فهرست مطالب masoomeh delbari

Improvement of saline soils is very important from the point of view of soil and water resources conservation. Excess Solution salts in the root zone can reduce water uptake by the plant due to reduced soil osmotic potential. Necessary strategy in dealing with this issue is reducing the soil salinity to optimum level by capital leaching. The purpose of this study was to investigate the application of different amounts of leaching water in reducing salinity and evaluating experimental and theoretical models in predicting final salinity. This research was carried out in a part of Mohammadabad lands of Sistan plain as a randomized complete block. This experiment are carried out with five treatments and four iterations using test plots with one-to-one meter. Leaching was performed using 100 cm of water in four intervals of 25 cm. The required physical and chemical analyses before and after leaching and after the application of each irrigation cycle in treatments and at different depths up to 100 cm of soil were performed on the collected data. The results showed that the use of 100 cm of water for leaching could reduce the soil salinity class from S2A2 to S1A2. Experimental and theoretical models were evaluated and the results showed that theoretical models predicted the final salinity better than experimental models. Of all the experimental and theoretical models studied in this study, the theoretical model of a single reservoir with a correlation coefficient of 94% had the best results.

This study aimed to calibrate the HYDRUS-2D model for simulating soil moisture distribution under tape irrigation in transplanting sugar beet, with and without mulching. The research was carried out during the cropping seasons 2017-2018 in 14 plots (7 plots with mulch and 7 plots without mulch) with 2 rows of 40x50 tape. To monitor soil moisture distribution, soil samples were collected from 12 plots, and access tubes of the PR2 device were installed at depths of 10, 20, 30, 40, 60, and 100 cm in two plots. The PR2 device was calibrated for a 7- day irrigation frequency and 6 irrigations. The calibration results showed that the default parameters a0=1.6 and a1=8.4 proposed by the PR2 manufacturing company were not appropriate for the loam-textured soil of the study area. For this research, a0 and a1 were modified to 1.8 and 8.3 for soil water contents higher than the filed capacity and to 1.2 and 8.6 for soil water contents lower than the field capacity, respectively. The PR2 device was then used to measure soil moisture contents for five irrigations (6 hours each irrigation), during and after irrigation until the next irrigation. The results showed that the HYDRUS-2D model performs appropriately in simulating soil water content in five irrigation intervals with and without mulch application. The mean absolute value of error (MAE) was less than 0.035, root mean square error (RMSE) was less than 0.042; correlation coefficient (R) was above 70%, and the sum of squared error (SSQ) was less than 0.41 percent. If the hydraulic parameters of the soil were recalibrated for the use of mulch, the HYDRUS-2D model provided the same efficiency. Therefore, the HYDRUS-2D model is recommended for designing and managing tape irrigation due to its good simulation during and after irrigation.

This study aimed to estimate the spatial distribution of soil properties including field capacity (FC), permanent wilting point (PWP) and  total available water (TAW) using ordinary kriging (OK) and random forest (RF) in agricultural lands of the southern Sistan Plain, covering an area of approximtely 147000 hectars. FC, PWP, and TAW and soil texture components were measured for a total of 200 surface soil samples (0-30 cm). Performance evaluation of the two methods based on the percentage of normalized root mean square error (nRMSE) revealed that the conventional OK with 5% less error in estimating FC, 3% less error in estimating PWP, and 5% less error in estimating TAW performed slightly better than RF. Comparing Bias values showed that OK underestimates both FC and PWP and overestimates TAW, while RF overestimates all three parameters. Spatial distribution maps of FC, PWP, and TAW produced by OK model showed that the highest amount of FC (23%) and TAW (14.4%) were in the west and northeast of the region, which had heavier texture and lower altitude from the sea level. In the southern and southeastern regions, which have lighter soil texture, the amount of available water was less compared to the western and northeastern regions. In the RF model, the most important variable extracted from satellite images was Digital Elevation Model  (DEM), and all three features had higher values in areas where DEM was lower. It seems that the flatness of the study area and the inadequacy of auxiliary variables caused the lower accuracy of the RF method.

The presence of salts in irrigation water and high evapotranspiration rate in Sistan and Baluchestan cause the accumulation of salt in the soil and as a result increases the osmotic force in soil. One method for crop irrigation planning is the use of Idso crop water stress index (CWSI) and Moran water deficit index (WDI). In order to investigate the effect of irrigation water salinity on CWSI and WDI and also wheat irrigation planning, an experiment was performed with three different irrigation water quality in Iranshahr, through the 1398-99 crop year. The experiment was performed as a randomized complete block design with 4 replications and 3 treatments including (1) irrigation water with salinity of 0.7, (2) irrigation water with salinity of 2.5 and (3) irrigation water with salinity of 5.2 dSm-1. The results showed that irrigation water with salinity of 0.7 and 2.5 dSm-1 have no significant difference in terms of yield and water use efficiency. However, the use of irrigation water with a salinity of 5.2 dSm-1 caused a significant reduction in yield and water use efficiency at a 1% level. Moreover, the salinity of irrigation water increased the upper baseline in the Idso diagram and the upper side of the proposed trapezoid of Moran, but it had no effect on the baseline (plant transpiration limit under standard conditions). As a result, according to the definition of CWSI and WDI indices, these two indices decreased by 19% and 22%, respectively compared to the control treatment. The average optimal CWSI and WDI in the first (non-saline) treatment were 0.39 and 0.38, respectively, and they were 0.33 and 0.32 in the very saline treatment, respectively. This showed a decreasing trend of irrigation frequency with increasing salinity of irrigation water.

The proper combination of cutoff time and lengths of border are among the criteria for high efficiency in surface irrigation. The objective of this study was to determine cutoff time and the optimal distance and flowrate in closed-ended border systems. The basic assumption in this method is that the point along the field, where the lowest water depth permeates should receive water equal to the required depth. For this purpose, the data applied were consisted of 18 vegetated and non-vegetated borders, and bed slope of 0.001 to 0.005, Maning's roughness of 0.017-0.211, border length of 100 m and flow rate range from 0.08 to 0.16 m3/min.m. After determining the cut-off time, the application efficiency obtained using the method presented in this study was compared with zero inertia (WinSRFR 4.1) model. The highest similarity of results was observed in closed end border for R-6, R-13, R-14 and R-17 border with 0% difference and the highest difference was for R-1 border with 8.67% difference in efficiency. Maximum efficiency was obtained in some border with decreasing length and in some border with increasing length. Generally, it can be concluded that the application efficiency obtained from both methods was close to each other.

In this research, the status of water productivity for wheat production and strategies to increase water productivity in different quantitative conditions of water was determined and analyzed in the irrigation district of Sistan Dam. In this regard, the SWAP simulation model was calibrated and validated by considering the current water resources operation, various quantities of irrigation water, and use of field information. Water production functions were used to determine the irrigation schedule (time and depth) for wheat crop. The results of field measurements in the crop year 2016-2017 in the irrigation district showed that farmers irrigate wheat on average four times in the current conditions. Farmers' average crop yield and water productivity were about 1450 kg/ha and 0.41 kg/m3, respectively. These results show that available water is not appropriately used and should be addressed with practical solutions to improve water productivity. The validation and calibration results of the SWAP model also showed the high accuracy of the model in the case study. The results of different management scenarios of eliminating some irrigation shifts compared to the existing conditions indicated that, although there was no significant difference in water productivity, crop yield decreases about 37%. The results of evaluating the scenarios of reducing the depth and frequency of irrigation (using 640 mm per season and applying 40 mm at each shift) showed that, with reliable and timely water supply and more frequent irrigation, water productivity could be increased by 30% compared to the baseline scenario; and crop yield can be doubled. In these scenarios, the presence of adequate moisture in the plant's root zone increases the yield, and the amount of deep percolation is greatly reduced. The amount of water used by farmers is excessive for various reasons. Therefore, it is recommended to train farmers on how to improve irrigation and crop management according to the water available in the irrigation district, so that they can distribute water according to the real needs of the plant and irrigate at the right time and sufficient quantity.

Estimation of reference evapotranspiration (ET0) is essential to determine water requirements of crops. In other words, to regionalize ET0 to a large area, some interpolation methods should be used (Goovaerts, 1997). A key parameter which may influence the proper performance of interpolattion methods, is the sequence of ET0 estimation process (Mardikis et al., 2005; Vilanova et al., 2012). That is why using some auxillary variables cross correlated with the main variable, could significantly improve the accuracy of interpolation methods. Therefore, this study aims to analyze the estimation process sequences while investigating spatial variability of annual and monthly ET0 in Iran. A comprehensive comparison of spatial interpolators is performed. Elevation is also used as a secondary variable in multivariate methods.

In traditional irrigation systems, inappropriate management of water allocation may lead to poor performance of the irrigation district, an increase in water losses, and a reduction in crop water productivity (CWP). Using optimization methods and nonlinear models along with the geographical information system (GIS) can improve the management of an irrigation district under uncertain condition. The objective of this study is to investigate the optimal water allocation and reasons for the CWP reduction in irrigation distribution district. To do so, in the first stage, those sites of water distribution district with the highest effects on CWP variability were identified. Then, the CWP was increased, later. For this purpose, fuzzy programming models and the possibility theory are used to prepare a water allocation plan for the irrigation system of Sistan Dam. The results show that the highest CWP of irrigation district for wheat fields is 0.45 kg/m3, which exist at the sites by the range of discharge losses in 360 to 400 L/m/hr. The analysis of these critical spots shows that the CWP values do not necessarily depend on crop productivity but they may depend on the management factors of irrigation district and the cultivated areas. Therefore, the spots with an optimized CWP could be considered as suitable patterns for increasing the management efficiency.

To investigate the effect of unconventional water and zeolite on yield and water use efficiency of sorghum, a split plot experiment was conducted based on randomized complete block design with 3 replications, at Gharakhil Agricultural Research Station in Ghaemshahr, Mazandaran Province. In this experiment, the main treatment was irrigation water quality (well water as control (W1); 75% well water and 25% seawater (W2); 25% well water and 75% seawater (W3); 100% treated urban wastewater alternated with 100% seawater (W4); 50% seawater and 50% urban treated wastewater (W5); irrigation with urban treated wastewater (W6)), and sub-treatment included three levels of zeolite as soil amendment (without zeolite Z1, calcic zeolite Z2, and potasic zeolite Z3). The results of data analysis showed that irrigation water quality treatments and zeolite levels had a significant (P<0.05) effect on plant yield. With increasing salinity, the yield of fresh and dry fodder sorghum decreased, but no significant difference was observed between treatments W5 and W4. The fresh and dry fodder yield in treated wastewater with calcic zeolite was higher than other treatments. The highest leaf area index was recorded in W6Z2 treatment (7.62) and the lowest was in W3Z1 treatment (3.80). The highest water use efficiency of fresh forage was observed in W6Z2 treatment (12.7 kg/m3) and the lowest in W3Z1 treatment (4.62 kg/m3).

Precipitation is one of the important climatic parameters in hydrological models. Therefore, the accurate estimation of its amount and spatial distribution in a watershed is of great importance. In recent years, due to the limited number of raingage stations especially in mountainous areas, the use of satellite precipitation data as an effective tool for predicting regional distribution of rainfall has gained lots of attention by the researchers. In the present study, the accuracy of TRMM 3B43 data, which is one of the TRMM products, was evaluated in 40 raingage stations and 9 synoptic stations in Hormozgan province in a monthly scale. Comparison of satellite data with the observation data was performed for the time period 1998-2012. To assess the agreement between TRMM rainfall data with observation data, the statistical criteria including Spearman correlation coefficients (Rs), Pearson correlation coefficient (Rp), mean absolute error (MAE), root mean square error (RMSE) and mean square error (MSE), as well as probability of detection (POD), false alarm ratio (FAR), true skill statistics (TSS) and critical success index (CSI) were used. Based on the results, the highest value of POD (1) was observed in August and the lowest POD (0.92) was obtained in May. Moreover, the highest FAR (0.91) and the lowest CSI (0.08) were observed in May, and the lowest FAR (0.16) and the highest CSI (0.83) was obtained in January. In addition, the highest Rp (0.64) and Rs (0.76) were seen in December while the lowest Rp and Rs were occurred in April, May and July. These results indicate that the TRMM satellite has the highest accuracy of predicting rainfall in winter and spring while it has the lowest performance in summer. In other words, the TRMM satellite is able to predict rainfall in cold months better than in warm months of the year. The results also showed that TRMM overestimates rainfall in most months of the year, however the results were significantly improved after calibration especially in August and December as seen in spatial distribution maps.

Subsurface drainage is constructed in the paddy fields with the purpose of drying of land at the time of rice harvest, mid-season drainage and lowering water level after the rice harvest. Most drainage equations have been developed for non-paddy land. However, given the unique special conditions of paddy fields in Guilan province (due to heavy textured soils with low permeability, heavy rain and shallow impermeable layer) compared to non-paddy lands, therefore introduced the equation that is most compatible with these conditions. So in this study, steady and unsteady equations were evaluated in estimating space of subsurface drainage for a second crop after rice harvest. Drainage treatments included: six conventional subsurface drainage systems including drainage system with drain depth of 0.8m and drain spacing of 7.5m (L7.5D0.8), drain depth of 0.8m and drain spacing of 10m (L10D0.8), and drain depth of 0.8m and drain spacing of 15m (L15D0.8), drain depth of 1m and drain spacing of 7.5m (L7.5D1), drain depth of 1m and drain spacing of 10m (L10D1), and drain depth of 1m and drain spacing of 15m (L15D1). From the rainfall occurred during the experiment, a three-day rainfall with a mean value of 23.9mm was selected for decision on the best equation. The equation that estimated the drainage space in the above rainfall with the least deviation from the best drainage treatment was chosen as the best drainage equation. The results showed that Ernst- Hooghoudt equation and Bouwer and Van Schilfgaarde equation combined with Hooghoudt equation estimated drainage space with the least deviations from the best drainage treatment (L10D0.8) and were selected as the best equations in the second cultivation season and the Delroix equation was introduced as the weakest equation in the design of subsurface drainage for paddy field in order to provide suitable conditions for the second cultivation.

In rice cultivation, mid-season and end-season drainage at harvest time are two important operations of water management which, respectively, increase yield and provide better conditions for harvesting rice. Due to the unique conditions of paddy fields of Guilan province, making decisions about the spacing and depth of drains and proper equation to determine the drainage spacing in paddy field requires research on the mid-season and end-season drainage. Therefore, in this research, the efficiency of drains spacing (L) and depth (D) of subsurface drainage in controlling water table and also accuracy of the steady and non-steady equations were evaluated at mid-season and end-season drainage stages in Guilan’s rice fields. Drainage treatments included six conventional subsurface drainage systems with rice husk envelope including drainage system with drain depth of 0.8 m and drain spacing of 7.5 m (L7.5 D0.8), (L10 D0.8), (L15 D0.8), (L7.5 D1), (L10 D1), and (L15 D1). All drain lines were 40 m long and made of PVC corrugated pipes with a diameter of 125 mm. Results showed that subsurface drainage with spacing of 15 m and depth of 80 cm (due to the proper water table depth and higher yield) and subsurface drainage with distance of 10 m and depth of 80 cm (due to the highest resistance to pentrometer penetration and the lowest soil moisture content) are recommended as the best drainage treatment for mid-season and end-season drainage, respectively. Dagan, Hooghoudt and Bouwer & Van Schilfgaarde equations combined with Hooghoudt equation were suitable equations for determining drainage spacing at mid-season drainage stage. Hooghoudt, Kirkham, Dagan, Bouwer & Van Schilfgaarde equations combined with Hooghoudt equation and Glover-Dumm equation were selected as suitable formulas for determining the spacing of subsurface drains for end-season drainage.

Soil moisture measurement is very important for better irrigation system management. One of the indirect methods for measuring the soil moisture content is to use Theta Probe device. Although the use of Theta Probe has many advantages over the other methods of measuring moisture content, but according to research conducted in most regions of the world, it has been shown that changes in the physical and chemical properties of the soil, and in general, all that affect the dielectric constant of the soil, cause an error in the accuracy of the measurement of moisture content with this device. Among the physical and chemical properties of the soil, the tissue type, the amount of clay contained in the soil, and electrical conductivity (EC) are the three main factors influencing the calibration of the Theta Probe system (Sarani and Afrasiab, 2012). The aim of this study was to investigate the effect of soil texture, irrigation water salinity (EC), and sodium absorption ratio (SAR) on the accuracy of Theta Probe device.

Energy is one of the most important demands in the development of human societies. As world population continues to growth and the limited and non-renewable resources of fossil fuels begin to diminish, countries must take action to facilitate a greater use of renewable energy resources, such as geothermal and wind energy. Iran has a high wind energy potential, but except in a few specific regions such as Binalud and Manjil, the use and exploitation of such clean renewable source is still not addressed enough. Wind speed in Sistan and Baluchistan province especially in cities like Zabol is very high and sometimes it goes near 120 km/h. So this study aims to investigate the feasibility of wind harvesting in synoptic stations of Sistan and Baluchestan province. Moreover, the trend analysis of wind data is investigated in this paper.
This study is based on wind data in 8 synoptic stations, for a period 10 years (2005-2014). The analysis was based on 3 hours interval wind speed data measured in 10 m height above ground surface.
The most widely used model to describe the wind speed distribution is the Weibull two- parameter. These two parameters include k and c: the first is the shape parameter and the second is the scale parameter. There are several methods for calculating these parameter.In this paper, these two parameter were determined through the maximum likelihood (ML) technique.The Weibull distribution functionis expressed mathematically as: (1) where f(v) is the probability density function, k is the shape parameter, c is the scale parameter (m/s), and v is the wind speed (m/s).The probability of having a wind speed between two values of interest V1 and V2 is given by the equation
P(V_1 Monthly mean and standard deviation of wind speed data were calculated for the selected stations during 2005-2014. The results showed that the monthly variation (2 to 5 m/s) of mean wind speed for all years is similar as the highest and lowest mean wind speed was happened in winter and autumn, respectively.
The wind speed characteristics required to evaluate the feasibility of wind energy utilization were calculated for selected stations. The results showed that the maximum wind power density was seen for Zabol with amounts of 257.227 W/m2 and 512.713 W/m2 in 10 m and 50 m heights, respectively. The lowest wind power density was seen in Iranshahr with amounts of 40.196 W/m2 and 80.12 W/m2 in 10 m and 50 m heights, respectively. Comparing these data and data calculated for other stations with the standard classification criteria indicated that Zabol, Zahak and Konarak are the most suitable sites for wind turbines installation. Moreover, Zabol has the maximum probability of having the wind speed of 3 to 25 m/s, i.e. 0.71 and 0.82 for 10 m and 50 m heights, respectively. Therefore given a wind turbine installed in 50 m height, the probability of blowing wind with the speed of 3 to 25 m/s, is about 0.82 multiply by total hours of wind existence during a year (82*7566 hrs/year), i.e. 5822 hrs/year.
The results of trend analysis by Mann-Kandal test showed that there are either an increasing trend or decreasing trend in selected stations, however, increasing trends (e.g. Zabol, Iranshahr, Zahedan and Konarak) were more often. Wind speed in Zabol has shown a positive trend for all months (except September). However the trend was significant in 41.6 percent of times.In annual basis, wind speed in Zabol has positively increased at a significance level of 5%. Wind speed in Iranshahr showed a significant positive trend in both monthly (except April) and annual scale. Overall, annual wind speed showed a positive trend in half the stations considered and a negative trend in others.
According to findings achieved in this study, wind speed is lower in the last months of the year for all stations in Sistan and Baluchistan province. The highest variation of wind speed was seen for Zabol. Based on trend analysis, some significant positive trend of annual and monthly wind speed was seen in Iranshahr, Zabol, Zahedan, and Konarak in descending order. According to the results, the highest wind power density in 50 m height was seen for Zabol (513 W/m2) and Zahak (434 W/m2) and the lowest one was seen for Iranshahr (80 W/m2). Overall, based on wind speed existence and its annual continuity, three stations Zabol, Zahak and Konarak were realized to be appropriate for installing wind turbines.

The Beerkan single ring infiltration experiment is a simple and inexpensive method for estimating soil hydraulic properties and is gaining scientist's attention in recent years. The purpose of present study was to compare the performance of modified forms of CL (cumulative linearization) and DL (derivative linearization) methods with the main Beerkan algorithm (i.e. BESTslope) in estimating saturated hydraulic conductivity (Ks) and the value of α (i.e. the ratio of gravity force to the capillary force) in the modified forms of CL and DL. For this purpose a number of 113 Beerkan infiltration experiments were carried out in Sistan Dam' research field. The negative values of Ks estimated by the BESTslope algorithm were eliminated and the 101 remaining experiments were selected for final analysis. According to the results, Ks values estimated by DL and CL were negative in most Beerkan experiments while the modified DL and CL methods resulted in positive values in all cases. The results were compared using a weighted coefficient of determination (ωr2). According to the results, the Ks values estimated by the modified DL were more similar to those obtained by BESTslope algorithm (ωr2 0.8135) when compared to those obtained by the CL method (ωr2=0.7506). Besides, the difference between Ks values estimated by BESTslope algorithm and the modified DL or CL methods decreased as the percentage of clay increased. The calculated value of α in modified DL (α =0.019) was more similar to that reported previous studies (in similar soil texture, i.e. loam and sandy loam) than that obtained in CL model (α =0.023).

In this study, some interpolation methods were evaluated for estimating groundwater depth in the Iranshahr- Bampour Plain during 2003, 2007 and 2012 in the months of May and October. Data used belonged to 42- 48 wells scattered across the study area. The methods used contained geostatistical approaches of Ordinary Kriging (OK) and Universal Kriging (UK), and deterministic approaches of Inverse Distance Weighting (IDW), Radial Basis Function (RBF) and Local Polynomial Interpolation (LPI). The performance of the prediction methods was evaluated through cross-validation with comparison criteria of determination coefficient (R2), root mean square error (RMSE) and mean bias error (MBE). The statistical analysis showed a high variance and coefficient of variation of groundwater depth and an increase in the average depth to groundwater during bygone years especially during 2003-2007 period. Directional semivariograms were calculated to find out the drift direction. UK method, with the first and second-order polynomials as drift, and different semivariogram models was examined. According to cross-validation results, the best geostatistical method for estimating groundwater depth was OK (with spherical semivariogram) for 1382 and 1391, and UK with J-bessel semivariogram model and second and first drift orders, respectively, for May and October 2007. Moreover, the cross-validation results indicated that LPI, with RMSE equal to 6.94, 5.87 and 8.65 m, respectively, for May 2003, 2007 and 2012, and 6.86, 6.54 and 8.68 m, respectively, for October 2003, 2007 and 2012 is the best method of interpolation among others. The generated maps of groundwater depth revealed a drop in depth to groundwater; therefore, an occurrence of water crisis over the study region during the recent years. Therefore, it is necessary to consider some management scenarios including exploitation control and alteration of crop pattern and irrigation systems for an optimum use of water resources and achieving a sustainable agriculture across the region.

Nowadays one of the key factors controlling development of sustainable agriculture is unsuitable quality of irrigation water. The use of unsuitable water for agriculture could cause some problems regarding salinity, infiltration and toxicity. The aim of this study is to predict the spatial distribution pattern of groundwater quality indices for agricultural purpose in Golestan province. The spatial variability of EC, TDS, SAR, pH, Na, Cl-, SO42-, Ca2, HCO3-, Na%, ES and PS of water samples obtained from 207 observation wells was investigated. The experimental semivariogram of all parameters was calculated and the best theoretical model was fitted. Ordinary kriging is then used to interpolate the values of investigated parameters at unsampled locations and their spatial distributions were mapped through GIS. The probability maps of exceeding a given critical threshold were produced for some parameters and indices using indicator kriging. The results showed that all water quality parameters have good spatial autocorrelation with spherical structure. Using the generated estimation maps the study area was classified into different water quality zones considering US soil salinity laboratory as well as FAO standard for irrigation. Based on US soil salinity laboratory, 95% of the study area has a moderate water quality (C3S1 and C3S2). Based on FAO limit, 89% of the study area has good and moderate water quality. The area with appropriate water quality is about 2592 sq km mostly contains eastern, southeastern and northwestern parts of the study area. Moreover based on the probability maps of ES and PS, there is a higher probability of salinity risk in central regions as well as some parts in west. The generated estimation and probability maps can help decision-makers and planners to make proper decisions for appropriate use of soil and water resources and development of a sustainable agriculture in the region.

Nowadays, the exploitation of the renewable energy sources such as wind plays a key role in human life. Although, Iran has a high potential for wind power generation, there is not an efficient energy planning yet. Environmental variables such as wind speed vary according to spatial points, so it seems reasonable to consider that there exists a spatial correlation between wind speed data at different locations. In geostatistics the spatial autocorrelation of data could be investigated by calculating the experimental semivariogram. The parameters of the fitted semivariogram model may be then used to better estimate the wind speed at unknown locations through kriging algorithms.
In order to describe the behaviour of wind speed at a particular location, the data distribution should be first fitted by a suitable distribution function. There are different wind speed distribution models used to fit the wind speed distributions over a period of time. Among them, Weibull distribution function has been found to be the best all over the world because of its great flexibility and simplicity.
The aim of this study was to simulate the daily mean and maximum wind speed probability distribution by using Weibull distribution function and to investigate spatial variability of the wind speed data. This study was also aimed to interpolate and map the means of Weibull distribution functions of daily mean wind speed data observed at stations spread over Iran.
Study Area and Data Set
The study is based on a long term (20 years) wind data recorded in 104 synoptic stations spread over Iran. The wind data are recorded at 10m above the ground level (a.g.l.) and contain daily mean and maximum wind speed (m/s).
The Weibull Distribution Function
For each sites, the daily mean and maximum wind speed data were fitted by a two-parameter Weibull distribution, whose parameters (shape and scale) were determined through the maximum likelihood (ML) technique. The Weibull probability density function is defined as follows: where V is wind speed (m/s), 𝑐 is the scale parameter (m/s) and 𝑘 is shape parameter (dimensionless). The high and low 𝑘 values indicate the sharpness and the broadening of Weibull peak, respectively. The Weibull probability density function curve could be draw if the 𝑘 and 𝑐 values are obtained. This could be done through different ways, among which is maximum likelihood method as: where Vj is the wind speed for jth sample and n is the number of sample data. Equation (3) is an implicit equation and could be solved through an iteration method.
Interpolation Two interpolation methods including inverse distance weighing and ordinary kriging were used to estimate the theoretical mean values of the previously determined Weibull distributions of the wind speed data at unsampled locations.
Inverse Distance Weighing (IDW)
In absence of data spatial autocorrelation, IDW is usually used as an alternative method for spatial estimation of random field. IDW is a weighted averaging interpolator in which data is weighted according to their distance to the estimation point such that more distant points get less weight than closer points.
Ordinary Kriging (OK)
OK is the most popular kriging approach used in the spatial interpolation of the regionalized variables. It needs the parameters of the best fitted semivariogram model to incorporate spatial dependence of data into the estimation process. The semivariogram quantifies the dissimilarity between observations as the separation distance between them increases.
According to the obtained results, Semnan and Bandar-Abbass had the lowest and highest shape (k) factor of the fitted weibull distribution functions to the daily maximum wind speed data, respectively. For daily mean wind speed data, Nehbandan and Bandar-Abbass had the lowest and highest shape (k) factor of the fitted theoretical Weibull distributions, respectively. A high k value means less variation of the wind speed.
The annual duration of daily wind velocity of exceeding 4 m/s is also calculated for each site in order to obtain a first diagnostic sign of the most promising areas in terms of wind energy potential. According to the results, Cities of Rafsanjan, Zabol, Torbate Jam, Khodabandeh, Ardebil, Bijar and Kahnouj are of the most potential areas in terms of high wind speed.
The auto-correlation analysis showed that wind speed is moderately correlated in space with spatial structure model of spherical and a correlation distance of about 500 km (Figure 1 (a)). There was no apparent drift within the range of 500 km. The best semivariogram model was selected according to the cross validation results as well as highest correlation coefficient (r) and lowest residual sum of squares (RSS) functionally of GS software.
To predict the spatial distribution pattern of wind speed over Iran, Weibull mean wind speed data were interpolated over a point grid superimposed to the map of Iran by using IDW and OK. The cross validation results showed both methods performed similarly however the maps generated were visually different. Besides, unlike IDW, OK represented the map of estimation error which is useful in decision-making as it is provides a measure of uncertainty.
According to wind speed map generated by OK (Figure 1 (b)), eastern Iran (e.g. the cities of Zabol, Rafsanjan and Torbate Jam), as well as northwestern provinces (e.g. Ardebil) are the most promising areas for wind energy planning.
The spatial variability of wind speed and duration across Iran has been investigated. First, the frequency distribution of daily mean and maximum wind speed data during recent 20 years was simulated by using Weibull function. Then the mean values of the theoretical Weibull probability distribution functions are used to investigate the spatial variability and predict the spatial distribution pattern of wind speed across the country. According to the results, wind speed is moderately correlated in space with an influence range of about 500 km. The maps of wind speed at 10 m a.g.l. generated using IDW and OK encourage the utilization of wind energy on the eastern, and northwestern regions. Besides, additional measurements may be considered in areas of highest estimation uncertainty.

The Beerkan single ring infiltration experiment is receiving more attention because of its simplicity and low expense in running in recent years. BESTslope algorithm is the original algorithm in Beerkan experiment, which estimates some soil hydraulic properties such as saturated hydraulic conductivity (Ks) and sorptivity coefficient (S).The purpose of this research is to compare the performance of BESTslope algorithm with a simple proposed method and to determine the value of a constant parameter as the ratio of gravity force to capillary force (α) for loam and sandy loam soils. For this purpose, a number of 113 beerkan infiltration experiments were carried out in Sistan research field. The results showed that the estimated Ks values for 31 selected experiments, in which the relative fitting error (Er) for each test is less than 5.5% are similar to those obtained for 59 selected experiments having an average Er of less than 5.5%. However, these results were largely different from those obtained for 101 selected experiments having an average Er of less than 10%. Overall, the magnitude of α for loam and sandy loam soils of study area was determined as the average of two α values obtained for 31 and 59 selected experiments and it is equal to 0.013.

In this study some vegetative and reproductive characteristics of Rosa damascene under three levels of irrigation water amounts (100, 70 and 40 % potential evapotranspiration) though surface and subsurface drip irrigation systems has been investigated. The investigated morphological characteristics were included the number of branches per plant, shading diameter, plant height, number of blooms, petals weight, fresh weight of one flower, percentage of dry matter of flower and total dry weight. The experiment was performed as a split plots design based on the randomized complete blocks with three replications at Research Center for Agriculture and Natural Resources of Kerman province (Research Station Joopar) during 2012 and 2013. Results showed that the plant height, number of blooms, fresh weight of one flower, petals weight and percentage of dry matter of flower were not significantly different for the two types of irrigation systems but all plant traits except percentage of dry matter of flower were significantly different under different irrigation management regims. Due to water resources limitation in the study area, the 70% potential evapotranspiration treatment was recognized as the best treatment because having 30% decrease in irrigation water amount, there were only 5.8% and 15% decrease in the number of blooms and the shading diameter, the most effective traits on yield (correlation coefficient = 0.99), respectively.

The knowledge about spatial variability of precipitation is a key issue for regionalization in hydro-climatic studies. Measurements of meteorological parameters by the traditional methods require a dense rain gauge network. But, due to the topography and cost problems, it is not possible to create such a network in practice. In these cases the spatial distribution pattern of precipitation can be produced using different methods of interpolation. Interpolation could be done only based on the data of the main variable (i.e. through univariate methods) or on the information obtained from both the main and one or more auxiliary variables (i.e. through multivariate methods). The classical interpolation methods such as arithmetic mean and linear regression (LR) methods are independent of the spatial relationship between observations, while geostatistical methods (such as kriging) use the spatial correlation between observations in the estimation processes (Isaaks and Srivastava, 1989). The previous studies showed that the choice of interpolation method depends on data type, desired accuracy, area of interest, computation capacity, and the spatial scale used. Hence, different interpolation methods, including geostatistical methods (OK, SK, Sklm, KED, UK and COK), univariate deterministic methods (IDW, LPI, GPI and RBF) and linear regression (LR) were compared to estimate monthly and annual precipitation in Sistan and Baluchestan Province. The auxiliary variables used in the multivariate approaches were DEM, distance to Sea and spatial coordinates. Study area Sistan and Baluchistan Province is located in southeast of Iran and covers an area of 181471 km2. It is located between the latitudes 25˚03ʹand 31˚27ʹN and the longitudes 58˚50ʹ and 63˚21ʹE. The precipitation data collected from 50 precipitation stations over the same period of 25 years (1988-2012) were used in this study. Interpolation methods Detailed description of geostatistical interpolation methods used in this study including OK, SK, Sklm, KED, UK and COK are provided in the variety of resources, such as Goovaerts (1997) and Deutsch and Journel (1998). In geostatistics the most important tool for investigating the spatial correlation between observations is the semivariogram. In practice, experimental semivariogram is calculated from the following equation: (1) where is the experimental semivariogram, N(h) is the total number of data pairs of observations separated by a distance h, Z(ui) and Z(ui+ h) are the observed values of the variable Z in locations ui and ui +h, respectively. After calculating experimental semivariogram, the most appropriate theoretical model is fitted to the data. Unknown values are estimated using the semivariogram model and a geostatistics estimator. Comparison method and evaluation criteria To assess the accuracy of interpolation methods and the best method for estimating precipitation, cross-validation technique is used (Isaaks and Srivastava, 1989). Evaluation criteria are including the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE). Statistical analysis showed a high coefficient of variation of precipitation in August, September and July. Kolmogorov-Smirnov test showed that precipitation data are normally distributed over the study area. The precipitation semivariogram was considered isotropic as a little change was seen for different directions. Results of autocorrelation analysis showed a high spatial correlation of precipitation in all periods (except for January and February) with a spherical semivarioram model. This confirms the results of previous studies (Lloyd, 2005; Haberlandt, 2007; Mair and Fares, 2010). The maximum sill was observed for months January, February and March with a higher amount of mean and variance. The maximum radius of influence was seen for January (511 km) followed by May (205 km). The performance of UK was evaluated using the trend function of the first and the second order polynomial. The evaluation results indicate that the first order polynomial is the more accurate one. The cross validation results showed that the best method for precipitation estimation was linear regression (precipitation versus elevation) for April, KED for May, UK for June and September, RBF for July, August, October, December, January, February and annual precipitation and SK for November and March. The LPI and GPI methods did not perform well in any of the time periods. This could be possibly due to large changes in surface topography of province. RBF method had the highest accuracy in most of the periods. The estimated values in this method are based on a mathematical function that minimizes total curvature of the surface, generating quite smooth surfaces (Zandi et al., 2011). Geostatistical methods had the highest accuracy for other periods. One of the reasons for good performance of geostatistical methods may be due to the low density of the meteorological stations. It confirms other researchers’ results (Creutin and Obled, 1982; Goovaerts, 2000). The use of elevation as covariate has improved the estimation results only for April and May. However, the distance to Sea did not improve the estimation results in any cases. The reasons for little improvement of the precipitation estimation through the multivariate methods could be due to the complex topography, low density of meteorological stations, and low correlation between precipitation and covariates. Geostatistical interpolation methods, in deterministic and linear regression methods, were evaluated for precipitation data in Sistan and Balouchestan province. According to the results of cross-validation, linear regression (elevation- precipitation) for April, geostatistical methods for May, June, September, December and March and RBF method for other periods had the highest accuracy. According to the estimation error maps produced by the geostatistical methods, the highest estimation errors were seen in the area with a low density of stations and the boundaries of the province. These areas are recommended for developing the meteorological network in the future. Also, due to the variability of climate, distance from Oman Sea and changes in the surface topography for the precipitation stations, we recommend that the province is divided into more homogeneous regions and the proposed approaches are investigated in each section, separately.

Salinization is the main characteristic of soils in arid and semi-arid regions which reduce the agricultural potential of irrigated lands. Therefore, soil reclamation as well as determination of the leaching requirement for salt control is very important for better plant growth. In this study, the effects of leaching on saline soils of Sistan region, southeast of Iran were examined using unsaturated disturbed soil columns. The experiment was conducted on four texture types (loam, sandy clay loam, sandy loam and clay loam) and three replications. Soil samples were purred in polyvinyl chloride (PVC) cylinders and leaching procedures were conducted in 10 stages with up to 5 pore volumes. Effluent from each column was collected and evaluated in terms of Na, K, Ca2, Mg2 and EC. At the end of the study, soil columns were cut and their corresponding samples were analyzed for Na, K, Ca2, Mg2 and EC. The results of leaching experiments showed that the water used in this study could reduce solute concentration and thus, this soil does not need any amendment. For most soil textures, it was also observed that almost 85% of the salts were leached after the fifth stage of the leaching process. According to the results, ions entry into the effluent solution is fast in the coarse textured soils. So, the difference between the amounts of irrigation water needed to transport the salts and leach the saline soils can be attributed to the soil texture. It seems that the main reason for these reactions is the cation exchange.

Soil saturated hydraulic conductivity is a key parameter needed in many projects including drainage. So it necessitates knowing about the spatial distribution pattern of hydraulic conductivity. However, to obtain the knowledge, it is needed to have a lot of field measurements carried out which is time consuming, tedius, and costly. Different types of kriging can be used for estimating and mapping hydraulic conductivity over a study area. However, the estimated results contain some uncertainties. Unlike kriging, stochastic simulation can be used to model the estimation uncertainty and incorporate it into the decision-making processes. In this paper, Sequential Gaussian Simulation (SGS) and non-parametric Sequential Indicator Simulation (SIS) approaches were employed to model the uncertainty attached to the hydraulic conductivity estimates in KheirAbad plain in Khozestan. A number of 200 equally probable simulated maps of hydraulic conductivity were generated through either of the methods. The results revealed that unlike the kriged map, the simulated maps could reproduce the histogram and semivariogram of the raw data, reasonably well. Regarding local uncertainty, the results showed that the kriging variance does not depend on the actual data values and so there is a limitation in its use. The accuracy plot and width of probability interval plot indicated that the uncertainty model obtained through SGS is more accurate than that obtained through SIS; however the goodness coefficient was slightly smaller for SGS (0.88) than for SIS (0.94).

The effect of two irrigation systems (surface vs. subsurface drip irrigation) on yield، irrigation water use efficiency and percentage of essential oil in Rosa damascene under three levels of irrigating water (100، 70 and 40 % of potential evapotranspiration) has been investigated. The experiment was performed as a split plots design based on randomized complete blocks of three replications at the Research Center of Agriculture and Natural Resources، Kerman Province (Joopar Research Station) during 2012 to 2013. Results indicated that the yield، irrigation water use efficiency and percentage of the rose’s essential oil did not significantly differ for the two types of irrigation systems. The treatment 100% of potential evapotranspiration yielded the highest flower (2716. 93 kg/ha). The highest irrigation water use efficiency (2. 14 kg/(ha. m3)) was found for the 40% potential evapotranspiration treatment. Moreover، the highest percentage of the flower’s essential oil was recorded for the treatment of 70% potential evapotranspiration.

The main problems relating to water quality for agriculture are salinity, soil infiltration and specific ions toxicity. Accumulation of the specific ions from the irrigation water in the plants reduces crop yields. One of the most common specific ions toxicities results from high concentration of chloride (Cl) ion. Knowledge of the spatial distribution of Cl concentration in groundwater is needed for a better management of the groundwater resources. As limited number of sample data is often available, some appropriate interpolation methods are needed to interpolate between the sample points. Ordinary Kringing (OK) is a geostatistical estimation method which uses a semivariogram model to predict the unknown values. However, it cannot predict properly the spatial distribution pattern of a highly skewed data. Besides, OK estimation variance is not a perfect measure of local uncertainty because it only depends on data configuration not data values. Unlike OK, indicator Kriging (IK) is a distribution-free approach, which has the ability to model local uncertainty of the estimated values through estimating a conditional cumulative distribution function (ccdf) corresponding to each point. Although this method has been used by many researchers for mapping and modeling local uncertainty of various environmental variables such as groundwater Cl and other groundwater quality parameters, in Iran it has not been employed for such purposes much. The objective of this study is, therefore, to model the local uncertainty of groundwater chloride over Kerman plain using IK. The performance of IK is compared with the traditionally used OK (with and without data logarithms). Study area and sample data This study is performed in Kerman plain. The study area is located in a semiarid and arid region. Its average elevation above Sea level is 1755 m. Because of the lack of surface water resources, groundwater resources are the main water resources for agricultural purposes in this area. Due to the importance of specific ions toxicity, the groundwater samples were collected from 76 agricultural wells and Cl concentration were measured in laboratory. Geostatistical analysis: First of all, experimental (indicator) semivariograms are calculated to investigate the spatial variability of (within class) Cl data. A suitable theoretical model is then fitted to the experimental values for kringing modeling of Cl. Then IK is used to map groundwater Cl and to evaluate the uncertainty attached to the estimates. The results were compared with those achieved from OK and log-kringing (LOK). The probability maps of not exceeding two threshold values 10 and 20 meq/lit were generated for Cl by IK. These two threshold values are selected according to the irrigation water quality standard proposed by Ayres and Westcot in 1989. In the following the geostatistical tools and methods used in this study are briefly described: Semivariogram: The semivariogram quantifies the dissimilarity between observed values as the separation distance between the sample points increases. In practice, experimental semivariogram, γ*(h), is computed for two values separated by a lag distance h as following: Where, N is the total number of data pairs of observations z(xi) and z(xi+h)separated by a distance h for a specific direction. Kringing needs the semivariogram values for any given lag, therefore, a theoretical model may be fitted to the experimental values and the characteristics of this model can be used. Ordinary Kringing: In OK, the values at unsampled locations are determined by a linear weighted moving averaging of values at sampled locations as: where z*(x0) is the estimated value of variable of interest at unsampled location x0, li is the weight assigned to the known value of variable at location xi determined based on a semivariogram model and n is the number of neighboring observations. OK produces an estimation variance attached to every estimate, which can be used to generate a confidence interval for each estimate assuming a normal distribution of errors. OK performed on lognormal transformed data is called Lognormal Ordinary Kringing (LOK). The estimates have to be back-transformed to the original space at the end. Indicator Kringing: Indicator Kringing (IK) is based on the coding of the random function Z(x) into a set of K indicator random function I(x, zk) corresponding to different cutoffs zk: After transforming the observed data to a new set of indicator variables, the experimental semivariogram is calculated for every set of indicators at each cutoff zk. The conditional cumulative distribution function (ccdf) at each unsampled location, e.g. x0, is then obtained by the IK estimator: where I*(x0; zk) is the estimated indicator transform at unsampled location x0 and li are the weights assigned to the indicator transform I at location xi. These discrete probability functions must be interpolated within each class and extrapolated beyond the minimum and maximum values to provide a continuous ccdf, which covers all possible range of the variable. E-type estimates, which are comparable with OK estimates, may be computed through post processing IK-based ccdfs. Local uncertainty measures, e.g. conditional variance and probability maps are also produced through post processing of IK-based ccdfs. In this study kringing methods are performed using the software package GSLIB. Evaluation of the A cross-validation technique with comparison criteria Mean Error (ME), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is used to evaluate the performance of the methods. The most accurate method is the one with the smallest amount of MAE and RMSE and with a MBE close to zero. Statistical analysis shows that Cl data distribution is strongly positively skewed. A logarithm transform is used to provide a normal frequency distribution of data. Experimental semivariograms are calculated for the raw and log-transformed data. For IK, nine thresholds 1.3, 2.4, 3.4, 4.4, 5, 7.4, 12.7, 18 and 24 (meq/lit) corresponding to 10, 20, 30, 40, 50, 60, 70, 80 and 90 percent of Cl cumulative frequency distribution functions are selected. Then, the observation of Cl data is coded according to these selected thresholds and the indicator semivariograms were computed afterwards. The results of semivariogram analysis show that chloride data values and its logarithms are strongly correlated in space and the best fitted semivariogram model is spherical with a range of influence of 42 and 72 km, respectively. However, according to the results, a higher spatial correlation is seen for log-transformed data. Cl data had a moderate to strong within class spatial correlation and indicator semivariograms often follow a spherical structure. Furthermore, the cross-validation results indicate that LOK with the smaller amounts of RMSE and MAE and a higher amount of correlation coefficient, R, are more accurate than IK for estimating groundwater chloride. Beside estimation, one of the main aims of this study is to model local uncertainty of Cl data over the study area. Both OK and LOK provide the uncertainty attached to each Cl estimate by calculating its estimation variance. Thus, where the estimation variance or standard deviation is smaller, the estimated value of Cl is more certain. The results show that OK and LOK estimation variance is more related to sampling configuration not to the actual values. In contrast IK conditional variance shows some relation with the sample data in addition to sampling location. Besides, IK conditional variance was more appropriate for representing the estimation error than the OK (and LOK) variance. Moreover, the produced probability maps showed that the probability of chloride exceeding critical thresholds 10 and 20 meq/lit is higher in the northwest and west of the study area. In this study non-linear indicator kringing is used to model the local uncertainty attached to Cl concentration estimates. Ordinariy (log) kringing is used to map the spatial distribution of Cl estimates. The results show that ordinary log kringing, which is faster and mathematically simpler than indicator kringing, provide more accurate results of Cl estimates. The correlation between indicator kringing conditional variance and estimation error is stronger than the correlation between ordinary (log) kringing variance and estimation error. This means that IK conditional IK produces the probability map of not exceeding a critical threshold for Cl concentration. These maps can be useful in many decision-making processes, e.g. water resource management.