Iranian Journal of Irrigation and Drainage
Year:2015 | Volume:9 | Issue:4|Papers Count:11

Ghanats are water structures of Iranian invention. Groundwater extraction is transferred by Ghanat, with the force of gravity and from underground and away from the evaporation. Structure of Ghanats created in Iran was changed from the used state to unused state, and the resistant structures or Kaval were used in order to prevent the destruction of structure of Kariz. The aim of this study is the optimal shape design of Ghanat Kaval using finite element method and particle swarm optimization. For this purpose, first, 3D modelling of Kaval and it’s around element was implemented in ANSYS software. Then, optimal shape design of Kaval was achieved using particle swarm optimization and linking it to ANSYS software. Weight of Kaval was considered as the objective of optimization problem, and the geometry parameters of Kaval were design variables. Also, stress of the body Kaval (compressive and tension stresses) was considered as constraint. The optimum results were shown that particle swarm optimization had high performance in the optimal shape design of Kaval so that the weight of Kaval in comparison with the existent Kaval was considerably decreased.

Shortage of water is developing in many parts of the world. One of the most effective strategies for production and sustainability of water resources is increasing water productivity in agricultural fields (or agriculture). Energy is an essential component of the agricultural sector, which has its costs. In order to enhance economic benefits and sustainability of water resources and energy in agriculture, we illustrate the productivity parameter. Productivity means more output per unit of input (water, energy, ...). Research conducted on the productivity of water and energy (electricity) in produced the seed corn, that the strategic importance of this crop in the world, is known for its high performance. This research done in agricultural company Barekat, dependent on, agro-industry Jovin (Farm Qadery) one of multiple farms in1391-1392.Water and electrical energy efficiency of maize seed were compared in the furrow, sprinkler and type irrigation systems. Irrigation water productivity in the production of seed corn for three, furrow, sprinkler (center pivot) and type irrigation system is respectively0.42, 0.46 and 0.47. Electrical energy productivity parameter (pumps and Center Pivot) for three system is 0.682 and 0.838 and 0.448 respectively. The results obtained show that the productivity of water parameters is high, in pressurized systems, particularly the type. Contrary to the impression, electrical energy productivity in the sprinkler system upper than type and furrow irrigation system.With attention to obtained parameters, we conclude in total that sprinkler systems (Center Pivot), is the best performance in terms of water and power consumption compared with furrow irrigation system and a type.

In this research, the applicability of DRAINMOD model for simulating water table fluctuations and subsurface drain discharge variations was evaluated at the consolidated paddy fields of Sari Agricultural Sciences and Natural Resources University. During free drainage period of a canola season (November 28, 2011-April 2, 2012), water table depths and subsurface drain discharges were measured in three subsurface drainage systems including drainage system with drain depth of 0.9 m and drain spacing of 30 m (D0.9L30), drain depth of 0.65 m and drain spacing of 30 m (D0.65L30), and drain depth of 0.65 m and drain spacing of 15 m (D0.65L15). The reliability of the model was tested using the statistics including model efficiency (EF), percent error (PE), coefficient of determination (R2), root mean square error (RMSE) and mean deviation (AD). Mean values of measured and simulated water table depths in the calibration treatment (D0.65L15) were both 32.4 cm. For validation treatments, such values were, respectively, 24.1 and 14.1 cm for D0.65L15 and 2.1 and 19.7 cm for D0.9L30. EF, PE, R2, RMSE and AD values for predicted drainage discharges ranged from -5.1 to -1.3, 9.1 to 21.6, 0.2 to 0.35, 0.08 to 0.11 and 0.07 to 0.1, respectively. Comparison of measured values with the simulation results showed that DRAINMOD model can be used as a practical tool for predicting the general pattern of water table and drain discharge variations in the study area.

Conflict over shared water resources has been occured in result of mismanagement, bad governance and increasing water demand. Therefore, equitable water allocation is one of the important tools reducing conflict.In this paper, we investigate application of six methods of bankruptcy theory: Proportional (PRO), Adjusted proportional (AP), Talmud (TaL), Pinile (Pin), Random arrival (RA), Sequential sharing rules (SSR based on PRO). The area study includesfive regions along Atrak riverin Golestan province. Total available water depending on water requirementsof each region is divided with bankruptcy theory. Since each method has different definition of fairness, so different results can be obtained. According to Plurality Rule, it shows that the results of PRO, AP, RA, Pin theories are the superior ones in this case study. Results of this study can be a starting point for negotiations and making agents’ final decision.

Water recourses planning in the arid and semiarid areas which are faced with water restrictions, both in quality and quantity, is very important, yet it is too difficult. Utilizing targeted low irrigation can be one of the approaches to deal with water shortages. According to their regions and physiological conditions, various plants show different reactions to water shortages. This survey in the form of the perfect randomized block program contains 9 different levels of irrigation in 3 repeating times on the SC704 corn cultivars had been applied in 2012 at the Islamic Azad University Golbahar branch farm in Mashhad. samples contain 7 levels of irrigation (11, 12, 13, 14, 15, 16, 17) respectively equivalents to 120%, 100%, 90%, 80%, 70%, 60%, and 50% of the plant water requirements, and two other levels based on the soil water depletion (SWD) (19 and 18 respective irrigation when 50 and 70 percent of the corps capacity moisture depleted).results indicate that change in the amount of water consumption, directly affects the crop performance. Also, targeted water shortage by optimized and low water consumption, can have more suitable performance on crops production. The highest plant height is related to the sample11 and equals to 209 cm and the least of them is related to the sample 17 and equals to 32.5 tons in per hectare, and it happens when the irrigation of about 50 percent of full water requirement has done. The results indicate that the efficiency of water consumption (seed performance) in samples that irrigation takes place based on the soil humidity is higher than the other samples.

However, several methods exist for calculation of reference crop evapotranspiration (ETo) but the FAO- 56 Penman- Monteith (FAO- 56 PM) method has been recommended by the Food and Agriculture Organization of the United Nations (FAO) as the standard equation. This method is difficult to use because it requires several weather parameters and complex calculations. On the other, over the last decades Artificial Neural Network (ANNs) have shown a good ability for modeling complex and nonlinear systems. The present study was carried out to investigate the sensitivity of the reference crop evapotranspiration to climate parameters using ANNs in Lorestan province. For this purpose in period 10 years (2001 – 2010) daily ETo were calculated using FAO-56 PM method based on weather data daily in the eight weather stations in Lorestan province. Then an Artificial Neural Network was designed with 18 scenarios. Combinations of six weather parameters (maximum and minimum air temperature, maximum and minimum relative humidity, wind speed and daily sunshine hours) which are required to calculate ETo with using FAO-56 PM method were considered as inputs and calculated ETo as output of the ANN in various scenarios. The results of this study showed that increasing the number of data in the input layers will not necessarily lead to improved outcomes of intelligence models. In case of weather data limitation, scenario 13 which was used maximum temperature and wind speed as input layer showed reliable results.

Discharges of urban, hospital, industrial and agricultural drainage wastes into rivers not only reduce the quality of river, but also it has irreversible effect on ecosystems. Estimation of pollution loads from various contaminants is important in quality and quantity planning of water resources. Pasikhan River is one of the major rivers of Guilan that is at risk of pollution due to adjoining to residential areas, fish ponds and paddy fields and their discharge of drains. In this research a new approach include of simultaneous application of water quality model and satellite images were studied to estimate the loading of pollution from agricultural activities in Pasikhan River. For this purpose, the WASP model was used to simulate spatial and temporal trend of nitrate and phosphate in Pasikhan River. For model calibration and validation, the quality of the river including of nitrate and phosphate was analyzed in 1390. The model simulated nitrate and phosphate with a determination coefficient 0.84 and 0.89 respectively. After determining the cultivation area of paddy fields in the study area and using the DEM map, nitrate and phosphate pollution loads into the river was estimated by calibrated model.Comparing observed pollution load (16.0 and 1.68 kg/ha) and model estimation (14.18 and 1.40 kg/ha) in terms of nitrate and phosphate, show appropriate accuracy of method for estimating pollution in agricultural land.

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

Infiltration and coefficients of infiltration equation are of the most important parameters in the design and evaluation of irrigation systems. The objective of this research was to compare the cumulative infiltration, infiltration rate and electrical conductivity of different soil textures including: sandy loam, silt and clay under saline water 0.58 dS/m, 5 dS/m and 10 dS/m with magnetized and non-magnetized water. Magnetized water was obtained by passing the water through a strong permanent magnet installed in a feed pipeline. Infiltration coefficients of different soils obtained based on Kostiakov-Lewis equation. The results showed that, magnetized water caused increasing of cumulative infiltration and also increasing of infiltration rate in different soil textures and all saline water treatments and this effect was significant (p<0.01). At the end season, in non-magnetized water and saline water of 5 dS/m and 10 dS/m, the average cumulative infiltration was reduced 10.8% and 25.6% respectively as compared to control treatment. But, in magnetized water and saline water of 5 dS/m and 10 dS/m, the average cumulative infiltration was reduced 6.4% and 13.95% respectively as compared to control treatment.In addition, using of magnetized water caused to decrease the electrical conductivity in different soil depths (p<0.01). Magnetized irrigation water had most effect on the infiltration capacity and electrical conductivity of clay and sandy loam soil, respectively. With increasing of infiltration using magnetized water, evaporation was decreased and therefore, irrigation efficiency is increased.

Hydraulic conductibility as a key soil property is essential for irrigation management purposes and plays an important role in understanding site-specific unsaturated water flow and transport processes. Since it cannot often be measured because of practical and/or cost-related reasons, data driven models such as artificial neural network may be applied for prediction of soil hydraulic properties. This paper aimed to assess uncertainty analysis in neural network prediction originated from different weights due to different training data sets. Here, we present a unique dataset that consists of 151 samples collected from arable land around Bojnourd City, containing of sand, silt and clay contents, saturated water content, Electrical Conductivity (EC), pH, real density, Organic matter (OM), Total Neutralizing Value (TNV) and bulk density (ρb). Bulk and real densities determined based on stepwise regression analysis as most important inputs to neural network model. Then a two layer perceptron neural network with 1000 different samples trained with some available transfer and training functions in Matlab. Results assessed with NUE as an integrated index defined as ratio of percentage of the observation coverage by 95 percent prediction uncertainty (95PPU) divided by average relative interval length of 95PPU. Uncertainty analysis results revealed that log-sigmoid and linear transfer functions with NUE values 0.57 and 0.59 performed better tan-sigmoid transfer function with NUE=0.25. Also all training functions (except of gradient descent training function) could predict saturated hydraulic conductivity with high reliability.