JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION
Year:2012 | Volume:2 | Issue:1|Papers Count:6

Proper estimation of crop water requirement is very important for water resource conservation in Iran. However, in most studies in Iran, the estimation of evapotranspiration is based on long-term weather data. However, use of simultaneous weather data of a non-agricultural weather station may underestimate or overestimate the crop water requirement. Therefore, it is essential for irrigation specialist to be aware of the error of evapotranspiration calculated by long-term weather data. The objective of this study was to evaluate accuracy of evapotranspiration of reference crop using long-term weather data in comparison with simultaneous weather data in drip and sprinkler irrigation. The period was designated to be from 1995 to 2009 in Isfahan weather station, Kabootarabad weather station and Shahid Beheshti airport weather station. The maximum value of n-RMSE using long-term weather data in comparison to simultaneous weather data were 24% for trickle irrigation and 18% for sprinkler irrigation management, 22% in spring, 20% in summer, 44% in fall and 56% in winter. According to results of this study, the application of long-term weather data in deficit irrigation management is not recommended. On the other hand, due to sensitivity of trickle irrigation to water stress, the application of long-term weather data for estimating of evapotranspiration in irrigation scheduling is not recommended. Finally, application of automatic weather stations and simultaneous weather data for accurate irrigation management particularly in deficit irrigation management in critical situation of water shortage is recommended.

Many factors affect soil erosion and runoff generation. However, the role of some factors such as freeze-thaw (FT) processes has not been well considered yet. The present study therefore aimed to simulate and explain the effect of FT cycle on runoff generation and sediment yield under rainfall simulation condition. Towards this attempt, air and different soil depth temperatures were analyzed in natural condition and 10 cm soil depth was targeted for the soil laboratory experiments. A FT cycle with three and two days for freezing and thawing was taken place. The rainfall storm with 1.2 mm/min and 30 min duration was simulated and conducted for three treatments in soil erosion and rainfall simulation laboratory of Tarbiat Modares University. The results showed that the induced FT cycle soil had a distinct and significant behavior and different from that seen for the control plot. So that, the times of runoff generation in freezed and FT cycle treatments were 2.74 and 3.25 times less, respectively, compared to that reported for the control plots. The runoff volume and sediment yield were also respectively reported in turn of 1.46 and 1.16 and, 11.92 and 2.65 times more for freezed and FT cycle induced treatments compared to those recorded for control plots. Ice lenses and frost heaves plus creating situations close to soil saturation after FT cycle were the most effective factors in distinct behavior of soil induced by Freezing and FT cycle.

Contamination of soil and water resources is a major concern for optimal use of these resources worldwide. The so-called distribution coefficient (Kd) is an applied parameter not only for modeling contaminant transport in soil but also for risk analysis of soil and water resources contamination. The most common quantitative model for estimating Kd is parametric method. The correlation coefficient of this model is usually low, however, the predicted Kd values may cause significant inaccuracy in predicting the impacts of contaminant migration or siteremediation options. The objective of this study was to investigate application of artificial neural networks (ANN) for improving Kd prediction of heavy metals. Consequently, three ANN types including multi layer perceptron (MLP), redial basis function (RBF) and hierarchical networks (HN) and two heavy metals of Chromium (VI) and cadmium were used for modeling purposes. The collected data were first divided into two training and test groups. The first group was used to train ANN and the second to evaluate generalized ANN models. The most suitable geometry of networks were obtained with trial and error procedure. The results of modeling Kd (Cr) revealed that both MLP and RBF networks are reasonable tools, but MLP was more accurate than RBF. Although the applied input data for training networks were not so much (at least 9 and the maximum of 16), but they were sufficient for modeling Kd (Cr). This finding is a promising result because direct measurement of Kd is expensive and time consuming. Further, usually limited numbers of available data are existing in each case. The results of predicting Kd (Cd) approved the preferences of MLP for modeling purposes. The ANN model can significantly enhance the correlation coefficient between predicted and measured data form 0.37 of parametric method to 0.63.

Quantitative assessment of soils hydraulic properties that are contaminated with oils is crucial for optimal soil and water resources conservation. The objective of this study was to investigate Kerosene effect on soil hydraulic properties. For this purpose, the soil retention curves and saturated hydraulic conductivity of Kerosene and water were determined by hanging column and constant head method, respectively. The soil retention curve parameters were obtained based on van Genuchten, Brooks-Corey and Campbell’s models. Unsaturated soil hydraulic conductivity for both fluids were predicted based on Mualem- Brooks-Corey, Burdine- Brooks-Corey, Mualem-van Genuchten and Campbell’s functions. The results indicated that Kerosene was retained less than water owing to its negligible surface tension, yielding less needed tension to drain kerosrene out from soils. Moreover, the magnitudes of the pore-size distribution parameters remained mostly unchanged and the bubbling pressure parameters were increased in Kerosene-air system compares to water-air system. The Kerosene-water matric potential ratio at the same soil fluid quantity was 0.48. The Leverett’s and suggested scale factors were determined 0.39 and 0.49, respectively, which indicates the soil fluid retention curves were appropriately scaled based on the recommended scale factor. Due to high kinematic viscosity of Kerosene and low soil retention for Kerosene, the saturated and unsaturated soil hydraulic conductivity of Kerosene was less than that of water.

Limitation of water resources in countries that are located in arid and semi-arid areas, urbanization development and population growth and pressure on conventional water resources, besides the difficulties and sanitation costs, caused more attention towards using wastewater in agriculture. Several investigations have shown that among different irrigation methods, drip irrigation has the best compatibility with wastewater applications. However, emitters clogging is the most important disadvantages of wastewater application in drip irrigation. To investigate the effect of weekly flushing on performance of emitters when using treated wastewater, a physical model of drip irrigation system was designed and constructed. Consequently, four prevalent types of emitters including Microflapper, Netafim, Emitting pipe and Antelco were selected and used. The results showed that flushing management treatment cause to increase the discharge rate, distribution uniformity, absolute distribution and Christiansen uniformity coefficient and to reduce the coefficient of variation of all the emitters when treated wastewater was applied. However, this increasing was depended on the type of emitters, such that the largest impact was obtained on Antelco emitter. The result further showed that Netafim and Microflapper dripper, with 8.5 and 27.8 percents, respectively, have the minimum and maximum discharge reductions when treated wastewater was used. Netafim dripper had high performance when flushing management practice was applied and it proven to have better performance than other emitters for long time period.

Although spatial and temporal variability of hydrological parameters are the main inputs for effective soil and water resources management in watershed scale, but this important subject has been scarcely taken into account. Hence, the runoff, suspended sediment and nutrient (Nitrate and Phosphate) yields from main tributaries of the Zarivar Lake were determined and their spatial and temporal variations were also investigated. A number of eight sampling points on the seven lake tributaries were monitored from March 2011 to April 2012 under base flow condition by daily sampling and event bases by hourly sampling intervals. The total yields of runoff, suspended sediment, nitrate and phosphate to the Zarivar lake during the study period were obtained to be 9.7 Mm3, 685.4 t, 25.4 t and 1.15 t, respectively. Almost seven storms were occurred during the same period due to which 1.61 Mm3 runoff, 685.4 t suspended sediments, 4 t nitrate and 211 Kg phosphate were transported. Some 8.1 Mm3 runoff, 718.65 kg suspended sediments, 21.4 t nitrate and 994.35 Kg phosphate were also entered into the Zarivar lake during base flow condition. The results of this study showed that northern parts of the watershed mainly contributed in runoff and nitrate yields. The maximum suspended sediments were also yielded by northern and western parts of the watershed. Whereas, the western parts of the watershed yielded the maximum phosphate to the Zarivar lake. The maximum contribution of runoff, suspended sediments, nitrate and phosphate were respectively happened in spring and winter seasons. About 82 percent of suspended sediments entered to the Zarivar lake during the study period just by one spring event with a duration of some 50 h. It clearly revealed more temporal variability of the suspended sediment and therefore further precaution for better management of soil and water resources of the closed Zarivar lake watershed.