جستجوی مقالات مرتبط با کلیدواژه « precipitation » در نشریات گروه « آب و خاک »

Iran and Afghanistan have a long history of facing climate hazards, leading to extensive damages to life, the environment, and economies. This research conducted a comprehensive comparative analysis of their long-term climate conditions on a national scale.Future climate scenarios based on IPCC6AR were projected to understand climatic similarities between the two nations. The results showed that recent years experienced the highest average temperatures in both countries, with significant changes occurring between 1991 and 2020 within the past 80 years. Monthly average temperatures over the last 80, 50, and 30 years exhibited a consistent upward trend, with the last decade being the warmest. Regarding precipitation patterns, the research revealed significant distribution changes in the last 80 years, particularly during 1951-1980 and 1971-2000 in Iran and Afghanistan, respectively. The study also examined natural hazards statistics resulting from climate change, including event frequency and the number of affected individuals over the past 40 years in both countries. The research emphasized preserving and revitalizing wetlands as an effective triple strategy to address climate change impacts in both countries. Given their shared exposure to climate change, conserving and restoration the Hamoun wetlands is crucial. Additionally, involving third parties in international discussions about wetlands or climate change can help resolve disputes.In conclusion, this study highlights the importance of understanding historical climate conditions and potential future scenarios for Iran and Afghanistan.

The weather has an ongoing impact on human living and working environments. Drought is a natural disaster that ranks first in frequency of occurrence, financial losses, and even human casualties among natural disasters that endanger humans and their environment. Due to its complexity and imperceptibility, this phenomenon—one of the primary and recurring features of various climates—has significantly impacted the human environment more than any other hazard. Its effects can also accumulate gradually over time and last for years afterward.Drought cannot be avoided, but if its nature and characteristics are researched and understood, we may be able to forecast when it will occur, lessen its adverse effects through planning and preparation, and perhaps even control it.

The political region of Iran is the subject of this study (Figure 1). Soil moisture and monthly precipitation are among the data used. The necessary data, which included 516 precipitation files and 516 soil moisture files with a spatial resolution of 0.5*0.66 in NC format, as well as drought and its characteristics calculated for all points, were acquired from the MERRA website for 43 years (1980–2022) to conduct this research. Iran's droughts were estimated over 3, 6, 9, 12, and 24 months using the MSDI. The characteristics of droughts, such as frequency, duration, severity, and magnitude, were computed, analyzed, and presented as a map in addition to examining the actual droughts.

Analyzing the drought characteristics for 1546 points across Iran revealed that the frequency and number of drought periods decrease as the time scale increases. In contrast, the values of other traits—such as continuity, intensity, and magnitude—also rise as the time scale increases. The increase in drought characteristics with increasing time scale, in terms of magnitude, duration, and severity, has been highlighted in studies by Nouri and Homai (2020) and Torabinejad et al. (2023). Geographically, the southeast and eastern parts of Iran have seen the highest frequency of droughts throughout the 43 years; in the southeastern part of the country, which is centered on the provinces of Sistan and Baluchestan, Kerman, and Hormozgan, there have been 28 to 34 periods of drought in 3 months. Conversely, the least amount of drought occurred in the northern coasts during the short-term periods of three and six months, with six to twelve periods, and in the coasts of the north and the southwestern region of the country during the long-term periods of twelve and twenty-four months, with two to six periods. The provinces of Sistan Baluchistan, Kerman, and Hormozgan in the southeast, as well as the far east of Khorasan Razavi and South provinces in the east of the country, experienced the worst droughts in terms of severity. Severe droughts lasting 12 or 24 months are concentrated in the southwest, in Bakhtiari and Chaharmahal, Kohgiluyeh and Boyer Ahmad, and part of Khuzestan.The findings from the computation and analysis of the duration and magnitude of droughts in Iran over the period under discussion indicated that, for two features, the country's northern regions, centered on the north coasts, mainly Mazandaran province, had more prolonged and severe droughts than other regions within the time scales of three and six months. By looking at the country's southwest from the perspective of location over periods of 12 and 24 months, the provinces of Fars, Bushehr, south of Kohgiluyeh and Boyer Ahmad, and Khuzestan province make up the central core of the most prolonged and most extensive droughts. 

Based on soil moisture and precipitation data, the results of a drought calculation in Iran demonstrated that droughts occur throughout the country with varying degrees of severity and weakness. Their occurrence has become a permanent feature of the country's climate, particularly in recent decades. The analysis of Iran's drought chronology revealed that, according to the MSDI index's range, most of the country's droughts are weak and moderate. It was also discovered that as time scales increase, the frequency of droughts decreases while the continuity, magnitude, and severity increase. Geographically, the country's southeast and east saw the highest frequency of droughts, while its northern coast—mainly the province of Mazandaran—and southwest—where the provinces of Chaharmahal & Bakhtiari and Kohgiluyeh & Boyer Ahmad—saw the lowest frequency. The maximum severity of drought in the country is -1.93 on the 3-month scale in the southeast and -2.2 on the 24-month scale in the southwest. Two of the study's notable findings are the continuity and high magnitude of the droughts that struck the country's north and southwest. Thus, the most extensive droughts, with values between -16 and -31 on short-term scales and between -35 and -68 on 12- and 24-month scales, have happened in the country's north and southwest, respectively. These results suggest an overall decrease in soil moisture and precipitation in these regions.

Determining the future climate situation by using climate models seems necessary to consider in the field of adaptation or reducing the adverse effects of climate change. In this research, the temporal trend of rainfall, minimum and maximum temperature in the four stations in the Qarasu basin and in addition to, investigated using Thiessen's interpolation method. Among the five models of the CMIP6, three models were selected as the best models and used for MME. Biass Correction was done with CMHyd software for scenarios SSP2-4.5 and SSP5-8.5 in periods 2026-2050, 2051-2075 and 2076-2100. The trend of variables in the base period (1990-2014) and future were investigated with Mann-Kendall test and sens slope. The results of analysis significant trends annual average maximum and minimum temperature of all stations and in catchment area according to SSP2.4-5 scenario in two near and middle future periods and for SSP5-8.5 scenario in all three future periods have a significant trend at the 99% level. In analysis significant trend seasonal rainfall according to SSP2.4-5 scenario in the summer season distant future all stations and in near future of the station area of Gorgan regional water company at the 95% level and for the SSP5-8.5 scenario only in the winter season in the distant future Ghafarhaji station has a significant trend at the 99% level. The future monthly rainfall in the catchment area according to scenario of SSP2.4-5 in August at the 99% probability level and SSP5-8.5 in March at the 95% probability level have a significant trend.

Agriculture, especially rainfed agriculture, is one of the branches that is largely affected by the effects of climate change. The previous studies regarding the estimation of the length of the dry season wheat growth period in the country are mainly related to the length of the growth period of the past years or based on the growing degree day method, which is not very realistic. The purpose of this research is to estimate the length of the average growth period of rainfed wheat for the Tabriz Plain for the past 20 years (1993-2017) and the future conditions under the simulation of GCM models, for the next 50 years (1394-1444), using the meteorological parameters of temperature and rainfall. is. In the first step, the state of future conditions was evaluated under the GCM models of the sixth report of the Intergovernmental Panel on Climate Change (IPCC) and under the SSP1-2.6 and SSP5-8.6 scenarios, and then with the proposed method of the ecological zoning guidelines of the Food and Agriculture Organization of the United Nations (FAO), the length of the growth period of each period has been estimated and compared with each other. According to the FAO method, the length of the growth period is equivalent to the period of time when the rainfall is more than 50 percent of the potential evaporation and transpiration. It has been done. The results of comparing the length of the growth period of the base period and the state of future conditions under the simulation of GCM models showed that in the Tabriz Plain, the length of the wheat growth period has increased by an average of 16 days for the next 50 years, and the end of the growth period by an average of It will happen 12 days later.

In this study, the trend of precipitation and temperature variables in basin scale across Iran was evaluated using ministry of energy weather stations data. Initially, among the total 2,142 climatology and 1,230 evaporation stations corseponding data of those with a minimum of 40 years record and a maximum of 3 months of missing data were selected, resulting in 476 and 116 climatology and evaporation stations, respectively. The study period was from water year 1973-74 to 2013-14. The monthly, seasonal, and annual precipitation and temperature data of the selected stations were spatially interpolated in different catchements across the country using the Thiessen method. The Mann-Kendall test was then employed to detect trends of times series. Considering the significance levels of 90, 95, and 99 percent, the results were analyzed at each station, second-degree basin, first-degree basin, and across the entire country. The findings of this research indicate an increasing trend in autumn precipitation for the northern part of the country (13 percent of stations), a decreasing trend in winter precipitation for 33 percent of stations, no significant trend in spring precipitation, and an increasing trend in summer precipitation. Additionally, an increasing trend was observed in all temperature time series. By comparing the changes over the last 5 and 10 years of study period (ending by 2013-2014) with the long term 40-years average, a significant decrease in precipitation in most regions of the country, was observed, this accompanied by an increase in the mean temperature, suggests a substantial reduction in available water resources in study period.

Greenhouse gases and the occurrence of climate change have occurred with the development of technology and the industrialization of human societies. long-term forecasting of climate parameters has always been interesting due to the importance of climate change for the earth and its inhabitants. General Circulation Models (GCMs) are one of the most widely used methods for evaluating future climate conditions. In the present study, the results of three general circulation models including the American model of GFDL-CM3, the Canadian model of CanESM2, and the Russian model of inmcm4ncml for the study area were evaluated and the CanESM2 model was selected as the superior model. The RCP scenarios 2.6, 4.5, and RCP 8.5 were used with the CanESM2 model to assess climate change conditions across the Hablehroud River basin for the period 2020-2051. According to the results, the total monthly precipitation shows an increasing trend in the coming decades 2020-2051 period compared to the period 1986-2017. The results of the study of temperature changes in the period 2020-2051 in the Hablehroud River basin also indicate an increase in the monthly average of maximum and minimum temperatures in the coming decades. The consequences of these conditions are of great hydrological importance in the study area, this condition necessitates the adoption of climate change adaptation policies in this watershed.

Statistics show that the occurrence of severe and long-term droughts, especially in sensitive and fragile areas of the country has caused severe economic and social losses, and the occurrence of drought, increased dust, storms, and desertification has caused a decrease in agricultural production. Extensive and dynamic monitoring of dryness by traditional methods is very difficult and costly due to the lack of soil moisture display points. Remote sensing technology is a practical and applied method for large-scale land monitoring. In this study, we tried to identify the drought in the Siah Kooh watershed area with TVDI dryness-temperature indices and the NDVI index resulting from MODIS sensor images and to investigate the relationship between drought and atmospheric elements in the region. The results of correlation as a total showed that the correlation values of TVDI index SPI6 and SPI12 are 0.68 and 0.71, respectively, and the correlation rates of NDVI values with SPI6 and SPI12 are 0.49 and 0.51, respectively. As a result, it can be said that the TVDI index, due to the use of thermal and reflective bands and soil moisture, is more accurate than the NDVI index, which considers only the amount of vegetation in the region. The TVDI index had an inverse correlation with the average of two months of rainfall of 0.54 and a direct correlation of 0.64 with the surface temperature of the earth. In contrast, the NDVI vegetation index with a two-month average rainfall has a direct correlation of 0.54 and an inverse correlation of 0.6 with the temperature. These linkages show that the correlation between vegetation and temperature is inverse (negative) and the correlation between vegetation and rainfall is direct (positive).

The climate of a region is influenced by many factors, some of which are planetary and some are regional and local. Teleconnection patterns are the origin of anomalies. Therefore, revealing the relationships between climatic parameters and teleconnection patterns is important for further understanding of climatic fluctuations and variability in each region. The purpose of this research is to investigate climate anomalies using Teleconnection patterns are the origin of anomalies that are seen Therefore, revealing the relationships between climatic parapatterns. For this purpose, the rainfall data of two observation stations (Khorram-Abad and Kermanshah) were collected during a period of 68 years (1951-2018). In this research, two types of data were used. 1- The monthly rainfall data of two synoptic stations of Khorramabad and Kermanshah were obtained from the National Meteorological Organization. 2- Data related to remote connection patterns including: Polar Oscillation (AO), North Atlantic Oscillation (NAO), Scandinavian Pattern (SCA), East Atlantic Pattern (EA), East Atlantic-West Russia Pattern (EA/WR) ), the North Tropical Atlas pattern (TNA), the Polar-Eurasia pattern (POL) and the Meridian Wind pattern (AMM), the Southern Oscillation (SOI), the combined pattern (Best) of the East Pacific-North Pacific Oscillation (EP/NP), Water surface temperature in Niño 1 and 2 (Niño 1+2) and water surface temperature in Niño 3 and 4 (Niño 3.4)) were obtained from the NOAA website.

Analytical statistics and inferential statistics methods were used to determine the effect of Teleconnection indicators on rainfall in the region. First, the rainfall time series of each station was tested for significance using the Anderson-Darling test at the 95% confidence level. Also, to evaluate the condition of independence of time series, the sequence test was used. Then, the Teleconnection indices were divided into two groups. Atlantic Ocean-based indices (AO, NAO, SCA, EA, EA/WR, TNA, POL, AMM) and Pacific Ocean-based indices (SOI, Best, EP/NP, Nino 1+2, Nino 3.4) And their interaction. In most statistical materials, parametric tests such as variance analysis, correlation analysis, regression analysis, etc., are based on the assumption that the measurements within each statistical population have a normal distribution and an equal variance-covariance structure.The hypothesis of establishing a normal distribution is related to the distribution of the studied population and not the samples.In order to be able to accept this hypothesis, that hypothesis must be substantiated in theoretical fields, that is, the values must be symmetrically centered around the average number. In this regard, data that have a skewness (lack of symmetry) or are strongly integrated in a part of the measurement scale, affect the variance-covariance between the variables. Analysis of variance is one of the parametric methods that evaluates the relationship between a dependent variable and an independent variable.In this approach, the independent variable is considered as the agent variable and the dependent variable as the response variable. In order for the results of the analysis of variance to be valid, several assumptions must be considered when applying its formulas. The first assumption is that the observations are independent. It means that each observation is uncorrelated with another observation. The second assumption is that the observations are normally distributed. That is, all observed measures of central tendency, including mean, mode, and median, should be the same. The third assumption is that the variance is homogeneous. That is, the sizes of the distribution of scores should be determined. This assumption is called homogeneity of variance. Therefore, before applying the statistical tests, first, the time series of monthly rainfall of Khorramabad and Kermanshah stations were tested for significance using the Anderson-Darling test at the 95% confidence level. If any of the precipitation time series is not normal, we tried to normalize that time series by using Johnson transformation functions. If these functions were not able to place the precipitation time series in the normal range. Kruskal-Wallis method, which is equivalent to non-parametric analysis of variance, was used to test the precipitation of these non-normal time series. In the following, in order to find out whether the average monthly rainfall of Khorramabad and Kermanshah synoptic stations has changed during the different phases of the teleconnection patterns, or in other words, whether the average monthly rainfall in the west has undergone changes due to the positive or negative phase of these patterns, first the values The standardized index of these patterns was divided into three levels: neutral, positive and negative. Then one-factor analysis of variance and Kruskal-Wallis test were implemented on these three levels as factors and the time series of monthly rainfall as the response variable.

The distribution values of Kruskal-Wallis statistics for the months of October and September and the values of the analysis of variance statistics for other months of the year revealed that the influence of the indices based on the Atlantic Ocean has less homogeneity and order than the indices based on the Pacific Ocean. In a way that the phase change of the Pacific Ocean indicators has caused a significant change in the rainfall of October and November in Khorramabad and October in Kermanshah. If the influence of the patterns based on the Atlantic Ocean does not have such an arrangement. In general, the patterns of the Atlantic Ocean have caused a significant change in precipitation mainly in the winter season, while the patterns based on the Pacific Ocean have had a significant effect on the precipitation in the autumn season. In this regard, the East Atlas-West Russia pattern had the most significant effect on the precipitation of these two stations, while the polar oscillation pattern, the Eurasia polar pattern, and the meridian temperature pattern caused a significant change in precipitation in only one month and one station. Also, the Scandinavian pattern has a significant effect on the October rainfall in Khorramabad, January, March and December in Kermanshah. On the other hand, the East Atlas pattern and the North Tropical Atlas pattern have had a significant effect on February rainfall in Kermanshah and October in Khorramabad. On the other hand, the East Atlas pattern and the North Tropical Atlas pattern have had a significant effect on February rainfall in Kermanshah and October in Khorramabad. The East Atlas and Scandinavian patterns have a significant effect on the October rainfall in Khorramabad and Kermanshah. In general, the patterns of the Atlantic Ocean have caused a significant change in precipitation mainly in the winter season, while the patterns based on the Pacific Ocean have had a significant effect on the precipitation in the autumn season.

The aim of the study is to evaluate the changes of mean temperature and precipitation parameters using IPCC Sixth Report models (CMIP6) under SSP scenarios (SSP1-2.6, SSP2-4.5 and SPP5-8.5) during the period of 2022-2100 in Kashan Plain. The mean temperature and precipitation data was obtained from 7 stations (Kashan, Kavir-e-Hosseinabad, Kamu, Ardestan, Alavi, Noushabad and Sensen) in Kashan plain considering the base period of 30 years (1984-2014). Also, 7 models were selected from the models of the sixth report (CMIP6). The post-processing of the output of the models was carried out using the linear ratio method. Nash-Sutcliffe indices (NSE), root mean square error (RMSE) and correlation coefficient (r) were used to determine the accuracy of the models. The annual trend of changes was investigated using Mann-Kendall test. Finally, the mean of IPSL-CM6A-LR and BCC-CSM2-MR models outputs was used to simulate mean temperature and precipitation changes in the future period. According to the results, in all of the studied stations, precipitation in the coming period will have a decreasing trend compared to the base period. The mean temperature will also increase in the future period compared to the base period. In Kavir-e-Hossein Abad, Ardestan, Noush Abad and Sen Sen stations, the intensity of temperature increase will be higher than Kashan, Kamu and Alavi. According to the predicted conditions, it is necessary to pay attention to comprehensive policies in the field of adapting to climate change in Kashan Plain.

The importance of heavy rainfall and its consequences have caused this phenomenon to be of special importance in environmental planning and water resources management. Using statistical methods and the discharge data of the Dez river, the heavy floods of this river were extracted to identify the effective factors in their creation. Among all systems leading to heavy rains, the Sudanian system has been the main factor of moisture advection in the lower layer of the troposphere with its expansion to the north and northeast. Due to its thermal characteristics, this system has been prone to receive considerable moisture from the surrounding warm seas. This system has provided the necessary conditions for the creation of surface instabilities in most transitional systems independently and in a smaller number in the integrated state with the Mediterranean system. In the investigation of the middle layer of the troposphere in all the systems that led to the floods of 1993 and 2005, it was observed that the anticyclone cell of Arabia was established over the Arabian and Oman seas and the east of the Arabian Peninsula by moving eastward. In this situation, a very deep trough has formed in the west of Iran, and the southern end of the trough has extended to southern Sudan and northern Ethiopia. The eastward movement of high pressure in Arabia and the significant southward expansion of the Mediterranean trough and the formation of a low-altitude center over Iraq is the most suitable pattern for the heavy cloud rains in the basin.

In terms of geographic location, the Dez basin is limited between 48°10' to 50°21' east longitude and 31°34' to 33°7' north latitude. The studied area is a part of the Dez river basin, which is located almost in the middle part to the end part. After the Karun branch of one of the largest and longest Dez rivers, Dez is formed from two main branches named Caesar and Bakhtiari, and after leaving the mountainous region north of Andimeshk and Dezful, it enters the plain of Khuzestan. For comprehensive and accurate interpretation of pressure systems and assessment of their environmental effects on the earth, the maps of the earth's surface and level of 500 hPa are very efficient. In most cases, the wind direction and temperature distribution at the 500 hPa level are completely affected by the topographic arrangement of the geopotential height at the 500 hPa level. Geopotential height maps are one of the most important and efficient atmospheric maps in synoptic analysis and interpretation. Heavy rains on representative days are analyzed, interpreted, and explained. Two statistical and synoptic methods were used for a more detailed investigation of the synoptic situation of heavy rains in the Dez river basin. In the statistical part, factor analysis, which is one of the widely used statistical methods in climatology, was used, and in the synoptic part, maps of different atmospheric levels were extracted and analyzed using Gardes software for the specified days. To determine the members of each group, the correlation of the scores of each factor with the sea level pressure map in the time period (1964-2020) was calculated, and the members of each group were determined. To determine the representative days, the correlation of the SLP maps of the days of each group was used, and the day that had a high correlation with more days was selected as the representative day. To perform factor analysis, the elements of the database were transferred from the Excel environment to the S-Plus2008 software environment, and then by performing various inferential analysis calculations, the most suitable method was selected to identify the main and effective factors.

As can be seen, the heavy rains of 2013 lasted for 6 days, and the system of 2011 lasted for 7 days. Of course, as mentioned, in some systems, days before or after the end of the activity period of the main system, scattered rains, even with high intensity, have been recorded in some stations. But these scattered rains are considered local rains. The highest peak flow rate of 8556 m3 belongs to the system in February 2013. This system has lasted for 4 days. It took 31 hours to reach the peak of the flood.

In the investigation of the middle layer of the troposphere in all the systems that led to the floods of 1993 and 2005, it was observed that the Arabian anticyclone has been established over the Arabian and Oman seas and the east of the Arabian Peninsula by moving eastward. In this situation, a very deep trough has formed in the west of Iran, and the southern end of the trough has extended to southern Sudan and northern Ethiopia. Therefore, the eastward displacement of the high pressure in Arabia and the significant southward expansion of the Mediterranean trough, and the formation of a low-altitude center cut over Iraq is the most suitable pattern for heavy cloud precipitation in the Dez basin. The peak discharge of floods with a delay of 12 to 24 hours from the day of peak rainfall has created a very intense discharge system. So, the floods of 2005 with a peak discharge of 8556 m3 s-1 and the flood of 1993 with a peak discharge of 4022 m3 s-1, while they were very heavy floods, there were two study periods.

Identifying the behavior of precipitation is one of the most important planning principles related to water resources. In this research, an attempt was made to analyze the trend of time changes in extreme rainfall profiles of the country by using the daily rainfall data of 3423 synoptic, climatology, and rain gauge stations for the period from 1970 to 2016 and by performing interpolation using the kriging method. Then, using percentile profiles (percentile less than 10, less than 25, 25 to 75, 75 to 90, and above 90) and regression analysis, changes in the frequency of member days of each of the percentile methods over time were calculated and mapped. The results showed that during the studied period, 86.6% of cells associated with days with the tenth percentile or less in the country had an increasing trend. On the other hand, the pixels associated with days with the 90th percentile and more have shown an increasing trend. Considering that the pixels with the 25th, 25th-75th percentiles (normal), and 75th percentile have shown a decreasing trend in terms of the number of days in their group, it can be concluded that the country's rainfall conditions and the days with rainfall are towards the limit values has moved and the possibility of drought or destructive floods has increased in the country.

Water resources management has long been the focus of residents in Iran. Knowing of the time and the amount of rainfall contributes to better planning for water resources management, and this can be examined according to the available statistical data. The need for knowledge about precipitation trends in the study areas facilitates and legalizes water resources management and planning and helps to supply water with a higher reliability factor. The purpose of this research is to estimate and analyze the precipitation trends in Kohgiluyeh and Boyer-Ahmad Province within the  Zohre-Jirahi basin.

This research is carried out in the Zohre-Jirahi basin in Kohgiluyeh and Boyer-Ahmad province based on the data from 1966 to 2018. In this regard, first, meteorological stations related to the studied area were located and their statistics were extracted from the received data. The stations’ data homogeneity is calculated based on the Kolmogorov-Smirnov method, and those without homogeneous data or with limited data are removed and, 30 stations are selected for data rebuilding. Rebuilding of missing data is done with Inverse Distance Weighted methods with the power of two and ordinary linear Kriging and after evaluating the methods by three criteria of Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination, the optimal method is selected to rebuild the missing data in this study area. After rebuilding the data, a multi-dimensional raster containing rainfall information related to the years of the statistical period is produced and the time series of the relevant data is created in an array and per surface unit. In this research, according to the surface of the study area, time series of 8915 points are analyzed, and the trend of changes based on the Mann-Kendall method and Sen's slope on an annual and monthly scale are assessed in these points and, raster maps are produced.

Among the methods used for rebuilding missing data, based on the evaluation of the models, the optimal method for rebuilding missing data in the study area was the Inverse Distance Weighted method with a coefficient of determination of 0.95.The results of calculations on an annual scale show that the average Sen's slope in the study area does not have a significant trend and is equal to 0.0011. The average Sen's slope in the study area on a monthly scale is 0.28 in April and has an upward trend, in May Sen's slope is equal to -0.03 and indicates a downward trend and in June and July, an unobserved trend, and the results of Sen's slope calculations are zero. In August, there is an upward trend, and it’s value is equal to 0.11. In September, there is an upward trend, and it is equal to 0.06. In October, there is no observed trend, and it is equivalent to zero. In November and December, the trend is upward, and the average Sen's slope in the study area is equal to 0.19 and 0.62, respectively, and in January, February and March, the downward trend is equal to -0.48, -0.55, and -0.14.

The results do not demonstrate a significant trend on an annual scale, however on a monthly scale, in December, April, November, August, and September, respectively, the highest upward trend is observed, while in February, December, March, and May, respectively, have the highest downward trend, and June, July, and October lack trends. The maximum average Sen's slope is calculated for December and equal to 0.62, and its minimum is in February and equal to -0.55. The management of water resources, especially in the agricultural sector as the main consumer, has large economic and social dimensions and is inevitable, and due to the great impact of water supply time to optimize and increase productivity, this research can be used to review the pattern and time of cultivation in this area. Groundwater artificial recharge, storage process, and consumption process should adapt to the new changes.

Global space coverage of the TRMM satellite imagery has provided a good opportunity to use the precipitation data estimated by this satellite in the country. Various studies have been conducted in the country to evaluate the accuracy of the above data in comparison to the measured data at ground stations. However, few studies have examined the efficacy of postprocessing methods in correcting TRMM precipitation data. The purpose of this study was to evaluate the performance of quantile mapping methods in improving TRMM precipitation data compared to ground data. For this purpose, 10 quantile mapping methods were applied to the gridded TRMM precipitation data in Kermanshah province on a monthly time scale (Apr-Oct) from 2005 to 2012. The ground precipitation data for the same time periods were collected from 13 synoptic weather stations and 82 rain gauges. The results showed that non-corrected estimations of TRMM precipitation data in the elevated (lowland) areas higher (lower) than the ground data. In addition, it was found that the difference between satellite- and ground-based estimates of precipitation in high-precipitation months was much greater than low-precipitation ones. The Parametric transformation of scale method with the least error, among the others, was introduced as the most appropriate quantile correction method. The results of data post-processing showed that the mentioned method could improve the accuracy of TRMM precipitation data. In addition, the correlation coefficient between ground measurements and satellite precipitation data varied in the range of 0.73 to 0.94 (significant at the 5% level), with the highest correlations obtained compared to the precipitation of synoptic and TRMM stations.

Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results.

Climate changes caused by the progress and industrialization of human societies have caused changes in the intensity and frequency of heavy precipitation and floods, which have caused irreparable damages. In order to reduce these damages, it is necessary to identify the changes in the threshold values ​​of precipitation and factors affecting it each region. Quantile regression methods are able to examine the trends not only in the median, but also in different ranges of the data series. Therefore, the purpose of this research is to investigate the seasonal trend of different amounts of precipitation and also to investigate the relationship between the climatic parameters of minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity and wind speed on different amounts of precipitation in different climates of Iran.

In the first step, the daily time series of climate data including precipitation, minimum and maximum temperature, minimum and maximum relative humidity and wind speed for a period of 45 years in different seasons for 5 synoptic stations of Babolsar, Shiraz, Bandar Abbas, Khorram Abad and Torbat Heydarieh were formed. In the selection of study stations, we tried to select stations with different climates and with appropriate statistical period. Then, to investigate the trend of seasonal changes of different amounts of precipitation in different quantiles (quantiles 0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95 and 0.99) was analyzed using the quantile regression method in all study stations. In the next step, the relationship between the climatic parameters on different amounts of precipitation (low to very high amounts of precipitation) in different seasons was investigated for each of the stations using the quantile regression method. Then the results were analyzed.

The results of examining the changes trend of daily precipitation are showed that the high amounts of precipitation in the spring season in Bandar Abbas, Shiraz, and Torbat Heydarieh stations were reduced significantly, but very high amounts of precipitation (0.95 and 0.99 troughs) in the station Babolsar and Khorramabad have increased. Also, very high daily precipitation amounts in summer have decreased in Bandar Abbas station but increased in Torbat Heydarieh and Khorram Abad stations, significantly. While in the winter season, different amounts of precipitation in all seasons have a decreasing trend and there was only a significant positive slope in very high amounts of precipitation (slope of 0.99) in Babolsar station. In the investigation of the parameters affecting the extreme precipitation, the results showed that the amount of impact on the occurrence of heavy rainfall was relatively higher than low to median rainfall. The parameters of minimum temperature, minimum humidity, maximum humidity and wind speed have a positive effect and the maximum temperature parameter has a negative effect on heavy rainfall in different seasons and stations.The highest positive effect coefficients were in spring for wind speed in Babolsar (1.8), in summer for wind speed in Babolsar (3.8), minimum and maximum temperature in Bandar Abbas (-4.03 and 1.53), in Autumn season for maximum humidity and wind speed in Babolsar (2.57 and 2.99) and wind speed in Khorram Abad (1.54) and in winter, for wind speed in Babolsar and Bandar Abbas (1.94 and 6. 2), and minimum temperature in Torbat Heydarieh (0.96). Also, the highest negative effect coefficients of maximum temperature were in autumn and winter seasons (-0.88 and -0.72) in Babolsar and autumn season (-0.63) in Shiraz.

The significant changes in increasing and decreasing precipitation are mostly related to the amounts of heavy precipitation, which are different in different seasons and climates. Also, the precipitation of the stations near the north and south coasts have been influenced by climatic parameters to a greater extent. In general, it can be said that flood precipitations are influenced by climatic parameters such as wind speed, humidity and temperature in order of importance and this effect is different depending on the location and time and the influence of different factors. Therefore, it is necessary to apply accurate planning for the correct use of received rainfall and optimal management in the target area using the results of such studies.

Global database and satellite products with high spatial and time-lapse power, can be seen as a suitable alternative source for conducting studies of water balance components in statistically deficient areas and areas with no uniform distribution of stations. Use of this data provided that it has sufficient accuracy, for Iran, which many of its parts, especially desert and mountainous areas, due to the low density of stations, the short statistical period of new stations always faces problems of accessing local and time information in the region. They will be, of great importance it is. The main goal of the research is to assess the global database and satellite products to estimate real rainfall, Evapotranspiration, and changes in water storage in the Tashk-Bakhtegan basin. Used GLDAS, PERSIANN - CDR, CHIRPS, NCEP database to assess the rainfall according to our objectives. For real Evapotranspiration, the real amounts of evaporation and absorption were first extracted based on the Balance Torrent White equation, and the results were evaluated by the GLDAS database, GLEAM. The GRACE satellite was used to estimate the changes in the region's water reserves and to assess it the GLDAS satellite was used to extract annual changes in groundwater. Results obtained showed that the PERSIANN - CDR database performed best and consistent with its observational data across all statistical indicators before and after the Bias correction. GLEAM also had the best statistical performance in estimating Evapotranspiration before and after correction with Balance Torrent White Equation data. Comparison of the observation levels of underground water with data extracted from GRACE and GLDAS satellites indicates the existence of a similar trend, and, based on the power of GRACE's low locality segregation and the low area studied, the results for groundwater changes and Ground water is acceptable. The results of the present study show data from the PERSIANN- CDR satellite for rain, GLEAM model for real evaporation and absorption and GRACE satellite for estimating ground water changes as a convenient tool for making early, quick and low - cost estimates on Water Balance Components It will be used.

Precipitation is one of the most significant climatic parameters; its distribution and values in different areas is the result of complex linear and nonlinear relationships between atmospheric elements-climatic processes and the spatial structure of the earth's surface environment. Classification of data and placing them in small and homogeneous zones can be effective in improving the understanding of these complex relationships and their results. In the present study, zoning and analyzing the distribution of rainfall in Iran concerning environmental factors was performed using the annual precipitation data of 3423 synoptic, climatological, and gauge stations in the country during the period from 1961 to 2015 and the altitude, slope, aspect, and station density data. After standardization and preparation of the data matrix, the optimal number of clusters was determined and the data set was entered into the neural-fuzzy network model (ANFIS-FCM). The results showed that the values of R2  and MAE  indices were 0.76 and 0.23, respectively which indicate the appropriate accuracy of the model. It was also found that in the four output zones of the model, environmental factors have a high impact on the spatial distribution of precipitation. In the first and third zones, the combination of high altitude and slope factors along with geographical proximity to precipitation systems has caused the average annual rainfall in these zones to be 318 and 181 mm, respectively. The mean annual rainfall has decreased to about 100 mm by the weakening of the role of environmental factors in the second and fourth clusters.

Arjan wetland is one of the most internationally important wetlands of Iran.  This wetland has been dried in the last two decades, except for short periods in other dry seasons. The purpose of this research was to study the long-term changes in meteorological and hydrological conditions using the data of stations located in the wetland watershed and adjacent watersheds. To this end the rainfall, evaporation, temperature and groundwater aquifers status and runoff of the wetland basin were analyzed. The data showed a decreased trend of precipitation and severe underground water harvesting in the studied area. Comparison of the amount of water stored in the reservoir of Arjan wetland in the current situation (95.49 mm3) to what reported in the Atlas of Water Resources of Iran (109.40 mm3) shows a significant decrease in recent years. Comparison of the current estimated runoff (13.90 mm3) with one reported in the Atlas of Water Resources (15.64 mm) shows a decrease in this factor. Moreover, the results showed that the groundwater level had a decreasing trend and the last estimated years had the lowest levels which clearly shows the negative impacts of numerous wells have been dug in the studied area. In conclusion, the results of this study show that the drying of Arjan wetland is affected by both climatic and anthropogenic factors. But the impact of anthropogenic factors is much more serious and it is strongly recommended to reduce the water harvest from aquifer to rehabilitate the wetland.