index (LST). Themaincomponentsof these PCA are definedasSynthetized Drought Indices, called SDI1, SDI2, and SDI3.Then, the performance of three output indices is evaluated by using the Standardized Precipitation Index (SPI) in 1 and 3 month scale.Validation results indicate that the synthesized index of PCA on the normalized VSDI, LST and PCI, provides the best performance in real-time monitoring of drought.This Index is called (OSDI). Then, 42 Landsat7 images were employed to evaluate the ability of suggested indices inheterogeneous land. The three normalized indices of NDVI, SWCI and VSDI, as the only effective factor in different performance of SDI1, SDI2 and OSDI, is produced by the spectral bands of Landsat7 images and their performance in various lands covers were evaluated using SPI index. High correlation between normalized-VSDI and one-month-SPIhas approved capability of OSDI in real-time monitoring of drought in heterogeneous areas. OSDI maps showed that in the provinces of Tehran and Qom of Iran, in 2008, 2009, and 2014, a severe drought has occurred in Central and South-East regions of Tehran, and also at central and northern parts of Qom.

Climate change impacts on precipitation and temperature world-wide are not yet well understood (especially for precipitation), due to their complexity and regional variability. In this study In order to explicit the impact of climate change on Precipitation and temperature in Iran, 15 AOGCM whose greenhouse gases scenarios ran under A2, A1B and B1 were used. Monthly precipitation and temperature data were calculated for 21 synoptic stations for two future periods (2016-2045 and 2070-2099) under all of three scenarios. Result showed that increases in temperature during summer months are higher in comparison with the winter months for both future periods. All three emission scenarios predict same increase in temperature (approximately 1 to 1.5oC) for the first future period while A2 and B1 scenarios predict the highest and lowest increase in temperature over the period of 2070-2099, respectively. It is expected that mean temperature of Iran increases about 3.5oC for the areas close to Caspian Sea and central regions and 4.5oC for other regions under the A2 critical scenario. It is also found that precipitation amounts will be increased in Gilan province and also slightly in Ardabil Province under second future period while the precipitation amounts will be decreased in other regions. Generally, it can be concluded that annual precipitation volume of Iran will be decreased about 1.02 (0.25 percent) and 16.52 billion cubic meter (4.13 percent) for the first and second periods, respectively.