احمدرضا قاسمی دستگردی

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

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.

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.

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.

Lack of drinking water and fresh water for agricultural purposes is one of the biggest problems in most arid and semi-arid countries. Therefore, desalination of saline water is necessary for the production of drinking water and irrigation. Due to the irradiative potential of south regions of Iran, the study of a solar subsurface irrigation system was performed experimentally and theoretically in Dehdasht. The system used a solar still and a subsurface reverse flow heat exchanger. The mixture of hot and humid air inside the solar still was directed to the underground pipe by a fan. Using the condensation process, the required water for subsurface irrigation was obtained. Using a suitable system for injecting compost tea into the pure water, a solution with a suitable composition was produced to irrigate and supply soil and plant nutrients. By measuring and monitoring environmental information and using MATLAB software, a fuzzy inference model was designed and Temperature changes, humidity ratio and mass of produced water were investigated. According to the simulation performed in MATLAB, the maximum temperature of the still reached 90°c in August and 60°c in April. Compared with its experimental values, the error values were reached 5.8% and 9%, respectively. The maximum and minimum amount of condensed water in the pipe was 6.13 liters and 3.09 liters per day for August and April, respectively. Compared to the experimental value obtained, the error values were 7% and 15%, respectively. According to the results, increasing the parameters such as solar radiation, ambient temperature, water temperature, diameter of condenser pipe and diameter of hot air pipe, as well as decreasing parameters such as condenser water temperature, relative humidity and pool water height, increased the amount of water produced. Therefore, this method is suitable for arid and semi-arid areas.

Management of water resources, especially groundwater, is important in arid and semi-arid regions. One of the important issues in optimum water resources management is the prediction of drought conditions. Groundwater is considered as the main resources of water supply for agriculture, industry and drinking uses in Boroujen plain. Therefore, it is important to investigate the drought condition of groundwater resources in the planning and sustainable management of these resources. So far, various methods have been developed and used by researchers to predict different types of droughts. One of these methods is the prediction of wetness conditions by the Markov chain. In most of the previous studies in the field of drought prediction, the Markov chain of first and second orders have been used. In this study, groundwater droughts in Boroujen Plain during the years 1985 to 2015 are assessed and the wetness conditions of this plain are predicted using the third order Markov chain model. In order to assess the groundwater droughts in the Borujen Plain, the GRI index values were calculated on the time scales of 1, 3, 6 and 12 months. For this purpose, the data of groundwater level of 13 piezometric wells in Boroujen plain during a 31 year period (1985-2015) was used. In order to predict the GRI index values in Broujen plain for the next months, the first, second and third- order Markov chain models were used and the performance of these models was evaluated based on contingency table method. After forming the contingency table from the results of first, second and third- order Markov chain models, the values of CSI, POD and FAR statistics were calculated. The CSI values for Broujen plain in the time scales of 1, 3, 6 and 12 months for the first order Markov chain model were calculated equal to 0.58, 0.50, 1.0 and 1.0, respectively. The CSI values for the second order Markov chain model in the time scales of 1, 3 and 6 months were obtained equal to. 0.45, 0.33 and 1.0, and for third order Markov chain model equal to 0.40, 0.38 and 1.0, respectively, which indicate the medium skill of the developed method in the prediction of wetness conditions at 1 and 3 months time scales, and good skill at 6 months’ time scale. Also, the delineation maps of GRI index were drawn by selecting the most suitable interpolation method. The delineation map of GRI in the Broujen Plain shows that the middle parts of the plain often experienced severe droughts. Comparing the performance of different orders of Markov chain in predicting the wetness conditions of Boroujen plain based on CSI, POD and FAR statistics showed that first order Markov chain method presented more accurate results than other models in predicting GRI values in all time scales. Therefore, it can be used to predict the groundwater drought in Boroujen Plain. The results of the GRI survey for Boroujen plain showed that during the period under study, the drought spell of groundwater began in 2008. In general, the severe droughts that have occurred in recent years due to reduce atmospheric precipitation, along with the overexploitation of groundwater have caused the severe decline in groundwater levels, which leads to groundwater quality degradation and land subsidence in the Boroujen plain.

Drought is a climatic phenomenon that begins slowly and has a hidden nature. The duration of its occurrence and the resulting damage occur gradually in different sectors. Therefore, assessment and investigation of drought is very important in planning and implementation of actions to cope with drought. Droughts are divided into different types. One of the common methods of drought assessment is the use of drought indices which everyone can describes a type of droughts. In recent years, the use of Composite Drought Index (CDI) have attracted the attention of researchers. In this study, a new Composite Drought Index was developed that combined the three components, including the Groundwater Resource Drought Index (GRI), the Modified Standardized Precipitation Index (SPImod) and the potential evapotranspiration (ETP). In the next stage, the drought condition of Shahrekord plain located in Chaharmahal and Bakhtiari province was assessed in a 31-year period (1985-2015) using the Composite Drought Index (CDI). The results showed that the Shahrekord Plain was frequently experienced some wet conditions during the years 1985 to 2000, but it has suffered from consecutive severe droughts from 2008. In general it can be said that the developed CDI index is well able to describe the composite behavior of meteorological and groundwater droughts in the study area and is recommended as a new index for monitoring and assessing regional drought.