فهرست مطالب kh. ghorbani

This study was aimed at comparing Viola spp. populations collected from north and northwest of Iran to investigate the adaptability and feasibility of their domestication under climatic conditions of Gorgan. The whole plant samples together with their rhizomes were collected from nine habitats in Golestan, Mazandaran, Gilan, East Azerbaijan, and Kermanshah provinces during the growing season. After botanical approval and receiving herbarium code, the rhizomes were cultivated under the same soil and climate with three replications. Their yield components and physiological, morphological, and phytochemical traits were measured after one crop year at flowering stage. The results showed that these populations were from V. alba Besser, V. odorata L., V. sieheana W.Becker, and V. ignobilis Rups. species. All populations produced flowers and seeds after one crop year. The populations differed significantly (P<0.01). V. ignobilis from Kermanshah had the highest amount of root and aerial parts fresh weight, aerial parts dry weight, and leaf area. The highest amount of root and aerial parts length belonged to V. sieheana from Alangdare. V. alba from Ziarat had the highest amount of total phenols (45.74 mg gallic acid per gram of dry extract) and total flavonoids (20.24 mg quercetin per gram of dry extract). The highest amount of chlorophylls a and b, total chlorophyll, carotenoids, and anthocyanins was observed in V. odorata from Bandargaz. The highest antioxidant activity by DPPH and ABTS methods was found in V. alba from Afratakhte and the highest one by FRAP was observed in V. odorata from Qarnabad. The highest amount of total antioxidant was measured in V. alba and V. oderata populations from Ziarat region. Overall, the results proved the considerable traits diversity in Viola spp. populations. This diversity could be due to the high genetic potential among different populations, differences in environmental conditions, or the interaction of population and environment.

The irrigation schedule of crops is the most effective way to increase agricultural water use efficiency. In irrigation planning, determining the irrigation time is more important and difficult than determining the depth of irrigation water. Among all methods of determining the irrigation time of crops, the methods which used plants are more accurate than other methods. In this study, the wheat water stress index has been used which is based on the air vapor pressure deficit and the difference between vegetation and air temperature (Tc-Ta). First of all, the diagram and the relationship between the top and bottom baselines were extracted, then the water stress index of wheat was drawn in the Karaj region. Secondly, to determine the optimal water stress index of wheat, four treatments including I1: 30% of maximum allowable depletion of moisture, I2: 45% of maximum allowable depletion of moisture, I3: 60% of maximum allowable depletion of moisture, I4: 75% of maximum allowable depletion of moisture were performed in four replications. The amount of water stress index of each treatment was calculated during the season separately, and the CWSI of the treatment with the highest water use efficiency was used to determine the irrigation time of wheat. The results showed that the relationship between the upper and lower baseline for wheat in the Karaj region is Tc-Ta = 3.6 0c and  Tc-Ta = -0.27VPD - 2.64, respectively. The treatment of 45% of maximum allowable depletion of moisture had the highest water use efficiency and the optimal water stress index for wheat was obtained at 0.36 in the Karaj region.

Due to many problems in the field of water supply, no study has been done on atmospheric water extraction devices. The objective of this research was to optimize the control parameters and the amounts of water produced from a physical model designed and built by the team including two-phase (refrigeration collector and sponge collector) in different atmospheric conditions were investigated. First, the effect of the sponge filter on the amount of water obtained, then the pilot in different weather conditions were studied to obtain optimal conditions. The suctions of 450 to 1400 rpm were applied at each humidity and temperature. In all the mentioned cases, the temperature of the device was studied at 2, 4, and 6 degrees below the dew point. Results showed that the sponge filter can increase the water produced by up to 15%. The first evaporator produced more water than the second evaporator. The optimal air suction speed inside the device depends on the relative humidity and temperature values. For the first and second evaporators, temperatures below 4 °C and 6 °C are optimal, respectively, and with increasing absolute humidity, the optimum suction speed increases. Parameters such as airflow velocity, number of collectors, temperature difference with dew point, and moisture absorption levels were influential in the amount of water produced. According to the qualitative study, the obtained water has a desirable quality for use in sensitive applications.

For projection of possible climate change impacts on agriculture and water resources climate models are used but there exist uncertainties in the outputs of these models. The aim of this study is to project the possible changes of precipitation and uncertainty involved in Hashem- Abad station, north of Iran, using general circulation models. For this purpose, precipitation observations for the baseline period of (1990–2010) were collected. Then the outputs of the 8 GCM models (including ACCESS1-0, CanESM2, CMCCCMS, GISSE2H, HadGEM2CC, MIROC5, MIROCESM, and IPSLCM5AMR) under the two scenarios of RCP 4.5 and RCP 8.5 were downscaled using change factor method and compared. The uncertainties of the GCM models for both emission scenarios were also investigated. Comparison of means of precipitation time series for the period 2010–2040 was performed by t-test. The results indicated a decreasing trend in most of the months. The GISSE2H model projected the most significant decreasing trend of total monthly precipitation. The highest uncertainty was observed in July for the both scenarios. Besides, although the outputs of the models didn't show a significant difference in 82% of the cases, the models’ uncertainty band indicated a major difference among the results. Further uncertainty analysis may be recommended for more scrutiny.

The drought defined as the lack of precipitation from its average along a period of time is one the most important subjects in meteorology and its prediction can play an effective rule in the field of water resources management. Therefore, both different modelling strategies and different affective variables on drought characteristics are applied for drought prediction. Considering the effect of spring rainfalls on annual drought conditions, the feasibility of the estimation of annual drought based on spring rainfall is investigated in thisstudy. However, Arazkooseh station located in Golestan province, Iran with 45 years recorded daily data were selected as case study. After calculation of drought severity factor based on standardized precipitation index (SPI), for 1, 3, and 12 months’ time scales, the correlation matrix was created. The results indicated highest correlation values between annual drought and drought in spring season. In order to find more accurate results, monthly drought indices for spring season were added to an M5 model. The results showed the predictability of annual drought using spring rainfalls as its correlation coefficient was 0.89 and the root mean square error was 0.57.

Univariate frequency analysis of hydrological events has some shortcomings caused by the lack of taking into account all characteristics of such events. Therefore, bivariate frequency analysis of hydrologic events such as flood can be useful in hydraulic design of structures and water resources management. The peak discharge and volume of flood are two important parameters in design of structures, thus, Archimedean copula functions were used for analysis of dependence structure between peak discharges and flood volumes. The time series of discharge for Arazkusehhydrometric station for a period of 40 years was constructed based on annual maximum (AM) discharge in daily scale. This station area is 1678.1 km2.The results showed that Gumbel extreme value copula was the best choice for fitting to data. Beased on design requirements, one can choose joint return period in "and", "or" and "conditional". For example, considering univariate frequency analysis results the return period of peak discharge equal to 50 years, while for a same value of peak discharge and volume in "and", "or" condition, the joint return periods are 72 and 38 respectively. The return period of "or" case was less than univariateand the highest value was belong to "and" case. This shows that planning or design based on "or" case is more confident, because results to larger values of peak discharge and volume quantile for the same value or univariate return period.

Soil temperature as one of the important environmental parameters, is measured only in synoptic weather stations and three times per day, while the other meteorological parameters such as air temperature and relative humidity are measured most in meteorological stations except rain guage stations. In this research which based on three methods of multivariate linear regression, artificial neural network and k-nearest neighbor were evaluated to estimate the soil temperature at different depth based on air temperature and relative humidity using daily data from synoptic meteorology stations of Gorgan from 1996 to 2005. The results showed that with increasing soil depth, the effect of meteorological parameters and estimation accuracy will decrease.more accuracy of shallow depth soil temperature is due to the greater influence of climatic factors on soil temperature and less time delay of heat transfer from the surface to the deeper depths. By comparing the observed and predicted soil temperature values from the various models, it will be concluded that the best performance is obtained from the k-nearest neighbor algorithm which has higher R2 and less RMSE. Artificial neural network and regression models, are in second and third place to predict soil temperature.

Evapotranspiration is one of the basic components in hydrologic balance that is important for the design and management of irrigation systems. This research investigates improvement of accuracy of ET estimation by H-S method based on adjusted coefficient of K. This coefficient, the ratio of evapotranspiration estimated by F-P-M method to that estimated by H-M method, is determined by M5 Decision Tree Model based on meteorological variables (air temperature, relative humidity, dew point) at three meteorological stations (including Astara, Rasht, Bandar-Anzali). Thirty years period (1360-1390) is used for this research. The data of each station is divided into two parts: eighty percent for training and twenty percent for validation. The estimated adjusted coefficient is multiplied by estimated evapotranspiration with H-S method. The results indicate higher performance of M5 Decision Tree Model relative to Neural Network model. In addition, mean difference between estimated evapotranspiration by two methods decreased from 0.41, 0.55, 0.7 to 0.31, 0.38, 0.28 for Astara, Bandar-Anzali and Rasht stations, respectively.