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

Airborne particles play an important role in the balance of atmospheric radiation and climate change. The relationship between aerosols and climate systems has received increasing attention as our understanding of these issues increases. A dust storm is a complex process that is influenced by the interactions of atmospheric systems and is basically caused by conditions such as high wind speed, bare soil and dry air.

Aerosol optical depth (AOD) is one of the most important parameters in the field of dust related research. Aerosol optical depth actually refers to the distribution of dust aerosols in the atmosphere. In this research, the dust condition was investigated using the AOD parameter of MODIS product. In order to investigate the relationship between environmental factors, NDVI was used. For each year, based on the total images taken, which is about 24 images per year, the average vegetation cover index was calculated in the Google Earth Engine system as a time series. In this study, among the climatic factors, annual rainfall data from synoptic stations were used to investigate the effect of rainfall on the amount of dust changes in the period from 2013 to 2022. Also, a 90-meter DEM was prepared from the SRTM sensor in order to prepare layers of slope percentage, slope direction and elevation classes.

In the research, firstly, to evaluate the dust situation, the aerosol optical depth product was obtained from the MODIS sensor. In order to highlight and monitor as much as possible, the dust phenomenon of the months of June to September, corresponding to the late spring and summer season of each year, when the majority of dust storms are concentrated in these months, was chosen. After downloading the images, the 9-year statistical period (2013-2022) was considered as the studied statistical period in Khuzestan province. The average optical depth of dust was obtained each year based on the images taken in the same year. The results show that most of the dust was spread in the years 2015, 2016, 2017 and 2018, so that a large part of the province faced the dust phenomenon in these years. The investigation of the dust situation in 2013 and 2014 showed that although most of Khuzestan province is facing a relatively low average AOD, the maximum AOD value is related to classes with values of 0.71 to 1.5 and more than 1.5 located in the southwest. is the province; The maximum value of AOD in these years was 2.82 and considering that AOD is between zero and 5 in the mentioned years, the southern regions have faced a high average dust phenomenon. The purpose of this study was to monitor dust and investigate its relationship with various factors of topography, vegetation and climate. The results of dust relationship with the mentioned factors showed that among all the factors, height and slope direction have the highest correlation (83 percent) and (72 percent) respectively with the phenomenon of dust in the opposite direction, so that an increase in the amount of height causes a decrease in dust and south-facing directions. And smooth areas will have an increase in the amount of this phenomenon. Therefore, by using remote sensing techniques, it is possible to identify dust centers as a valuable tool to combat the expression factor.

Desertification is one of the most obvious ecological and environmental problems in arid and semi-arid regions. n this study, to identify dust spots, changes in the average occurrence of dust storms were obtained using MODIS sensor data from 2013 to 2022, and then its relationship with climatic factors, sun cover with NDVI index, and topography were evaluated. A decrease in the amount of precipitation that can lead to a decrease in soil moisture, vegetation cover and an increase in drought; Also, the changes in land use in recent years are all among the factors that have led to an increase in the phenomenon of dust, especially in Khuzestan province. Examining the influencing factors such as topography, vegetation and climatic factors is of great importance in identifying and prioritizing the control of dust centers, in addition, the MODIS optical depth sensor product can be of great help as a valuable tool for monitoring and managing this event.

As a dreadful natural disaster caused by a severe reduction in precipitation rate, drought brings about, compared with other natural disasters, far-reaching spatial and temporal consequences, incurring severe damages. On the other hand, in late the 20th century, drought monitoring approaches underwent a paradigm shift, and advances in remote sensing and earth observation technologies allowed observations and monitoring of key drought-related variables over larger temporal and spatial scales than what the then conventional methods had already made possible. There are different remote sensing indices used to assess drought, including the PDI index which has been developed based on the spectral patterns of soil moisture changes in the NIR-Red space using the red and near-infrared bands of the ETM+ sensor. Therefore, as Khuzestan province is suffering from drought consequences, including but not limited to dust storms and economic difficulties, this study sought to identify the spatial and temporal trends of drought in the center of Khuzestan province.

The study area is located in southwestern Iran and the center of Khuzestan province at 31° 0ʹ 17ʺ to 31° 43ʹ 69ʺ N latitudes and 48° 35ʹ 51ʺ to 49° 32ʹ 2ʺ E longitudes covering an area of 7635/36 km2. To conduct the study, some twenty ETM+ remote sensing images of level-1 data taken from 1999 to 2018, (path/row 168/35) were collected from the United States Geological Survey website.After gathering the required data, some 411 random points were selected on the collected images, the pixel values of red and near-infrared bands were extracted and plotted against each other, and the slope of the best-fitted line, known as the soil line, was obtained. Then, the PDI drought index values were calculated using the slope and the values of the aforementioned bands. Finally, by applying a natural break classification method, different degrees were separated, and the drought’s trend of spatial and temporal changes was identified using Mann-Kendall's seasonal trend test at different significance levels.

The results of the spatial trend analysis of drought suggested that the trend was significant only in low drought and non-drought conditions. For the non-drought conditions, the probability of spatial changes was lower than the confidence level at 5% and 10% significant levels, indicating the significance of the conditions at these two levels, and thus, rejecting the null hypothesis at merely the 1% level. On the other hand, as the low drought conditions showed significant spatial changes only at the 10% significant level, the null hypothesis is rejected at the 1% and 5% levels. However, moderate and severe drought conditions revealed no trends in terms of spatial changes due to the higher probability values ​​of 0.28 and 0.3, respectively, which were higher than the determined significance levels. Moreover, the results of temporal trend analysis indicated no trend for the non-drought conditions, considering the fact that the null hypothesis was rejected at all significant levels. On the contrary, in the moderate drought conditions, a temporal trend was confirmed at all significant levels with the probability rate of 0.006 which was lower than all the assigned levels. Also, a temporal trend was found at low and severe drought conditions at 5% and 10% significance levels with a probability rate of 0.023 and 0.014.

The spatial analysis of the drought trend suggested that only the area with non-drought conditions had a significant increasing trend, which could be justified by the increase in the area of ​​irrigated land around water bodies in the area, especially around the Karun River in the west of the study area. The reduction in the area of land in the northeast of the study area with the low-drought conditions could be attributed to the rangeland degradation containing low and moderately dense vegetation.Moreover, the status of drought conditions in some sandy areas has changed from low in 1999 to moderate in 2018 due to vegetation destruction. The decreasing and increasing trends in areas of land with moderate and severe drought conditions, respectively, indicated the worsening of the drought conditions in the study area. Taking the changes in the drought index into account, it could be said that merely the areas with non-drought conditions remained unaffected by any significant increase or decrease in drought conditions, considering the fact that such areas are mainly wetlands, irrigated farms, and fish farms (that are naturally wet).However, the trend of the drought index value was found to be (highly) significant for other drought conditions, especially for moderate-drought conditions, indicating an increase in the severity of the drought conditions during the studied years. The frequent occurrence of dust storms in Khuzestan in recent years suggests that the results of this study correspond to the current reality of the region. In fact, it could be argued that during the last decade, the exacerbation of climate change and drought conditions on the one hand, and the development of construction projects and excessive extraction of water resources, on the other hand, have led to the dryness of many wetlands and wet areas, thus creating small deserts which are regarded as the main sources of dust storms in Khuzestan province within the past few years. Moreover, according to the findings of recent studies, desertification and drought trends have been increasing in recent years, indicating a great increase in the significance level of desertification in number 3 and 4 desertification centers in the east and southeast of Ahwaz, and a significant increase in the severity of drought conditions. This study proved the efficacy and applicability of the PDI drought index in drought monitoring.

Rapid population growth, increasing water demand, decrease in precipitation, and occurrence of drought may increase the use of water resources, especially the extraction of groundwater resources, leading to a drastic decline in groundwater level, and consequently the occurrence of land subsidence phenomenon. There are various methods for monitoring land subsidence. However, from among ground and space-based methods for the detection of earth crust deformations, the application of Interferometric Synthetic Aperture Radar (InSAR) on the collected data is considered as the most proper method in terms of accuracy and continuous spatial coverage. 

Located in central Iran in the west of Yazd Province, Abarkouh plain is a part of the Abarkouh – Sirjan basin, covering an area of 1390 km2. The area consists of alluvial fans and plains, surrounded by mountains on the west, south, and southwest and bounded on the east by Abarkouh Playa. This study used 46 satellite images taken from 2014 to 2018 to measure the amount of land subsidence in the Abarkooh plain. Moreover, the Shuttle Radar Topography Mission (SRTM) DEM was applied with 30 m resolution to remove the topography effect. A small Baseline Subset (SBAS) time series analysis was also performed to examine the short-term and long-term behavior of the subsidence.Decline in groundwater level and the subsurface sediment thickness are the two most important factors affecting the subsidence. The data used in this study were collected from 34 piezometric wells and 77 geologic logs. Finally, the most effective factors involved in subsidence and their relationship with other factors were investigated by comparing the output of the subsidence map and other existing maps.

The study's results indicated that the subsidence occurred in the east, northeast, and north of the area with the maximum accumulated value of 21, 10, and 6 cm, respectively, over four years. Moreover, the study of groundwater level and the thickness of fine-grained sediments showed that despite the decline in water level throughout the whole plain, subsidence is observed only in regions with clay subsurface sediments. According to different trends of decline in the groundwater level of the study area, groundwater level variations are changed during three periods. Accordingly, the water level declines during the first period in the east, northeast, and north of the area, while it increases in the west and southwest of the region. However, the decline in water level occurs throughout the whole region during the second period, and it is decreased at a lower rate in the east, north, and northeast during the third period.

In the first period, the comparison of the location of areas with increase or decrease in their water level with their corresponding areas on the Landsat showed that the water level declined in those residential and agricultural areas where there are more water wells, and, therefore, the subsidence rate is much more than other areas. On the other hand, the study of areas with an increase in water level suggested that the aquifer of these areas was recharged by mountains and alluvial fans.In the second period, those areas whose water had declined in the previous period experience more decline. Therefore, it can be concluded that the aquifer had not sufficiently been recharged in wet periods. In other words, the increase in the decline of the areas' water level occurred due to the decrease in the recharging of the underground waters because of several years of drought, and the increased groundwater withdrawal caused by the development of agricultural lands. However, despite the sharp decline in the areas' water level, no subsidence was found in the region.In the third period, some piezometric wells were dried, and the water level decline was significant in the west and southwest areas, which could be attributed to factors such as increased acreage, creation of new industrial centers, etc. Therefore, it could be argued that the subsidence rate of this four-year period will certainly return to the hydraulic conditions before this period. Thus, it can be concluded that in addition to the decline in groundwater level, other geological and hydrogeological factors play an important role in causing subsidence.

In this research, for analysis the spatial- temporal trend of precipitation in Caspian basin, the monthly data of GPCC data base in the spatial resolution of 0.5°×0.5° during 63 years period(1951- 2013) have been used. In order to analyze of the spatial- temporal trend of precipitation, the mean of annual, seasonal and monthly amounts of precipitation were prepared and then by applying Mann-Kendall nonparametric test at 0.95 level of significance, 17 maps precipitation spatial trend were produced in GIS. To examine the temporal trend of precipitation, first 17 graphs were prepared using the weighted amount of precipitation and then the trend was evaluated using Mann-Kendall nonparametric test at 0.95 level of significance. The evaluation of monthly precipitation spatial trend maps indicated a significant downward trend at 0.95 level of significance in March and April on the western, eastern and southwest regions of Caspian basin. Only in spring and autumn there have been a significant downward trend and in none of the other seasons no meaningful trend was detected. Also, the analysis of yearly precipitation spatial trend illustrated a significant downward trend on western region. The analysis of precipitation revealed significant downward trend on monthly time series at the 0.95 level of significance in March on Caspian basin.

In the present study, considering the importance of wind speed forecasting, one of the important factors especially in the discussion of evaporation in watershed, is the construction of water structures and dams ponds so For this purpose we use the SARIMA linear time series models and non-linear SETAR linear time series models to predict wind speed of Ahwaz synoptic station with a statistical period of 22 years 1988-2002. Correctly fitted Sarima models were selected based on self-correlation and partial correlation functions and independent residue test (Logon-Box). The SETAR models were also used to assess the adequacy of the remaining models of the model. The results of the evaluation of the matched models showed that the SETAR model (2; 6.2) with the coefficient of explanation of 0.74 compared to the SARIMA linear model had a precision It is acceptable to predict the monthly wind speed of Ahwaz synoptic station.

By understanding the temporal and spatial variations of water resources in a region, the better management and planning of water resources and water consumptions can be done. Time series modeling, if used correctly, will lead to acceptable results in this regard. For this purpose, the monthly runoff time series for 28 years was prepared at the hydrometric stations of Mehregerd region and the presence of trend was studied using the Mann-Kendall method. Then, different patterns of ARIMA were fitted to the data using the univariate Box-Jenkins method, and the most suitable pattern for predicting the future years was chosen. The results indicated that runoff had a decreasing trend in all months and seasons. The role of increased water consumption in the process of decreased water resources of the area was investigated and it was observed that the water consumption in the region has significantly increased since 2006. The consumption level in this basin is estimated about 46 Mm3/year. The forecast of runoff by 2023 using ARIMA (0, 1, 0) * (0, 1, 1) was performed as the most suitable model and it was found that the surface water resources in the upcoming years would experience a significant decrease if the current trend of exploitation is continued.

Water is a vital source of growth and development in human societies. In dry and semi-arid areas like most areas of Iran, because of lack of surface water, groundwater resources are used. This research studies the process of extraction of groundwater resources from the six main watersheds of Iran during the years of (1982-2018) and predicts the rate of harvesting of these resources by 2025. For this purpose, the Time series model was used in the ARIMA method. The results showed with current management that the rate of harvesting of these resources in the future will increase in the most of the basins and this will endanger the availability of these resources and human life. Pricing of water resources used for agriculture, the installation of the smart meters and the sustainable management of groundwater resources are recommended to improve the predicted state.

This paper was carried out to determine the cycle of dust storm in relation with precipitation in 12 months of the year, using processing of digital time series and their correlation with two variables mentioned above. Hence to accomplish the aim of the paper, the frequency of time series for dust storm and rainfall have been analyzed for the long time statistical data collected from 45 synoptic stations(1986-2016), located in the south east of  Iran. In order to understand whether or not any correlation with these two variables, firstly a diagram of the daily changes in dust storm and rainfall cycles was drawn for each month separately. Then, to make sure that the statistics data are normal, the data were analyzed using Kolmogorov-Smirnov test in SPSS software. The results of data normality showed that all statistical data of the parameters used in this research were normal. The results of calculating the correlation coefficients of each month during the course of thirty years, indicated that the correlation of data are significant at (P<0.01 & P<0.05) only in the cold months of the year (January, February, March, April, May, November and December). Also, this study showed that the relationship between the amounts of daily precipitation with an abundance of dust events in the area was negative, and this indicates that the dust could be significantly as reducer or repressive downpours in the region.

Selecting appropriate inputs for intelligent models is important due to reduce costs and save time and increase accuracy and efficiency of models. The purpose of this study is using Shannon entropy to select the optimum combination of input variables in time series modeling. Monthly time series of precipitation, temperature and radiation in the period of 1982-2010 was used from Tabriz synoptic station. Precipitation, temperature and radiation parameters with different delays are considered as input to the Shannon entropy. The results showed that time series with three delays provide the better results for the modeling. Applying Bayesian network and multivariate linear regression analysis were performed. Models performance was evaluated using three criteria: coefficient of determination (R2), root mean square error (RMSE), and the dispersion. Index (SI). The results indicated that Bayesian neural network model shows the best performance to simulate time series of precipitation, temperature and radiation in compare to multivariate linear regression analysis. The results showed that Shannon entropy has better performance in selection of the appropriate entry into intelligent models.

Nowadays, water supply is more limited and providing water is more difficult due to increasing population and demand for water. Thus, due to rainfall shortage and impacts of drought, the need for forecasting monthly and annual rainfall and flow discharge through time series analysis is acutely felt. One of the key assumption in time series is their static condition. However, hydrological time series are sometimes dynamic due to independent occurrences and variations. The objective of this study was to evaluate a suitable forecast model for water flow discharge  through evaluating whether time series data of rainfall in Jamishan river, Kermanshah, Iran were static. The 25-year data of monthly rainfall (1988-2013) were used and assumed to be a dynamic series. Non-seasonal parameters of SARIMAS model were analyzed through MINTAB software. The accuracy, reliability, normality and independence of data were also evaluated. Finally, water flow discharge was modeled and predicted by SARIMA (3,0,1) x (3,0,1)12 which showed minimum mean error.

Introduction Vegetation cover is an important indicator in the arid and semi-arid areas and plays an important role in balancing the ecosystems. Monitoring vegetation cover changes is of great importance because of vegetation effects on the environment. This monitoring provides detailed quantitative and qualitative information. It is therefore important to monitor dynamic changes in vegetation and to investigate the factors that are driving these changes in order to guide regional environmental management. Changes in vegetation could be tracked by satellite time-series data and trend analysis. Analysis of vegetation time series improves considerably our understanding of vegetation annual changes. Touran Biosphere Reserve accommodates three areas of Wildlife Refuge, National Park, and Protected Area. This reserve is the second largest biosphere reserve in the world. In terms of international categories, Touran is a special habitat of steppe grasslands in Central Asia and Desert-Saharas peculiar to West Asia. The aim of this research is to evaluate vegetation trend in Touran Protected Area using MODIS vegetation time series data from 2001 to 2015.
Materials and methods The 16-day composite MODIS VI product with 250m spatial resolution from Terra (MOD13Q1) was downloaded from the Land Processes Distributed Active Archive Centre (LP DAAC) for the Touran protected the area and the years from 2001 to 2015. The MOD13Q1 product includes normalized difference vegetation index (NDVI), Enhanced Vegetation Index (EVI), infrared band, red band, and quality assessment (QA) information. Before the time series analysis, different vegetation indices (NDVI, EVI, TSAVI1, and PVI1) were created. Then, the vegetation indices were aggregated to monthly composites by applying the arithmetic max of each month and standardized anomalies (Z score) of the monthly vegetation indices data were calculated to remove the seasonality. Several studies using the more robust Theil-Sen trend analysis and Mann-Kendall tests were conducted to explore the trend analysis of vegetation using NDVI, EVI, TSAVI1, and PVI1. A field survey was conducted to select a suitable vegetation index.
ResultAccuracy assessment results showed the Kappa coefficient for the PVI1, TSAVI1, EVI, and NDVI indices to be 0.78971, 0.5674, 0.5288, and 0.4159, respectively. Field survey results showed that PVI1 is the best index in the study area. According to the results of the Theil-Sen slope analysis and the Mann-Kendall test, the trend was divided into 3 levels. Vegetation with Theil-Sen slope 0, but with Z values ranging from -1.96 to 1.96 has no trend, and vegetation with Theil-Sen slope > 0 and Z > 1.96 has a significantly increasing trend. Results related to inter-annual trends show that vegetation degradation or decreasing trend occur in the northeast, east, and southeast, as well as the west and southwest region. Vegetation cover improves in central and southern of Touran protected area. Areas without a trend or stable regions are dispersed in the entire region. Vegetation cover improves in central and southern of Touran protected area. Areas without trend or stable regions are dispersed in the entire region. Results indicated that vegetation cover declines in 30% of Touran protected area, vegetation cover increases in 26% of study area but in 44% of Touran protected area no trend in vegetation cover was detected.
Discussion and Conclusion Combining methods including the coefficient of variation, the Theil-Sen median trend analysis, and the Mann-Kendall test provide an effective way to investigate the characteristics of variations in vegetation. According to experts in this area, the overgrazing on rangelands of this area is one of the dangers that threaten the biosphere reserve. Overall, this study showed that the creation of distant-base vegetation indexes from the MODIS Time Series product could improve the evaluation of the long-term trend on a local scale. The method of this paper provides useful information for identifying vegetation changes in arid and semi-arid areas. For future studies, it is suggested studying the changes in vegetation trends in arid ecosystems in order to evaluate remote-sensing time-series images.

Common causes for groundwater quantity and quality changes are infiltration through the seasonal floodwater, effects of the artificial recharge projects and adjacent aquifers, and groundwater extraction for deferent uses. However, recognizing the impact of their contributions to these changes can be often challenging. Still, to understand the changes, monitoring groundwater level and several chemical factors are taken into consideration. Certain problems of course can complicate the analysis of the observations; for instance, insufficient data and manually prepared and measurement intervals can degrade the accuracy of such observations. In this paper, device-measured daily time series of Electrical Conductivity (EC), Temperature (T) and Groundwater Head (GH) are analyzed. The main purpose is to evaluate the groundwater changes and its relation to the processes on the aquifer surface in an arid region. Two wells (PZ2 and W20) with 1,200m distance from each other selected and installed a sensitive device for recording the groundwater level, temperature and salinity fluctuations. Time series of changes from December 2012 to July 2013, were analyzed. Groundwater head in PZ2 showed a decline but in W20 showed a minor rise. Salinity variations were different and were 15.3mS/cm in PZ2 and 1.7mS/cm in W20. The backdrop of increased salinity in the surface layers of groundwater in PZ2 identified and showing a correlation between groundwater head and salinity fluctuation with 83.5% R squire.

Choosing the appropriate inputs for intelligent models is important. Because it can reduce costs and save time and increase accuracy and efficiency of the models. This work aims at the use of gamma test to select the optimum combination of input variables including delayed records of in time series modeling precipitation. Monthly time series of rainfall for the period 1383 to 1393 was used for Rasht station Rainfall data as with different lags were employed as input to gamma test. Results showed that time series with three delays (lags), provides better results. The simulation was performed using Bayesian network and multivariate linear regression. The performance of models was assessed using three criteria, i.e. coefficient of determination (R2), root mean square error (RMSE), and dispersion index (SI). Bayesian neural network using a three-month delay the coefficient of determination of 0.82, root mean square error of 17.84 and a diffusion index of 0.17 showed better performance as compared with multivariate regression. The results established the significant role of the gamma test integrated with intelligent models in the appropriate selection of input variables..

Drought is one of the most important natural disasters that is effective in different parts such as; natural resources, surface and subsurface water resourcesand etc. Notice to the changes trend of drought and its forecasting, can be effective in planning of drought control and reduction of drought impact. In this study climate data of 7 stations of southwest of Iran from 1980 to 2014 was used and the Intensity of seasonal drought was calculated based on RDI index. Then drought index predicted from 2015 to 2019 using time series models and ITSM software, then the changes trend of drought based on parametricand non-parametric tests were evaluated. According to results in Abadeh, Shiarz and Fasa stations MA (5) Innovation, in Bandar Abass station AR (15) Burg, in Bandar Lengeh station AR (15) Yule-Walker, in Boushehr station MA (5) Hannan-Rissanen and in Jask station AR (13) Burg model were the best time series model that fitted in data. Correlation coefficient between observed data and predicted data, P- Value of Ljung – Box test, white noise of models and ACFand PACFResidual of models showed that predicted models were Reliable. Results showed that the changes trend of seasonal droughtin all stations from 1980 to 2014 and 1980 to 2019 periods were non-significant at 0.95 level based on parametric and non parametric statistical methods. However slop of changes trend in (1980-2019) period was more than (1980-2014) period.

Drought is one of the most important natural hazards that should affect Agriculture and water resources. The frequency of its occurrence, especially in arid and semi-arid areas of Iran is very high. Therefore simulation and drought forecasting is necessary more than ever. This factor is importance in the planning and management of natural resources and water resources. In this study seasonal drought over the 5 next years (from winter of 2015 to autumn of 2019) in Tehran station was evaluated using RDI drought index, time series models and ITSM software. In this research climate data of Tehran station from 1967 to 2014 was analyzed (with average precipitation of 239.67 mm/year). Based on Hannan-Rissanen MA (5) model method was the best model fitted to the data. According to results coefficients of model (Z(t-1)) at 3 and 4 lags are insignificant (at the 95% level), therefore this coefficients set zero. According to p-value of Ljung - Box test (0.894) in different lags that is significant in 95% level can be said that the prediction is Reliable. Based on results seasonal drought condition in 50% of predicted seasons will be normal, in 45% of predicted seasons will be near normal and in 5% of predicted seasons will be moderately drought.

Evapotranspiration is one of the most important components of the hydrological cycle and hydrological models for planning irrigation system water. In this study, the amount of evapotranspiration using the FAO Penman-Monteith for synoptic weather stations of Isfahan and Rasht that located in parts of central and northern Iran in the period 1970-2011. Some of the most important meteorological variables affecting it in the scales of seasonal and annual was calculated. In addition, changes were also analyzed and compared. For this purpose, non-parametric test of Mann-kendall and parametric test of regression was used. The results showed that the annual scale evapotranspiration changes in Rasht, and Isfahan stations had the significant increasing trend (5% level). At these station, the temperature increased and the minimum relative humidity decreased but of course was greater than other variables. In the seasonal scale both positive and negative trends have been observed. Isfahan Station evapotranspiration trends in other seasons except in winter is negative (5% level). Rasht Station evapotranspiration trends in other seasons except in spring was positive (5% level). Among the studied variables, seasonal and annual of number of sunshine hours was increased (level 5%). Maximum and minimum air temperature trend at both stations in the annual scale was increased (5% level). Comparing the results of the review process, potential evapotranspiration and temperature changes by both tests showed contradictory phenomena in the process of evaporation variables in Isfahan Station.

Groundwater drought is one of the drought types that caused by lack of sufficient groundwater recharge. Groundwater Resources Index (GRI) is a method to express the state of this type of drought using ground water level data. Various methods and models have been presented in order to forecast and model, but selecting a reliable model is a difficult task. So, it would be better to use a combination of acceptable models instead of using just one model. In this study, the GRI values over 1984-2011 period were calculated in south of Qazvin province and its relationship with meteorological parameters such as precipitation, discharge, evapotranspiration, temperature (Mean, Max, Min) and large scale climate signals (MEI, SOI, AMM, AMO, PDO) was modeled by artificial neural network based on the Gamma test and in three structures. The results show that SOI and temperature have higher significant correlation with GRI values and also using the meteorological parameters as input parameters lead to improving the artificial neural network performance. Moreover, the ARIMA (1, 1, 3) (2, 0, 1) was selected for forecasting of GRI based on evaluation measures such as AIC and SBC. Finally, ANN-ARIMA modeling revealed better performance compared with the ANN and ARIMA(R2=0.94, RMSE= 0.05).

Within the climate change debate and its probable impacts on water resources systems, design and operation of management plans based on the assumption of stationary hydrology may cause serious challenge to accurately predict future supplies. Therefore this case study is trying to assess trend in hydroclimatic variables of Karkheh Rivers upstream by applying modified Mann-Kendall trend test on long term daily time series of temperature, precipitation and discharge. Temperature variables are mostly showing meaningful increasing trends but observed changes in assessed stations were not spatially uniform for precipitation. Streamflow variables depict a decreasing trend, though more noticeable in base flows. Decreasing trend is meaningful for annual discharge median in Holailan at 90% confidence level. Total yearly precipitation, number of precipitation days and number of days with precipitation equal to, or greater than, 10 mm/d show the most correlation with stream flow variables. Comparing monthly discharge with temperature and precipitation variables in the studied gages indicates a time-delay in system response to inputs. This may related to snowmelt contributions or contributions of water into streams after passing through different hydrological pathways such as groundwater. Some parts of streamflow changes, especially about base flows, is not completely verified by precipitation changes and can be attributed to changes in temperature or another factors such as groundwater overexploitation.

Different types of time series analysis models are commonly used for predicting hydrological factors. In this study, the situation of Soleimanieh spring discharge in Kashan was investigated using various time series models and mean monthly flow during 11 year period. Then, spring discharge predicted using the best modals for future 9 years. In this research, the data were analyzed using 12 time series models including Autoregressive, Moving average, Autoregressive-composite moving average, seasonal and non-seasonal models such as Box and Jenkins. Finally, the results showed that the value of AIC is the lowest and model parameters don,t exceed of one in SARIMA (1, 1, 0) (1, 1, 1) [12] model. So, this model was selected to predict discharge data. Then, Komogorov-Smirnov test was used to investigate the normality situation of the predicted data. The obtained results showed that predicted data are normal. Therefore, according to the results, it can be conducted as the type of selected model is very important and it affects the accuracy of output response. Also, according to the uncertain nature of hydrological issues, time series models are one of the best methods in hydrological prediction.