M. R. Emdad; A. Tafteh
Abstract
Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can ...
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Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can be used to make or predict appropriate management scenarios according to the region's conditions and to predict plant performance in the field. Since the performance of irrigation scenarios in field conditions are costly and time consuming, and due to the limited water resources in the country and the necessity of optimal water use in agriculture, using the efficient and generic models can be useful tool for simulating crop production and soil salinity variations. This research has been conducted in order to simulate soil salinity and yield production using SALTMED model in Azadegan Plain of Khuzestan province. Materials and Methods: This study was carried out in wheat fields of Azadegan plain in Khuzestan province during 2014-2015 in three regions including Ramseh (as saline soil), Atabieh (as very saline soil) and Hamidieh (as control, non-saline soil). Three 10-hectare plots were selected in each area and a pilot with area of 2000 m2 was used for evaluation and measurement in each plot. First year data were used to calibrate the SALTMED model and second year field data were used to validate the model and to achieve the results in three conditions. The dominant soil texture in the area was clay loam. The quality of used irrigation water with average salinity of 2 dSm-1 was classified as C3-S1(high salinity with low sodium absorption ratio) and had no effect on wheat yield loss. In this study, version 3-04-25(2018) of SALTMED model was used and after calibrating in the first year, the results of simulated wheat grain yield and soil salinity variation values were used for model validation in different regions and in soils with different degrees of salinity, in the second year. Results and Discussion: The average measured and simulated biomass yield in the first year were 6.6 and 6.1 t/ha, respectively. Furthermore, the average of measured and simulated of wheat grain yield was 2.9 and 2.6 t/ha, respectively. Some statistical indices including mean bias error, normalized root mean square error, and root mean square error for grain yield were 0.11, 0.04, and 0.12 t/ha, respectively. The values of the same statistical parameters for biomass were -0.49, 0.1, and 0.61t/ha, respectively. These results showed that the measured values of grain yield and wheat biomass were in good agreement with the simulated values using SALTMED model. The simulated and measured variations of soil salinity at three soil depths of 0-30, 30-60, and 60-90 cm, showed close agreement with each other in three layers. Root mean square error, normalized root mean square error, and mean bias error for soil salinity values were 1.3, 0.20, and -0.06, respectively. After calibrating the model in the first year, to validate this model in the second year, the results of three pilots locations in three regions of Ramseh (saline), Atabieh(very saline) and Hamidieh(non-saline) were used. Comparison of simulated and measured wheat grain yield and biomass values showed that there was no significant difference between simulated and measured values. The simulated values of grain yield and wheat biomass in the three non-saline, saline and very saline soils had high correlation with the measured values, indicating high accuracy and efficiency of this model in simulating grain and biomass yield in different degrees of soil salinity. Moreover, the trend of soil salinity changes simulated by the SALTMED model in three highly saline, saline and non-saline soils (for three soil layers) was close to the measured values. The SALTMED model with normalized root mean square error and mean bias error of 0.18 and -0.13, respectively, showed good accuracy in different salinity conditions. There was no significant difference (5% level) between the measured and simulated salinity values of the different soil layers. The mean standard error at the 0-30, 30-60, and 60-90 cm layers was 1.1, 1.05, and 0.81 dSm-1, respectively. Therefore, based on the results and statistical indices, it was found that SALTMED model had good accuracy and efficiency in simulating yield, biomass and soil salinity under different salinity conditions. Conclusion: According to the results and statistical indices, SALTMED model had good performance and accuracy in simulating grain yield, biomass and soil salinity variations in different soil salinity conditions and so it can be used to predict wheat yield, yield components and soil salinity in different soil condition with different degrees of soil salinity to sustain soil and water and improve water productivity in similar areas.
iman babaeian; Maryam Karimian; Hamed Ashouri; Rahele Modirian; Leili Khazanedari; Sharare Malbusi; Mansure Kuhi; Azade Mohamadian; Ebrahim Fattahi
Abstract
Introduction: Southeast watersheds of Iran including Great Karoon, Karkheh, Jarrahi and Zohreh have the most significant contribution in the water supply of the agriculture, industry, drinking water and hydroelectric power plants over Iran. 25 percent of the country’s electricity is produced from ...
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Introduction: Southeast watersheds of Iran including Great Karoon, Karkheh, Jarrahi and Zohreh have the most significant contribution in the water supply of the agriculture, industry, drinking water and hydroelectric power plants over Iran. 25 percent of the country’s electricity is produced from hydroelectric power plants located in this region. The existence of a monthly relatively high resolution gridded precipitation dataset is of the most important needs of water resources management for such as deciding on the suitable time of dewatering and discharge of dams, calibration of dynamical monthly forecasting models and drought early warning. Even considering all observation stations governed by Meteorological Administration and Ministry of Power, the density of stations is not so enough to use them for calibration of hydro-climate model outputs. To overcome this deficiency, one way to fill the gap is using bias corrected global gridded precipitation dataset such as APHRODITE, CMORPH, PRESIANN and other newly generated data.
Material and Methods: Watershed of Karkheh, great Karoon, Jarrahi and Zohreh are the area of study which covers southwest provinces of Khuzestan, Kermanshah, Ilam, Chaharmohal-Bakhtiari, Kohkiluyeh and Buyerahmad, Isfahan, Hamadan, Fars and Lorestan, which is shown in figure 2. There are 135 observation station in the area of study which governs by Iran Meteorological Organization and Ministry of Power. Area of study covers by 75 grids of 0.5×0.5 degree latitude and longitude. For each grid there is an APHRODITE precipitation data. In the 34% of grids, there is no observation station. The main goal of this study is to attribute a reliable monthly precipitation data to all grids without any observation station. Period of APHRODITE data set is 1987-2007, which is same to observation period. Firstly regional bias of APHRODITE data set has been computed by comparing observed precipitation with APHRODITE one. Then bias corrected APHRODITE precipitation (Composite APHRODITE Observation dataset) has been placed in non-observation grids. Efficiency of composite precipitation data has been determined by statistical parameters of bias, correlation and Nash-Sutcliff indices.
Results and Discussion: In this research the results have been evaluated at monthly and seasonal time scales. In the case of seasonal time scale, we found that the minimum APHRODITE’s bias of 1.2 mm has been occurring in summer, while the maximum bias has been occurring in winter by 40.9mm. It means that the bias is high in the rainy season. Seasonal correlations were statistically acceptable in 0.05 significant levels, showing same seasonal fluctuations in APHRODITE and rain gage data. To provide seasonal composite APHRODITE-Observed precipitation gridded data set, mean seasonal bias of APHRODITE has been removed, while preserving seasonal fluctuation. The highest spatial correlation of 0.8 was detected in autumn, while it was about 0.7 for spring and winter. The minimum seasonal correlation was in summer by 0.5. There were also a good agreement between area averaged observation and APHRODITE data, when considering statistical indices of bias, Nash-Sutcliff and relative percentage errors. Results show the cumulative distribution function of APRODITE data is behind of the observed cumulative distribution function data, meaning that APHRODITE reaches its maximum earlier than observation data. This implies that APHRODITE cannot capture well the extreme monthly precipitation. Monthly correlations are approximately greater than 0.9, but the only exception is September with a correlation coefficient of 0.52. All correlations are significant in 0.05 levels. The highest spatial correlation was occurred in Novembers. Monthly Nash-Sutcliff was 0.96 in monthly time series. The categorical percentage score was 94.1%. These results strongly confirm that APHRODITE precipitation data is a good option for replacement in grid cells without observations. The number of observation stations per cell is varied from 1 to 7. We found that the maximum monthly correlations occur in grid cells of 0.5×0.5 degree latitude and longitude which having at least 3 observation stations. The three-station bias has been applied to APHRODITE data, then bias-removed data has been replaced with grid cells without observations. Spatial patterns of new composite APHRODITE-observation data set has good agreement with observation in the areas having intense observation stations. They also can capture well the spatial precipitation distribution of rainy areas located in the center of basin and low rainfall areas located in the southwest of the region. The results of this research can be used in calibration of dynamical seasonal forecasting outputs, drought early warning and rain-runoff simulation.
R. Beitlefteh; A. Landi; S. Hojati; Gh. Sayyad
Abstract
Introduction: Recently, air pollution due to the occurrence of dust storms is one of the worst environmental problems in Western and Southwestern Iran, especially the Khuzestan Province (12, 13). According to the reports of the Meteorological Organization of Iran the average number of dusty days in the ...
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Introduction: Recently, air pollution due to the occurrence of dust storms is one of the worst environmental problems in Western and Southwestern Iran, especially the Khuzestan Province (12, 13). According to the reports of the Meteorological Organization of Iran the average number of dusty days in the cities of Ahvaz and Abadan in the Khuzestan Province reaches 68 and 76 days each year, respectively (6). Previous studies have shown that the yearly damage costs of wind erosion and occurrence of dust storms in the Khuzestan Province reach about 30 Billion Rials (5). However, very few studies have been conducted on the characterization of dust particles and also the identification of their origins in Iran, especially the Khuzestan Province. Hojati et al. (10) reported that dust deposition rate, mean particle diameter, and concentration of soluble ions in samples taken from Isfahan and Chaharmahal and Bakhtiari Province decrease with altitude, with a significantly lower gradient in periods with dust storms. They reported three factors that control the rate and characteristics of dust deposited across the study transect: 1) climatic conditions at the deposition sites, 2) distance from the dust source, and 3) differences between local and transboundary sources of dust.Therefore, this study was conducted to investigate the effects of dust storms on deposition rate, mineralogy and size distribution patterns of dust particles from twelve localities around the Houralazim lagoon.
Materials and Methods: Dust samples were collected monthly during a 6 month experiment from August 2011 to February 2012. In order to differentiate between the contribution of dust production by local soils and other sources, surface soils were also sampled from the vicinity of the dust sampling sites. The collection trays were made of a glass surface (100 × 100 cm) covered with a 2 mm-sized PVC mesh on the top to form a rough area for trapping the saltating particles (Fig. 2). Dust samples were collected by scraping materials adhered to the glass trays using a spatula. All the trays were wet cleaned before the next collection. The collected dust and soil samples were examined for their grain size distribution using a Malvern Hydro 2000g laser particle size analyzer, as well as their mineral compositions by a Philips PW1840 X-ray diffractometer and a LEO 906 E transmission electron microscope (TEM).
Results and Discussion: The results showed that wind speed and direction patterns during the periods with dust storms and those without dust storms were different. Accordingly, in periods with dust storms (3, 5 and 6) the contribution of winds with speeds greater than 11.1 m/sec, especially from the Northwest direction, increased when compared with those from the periods without dust storms (1, 2 and 4). Besides, the direction of prevailing winds in periods without dust storms were mainly from the West and the Northwest. However, in periods with dust storms East-directed winds were also observed (Fig. 3). These show that the source areas of dust particles in these periods are probably different. The results also illustrated that the average amount of deposited particles in the periods with dust storms (12.5 g m-2 month-1) was considerably more than that of the periods without dust storms (7.5 g m-2 month-1) (Figs. 4 and 5). The difference in dust deposition rate between periods having dust storms and those without dust storms seems to be due to dust input from a source outside the study area. Particle size distribution analysis showed that dust particles collected from the study area in both periods (with and without dust storms) are mainly silt-sized particles. This fraction contributes to 60 to 76 % of the particles collected from periods without dust storms and 66 to 82 % of particles affected by dust storms (Table 2). The results also imply that in both periods (with and without dust storms), dust particles collected from the study area had a bimodal distribution pattern which suggests mixing of settled particles from different sources and/or deposition processes (Fig. 6). Mineralogical composition of dust particles were collected from both periods (with and without dust storms) and those from the soils contained quartz, calcite, feldspar, halite, dolomite and palygorskite (Figs. 7 and 8). Moreover, the TEM images of dust particles collected in periods with dust storms showed higher amounts of palygorskite than in periods without dust storms (Fig. 9).
Conclusion: The similarity in the physical properties of local soils and deposited particles of the periods with and without dust storms implies that the contribution of local soils and sediments in producing dust particles is high. However, it seems that in periods with dust storms the contribution of a transboundary origin such as Iraqi arid lands in producing dust particles increases.
M. Jamei; M. Mousavi Baygi; M. Bannayan Awal
Abstract
Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily ...
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Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily Grid Precipitation data during the 2000-2007 years were compared with 44 meteorological stations. Four interpolation methods i.e. Inverse Distance Weighted, Ordinary Kriging, Cokriging, and Regression Kriging were assessed to determine the most appropriate interpolation method for daily precipitation.For the variography analysis in Kriging models, five variogram models including spherical, exponential, linear, linear to sill and Gaussian fitted on the precipitation data. Near neighbor method was used to compare APHRODIT Daily Precipitation data with station recorded data. Cross validation technique was employed to evaluate the interpolation methods and the most appropriate method was determined based on Root Mean Square Error,Mean Bias Error, Mean Absolute Error indices and regression analysis. The result of error evaluation of interpolation methods showed that regression Kriging method has the highest accurate to interpolation of daily precipitation data in Khuzestan province. Therefore, regression-based interpolation methods which using covariates would be improved precipitation evaluate accurate in the area. Comparison of error indices and regression analysis of regression Kriging interpolation method and estimate of APHRODITE show that on most days the accurately estimate of regression Kriging is higher than the APHRODITE. Therefore to understanding of spatial distribution and estimate of daily precipitation data in Khuzestan Province, Regression Kriging interpolation method is more accurate than available APHRODITE data
A. Mahjoubi; A. Hooshmand; AbdAli Naseri; S. Jafari
Abstract
Sugarcane is one of the high consumption plants that has very high drainage coefficient. Irrigation frequency of Sugarcane in the maximum of consumption month is near 5 days and drainage systems often are removing drain water more than enough from the soil. This study was carried out to investigate the ...
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Sugarcane is one of the high consumption plants that has very high drainage coefficient. Irrigation frequency of Sugarcane in the maximum of consumption month is near 5 days and drainage systems often are removing drain water more than enough from the soil. This study was carried out to investigate the impact of controlled drainage on reduction of drainage coefficient and drainage volumes in three fields of Imam Khomeini sugarcane agro- industry. Two treatments were controlled drainage with water table controlling in 70 and 90 cm depth from soil surface (CD70, CD90) and the third one was free drainage (FD) treatment. According to the results, the CD treatments significantly reduced drainage coefficient during the study, compared to free drainage treatment. Average drainage coefficient in during the study in CD70, CD90 and FD treatments was 3, 4.12 and 8.98 mm/day respectively. Controlled drainage treatments (CD70, CD90) reduced drainage coefficient by 67% and 54% respectively, compared to free drainage treatment. CD70 treatment reduced drainage coefficient by 27%, compared CD90 treatment, too. The use of controlled drainage did not limited for plant growth and did not reduce sugarcane yield. Using of this method, in addition to the economic benefits will cause decreasing river pollution load and has a positive environmental impact.
