vahid varshavian

Water shortage is the most important factor in reducing the yield of agricultural products all over the world and this factor is more effective in arid and semi-arid regions. Iran is a country whose average rainfall is about one-third of the global average, and in terms of water resources, it is in dire straits. Therefore, it is necessary to help preserve limited water resources by optimal use, saving and increasing irrigation efficiency. This research was defined according to the consumption of the strategic product of corn and the importance of managing the country's limited renewable water resources, on top of that, the use of pressurized irrigation systems in the region and in the climatic conditions of Urmia.

In order to investigate different levels of irrigation on performance indicators and plant traits of sweet corn, under the subsurface drip irrigation system in the form of a complete randomized block design with four treatments of 20% over-irrigation, equal to the water requirement, 20 and 40% under-irrigation in four replicates in the research farm of the Engineering Department. water of Urmia University. The plant index values of stem diameter, leaf area, grain yield, number of leaves per stem, biological yield (wet), plant dry matter and cob yield during the growing season on specific dates and also on the day of harvest. was taken and evaluated.

The results showed that in general, the effects of under-irrigation and over-irrigation on biological yield, dry matter, uncoated cob yield, 300 seed weight, number of leaves and stem diameter were significant. Also, based on the results, it can be stated that with the reduction of water consumption, the cob yield decreases linearly. The highest and lowest cob yields were obtained in overwatering and 60% water requirement treatments with an average of 1.83 and 1.13 kg/m2, respectively.

In general, under-irrigation and over-irrigation have a significant effect on biological yield, dry matter, uncoated bale yield, three hundred weight, number of leaves and stem diameter. Also, based on the results, it can be stated that with the reduction of water consumption, the quantity of cobs decreases linearly. The highest and lowest cob yields were obtained in over-irrigation and 60% water requirement treatments with an average of 1.83 and 1.13 weight per square meter, respectively. According to the results of the research, with a 20% reduction in water consumption compared to full irrigation, only 5.02% of the biomass weight is reduced, apart from significance. Control (equal to water requirement) and 20% less irrigation in dry matter traits, uncoated cob yield, 300 seed weight, number of leaves and stem diameter, no significant difference was observed. The process of changes in the accumulation rate of biological yield of sweet corn initially started from 21 days after planting and after 6-9 days it entered the linear growth stage and then continued steadily. The highest weight of biological yield of sweet corn in different treatments was observed in 87 days after planting.

Planning for the best use of water resources in agriculture is inevitable. In this regard, the method of subsurface drip irrigation can, in addition to reducing the amount of irrigation water, increase crop yield. This study was conducted to investigate the different levels of under-irrigation and over-irrigation on yield indices and plant traits of sweet corn cultivar CHASE, under subsurface drip irrigation system in a randomized complete block design with four treatments of 20 Percent over-irrigation, equal to water requirement, 20 and 40 Percent under-irrigation in four Repeated. The results showed that in general, the effects of under-irrigation and over-irrigation on cob yield, 1000-seed weight, uncovered cob diameter, pod-free cob length, number of cob rows and number of seeds were significant. Also, based on the results, it can be stated that by reducing the amount of water consumption, the yield of the product decreases linearly. The highest and lowest cob yields were obtained in over-irrigation treatments and 60 Percent of water requirement with an average of 30.4 and 19.9 tons per hectare, respectively. Accordingly, with a 20 Percent reduction in water consumption compared to full irrigation, only 4.72 Percent of the crop is significantly reduced. This is while with a 20 Percent increase in irrigation water, the yield of cob increases by 10.54 Percent. 1000-seed weight in full irrigation and 20 Percent low irrigation treatments was 375.7 and 372.6 g, respectively. There was no significant difference between over-irrigation and 20 Percent under-irrigation treatment. There was a significant difference between the means of ear yield indices, length and number of seeds per ear row in all irrigation treatments.

The impact of climate change as the most important factor affecting agriculture, especially rainfed cultivation has challenged the management of these resources. This study tries to investigate the effect of climate change on the yield and length of the dryland wheat phenological stages in western Iran. For this purpose, two downscaling models, SDSM and LarsWG, were used to simulate the climate over the next 30 years. To model the performance and phonological stages, two models of AquaCrop and DSSAT in the base period and the future period were used, considering the three RCP climate scenarios of 2.6, 4.5, and 8.5. The results showed that the AquaCrop model has better performance and less error than DSSAT. So that the value of the coefficient of determination between observed and simulated data in the base period with AquaCrop model in Kermanshah, Sanandaj, and Ilam stations are 0.86, 0.64, and 0.89, respectively; and RMSE coefficient values are 198.6, 274.6 and 192 kg/ha, respectively. While, in the DSSAT model, the coefficient of determination is 0.90, 0.11, and 0.82, respectively, and the RMSE coefficient is 219.9, 288.1, and 238 kg/ha, respectively. The general results show that in LarsWG downscale model with AquaCrop and DSSAT agronomic model, the lowest yields are allocated to Kermanshah, Sanandaj, and Ilam in 8.5, 4.5, and 8.5 scenarios, respectively, and the highest yields are obtained in 2.6, 2.6, and 4.5 scenarios; which indicates a decrease in performance in the scenario of rising temperature and rising carbon dioxide. However, in the SDSM downscale model, the highest yield of dryland wheat is mainly in scenarios 4.5 and 8.5, and the lowest yield will be in scenario 2.6, which is different from the results of the LarsWG model. According to these results, it can be stated that the type of downscale model and crop model can be effective in the obtained results.

It is important to prediction of precipitation changes to know how much it will change in the future and to consider the necessary measures to mitigate the adverse effects of climate change. Therefore, in this study, precipitation changes in the west of the Iran were predicted. For this purpose, the data of CanESM2 model were used under three scenarios of RCP2.6, RCP4.5 and RCP8.5 using the SDSM downscaling model and was investigated the changes of precipitation in the period (2021-2050) compared to the basic period (1989-2018). The MAE, MSE, RMSE and R2 indicators were also used to ensure the calibrasion and validation of the SDSM model. The results indicate the appropriate accuracy of this model in the study area. The results of a monthly survey of precipitation at the study station showed that precipitation will increase during the period (2021-2050) in most areas from October to December, and in some areas in addition to January to April. Based on this, it is expected that the amount of precipitation in the next period compared to the basic period will increase between 0.05 and 15% on average in the study area. Most of the changes are related to the northern and eastern areas of the study area and the least changes are related to the southern areas of the study area. Also, the highest and lowest changes are predicted based on RCP8.5 and RCP2.6 scenarios. Due to the complexity of the precipitation process, there are always uncertainties in the field of precipitation forecasting, and it is necessary to use the results of the models in this field with caution and to increase the accuracy of forecasting from different GCM models and microscale methods. Different used. Also, considering the increase and intensity of rainfall and mountainousness of the study area, the necessary strategies to deal with floods and its management should be considered.

Considering the significant effect of chilling and frost phenomenon on agricultural production in Iran, the aim of this study is projection of minimum temperature in three provinces of Kurdistan, Kermanshah and Ilam, Western of Iran. For this purpose, the data of 17 meteorological stations during the baseline period of (1989-2018) were collected. Then, the HadGEM2 and CanESM2 climate models outputs were statistically downscaled using LARS-WG and SDSM weather generators under three climate change scenarios of RCP2.6, RCP4.5 and RCP8.5 during future period (2021–2050) and compared with observed data. The performance of the two weather generators, were compared using MSE, RMSE, MAE and R2 indices. The results indicated the good accuracy of both statistical models in simulating the minimum temperature in the study area; however the SDSM model performed better than the LARS-WG. The projected changes of minimum temperature compared to the baseline period revealed a significant increase varying between 0.6 – 1.5 oC in study stations. The most significant change was observed in northern parts of study region especially in the Saqhez and Zarineh stations. The findings of this study can be used in frost risk management and agroclimatic planning in the region.

Frost and freezing are important and risk generating factor in the agricultural sector of the country. Frost is a phenomenon that at low temperatures which could damage or destruct the plant organs. Various studies by researchers show that last spring frost (LSF) and first autumn frost (FAF) along with gradual global warming have significant impacts on agriculture and natural resources. Teleconnection patterns represent large changes which can periodically alter other atmospheric patterns such as temperature, wind, humidity and precipitation in regional and global scales. The purpose of this study was to investigate the effect of different teleconnection indices on variability of FAF and LSF events during the last 31 years at 12 Synoptic stations of Iran.

To detect the relationships between frost events and teleconnection patterns we used two different data sources. As the first source, the screen daily minimum air temperature (Tmin) were used as the frost indicator. We also applied 10 different teleconnection indices in daily and monthly scales. Daily minimum air temperature were obtained from the Iran Meteorological Organization (IRIMO) for the historical period of 1985-2015. The teleconnection indices were also utilized for the same period (http://www.cdo.noaa.gov). After the data quality control, the outlier data were removed from the analysis. Normality of data was evaluated by Kolmogorov-Smirnov test. For time series with normal distribution (P <0.05), the Pearson’s significance test was performed. For other time series, when the normal distribution was not the case, Spearman's nonparametric test was applied.

In case of lag-free correlations in the monthly time scale the strongest correlation between teleconnection indices and minimum temperature was observed for NAO- (-0.76), in February for Isfehan Station, which can cause a delay in occurrence of last spring frost. By applying the time lag to the correlation, the strongest correlation was found for AMO+ with time lag of 11 month. In seasonal time scale, the strongest lag-free correlations were found for AO-during the last spring frost (LSF). By applying time lag-correlation, the strongest correlation was evident between Tmin temperature and AMO+, indicating that this index was the most influencing teleconnections (with 3 seasons lag). In comparison with the normal phase, the occurrence of El Niño causes earlier autumn and spring frost events. In contrast, La Niña event will postpone the dates of autumn and spring frosts. This means that if La Niña occurs in winter, the strongest effect will be appeared in the autumn of the following year. The correlation between the El Niño, La Niña and Normal phases with minimum temperature showed that the La Niña phase has the highest effect on shifting the dates of the minimum temperature event.The strongest correlation between the minimum temperature of autumn and teleconnection patterns (correlation coefficient of 0.382) was observed for AO- , highlighting the fact that the occurrence of the Negative phase of this phenomenon leads to the cause a delay in occurrence of first autumn frost (FAF). For the last spring frost (LSF), the strongest correlation was found for SOI- index (correlation coefficient of 0.665) for Hamedan station, leading to early spring cold in the region. The correlation between the phases of the El Niño, La Niña and Normal with minimum temperatures showed that the La Niña phase has the most effective phase affecting the minimum temperature. The results of this research can be used for wise managing of risk factors and arranging appropriate time of planting agricultural products, as well as insurance for agricultural products.

This study highlighted the impact of teleconnection phenomena on shifting the dates of autumn and spring frost. It was shown, that the occurrence of teleconnection can significantly shift the date of first and last frost in the study region. For some teleconnections cause the advance, but for others may postpone (up to 50 days) the frost event. We found different results for lag-free correlations compared to lag-correlations. For instance, for monthly analysis of lag-free correlations, the strongest correlation between teleconnection indices and minimum temperature was found for NAO- (-0.76), but for lag correlations, the strongest correlation was found for AMO+. In comparison with the normal phase, the occurrence of El Niño causes earlier autumn and spring frost events. The results showed that Shifting the Date of First )Autumn( and Last )Spring( Frost Events at the stations were related to a number of teleconnection patterns such as AMO, SOI, NAO, AO and MEI. In contrast, La Niña event will postpone the dates of autumn and spring frosts. Further works are required to better understanding of the impacts of teleconnection events on other meteorological parameters such as humidity which was not investigated in this research.

Due to excessive withdrawal of groundwater, it's modeling in warm and arid regions are of very importance. Prediction of groundwater is important from different aspects of water resource management. In current years, the application of intelligent models in simulation has good results because of simplicity and high accuracy. Based on the previous studies, tin more investigations were used weather and hydrogeology parameters as input in intelligent models. While the effect of the ENSO phenomenon on climate and hydrology and hydrogeology was evaluated. The purpose of present study was an evaluation of intelligent models including artificial neural network (ANN) and co-active neuro-fuzzy inference system (CANFIS), wavelet transform combined with ANN (WANN) and WCANFIS models in forecasting groundwater level base on ENSO indexes.

Evapotranspiration is a key principle of water balance and an important element of energy balance. Therefore, forecasting and estimation of evapotranspiration in agricultural water management, forecasting and monitoring drought, and the development and exploitation of effective water resources might be valuable and practical. The purpose of this study was to model the Reference Evapotranspiration time series (ET0) at Rasht Synoptic Station with two SARIMA and GRNN models during 1956-2017 and its forecast for 2018-2027.

Rasht is located in the temperate and humid parts of northern Iran and in the southern strip of the Caspian Sea. In this study, the Adjusted ThornthWaite method (ATW) was used to estimate ET0, the credibility of which was previously confirmed by researchers for estimation of reference evapotranspiration rate in Rasht. Evapotranspiration values were estimated for the time period of 1956-2017. Two models were selected for modeling and validation of the ET0 series. The SARIMA model is based on seasonal stochastic models, and the GRNN model is based on artificial intelligence. The models’ inputs were selected on the basis of three previous monthly and yearly. The target-input matrices were divided into calibration (75%) and validation (25%) sections. Autocorrelation Function (ACF) indicated a seasonal trend in the ET0 monthly series, with a return period of 12. Four times seasonal differentiation, revealed that the best degree of SARIMA’s seasonal integrated degree was the first-order. Other SARIMA operators, including seasonal and non-seasonal autoregressive, and average seasonal and non-seasonal moving, were selected by trial and error. Optimization of the GRNN model was accomplished by trying and error of the spread parameter. In this study, criteria such as RMSE, NS, and R were used to check the error and correlate the outputs of the model.
and NS values of 8.89 mm and 0.97, respectively. The GRNN model had its best performance by applying the total inputs. The RMSE and NS values were 9.22 and 0.96, respectively, for GRNN’s best output. The difference between the two models was reported in predicting the year’s minima (January-February), which showed SARIMA’s better performance. To compare these two models, the Taylor diagram was also used, which showed that the accuracy of SARIMA not only in error but also in the correlation and estimation of the true deviation of the real values was slightly more accurate than GRNN. After evaluating the models and assessing their acceptable performances, best extracted models from both SARIMA & GRNN were used for ET0’s long-term forecasting up to the next ten years (for the period of 2018-2027).

The results of the forecasts for Rasht’s future showed a sharp ascending trend in the rate of evapotranspiration in the years 2018-2027 (compared to the period of 1956-2017). This is a warning of a rapid increase in the evapotranspiration rate in the years ahead, in the wet area of Rasht. This issue is very important for the surface water and groundwater resources planning, agricultural uses, and will be a serious warning to farmers and water managers in this area.

Solar radiation is the main source of all energies on the Earth and is an important parameter in hydrology studies, water resource management, water balance equations, and plant growth simulation models. The most common instrument for recording global solar radiation data (GSR), is using pyranometer; however, because of the high costs of installation and maintenance, it is not possible to establish a radiation site for such purposes. In areas where ground measurements are not available, the Global Solar Radiation (GSR) can be estimated by empirical and semi-empirical models, satellite techniques, artificial intelligence models and other geostatistical approaches. In artificial intelligence models such as neural networks, various meteorological parameters like air temperature, relative humidity, sunshine hours, etc. are easily integrated to estimate global solar radiation. In most commonly used radiation models (e.g. Angstrom-based models) for estimating daily GSR, the sunshine hours and cloud cover are two important input parameters. Unfortunately, those parameters are not measured very accurately in weather site. Moreover, for time scales less than daily (e.g. hourly) using sunshine hour as an input, is not possible for predicting the sub-scale temporal GSR. The main purpose of this study, is comparing Multiple Linear Regression model and three types of artificial intelligence models (MLP, GRNN, ANFIS) against each other to estimate GSR in cold semi-arid climate of Hamedan, in order to present the most accurate model by including the soil data and ignoring the sunshine hours. Study Area: According to the Extended De-Martonne climate classification model, Hamedan is located in a semi-arid-very cold area and has a mean altitude of 1851 meters above sea level. In this study, GSR and meteorological variables (daily values of maximum air temperature, mean air temperature, minimum air temperature, air pressure, air relative humidity, soil temperature and rainfall) recorded at Bu-Ali Sina University weather site, located at latitude 34’48” and longitude 48’28”. These data were recorded every 10 minute during 31 Dec. 2016, to 10 Mar. 2018 by using an automated Spanish GEONICA Logger. Models: Multiple linear Regression (MR): This model is a simple and linear model that estimates the target variable by assigning a constant optimized coefficient for each input variable. Adaptive Neuro-Fuzzy Inference System (ANFIS): A multi-layered network model that uses advanced neural network learning algorithms and fuzzy logic, to describe the relationships between inputs and outputs. This model uses the neural network’s Learning ability and fuzzy rules, to define the relationships between input-output variables. Generalized Regression Neural Network (GRNN): Is a three-layered neural network, which the number of neurons in the first and last layers like other neural networks, is respectively equal to the input and output vectors. But, unlike other networks, the number of hidden layers of neurons in GRNN model is equal to the number of observational data. Evaluation criteria: To evaluate the models performances against actual field measurements, the Root Mean Square Error (RMSE) and Coefficient of Determination (R2) have been used. The correlations of models input variables (eight independent variables) versus GSR (dependent variable) were evaluated. Results revealed that maximum air temperature, average air temperature, relative humidity and soil temperature are respectively the most influencing inputs for modeling GSR, if using minimum numbers of meteorological parameters. Among them, maximum air temperature, minimum air temperature, atmospheric relative humidity and soil temperature, were selected as the best inputs, for modeling with least parameters. By using correlation test, as a 2-variables input matrix (relative humidity and soil temperature) 3-variables (mean air temperature, relative humidity and soil temperature) and the whole 4 parameters, were selected as 4-variables input matrix. The percentage of train and test data was 75% and 25% respectively. In this research, the models were run by using two different samples: Random and non-random samples. The results of the evaluations showed that random samples had higher accuracy in GSR estimates. In MR model, the 4-variables input, and in three artificial intelligence models (GRNN, ANFIS, MLP), 3-variables input showed the superior performances. Finally, the models were evaluated by using all of the eight inputs. At this stage, MLP with RMSE=3.04 Mj.m-2.day-1 and R2=86.33%, ANFIS with RMSE=3.26 Mj.m-2.day-1 and R2=84.43%, GRNN with RMSE=3.41 Mj.m-2.day-1 and R2=82.86%, and MR with RMSE=4.11 Mj.m-2.day-1 and R2=75.20%, provided the best GSR estimates respectively. The results showed that, in all numbers of input variables, random and non-random samples, artificial intelligence models present better performance than linear regression. By availability of the whole eight meteorological variables (daily values of maximum air temperature, mean air temperature, minimum air temperature, air pressure, air relative humidity, soil temperature and rainfall), MLP model can present the best GSR estimates. If all input parameters are not available, employing Generalized Regression Neural Network (GRNN) model and 3-variable inputs of mean air temperature, relative air humidity, and soil temperature is suggested for estimating the Global Solar Radiation (GSR) in cold semi-arid climate of Hamedan. It is noteworthy that in estimating GSR, two important parameters of sunshine hours and cloud cover were not used in our research. Testing the models performances in other climate types is suggested as future works.