فهرست مطالب reza modarres

In the present study, the variable of the time interval between the peak flood and the peak of drought in thirteen basins of Karkheh basin was investigated. Since the time interval variable from the flood peak and the peak of previous drought has a discrete nature, eight well-known discrete probability distribution functions were tested for frequency analysis. In general, the results of the research showed that in most of the studied small and fourth-degree basins, with the increase in the intensity of annual droughts, the corresponding peak flood discharges will decrease up to SPI= -1, and after that, as the droughts continue to intensify (SPI <-1), the discharges will decrease. The flood peak will increase with an exponential and quadratic pattern. While in large basins and the third level, with the intensification of drought, the peak discharges of floods will also decrease. The results of the frequency analysis determined that the probability distribution functions of DPO and NBI have the most appropriate fits to the time interval variable between the peak of flood and the peak of drought. Based on the frequency analysis, it was found that the time interval between the peak of flood and the peak of drought is approximately 6 to 8 months. The defined variable reflects the interaction and impact of two extreme events of flood and drought in a basin.

Chl a is the main pigment of phytoplankton, which is an indicator of phytoplankton biomass and reflects the primary production in the marine environment. In this study, level 3 (4 km) data of Chl a concentration of Persian Gulf and Oman Sea for the period of 2003- 2018 were used. The data was converted to raster format in ArcGIS10.5 environment and then the numerical values of each pixel were extracted in R (version 4.0.2). Missing data were observed in Chl a data, to solve this problem, DINEOF algorithm was applied and non-parametric Mann-Kendall and Sen’s Stimulator tests were used to analyze Chl a concentration trends. The results showed that the maximum concentration of Chl a is in September (0.09 to 18.75 mg / m3) and October (0.23 to 18.03 mg / m3) and the minimum concentration of Chl a in May (0.22 to 5.74 mg / m3) and June (0.20 to 5.12 mg / m3). The trend of Chl a concentration variability over the study period was negative in most areas and not significant. These analyses provide an overall description of Chl a concentration variability in the Persian Gulf and Oman Sea based on satellite observations; however, further investigations based on in situ observations are needed to achieve better understanding of the patterns of of Chl a concentration alterations.

This study was conducted to monitor the current water quality of Beshar River by the Iranian Surface Water Quality Index (IRWQIsc). Monitoring of the river water quality was done based on sampling 12 stations along the river in October of 2021 as the period of water shortage and January of 2022 as the period of high water flow simultaneously with sampling two other stations (fish farm effluent and Yasuj wastewater treatment plant effluent) as the point source pollution entries. The results of zoning Beshar River based on IRWQIsc in the period of water scarcity showed a decrease in river water quality from the upstream to the downstream part. 41.66% of the upstream part of the river (Tang-e Tizab to Dehno) was in a relatively good condition, 8.34% of the river (entrance of Yasuj City) was in a good condition, 8.33% (middle part of the river in Shah Mokhtar Area) was in a relatively bad condition and 41.66% (downstream of the river) was in a moderate condition. Despite the increase in discharge and rainfall, no significant increase in water quality was observed in the high water flow period. In this period, 25% of the river (Tang-e Tizab, Tang-e Sorkh, and downstream of Qalat) was in a moderate condition and 75% of the river was in a relatively good condition based on IRWQIsc. Comparison of the flow rates and IRWQIsc indices showed that only during the high water flow period in the lower reaches of the river, the increased river discharge enhanced the river water quality , but no relationship between the river flow and the IRWQIsc was found at the upstream part of the river and in the period of water scarcity.

In the present study, to investigate the air pollution of the Persian Gulf Special Economic Zone in the west of Bandar Abbas, at first inversions which occurred between 2010 and 2020 were extracted using radio sound. Next, using the AERMOD model, the concentration of PM2.5 - 10, SO2, CO and O3 emissions from 60 chimneys for inversion days in a radius of 20 km was predicted. The results showed that the highest inversion was in January and February with 28 and 26 days per month and the lowest with 11 and 13 days in August and July, respectively. The lowest base of the inversion layer was related to January and February with 10 and 13 m, respectively, and the highest with 408 m was in September. The average annual inversions were 250.8 days per year, which 157.4 were radiation and 93.4 were subsidence. The AERMOD model showed that the concentrations of PM2.5, SO2 and O3 were higher than standard, but PM10 and CO were lower than standard. Southeast winds are the most important parameter in the distribution of pollutants, which causes the smoke column move to the northwest. Predicting the distribution of pollutants can be useful in formulating strategic management in environmental protection and sustainable development.

The DINEOF algorithm is a parameter free technique based on iterative EOF analysis that is used to calculate the missing data in a given satellite data set (without requiring any prior information). In this study, the DINEOF technique has been used to fill the gaps in chlorophyll-a data series in the Persian Gulf and Oman Sea. Level 3 data (4 km spatial resolution) of chlorophyll-a concentration obtained from MODIS sensor (2003- 2020) for the study area were used. In some of the images several gaps were found in different months of the year. Images with gap in the Persian Gulf and Oman Sea were reconstructed by rtsa.gapfill R-package and DINEOF algorithm in R software. The linear regression analysis was performed between the missing and reconstructed data, and also parameters such as RMSE, MSE, MAD and SNR were calculated to evaluate the validity and performance of the DINEOF algorithm. The maximum number of the gaps in data series were found in July. Hence, the images of July have been examined and reconstructed as the case study. The original maps of chlorophyll-a concentration showed that the maximum number of the gaps were in July 2009 and 2015. Evaluation of the results showed a high accuracy of DINEOF-reconstruction method (e.g. in July 2014, R2 = 0.83, RSME = 0.34, MAD = 0.14, MSE = 0.10). The results showed that the implementation of the DINEOF algorithm (in R) to reconstruct the gaps in chlorophyll-a concentration images could serve as a rapid and efficient technique.

Drought occurs due to lack of humidity and deficiency in precipitation amount. Therefore, it is important to recognize and investigate the effective factors on precipitation deficiency and consequently drought occurrence. Enso is a phenomenon of climatic teleconnection that can affect weather in different regions of the world. Enso is one of the important large-scale phenomena affecting the temporal and spatial distribution of rainfall and consequently the drought that occurs in the tropical Pacific. The objective of this research was to investigate the effect of Enso on meteorological drought in the midwest of Iran.

First, precipitation data of synoptic stations in 14 locations were obtained in the west of Iran. Then, SPI values was calculated at different time scales of 1, 3, 6, 9, 12, 15, 18, 24, and 48 month using the spi-sl-6.exe software package. In the next step, using the data of Enso including Nino1+2, Nino3, Nino3.4, and Nino4, the Spearman correlation test was simultaneously performed in the Minitab16 program. Then, the asynchronous relationship was investigated with the cross-correlation function between Enso and SPI in time series of months 1 to 48 in SPSS16. Finally, linear regression modeling was performed. Multivariate regression test with simultaneous and delayed states of 1-6, 12, 24, and 48 months was used based on the stepwise method.

Results are presented in three sections: simultaneous, asynchronous relations, and regression equations. Results showed a high correlation between seasons of autumn and spring with indices of Nino4, Nino3.4, Nino3, and Nino1+2. In addition, the asynchronous relationship was better than the simultaneous mode. Better significance in the asynchronous mode is due to the effect of Enso on weather and drought in Iran. Considering the p-value <0.05, it was shown that the highest significance was observed between Enso indices and drought index in Bushehr station. In addition, in a number of stations, including Arak, Urmia, Shiraz and Hamedan, no significant relationship was observed in their simultaneous state. The regression analysis showed that Nino4, Nino3.4, Nino3 and Nino1+2 indices had the highest effect in different time series of SPI with the highest positive and negative coefficients of atmospheric oceanic index in all stations. The results showed that high correlation between Enso and SPI can affect predicting climatic conditions, drought, floods, planning, and crisis management in watershed scale.

Enso can be used as predictors of long-term climatic factors such as precipitation and temperature. Enso phenomenon in Nino1 + 2, Nino3, Nino3.4 and Nino4 regions can be used as predictors of climatic factors such as rainfall for long-term forecast of precipitation and finally drought and wet season in different parts due to the universal Enso phenomenon. It seems that due to the mechanism of the Enso phenomenon and its relationship with natural disasters, further studies can be effective in predicting climatic conditions, drought and floods in Iran which ultimately can be used in predicting hydro-climatic events.

During last century, anthropogenic forcing and climate change have influenced the stationarity of the many extremes. Therefore, using of stationary pre-assumption-based Frequency Analysis (FA) techniques for estimating the risk of extremes may not be reliable anymore. In present study we introduced a nonstationary FA method to estimate risk of 24-hour maximum precipitation variable, as one of the major factors for generating floods in West of Iran. Nonstationarity assessments showed that among 53 investigated stations, only 4 stations (i.e., Khorramabad and Poldokhtar stations with increasing linear trends and Izeh and Ramhormuz stations with decreasing linear trends) have significant nonstationarity. Three hydrological homogeneous regions were characterized based on statistical properties of 24-hour maximum precipitation variable at study stations. Results revealed that stations in the Region_II, mostly including the stations in Fars, Chaharmahal and Bakhtiari, Bushehr and Kohgiluyeh and Boyerahmad Provinces, have higher risks for more severe 24-hour maximum precipitation compared to stations in other regions. Also, results showed that if nonstationarity in Khorramabad and Poldokhtar stations is ignored, 100-year quantiles of 24-hour maximum precipitation will underestimate around 10.1% and 13.8% in those stations.

We study the blocks of interpoint distances, their distributions, correlations, independence and the homogeneity of their total variances. We discuss the exact and asymptotic distribution of the interpoint distances and their average under three models and provide connections between the correlation of interpoint distances with their vector correlation and test of sphericity. We discuss testing independence of the blocks based on the correlation of block interpoint distances. A homogeneity test of the total variances in each block and a simultaneous plot to visualize their relative ordering are presented.

Abstact:The relationship between peak flood and watershed area has always ben used for predition at ungaged basins. In a region with homogenous hydrologic resonse this relationship changes with a similar ratio which is called simple saling. If this ratio changes it would result in multiscaling relationship between peak floos and watrshed area and therefore, basin area cannot be used for flood discharge estimation at ungagfed basin alone. Therfore it is necessary ro evalute this before developing regional flood model.This study investigates the spatial scale method in two Caspian Sea basin and Karkheh river basin and then compares the results of two basin. To this end, peak discharge for the 46 hydrometric stations in the Caspian Basin and 24 stations in the Karkheh basin have been selected. These stations have similar hydrological behavior among the stations in their basin. The statistical method of the spatial scale at these stations was performed using the probability weight moments of annual peak discharge and area covered by each station. The results showed that in the Caspian Sea basin the parameter of the spatial scale is simple and in the Karkhe basin the multiscale mode of the spatial scale exists. Therefore, it can be said that for estimating the discharge in the ungaged regions for the Karkheh basin, it is not possible to use the flood index and the discharge-area power relationship. While this is feasible in the Caspian Sea basin.

Flood control and management is a fundamental issue for hydrology researchers and managers. Regarding the design and construct of different hydrologic structures such as reservoirs and dams as effective techniques for flood control, the appropriate estimation of the dimension and resilience of these structures needs correct estimation of the magnitude and return periods of flood for which the design flood in different return periods or probability level is applied. Design flood estimation is done through frequency analysis with the key stationary assumption. Nowadays the factors such as land use change, inappropriate management and climate change has influenced stationary conditions of flood peaks. Therefore, in the existence of non-stationary, the estimation based on stationary assumption is not confident and may have large errors. In this regard, this study for non-stationary flood frequency analysis the GAMLESS model for location, scale and shape parameter estimation as well as visual inspection of non-stationary are introduced and developed for quantile estimation in non-stationary conditions. Six hydrometery stations from different provinces in the North of Iran were selected. Frequency analysis with and without stationary assumptions was done for each station. Results indicated that location and scale parameters have trend with the linear and quadratic manner. In addition, results indicated that design flood estimated by non-stationary procedure has increased around 3 times higher than stationary estimation in Nodekhormalo station. Results also demonstrated that in a station with increasing non-stationary trend, return period of big floods is decreasing and for the same return periods, flood quantiles has increased.

Investigation of the future trend of changes in the density and health of the vegetation due to climate and land use/land cover changes can be useful in ecological and environmental studies using time series data especially in dry and semiarid brittle ecosystems. Time series analysis is a powerful tool for investigating the trends of natural phenomena in the past and present. The Satellite Time Series provides the most appropriate data for analysing the trend of changes in natural and human phenomena and predicting for the future. A time series is the collection of statistical data collected at regular intervals. Determining the trend (increasing, decreasing and unchanged) of the vegetation can be one of the ways to help manage vegetation monitoring. In this regard, the use of statistical tests to determine the trend of time series data is essential. There are different methods for determining the process, which are divided into two sets of parametric and nonparametric methods. Since satellite imagery is one of the best time series illustrations, the use of these data in trend studies is fruitful. Vegetation indices are used to monitor and evaluate vegetation dynamics using satellite imagery. NDVI is the most famous useful in plant monitoring. The NDVI, is used as a useful indicator of photosynthesis capacity identification, usually used to examine environmental and ecological changes. Therefore, remote sensing through the NDVI satellite index can measure surface vegetation changes due to the strong correlation between the vegetation and this index. The NDVI is also reliable for identifying vegetation stress because, as a result of degradation of vegetation in the land, vegetation characteristics such as health, density are majorly changed Materials and methods Monthly MODIS NDVI times series images from the MOD13A3 series, were collected from the site of NASA from 2003 to 2014 (144 images). Then the images from Gavkhuni basin have been extracted to determine the trend of vegetation changes. Mann-Kendall test was presented based on the significance of the Tau Kendall correlation coefficient (τ) and then developed. Correlation coefficient measures the relationship between two series of variables to determine if the first variable increases with increasing the second variable, or decreases or the patterns of change are not relate together. This method is widely used to handle time series. In general, Mann-Kendall statistical test can be used to determine the uniform trends of ubnormal distribution data based on the ranking. Given that the Mann-Kendall test, in addition to the trend, also specifies the type of trend occurring, this model does not require the input of normal data. In the test, for time series data trends, positive values indicating incremental trends and negative Z values represent decreasing trends. Results Vegetation fluctuations of mean monthly MODIS NDVI time series images of Gavkhoni basin from 2003 to 2014 were presented. The maximum vegetation cover is observed in spring months. This increase reached its maximum during the course of the study in 2007, and then reached its maximum four months later and then sharply declined in 2008. In 2009, vegetation cover in Gavkhuni basin is relatively modest. In fallowing, the fluctuations are almost the same, but declined sharply in 2014. In addition, the highest vegetation density is observed in the western and central regions of Gavkhuni basin. Also, this figure shows that vegetation density is high in 2003-2006 and has declined significantly by 2014. Accordingly, the vegetation cover increased in January 2003 through January, a dramatic drop in February, a slight decrease in March to May, a modest increase in June to July, a slight increase in the months of July to September, in October And November has been a slight decrease, and December has seen an increase in vegetation. The trend of long-term vegetation changes in Gavkhuni basin based on 144 images of the monthly MODIS-NDVIwere implemented in the Idrisi Tersset software. At first 144 time series images were converted into TSF file format. The resulting trend map indicates that the vegetation trend is incremental with a value of τ equal to 0.68 to a decreasing trend with the value of τ equal to -0.65. Accordingly, the trend of increasing vegetation is more dispersed in the north-eastern and central to west parts of the basin, and slightly to the south of Gavkhuni basin. In addition, the largest decline in vegetation occurs in the central regions of the east and south of Gavkhuni basin. This trend is evident in the northern and western basins as well as in the south of the basin. In addition, decreasing trend of vegetation is more concentrated in areas of basin which referring to the use/land cover map are aquaculture. This trend indicates a sharp decrease in the rain-fed agriculture. Referring to the land use/land cover map, the vegetation cover of the Gavkhuni wetland during the study period has also been subject to decreasing or unchanged trend. The vegetation of the rocky areas of the north-east of the basin has also increased marginally. Accordingly, the most extensive areas indicates unchanged trend of vegetation in Gavkhuni basin. The results showed that the largest decline in vegetation was observed in 2007, when the catchment area was faced with drought at this time. Also, the annual average vegetation map in 2014 with the least amount of vegetation density has been encountered. Due to the fluctuation of vegetation, the need for serious attention of planners is required because it can be said that vegetation as the representative of environmental health also reflects the characteristics effective biophysics in growth, such as adequate water, undiluted and nutritious soil, etc., provide both the protection of wildlife and soil conservation against erosion. Also, since vegetation cover has declined from 2003 to 2014 in the months of April and May (when is the beginning of the growth season in the Gavkhuni basin), it indicates a crisis in agriculture in the Gavkhuni basin. The regional variations of vegetation in Gavkhuni basin from 2003 to 2014 through the time series images of MODIS NDVI was determined using non-parametric Mann-Kendall Tau and contextual tests, and areas with increasing, decreasing and unchanged vegetation cover were determined. The results showed that the extent of the vegetation variation trend as well as the extent of the decreasing trend in the Contextual Mann-Kendall method was more than Tau Mann-Kendall, and the trend in most of the area remained unchanged, which only indicates whether vegetation is present in this area or not. This case is achievable by comparing the trend map with land use / land cover map in 2014 or following years. In addition, it was revealed that the decreasing trend with the sever vegetation decline has occurred in the central regions of the Gavkhuni basin and along the Zayandehrud River and also in the urban areas, which can indicate the high urban growth and land use /land cover change from the vegetation to the construction site in this basin. Accordingly, a sharp decreasing trend was observed in the northern and western regions as well as in the south of the basin, which with referring to the land use /land cover maps and type /vegetation maps, these areas are highest proportion of rangeland vegetation and fertile farming along the Zayanderroud River. These lands affected by land cover/land use and have been severely subjected to land degradation due to the dryness of Zayandehrud Rive in recent years. This urged urgent measures to prevent land degradation. In addition, since the highest reduction in temporal monitoring in the Gavkhuni basin has been observed in the months of April to June, and these months are growth season and cultivation in the Gavkhuni basin, this necessitates a serious actions to mitigate the risks. Also, the study of vegetation trends showed that during the study years, saline lands in the Gavkhuni basin increased and the agricultural land was significantly reduced.

The main purpose of the present study was to evaluate the spatial pattern of the mean, time trends and frequency distribution of Sea Surface Temperature (SST) in the warm season as well as the extreme events in the southern part of the Caspian Sea. This study was performed by using long-term, high-resolution and pixel-based satellite-derived SST data. The SST changes during the warm season and the extreme events were explored and statistically analysed throughout the study period (1982 to 2016). The average range of temperature changes in the study area is between 25-27.6°C. The maximum and minimum long-term mean water temperatures during the warm season were observed in the south-eastern coasts and the northern regions of the study area, respectively. The mean range of temperature changes in the extreme events were 27.4-29.2°C in the Southern Caspian Sea. The highest long-term average of water temperatures during the extreme events were observed in the east to the south-eastern regions of the Southern Caspian Sea. During the warm season and extreme events, the trend of sea surface temperature changes in the southern part of the Caspian Sea was 0.04-0.08 and 0.04-0.084 ° C per year, respectively. Also, throughout the study period (1982 to 2016), there was no obvious negative trend (indicating a decrease in temperature) in the warm season temperatures and the extreme events. Based on analysing the probability density functions, the temperature changes during the extreme events are more intense than the temperature trend during the warm season in the Southern Caspian Sea. Increasing the skewness in the probability density function of the temperature changes in the extreme events compared to that of the warm season is indicative of increasing the numerical values of the sea surface temperature trend in the extreme events comparing to the warm season in the study area.

Change detection algorithms of remote sensing image can be divided into two categories: pixel-based and object-oriented, according to the minimum processing unit. This paper deals with the comparison between application of pixel-based and object-oriented approaches in land use classification in Isfahan-Borkhar, Najafabad and Chadegan plains and evaluation of land use changes with Landsat TM (1985) and OLI (2015) data during the study period. The object-oriented approach involved the segmentation of image data into objects with multi-resolution segmentation algorithm by eCognition software. Then objects were assigned and classified with the nearest neighbour algorithm in object-oriented classification The supervised pixel-based classification involved the selection of training areas and a classification using a maximum likelihood algorithm. Accuracy assessments of both classifications were undertaken. The results show better overall accuracy (higher 90%) of the object-oriented classification over the pixel-based classification. The land use maps indicate that residential area is increased 2.09, 9.66 and 3.74% and rangeland area is decreased 7.48, 10.94 and 17.73% in Isfahan-Borkhar, Najafabad and Chadegan plains in the study period, respectively. In Chadegan plain the increase in agriculture and fallow land use has been equal to 8.31 and 5.64%, respectively.

Cysts are one of the most common lesions in the oral cavity, which can appear in the soft or hard tissues. The aim of this study was to determine the frequency of jaw cysts at five major centers for oral and maxillofacial surgery in Tehran including Shariati, Taleghani, Baghiatollah hospitals, and Shahid Beheshti and Azad Faculties of Dentistry from 1992 to 1994. Method & Material: This descriptive study of existing data was performed by means of data forms. By reviewing the patients files, 317 files belonging to cases of jaw cysts were selected and information such as age, sex, the type and location of the cysts and biopsy results were registered in data forms. The results were analyzed by means of SPSS statistical software.

The samples were ranged from 6 to 78 years. Odontogenic cyst was the most common type (%80.75), and periapical cyst was the most frequent odontogenic cyst (%33.8). Except lateral radicular, paradental, and traumatic bone cyst all the remaining cysts were found more common in men (%60.2) than women (%39.8). The maximum occurrence of cysts observed in men and women between 11-20 (%26.9) and 21-30 (%25.86) years respectively, and the minimum range was between 71-80 (%1.26) years though dentigerous and eruption cysts did not follow this pattern. There was a greater frequency of cysts cysts in the mandible (%56.2) than the maxilla (%43.8) except periapical and traumatic bone cysts.

Odontogenic cysts are most frequently observed in posterior portion (%76.2) and left side (%67.6) of mandible and anterior portion (%60.9) and right side (%49.54) of maxilla.