مقالات رزومه احمدرضا قاسمی

The pan evaporation is used as a practical parameter in various fields, such as estimating water loss from lakes and dams, as well as estimating the Plant water requirement, especially in areas where there is no lysimeter information. Modeling this parameter can be useful in the estimation of missing data and long-term planning of water resources and agricultural development. In this research, by using an artificial intelligence model (Gene Expression Programming, GEP) and two time series models (Fourier and ARIMA), evaporation from the pan at Zayanderud dam station, was modeled in the period from 1344 to 1396 (53 years). The time series of pan evaporation on daily scale for the months of June (Khordad), July (Tir), August (Mordad), September (Shahrivar), and October (Mehr) as input to the Fourier and ARIMA models and 4 different patterns including the use of daily evaporation data 1 month, 2 months, 3 months and 4 months ago, were used as input for gene expression programming model. The results showed that the GEP model has acceptable results only in Mehr, and for other months the results are not acceptable in terms of statistical indicators. The daily evaporation estimated error was found to be 0.38 mm in Mehr. This error was acceptable based on the coefficient of determination of 0.84, the Nash-Sutcliffe coefficient of 0.83 and the Willmott's index of  agreement of 0.95. Unlike the gene expression programming model, the Fourier model provided acceptable results in all the studied months. The error values were obtained between 1.02 and 0.7 mm per day in all the studied months, which is equivalent to 5.2 to 8.8 percent. Comparing the results of the above two models with the ARIMA model showed that, the error values of the ARIMA model in all months are higher (9.4 to 19.6%) than the Fourier model, and Gene Expression Programming model. Therefore, the best model for estimating pan evaporation in October (Mehr) is the Gene Expression Programming model and in the rest of the months, the Fourier model. The evaluation of the model in estimating the extremes daily evaporation data also showed that the highest and lowest accuracy is in October (Mehr) and June (Khordad), respectively. In total, according to the statistical indices, the ability of the Fourier model to estimate the daily evaporation in the Zayanderud dam station was proved. Therefore, this model can be recommended to estimate the daily evaporation and also to reconstruct the missing data in this station

Evaporation is a physical phenomenon by which water particles enter the atmosphere in the form of vapor from the surface of water or the surface of wet soil through receiving solar energy. Evaporation process is an essential part of the hydrological cycle and it takes place continuously in nature. The importance of evaporation estimation in water resources and agriculture studies is undeniable. The evaporation pan is used as an indicator to determine the evaporation from the water level of lakes and reservoirs all over the world due to the ease of interpreting its data. Evaporation prediction is one of the important variables in planning and managing water resources. Various models are used to predict evaporation, such as stochastic time series models, experimental models, data mining models. Due to the fact that evaporation is affected by many parameters that are non-linear, non-stationary and random and occurs in a dynamic and complex manner, the use of experimental methods to predict evaporation that is based only on weather information such as temperature, relative humidity, wind speed and solar radiation are used, the accuracy is not enough. So far, various methods have been presented to calculate and estimate evaporation, such as the water balance method and experimental methods. These methods have flaws such as the inability to generalize to other places and different climatic conditions. Therefore, researchers tried to find a way to reduce the error as much as possible. The use of machine learning models are among these methods.

In this research, two techniques of support vector machine and network with long short-term memory will be used to estimate the amount of evaporation from the pan in Kohgiluyeh and Boyer-Ahmad Province, and the results will be compared with the actual evaporation values recorded in evapotranspiration stations and some experimental methods of evaporation estimation. In order to model and estimate the amount of evaporation, daily data of parameters of pan evaporation, rainfall, wind speed, maximum and minimum temperature, cloudiness, soil temperature, maximum and minimum relative humidity, average dew point temperature, sea level pressure and actual vapor pressure has been prepared from the regional water organization for 6 stations including Yasouj, Imamzadeh Jafar, Dehdasht, Dogonbadan, Siskhet and Likk. The length of the statistical data period varies from 39 years in Dogonbadan to 14 years in Likk. In this research, two SVM and LSTM models, as well as three experimental methods of Mayer, US Civil Engineering Organization (USBR) and Ivanov were used to estimate evaporation from pans. To use the models to estimate evaporation, first the data were randomly divided into training and testing stage. 70% of the data were considered as training data and 30% of the data were considered as test data. Finally, in order to evaluate the models, the statistical indices of explanation coefficient (R2), root mean square error (RMSE) and mean absolute value of error (MAE), model efficiency index or Nash Sutcliffe coefficient (N.S) and residual mass index (CRM) were used.

The results of the SVM model show that the error values of daily pan evaporation estimation in the SVM model in the training stage vary between 1.6 and 1.2 mm per day. The efficiency coefficient also shows that the model is in a satisfactory condition in terms of estimation and forecasting ability in the Siskhet station, in a good condition in the Yasouj station, and in a very good condition in other stations. LSTM model has weaker results than SVM model. The error values in this model are higher than the SVM model and vary between 2.2 and 3 mm per day. Also, the results of the efficiency coefficient of the model (N.S) also show that this model has not provided acceptable results for estimating pan evaporation in the two stations of Sisakht and Yasouj. In part of the models test, the LSTM model also showed a weaker ability than the SVM model in most of the stations. The LSTM model has provided better results than the SVM model only in Imamzadeh Jafar station. The best result among all the stations and the two investigated models was obtained at the Dehdasht station with an error of 1.6 mm per day and a Nash coefficient of 0.87 for the SVM model. Comparison of the results of the models with 3 experimental methods of Mayer, Ivanov and USBR also showed that, except for Ivanov's method and at Doganbadan station, none of the experimental methods could simulate the daily evaporation values well.

In summary, the results of this research show that the SVM model has estimated the northern and central regions of the province with an error between 15 and 18 percent, while the LSTM model estimates the same regions with a greater error and about 22 to 26 percent. In the south of the province, the results is almost reversed and the SVM model has an error of about 19 to 21 percent and the LSTM model has an error of between 13 to 21 percent. In the east of the province, the amount of error in the estimation of pan evaporation in the LSTM model is also higher than in the SVM model. In total, the comparison of the results of two models and three experimental methods of estimating evaporation from pans in Kohgiluyeh and Boyer-Ahmad provinces showed that LSTM model is the best model for Imamzadeh Jafar station and SVM model is the best model for the other 5 investigated stations. None of the three experimental models, provided acceptable results for the province. The results of this research show that, despite the emergence of powerful models that are capable of detecting complex and non-linear behaviors of variables based on mathematical relationships that can well recognize and simulate the pattern of data variability, the use of experimental methods to estimate pans evaporation is not recommended.

Landslide is defined as the movement of a mass of rock, debris, or earth down a slope. Sedimentary mass movement can be various factors that play an important role in the destruction of communication roads and residential houses, the destruction of pastures and gardens, as well as erosion and sediment transport in the basins. Various studies have been conducted on landslide risk zoning using logistic regression model. Which factors such as distance from fault, slope, slope direction, land use, rainfall, lithology, elevation and distance from river and road were reported as the most important factors affecting landslides.

In this study, Chaharmahal and Bakhtiari province as an important mountainous region in the country was studied. Reviewing past research showed that among the various factors affecting landslides, 9 factors including distance from fault, slope, slope direction, land use, precipitation, lithology, elevation classes and distance from river and road as independent variables, are the most important factors. To analyze the data, first 200 landslide areas were randomly selected, and another 200 points in the whole area without landslides were also randomly selected. After preparing the layers, the logistic regression model was performed to investigate the role and weight of each of the 9 independent variables. The accuracy of the model results was checked using three statistics, R2 Naglerk, R2 Cox and Snell and factor-2LL.

The results of running the logistic regression model, showed that among the studied variables, the texture and slope of the land, respectively, can be considered as the most effective factors in creating landslides in the region. After that, the distance from roads and finally the amount of rainfall has the greatest impact on landslides in the studied region. The evaluation results of the model obtained from these five parameters, showed that the overall accuracy of the model is 90.9% and acceptable. After that, the distance from the roads is an effective factor in creating this phenomenon, and in the end, the amount of rainfall is effective on the landslides. The results of evaluating the correctness of the logistic regression model show that the factor -2LL in the last iteration of the model is equal to 117.893 and the coefficients of R2 Naglerk, R2 Cox and Snell are 0.65 and 0.42, respectively, which indicate the accuracy of the model. Based on the obtained results, a landslide risk zoning map was prepared for the study area.

Due to the high number of landslides recorded in Chaharmahal and Bakhtiari province and the risk of new landslides, this study was conducted on the area to determine the factors that have the greatest impact on this phenomenon and areas with a high risk of landslides. Based on the results, it can be stated that in addition to natural factors (land type, land slope and rainfall), some human factors, including unprincipled road construction, have an important role in landslides. These factors have caused 13.8% of Chaharmahal and Bakhtiari province is at high risk of landslides. To reduce the risks, ecosystem change and land use of the regions should be avoided as much as possible.

Considering that wheat and barley are important and strategic products in Iran, the Sunn pest is one of the most important pests of these products. Every year, the country's plant protection organization monitors this dangerous pest so that an order to fight it can be issued at the right time. Farm monitoring is a very difficult, time-consuming, and destructive task; it also requires a large number of specialized human resources. One of the goals of this research is to create a non-destructive method for such an operation in the identification of Sunn pests. In this research, the counting of Sunn pests in cereal farms was done by two methods using aerial image processing by recording 954 images and framing method and eye counting in the farm by an expert in two farms with different numbers of this pest. The number of pests in each image was obtained after processing the images using the Python programming language in the PyCharm learning library. The statistical study results showed no significant difference at the 1% probability level between the average data of the two methods. This method can be used confidently to monitor the Sunn pest in cereal farms. In this research, by using UAV image processing, a fast, accurate, and non-destructive method was proposed and evaluated for counting the pests and issuing spraying orders in cereal farms.

Thunderstorms, also known as electrical storms or lightning storms, are one of the most damaging weather hazards and often damaging to the environment, crops, cities, and property. Therefore, forecasting them is very important for decreasing the possible damages. To forecast these phenomena, investigating the thermodynamic structure of the atmosphere and analyzing instability indices is necessary. The purpose of this study is to investigate the general characteristics of thunderstorms and the ability of GFS reanalysis data in identifying the spatial distribution of thunderstorms in Iran. For this aim in addition to GFS data, 8-year thunderstorm reports (2008 to 2015) in 361 weather stations in Iran from April to May were also used. At first, based on the thunderstorm frequency, each data series was divided into 4 quarters. Then, 10 atmospheric instability indices were calculated for daily GFS data and the results were analyzed. The results showed that the CIN index has the best performance. However, this index showed more error in Quarter-4 which had strong instability. The KI index showed moderate performance in forecasting weak instabilities but showed better results as instability increased in the fourth quarter. Both TT and 4LFTX indices had the same results and showed strong instabilities in the western parts of the country. Investigation of the average ability of the indices, induced that the four indices, including CIN, TT, 4LFTX, and KI had good performance in detecting instability. This performance varies from 90% in the CIN index to 60% in the KI index. The other investigated indices did not have an acceptable performance. It is recommended to redefine the threshold values of the instability indices according to the climate type in the studied stations.

Evaporation from water levels is one of the most important processes in meteorology and hydrology. Optimum use of available water resources is necessary due to the high costs of water storage in the dams. Every year, it is evaporate millions of cubic meters of fresh water from the dam’s water. On the other hand, evaporation from the pan helps to estimate the amount of the plant water requirement in areas where there is no lysimeter information. A wide variety of methods can be used to measure or estimate water use by plants. The evaporation pan method is a simple, inexpensive, and readily understandable way to estimate irrigated crop water use. It also requires little attention and can provide farmers with reliable estimates of plant water use. Pans provide a measurement of the integrated effect of radiation, wind, temperature, and humidity on the evaporation from an open water surface. The pan has proved its practical value and has been used successfully to estimate water use by observing the evaporation loss from a water surface and applying the crop coefficients to relate pan evaporation to irrigation requirement. For this reason, the study of evaporation from the pan has always been of interest to researchers. Modeling this important parameter can be very effective and helpful in accurately estimating of missing data.

In this research, by using of the Gene Expression Programming model (GEP) and some time series models, including Exponential smoothing model, Holt’s model, ARIMA, Brown’s linear trend and Winters’ model, the daily evaporation from pans in Khuzestan province was evaluated. For this purpose, daily pan evaporation for 20 years (2000 to 2020) of three synoptic stations of Ahvaz, Abadan and Dezful were used. These stations have the longest and most complete data among the stations of this province. For modeling the pan evaporation data, 80% of the data was used for the training stage and the remaining 20% was used for the model testing stage. In this research, seven patterns were defined for data modeling. These patterns include the use of data from 20 months ago (M20), 16 months ago (M16), 14 months ago (M14), 10 months ago (M10), 6 months ago (M6), 4 months ago (M4) and 2 months ago (M2). Three evaluation statistics ware also used for evaluate the results of the models. This statistics are include the Root Mean Squared Error (RMSE), Nash- Sutcliffe coefficient (N.S) and Coefficient of Determination (R2). The model with the lowest root mean squared error value and the highest Nash- Sutcliffe coefficient and coefficient of determination was selected as the best model.

The evaluation of the results based on the 3 evaluation indices, showed that the patterns M14, M16 and M20 have the highest accuracy in modeling in both training and test stages. In other words, by increasing the memory of the model, the results show more accuracy and less error. Also, based on the evaluation statistics, the amount of simulation error in the cold months (October to March) was 28% lower than in the hot months (April to September). However, in all 12 months of the year, at least one suitable patterns with acceptable accuracy was obtained for simulating the daily evaporation data. The best results in the warm months were observed for Dezful and Abadan in August and for Ahvaz in May and in the M20. The error in these stations is 0.93 mm, 0.95 mm and 0.95 mm per day, respectively. The Nash- Sutcliffe coefficient (N.S) is more than 0.80 in all stations. In the cold months of the year, the lowest error (0.70 mm per day) was observed in December of Abadan, along with the Nash- Sutcliffe coefficient of 0.82, which indicates the high efficiency of the GEP model in data simulation. Comparing the results of the Gene Expression Programming model (GEP) with the studied time series models also showed that the GEP model has lower error and higher R2 and N.S coefficient in all months in Abadan. In Dezful, the Holt model provided better results than GEP in January and September. The error rate in these two months is 61% and 71% less than GEP model, respectively. In Ahvaz, the Exponential smoothing model in October and the Holt model in November estimated the daily pan evaporation with 48% and 68% less error, respectively.

Because groundwater quality monitoring is very expensive and time consuming, it is necessary to find a method for estimating the hydrochemical variables with the minimum sampling. The aim of this research is to determine the optimal number of sampling wells in Khanmirza plain in Chaharmahal and Bakhtiari. For this purpose, the results of chemical sampling of 19 wells and seven water quality variables including SAR, EC, pH, TH, TDS and the sum of anions and cations for the period 1991 to 2011 were used. At the first, the best geostatistical method for drawing zones for each variable is selected. Then by means of semi-variogram curve the radius of influence of each well is calculated for each variable. According to the amount of radius overlap the minimum number of wells were determined to estimate the average of each variable in the plain. The results showed that to estimate the pH variable one sample well, for the SAR variable 3 sample wells, for the sum of anions and total cations 4 sample wells and for the variables EC, TDS and TH only 5 sampling wells are enough. The results of two-way t-test to validate the results also showed the ability of the proposed wells to properly estimate the studied variables in the plain. Therefore, it can be concluded that in special cases that require necessary and immediate information on groundwater quality in Khanmiza plain, instead of sampling from 19 wells, sampling can be done from a limited number of wells

Identifying potential sites for small rainwater harvesting can help to improve crop water productivity especially rainfed cropping systems.In present study, suitable areas for creating small rainwater collection systems in Isfahan province were determined using multi-criteria decision making models including hierarchical analysis process and network analysis process. The selected variables were annual rainfall ( 111 synoptic, climatology , evaporation and rain gauging stations), land slope, soil permeability, vegetation cover, soil fertility, distance from city and village and distance from road. The results showed that in both AHP and ANP methods, Khomeni-Shahr region with about 63% and Mobarake with about 55% of area , have the largest suitable coverage (good and very good condition) for the small rainfall collection systems construction and Aran and Bidgol (4%) and Ardestan (6%) have the lowest percentage of suitable areas, respectively. For the entire province, according to the AHP and ANP methods, about 24.8 and 21.8% of the lands are categorized as good and very good class for the creating small rainwater harvesting systems, respectively. The results also indicated that for both AHP and ANP methods, about 76.3% of lands with good and very good conditions for the construction of these systems are located in dry and semi-arid areas and 23.7% in semi-humid areas. The statistical comparison between the two approaches showed a significant difference between the provided suitable zones for constructing these systems at 0.01 level. The defference may be attributed to assigned weight of input layers.

This research was conducted with the aim of investigating the snow cover changes in the northwest of Chaharmahal and Bakhtiari province (Kohrang region) in the second decade of the 21st century (2014 to 2020) and comparing it with the average snow cover in the last decade of the 20th century. For this purpose, the images of Landsat 5 and 8 satellites for  February from 2014 to 2020 and also four passes in the years 1991, 1995, 1997 and 2000 (to calculate the average snow cover of the last decade of the 20th century) were analyzed. The amount of snow cover was calculated using the normalized differential modified index of snow cover (NDSI) index. The results of the NDSI showed that the lowest amount of snow cover is 1363.8 square kilometers in 2018 and the highest is 4263.7 square kilometers in 2014., The results also showed that the amount of snow cover in the investigated years in the second decade of the 21st century decreased by 14% (in 2014) to 72.5% (in 2018) compared to the average snow cover in the last decade of the 20th century. The results of Mann-Kendal analysis of trend in snow height at the Kohrang station also indicate a significant decrease in snow height by 78 mm per year in January and 151 mm per year in February

In recent years, the use of soil amendments with the aim of improving texture or increasing water capacity has become popular. This can affect the physical or chemical properties of the soil. In the present study, changes in electrical conductivity and soil permeability due to the addition of biochar and betonies were investigated. For this, 13 samples with two levels of 2% and 3% walnut wood biochar at two temperatures of 400°C and 600°C, were prepared, together with three levels of 2%, 5% and 10% bentonite and one soil sample without materials was used. The samples incubated for five months. The t-test was also used to check the difference between the samples. The results showed that both biochar and bentonite are able to significantly increase EC and decrease soil permeability. The average permeability value for samples with 2 and 3 percent biochar is a quarter of the permeability of soils without amendment. Adding bentonite to the soil reduced permeability such that the permeability of samples containing 10% bentonite (0.25 mm/min) was approximately ten times lower than that of the control sample (2.6 mm/ min). The electrical conductivity value in the samples with 10% bentonite is more than 4 times higher than in the soil without amendment, and in the samples with 2 and 3% biochar, it is on average about 2.3 times higher. Considering the increase in soil electrical conductivity, it is recommended to use biochar and bentonite with more accuracy.

The pattern of root distribution in layered soils is one of the significant issues in the calculations of soil water and irrigation management and planning. The objective of this study was to determine the pattern of root distribution of soybean in layered soils and its effect on water uptake. The research was conducted in a completely randomized design with 15 treatments consisting of three different textures of soil (light, heavy, and medium) in four replications. The pattern of root distribution was monitored by the sampling of columns at the end of the growth period of the soybean. It was observed that the presence of the layer with medium texture has led to better plant development and growth after comparing the treatments in terms of plant growth. In general, root length density decreased with increasing soil depth, except in cases where there were different layers of soil, and root length density takes place in the following order: root length density in layers with medium texture≥ heavy texture≥ light texture. The rate of root water uptake rate was highest in the sandy layers, intermediate in clay, and lowest in loamy texture. Also, the rate of root water uptake rate increased significantly with increasing depth regardless of treatments. It can be concluded that the pattern of root distribution and plant growth is significantly affected by soil texture and its stratification.

The sharp decline of surface freshwater and groundwater resources in recent decades in most parts of the country are the most important issues of Iran today. One of the serious problems of the plains of Iran is the lowering of the water level of the wells the drying of the Qanats and the salinization of their water followed by the salinization of valuable soil of agricultural lands especially in dry and arid areas. Therefore appropriate strategies for exploiting unconventional water resources such as saline groundwater and seawater and agricultural drains can help reduce the negative effects of depletion of water resources. In this study one of the ways to exploit saline water resources was investigated. Due to its location in an arid and semi-arid region Iran has a high potential in the solar energy. Desalination of saline water by evaporation and condensation with the help of solar energy for the use of plants (distillation irrigation) is one of the simplest methods to produce fresh water that can be used in agriculture.

In this study by implementing plastic and polycarbonate structures the effect of material and size of different distillation surfaces in the distillation irrigation method on the yield and water use efficiency of tomato plants were investigated. This research was conducted in the research farm of Shahrekord University and from June to October 2016. Experiment in 7 treatments and 4 replications as a factorial experiment in a completely randomized design with two types of distillation surface materials including transparent plastic plate (P) and polycarbonate plate (PC) with two transparent walls and three external surface sizes (0.48 72 0. and 0.96 square meters) were done. The salt water used was also supplied from the salt water spring of Ardal city of Chaharmahal and Bakhtiari province. To compare the results a treatment with subsurface drip irrigation (fresh water irrigation) was also considered as a control treatment. In this experiment the parameters of fruit number fruit weight shoot weight and dryness fresh water volume produced in each treatment ion leakage rate leaf surface temperature and water use efficiency were measured.

The results showed that P3 treatment (plastic plate with external surface of 0.96 square meters) had the highest amount of fresh water production during the experiment (21 liters). In contrast PC3 treatment (polycarbonate plate with external surface of 0.48 square meters) produced the lowest freshwater (16.5 liters). Transparency of the plastic treatments and consequently more light passing into the structure than polycarbonate treatments can be the reason for the difference in fresh water production. The maximum light transmission through the transparent plastic and double-walled polycarbonate is about 91 and 80% respectively.Due to the production of the highest amount of fresh water in P3 treatment the highest number of fruits heavier fruits and heavier and drier plants were also observed in this treatment. In contrast the lowest amount of fresh and dry plants in PC3 treatment and the lowest number and weight of fruit were observed in PC3 P1 and P2 treatments which did not show a significant difference. The lowest amount of ion leakage and leaf surface temperature were observed in the control treatment which indicates that this treatment was not under water stress.Among other treatments the lowest amount of ion leakage was observed in P3 treatment which produced the highest amount of fresh water. The highest amount of ion leakage and leaf surface temperature were also observed in PC3 treatment this treatment had the lowest amount of sweet water production. Therefore it can be said that this treatment was under water stress. The lowest amount of fruit and plant material production in this treatment is also shows this issue. The highest water use efficiency in terms of fresh weight of fruit was about 11 g/l in P3 treatment which is not statistically significant with PC1 and PC2 treatments. The lowest value is related to PC3 treatment with a value of about 4 g/l which is not significantly different from P2 treatment. The control treatment had the lowest water use efficiency compare than the other treatments because it receives water based on its water requirement and part of the water is also lost by gravity. The P3 treatment with transparent plastic produces more fresh water and its water use efficiency is higher than the other treatments.

Meteorological drought is defined as a lack of rainfall over long periods, which reduces soil moisture and river flow. One of the critical drought assessment tools is drought indices (Tsakiris & Vangelis, 2005). So far, many drought indicators have been developed by researchers, for example, the RDI )Reconnaissance Drought Index) (Tsakiris & Vangelis, 2005). The difference between this index and other drought indices is that it is estimated based on two variables of precipitation and potential evapotranspiration. For this reason, it is more accurate than indices that are calculated only based on precipitation. So far, some studies have been used the RDI for drought assessment. Zarei et al. (2016) studied the spatial pattern of drought using the RDI index in southern Iran. The results showed that the area with dry conditions had an increasing trend. Asadi Zarch (2017) investigated the drought trend in Yazd province between 1966-2009 using the RDI index. The results showed that drought occurrence in Yazd increased during the studied period. Because, unlike other natural disasters, it is difficult to accurately determine the onset and the end of the drought period (Moried et al., 2005). Accordingly, it is difficult to diagnose and evaluate the drought phenomenon. Therefore, monitoring and predicting drought is very important in water resources management. The use of wavelets is a new and very effective way of analyzing signals and time series. Application of Wavelet in Wavelet- Artificial Neural Network (WANN) models as a function for training has recently been used as an alternative for Artificial Neural Network (ANN) models. In recent years, the combination of wavelet theory and artificial neural networks has led to the development of wavelet neural networks (Thuillard, 2000). Zhang et al. (2017) applied the ARIMA, ANN, WANN, and Support Vector Regression (SVR) models to predict droughts in China's northern Haihe River basin using the SPI index. The results showed that the WANN model performed better than other considered models for predicting the SPI values at 6 and 12 months time scales. This study aimed to predict the meteorological droughts in the Kuhrang region using ANN and WANN models. To this end, the efficiency of ANN and WANN models in predicting precipitation and potential evapotranspiration will be evaluated. Then, the Resilience Drought Index (RDI) will be calculated based on the predicted values by ANN and WANN to describe and prediction of Kuhrang wetness conditions.

Water scarcity is the most important problem in agricultural development, especially in arid areas. Therefore, the study of strategies to increase water use efficiency must be considered. Providing methods to control and reduce soil evaporation can be a suitable way to increase water use efficiency. Soil surface Evaporation can be considered as the main part of water balance components in arid areas that cause water wastage. One of the ways to control and reduce evaporation from the soil is to use natural amendments. Therefore, this study was conducted to investigate the effect of three minerals including bentonite, vermiculite and zeolite on the amount and intensity of evaporation from the soil surface in a period of 4 months from Tir (July) to Mehr (October).

This study was conducted as a completely randomized design (CRD) with 10 treatments and three replications at Shahrekord University. The studied treatments in this research include 3 types of natural amendments (bentonite, vermiculite and zeolite) at three levels of 0.5, 1 and 2% with a control treatment (soil without any amendment). These natural minerals were mixed in soil of whole pots. The amount of evaporation was measured by measuring the amount of irrigation water of each pot and outlet water of them and based on the water balance equation. Moisture content was measured with a hygrometer at two depths of 5 and 15 cm. Finally, the soil moisture characteristic curve for each treatment by using of pressure plate was obtained and compared.

The results showed that among the three studied minerals, the highest reduction in soil evaporation was related to bentonite. The two minerals vermiculite and zeolite also significantly reduced evaporation compared to the control treatment, but the difference between them was not significant. Among the studied treatments, the highest reduction effect on evaporation was observed in 2% bentonite mixed with soil and the lowest effect was observed in 0.5% zeolite treatment. This treatment did not show a significant difference with the control, but 2% bentonite treatment significantly reduced the rate of evaporation compared to the control by 6.4%. The results the soil moisture in the treatments was the opposite of the evaporation results. In the treatments where the evaporation rate was lower, the moisture content increased. Due to the significant effect of the three minerals on the soil moisture, the soil moisture curve in the studied treatments has also changed compared to the control.

The use of natural soil amendment in addition to preventing soil contamination, helps to improve soil structure to reduce evaporation. Overall, due to the highest effect of using 2% bentonite on significant decreasing of soil evaporation therefore, this soil amendment can be recommended to control of evaporation from soil.

Drought and the resulting stress are one of the most important and common environmental stresses which limit agricultural production. Researchers have used organic amendments such as biochar to improve soil properties and reduce the effects of the undesirable stresses. The purpose of this study was to investigate the effect of using the provided biochar of walnut wood and walnut green shell on retention curve and hydraulic parameters of a sandy soil in completely randomized design. For this purpose, the biochar were produced at 400 0C for 2 h. The amendments (walnut wood and walnut green shell and their biochar) at 1 and 2 w/w% ratios (36 and 72 t ha-1, respectively) were mixed with soil sample in three replications and were incubated for 120 days in greenhouse conditions. The retention curve of the incubated soils was measured using sandbox and pressure plates apparatus. Statistical analysis showed that the applied biochar had significant effects on bulk density, porosity, stability of aggregates in wet sieving, and van Genuchten’s parameters (except residual moisture parameter). Application of 2% walnut green shell biochar led to 17% reduction in bulk density (17%) compared to the control and increased the total soil porosity. Application of 2% biochar had the highest effect on the soil water retention curve. Also, the addition of biochar significantly increased soil water retention at low tension and decreased the difference in soil moisture content between treatments. Since water retention curve in lower tension is affected by coarse soil pores and soil structure it could be concluded that addition of biochar improved soil structure and increased coarse soil porosity. Application of appropriate biochar as an amendment in agricultural soil can partially improve the physical and hydraulic properties of sandy soils in semiarid region.

Because of the limited water resources in the world, especially in arid and semi-arid regions, the study of water loss causes is necessary for optimal use of them. The results of such research can be effective in water management and increase irrigation efficiency especially in low water areas. Since evaporation from the uncovered soil surface as one of the most important parts of water balance components, causes water loss, providing solutions to reduce it seems necessary. The use of soil amendment (such as biochar) can be an effective solution for decreasing evaporation from soil. Therefore, this study was conducted to investigate the effect of walnut raw material (sawdust) and it’s biochar on evaporation and soil moisture in Shahrekord university research farm, during the 5 moths form Mordad to Azar.

The experiment was conducted in a completely randomized design with 13 treatments and 3 replications. The investigated factors include the experimental material including walnut raw material, as sawdust, and its biochar at two levels of 1 and 2% and their distribution depth in soil (mixed at 3 and 8 cm above the pot and mixed in 30 cm or the soil of the whole pot). Evaluation of soil evaporation in the treatments was done by measuring the amount of inlet and outlet water to the pots and based on the water balance equation.

Comparison of the mean of different biochar and walnut raw material treatments showed that walnut raw material treatments significantly reduced evaporation from soil surface compared to the control treatment (soil without any amendment). The average amount of evaporation in treatments containing walnut raw material is 1450 ml, while this amount is higher for treatments containing biochar and 1820 ml. The average of soil moisture content in these treatments is 27.8% that was also significantly higher than the control treatment (25%). Regarding biochar effect on soil evaporation and moisture, the results showed that except for 2% biochar treatment and distribution in the whole pot soil in, the difference between the other treatments and the control was not significant. The results also showed that the soil evaporation in treatments containing raw material (1450 ml) is significantly lower than treatments containing biochar (1820 ml). Unlike biochar, the depth of distribution of the raw material in the soil also had a significant effect on evaporation. The evaporation values for distribution depths of 3, 8 and also mixed in the soil of the whole pot (30 cm), were obtained 1360, 1440 and 1550 ml, respectively, which had significant difference with the control treatment (1800 ml) at 5% significant level.

Overall, due to the decreasing and significant effect of using 1% and 2% of raw walnut material (in the form of sawdust) compared to its biochar, on the soil evaporation and considering the rational and economical use of 1% and 2% raw material, it can be recommended to use this material with the mentioned percentages to reduce evaporation form the soil surface.

Freshwater resources are fundamental for agriculture and food production. The increasing demands for water and climate changes cause shortage of fresh water on the planet. The water shortages can also limit the agricultural production which can endanger human food security. Due to the restriction of freshwater resources in the recent decades, many researchers have focused on the use of the new methods for sweetening saline water. The sweetening saline water need to energy and this cause restriction use of these processes. Use of renewable energy is one of the best solutions for this problem. Solar energy is an environmentally responsible method of generating power, and also makes financial sense. In addition to being renewable, solar energy is typically labeled a green source of energy due to the lack of harmful environmental side effects associated with its use. Iran has a high solar energy potential, because it if located in mid latitude and also arid areas of the earth. Sweetening saline water by using solar energy is one of the methods in which saline water first evaporates and then converts to fresh water by condensation. When this method used for irrigation, it is called condensation irrigation. Condensation Irrigation (CI) is a combined system for solar desalination and irrigation. The Condensation Irrigation system presented in this research uses solar thermal energy to evaporate saline water. At the first the saline water convert to vapor by solar energy and then the vapor become cooled and precipitates as freshwater on the distillation container walls.

In this study, condensation irrigation method was used for irrigating chili pepper in Shahrekord. This experiment was arranged as factorial experiment based on completely randomized design with four types of water (distilled water, drinking water and water with salinity of 4 and 6.5 dS/m) and three types of distillation container including of transparent and dark plastic hemisphere and pyramidal glass, as distillation areas. Two types of saline water used in this research were obtained from two regions of Naein in Isfahan province. The region is a desert region located on the margin of the desert areas of Iran and its water grounds are generally salty. After preparing the culture medium, the chili pepper seedlings were transferred to the field. Some growth related characteristics and also some physiological parameters such as, total fresh and dry weight of the Shoots, total fresh weight of fruit, plant height, number of fruits, leaf relative water content and chemical indices including chlorophyll a, b and total chlorophyll, ion leakage, carotenoids and also water use efficiency and the volume of consumed water were measured and evaluated. In order to analyze the results, SAS software (version 1/9) was used. Mean comparison was also performed by LSD test.

The results showed that, with increasing the salinity of water, the fresh and dry weight of the fruit, shoot weight, relative water content, carotenoids, water volume and water use efficiency decrease compared with distilled and drinking water, so that the water with salinity of 6.5 dS/m showed the most decrease in the studied indices. By increasing the salinity of the water inside the containers, the amount of evaporation decreases, resulting in the amount of fresh water produced on the distillation containers. In other words, the amount of available water to the plant decreases. Indices such as ion leakage that indicating plant damage due to water stress was also higher in two saline waters (water with salinity of 4 and 6.5 dS/m). Also, the amount of carotenoids in the two saline waters was lower. By reducing available water for the plant (in other words, the more water stress for the plant) these indices become lower. Among the distillation containers, the highest values of the above indices were observed in the pyramidal glass. The highest amount of water use efficiency was obtained in the two types of fresh water with an average of 4.4 kg / m3 and the lowest value (1.6 kg/ m3) in water treatment with salinity of 6.5 dS/m. Finally, the results showed that if the distilled irrigation with water treatment with salinity of 4 and 6.5 dS/m and glass pyramid distillation is used, this method can supplies 38% and 36% of water requirement and the rest of the water have to provide by supplemental irrigation.

amount of water that use by evapotranspiration. Estimation of evapotranspiration (crop water requirement) is also one the most significant stages for designing different irrigation systems, programming and correction of the water resources management. Determining crop coefficient is a necessary part of estimating evapotranspiration. Crop coefficients (Kc) are properties of plants which vary in different growth stages. Potential evapotranspiration occurs if a field of the crop had an ideal unlimited water supply. The effect of both crop transpiration and soil evaporation are integrated into a single crop coefficient. The Kc coefficient incorporates crop characteristics and averaged effects of evaporation from the soil; while in dual crop coefficient two sub-coefficients (basic crop and evaporation coefficient) are considered separately. Therefore, the dual crop coefficient approach is more complicated and computationally intensive than the single approach. Due to the importance of accurate determining of crop water requirement many researches have been done for measuring single and dual crop coefficient. The aim of this research is to determine the actual evapotranspiration and single and dual crop coefficients of linseed by lysimeter, during various stages of growth in the Shahrekord area. This research was conducted in Shahrekord University. Shahrekord is located 97 kilometers southwest of Isfahan with the geographical coordinates from 32°18'22'' to 32°21'50'' N and 50°49'22'' to 50°53'44'' E. It is topographically situated in the north of the Zagros Mountains. The annual average temperature and precipitation in Shahrekord is 11.5°C and 320 mm, respectively. This study was done in the crop year of 2014-2015. Four lysimeters were used with the length, width and depth of 190, 65 and 140 cm. respectively. After filling lysimeters with soil and before planting, samples were taken for determining the soil characteristics, in depth of 0-30 cm. One of the lysimeters was allocated to grass, for measuring the reference evapotranspiration, and three other lysimeters to the linseed. After each irrigating the drainage water was collected and then its quality and quantity was measured. The crop evapotranspiration (ETc) and crop reference evapotranspiration (ETo) were calculated by using of water balance equation. Single crop coefficient was calculated from ratio of the measured crop evapotranspiration (ETc) and measured reference evapotranspiration (ETo) and dual crop coefficient was determined using the FAO 56 method. The results showed that the initial, development, middle, late and total growing periods of linseed are 20, 14, 22, 24 and 80 days. The total values of growing degree days (GDD) for these stages are 226.5, 226.7, 379.1, 420.7and 1253, respectively. The average value of the single crop coefficient in the initial stage is equal to 0.74, in the middle stage is 1.23 and at the end of the final stage is 0.61. The single crop coefficient was also calculated using the FAO 56 method. The results indicated that the difference of single crop coefficients in these two methods was very little in the initial and middle stages, but it increased in the final stage. The basal crop coefficient, soil evaporation coefficient and dual crop coefficients were calculated using the FAO56 method. The basal crop coefficient for linseed at different growth stages of initial growth stage, middle and end final are 0.15, 1.15 and 0.2, respectively. During the initial stage, because of low green cover, the soil evaporations is the highest and it became decrease by increasing the green cover in the next growth stages. The actual evapotranspiration value of linseed that obtained from three lysimeters was equal to 444 mm during the growing season. The evapotranspiration value of linseed that calculated by dual crop coefficient (using FAO56 method) was 445.5 mm, while this value was equal to about 391 mm for the single crop coefficient (using FAO56 method). The comparison of these values clearly indicated that the dual crop coefficient can estimate the evapotranspiration more accurate than the single crop coefficient. The results showed that evapotranspiration of linseed and grass are 444 and 429 mm, respectively and it daily variations for linseed are 1 to 10 mm and for the grass are 4.3 to 8.5 mm. The main conclusion that can be extracting from this research is that the calculating of dual crop coefficient is necessary for estimating the accurate crop water requirements.

Wind is one of the main climatic parameters that can formed the climate of a region. So the temporal changes of this parameter can affect the global and rigional climate. The declining trends of surface windreported by previous studies generally are small but, it is still important to assess the distribution of the trend of wind speed at the continental to global scale in order to betterunderstand the causes of the trend. In thisstudy, a 50-year wind speed data (1961-2010) wasused to predict the wind speed over 34 stations in Iran. The results showed that the Holt–Winters model can very well predict the wind speed in the country. The determination of coefficient varies between 0.39 in Abadan to 0.80 in Babolsar stations. The results of Kolmogorov-Smirnov test was used to evaluate the normalized residuals of model showed that the model residuals are normal in most of the stations that show the suitability of the model to estimate and predict the wind speed in Iran.The results also showed that the wind speed at the stations located in the western and central regions and some northern stations will decline in 2020. The station is located in the eastern half and southern Alborz will be an increasing trend. In other words we can say that, the wind speed in most arid and semiarid regions of the country in the future will increase, while moderate and mountainous areas in western parts of the country will experienced a decreasing wind speed.