Showing 189 results for Ssi
S. Youneszadeh Jalili, M. Kamali, P. Daneshkar Arasteh,
Volume 20, Issue 78 (1-2017)
Abstract
Integrated management of watershed basins depends on deep knowledge of basic concepts such as the arrangement of lands and their uses. Location and distribution of agricultural land use help to balance water resources in the watershed basins. In this research with the help of satellite images of Landsat 5 and 8, and the method of maximum likelihood classification algorithm, land use types of water, barren areas and salt lands, and irrigated agriculture were studied in the Urmia watershed in the years 2010 and 2013.Then applications of modis images and product Urmia watershed land cover for years 2010 and 2012 were compared and finally modis and Landsat land covers in 2010 were compared. Results showed that the area of irrigated farmlands of Urmia basin has increased in the years between 2010 and 2013; while, the water zone has declined. Comparison between modis and landsat in 2010 showed that modis can estimate irrigated lands and water zone better than barren areas. The kappa coefficient for years 2010 and 2013 in Landsat images are 0/77 and 0/87, respectively.
S. Moradi Behbahani, M. Moradi, R. Basiri, J. Mirzaei,
Volume 20, Issue 78 (1-2017)
Abstract
Salt cedar is widely spread out in most part of the country but there is lack of information about its symbiosis with arbuscular mycorrhizal fungi. Then, the main objective of this study was to evaluate the symbiosis of AMF with salt cedar and its affectability by distance from river and soil physiochemical properties. For this purpose, riparian Maroon forest width was divided to three locations including riverside area, intermediate area and the area far from river with 200-hundred-meter interval. In each site 10 salt cedars were randomly selected and soil plus hair root samples were gathered from the salt cedar rhizosphere. Our result indicated that root colonization and spore density in the intermediate distance had the lowest and highest values, respectively. These values were significantly different compared to the other two sites. The average root colonization percent in the riverside area, intermediate area and the area far from river sites were 82.37, 73.77 and 80.17, respectively. While the average spore density in the riverside area, intermediate area and the area far from river were 189, 245.5 and 188.8 in five gram soils, respectively. Root colonization had significant positive correlation with soil potassium while spore density had significant correlation with studied soil physiochemical properties. Also, soil nitrogen, organic carbon, potassium and clay showed 52.6, 51.19, 50 and 23.4% decreasing trend from the riverside area to the area far from river. Regarding this research results, salt cedar showed high level of symbiosis with arbuscular mycorrhizal fungi but this symbiosis could be affected by distance from river in riparian forest.
Mh. Rasouli Sadaghiani, S. Sadeghi, M. Barin, E. Sepehr, B. Dovlati,
Volume 20, Issue 78 (1-2017)
Abstract
Potassium is the most abundant nutrition element in the surface soil but most of the potassium is unavailable to the plants. The present study was conducted with the aim of isolation of potassium solubilizing bacteria from rhizosphere soil and evaluation of quantitative ability of released potassium from different sources of silicate by strains. For this propose, laboratory and greenhouse evaluations were carried out on corn (Zea mays L. Cv. single cross 640 (as a factorial in a completely randomized design with three replications. Laboratory factors were potassium sources (four levels), incubation time (seven levels) and microbial inoculation (six strains) and greenhouse factors were potassium sources (five levels) and microbial inoculation (four strains). The results showed that among the bacterial strains KSB13 had maximum dissolution diameter (25 mm) and solubilisation index (SI=3). The highest potassium content (3/32 µg/mL) was released from biotite by strains of KSB10 after ten days incubation. The microbial inoculation increased root dry weight and plant height for 30 and 25 percent, respectively, compared to control treatments. Also the mean shoot dry weight and K content in microbial treatments of silicate minerals were respectively increased 3/75 and 1/57 times higher than control treatment. It can be concluded that microbial inoculation causes potassium release from silicate minerals and improved plant growth.
F. Gavazi, E. Maroufpoor,
Volume 21, Issue 1 (6-2017)
Abstract
The main purpose of this study is investigation of hydraulic properties in drip irrigation tape. In this study, 10 types of drip irrigation tape were tested, and the effect of 4 temperatures of water, 13, 23, 33 and 43 °C, was investigated according to the standard ISO 9261 and ISO IRISI. Initially all experiments were performed in standard temperature (23°C) in order to obtain qualitative evaluation indexes of tapes. The results obtained were as follows: According to the Cv, 8 models of tapes were ranked as good and 2 models as medium. According to the difference between the actual and nominal flow rates, 3 models were ranked as good, 3 models as medium, 3 models were acceptable and 2 models were unacceptable. According to the EU, 9 models were ranked as excellent and T3 was ranked as good. According to the UC, UC of all models was more than 70% and their flow rate variation follows normal distribution. According to qvar, flow rate changes in 3 models were acceptable, 2 models were ranked as good and 5 models were unacceptable.
F. Abbaszadeh Afshar, ِ S. Ayoubi, A. Jafari,
Volume 21, Issue 1 (6-2017)
Abstract
Mapping the spatial distribution of soil taxonomic classes is important for useful and effective use of soil and management decisions. Digital soil mapping (DSM) may have advantages over conventional soil mapping approaches as it may better capture observed spatial variability and reduce the need to aggregate soil types. A key component of any DSM activity is the method used to define the relationship between soil observations and environmental covariates. This study aims to compare multiple logistic regression models and covariate sets for predicting soil taxonomic classes in Bam district, Kerman province. The environmental covariates derived from digital elevation models, Landsat imagery, geomorphology map and soil unit map that were divided into two different sets: (1) variables derived from digital elevation models, remote sensing and geomorphology map, (2) variables derived from digital elevation model, remote sensing, geomorphology map and the soil map. Stratified sampling schemes were defined in 100000 hectares, and 126 soil profiles were excavated and described. The results of accuracy model showed that data set 2 increased accuracy of model including overall accuracy, kappa index, user accuracy and reliability of the producer. The results showed that the multiple logistic regression model can promote traditional soil mapping and it can be used to large group of other scientific fields.
Sh. Kouhestani, S, Eslamian, A. Besalatpour,
Volume 21, Issue 1 (6-2017)
Abstract
This study aims to investigate the changes of minimum and maximum temperature variables under the impact of climate change for time period of 2015-2100 in the Zayandeh-Rud River Basin. The outputs of 14 Global Climate Models (GCMs) under three green-house emission scenarios (RCP2.6, RCP4.5, and RCP8.5) are employed from the Fifth Assessment Report (CMIP5) of Intergovernmental Panel on Climate Change (IPCC). A novel statistical downscaling method using a Bayesian Relevance Vector Machine (RVM) is used to project the impact of climate change on the temperature variables at regional scale. The results of the weighting average of the GCMs show that the various models have different accuracy in the projecting the minimum and maximum temperatures in the study area. The results demonstrate that the MIROC5 and CCSM4 are the most reliable models in projecting the maximum and minimum temperatures, respectively. The highest increase for both maximum and minimum temperatures was obtained in winter.
On the annual basis, the maximum temperature will increase by 0.18-0.76 °C and 0.25-1.67 °C, respectively, in the near and long-term future periods under different emission scenarios. The annual minimum temperature will increase by 0.28 to 0.82 °C and 0.24-1.56 °C, respectively, in the near and long-term future periods. In a general view, changes in maximum temperature will be slightly higher than minimum temperature changes in the future.
K. Roshangar, R. Valizadeh,
Volume 21, Issue 2 (8-2017)
Abstract
Hydraulic jump is the most common method of dissipating water’s kinetic energy in downstream of spillways, shoots and valve. In this paper, Support Vector Machine (SVM) method, as a machine learning method, have been used to estimate hydraulic characteristics such as the sequent depth ratio, jump length and energy loss in three different sudden expansions stilling basins, and the rate of influence of input parameters in each jump has been analyzed. In order to evaluate the performance of proposed method, 936 sets of the observed data have been used for training and testing process of three kinds of expanding channel models. Furthermore, a comparison between semi-theoretical approaches and the data obtained from the best SVM models have been carried out. The results confirmed the efficiency of SVM method for estimating the hydraulic jump characteristics and proved that this method performed well in comparison to the semi-theoretical relationships. The obtained results revealed that the superior model for the sequent depth ratio and relative energy dissipation was the model with (Fr1,h1/B) parameters and the superior model for the length of hydraulic jump prediction was the model with (Fr1, h2/h1) parameters.
M. Isazadeh, P. Mohammadi, Y. Dinpazhoh,
Volume 21, Issue 4 (2-2018)
Abstract
Statistical analysis and forecast discharge data play an important role in management and development of water systems. The most fundamental issues of statistical analysis and forecast discharge in Iran are lack of data in long term period and lack of stream flow data in gauging stations. Considering the issues mentioned in this study, we tried to estimate the daily data flow (runoff) of Santeh gauging station in Kordestan province using the nearby hydrometric and meteorological stations data. This estimation occurred based on the sixteen different input combinations, including data of daily flow of hydrometric stations Safakhaneh and Polanian and daily runoff in Santeh precipitation gauging station. In this research, the daily flow estimation of the Santeh station in each of the months of the year was evaluated for sixteen different combinations and artificial neural network models and multiple linear regressions. The performance of each model was evaluated with the indicators RMSE, CC, NS and t-student statistic. The results showed good performance of both models but the performance of the artificial neural network model was better than the regression model in estimation of the daily runoff in the most months of the year. Mean error of artificial neural network and multiple linear regression models was respectively estimated as 6.31 and 8.07 m3/s in the months of the year. It should be noted that the artificial neural network, for each sixteen combination used, had better result than the regression model.
S. Z. Atar Shahraki, A. R. Hosseinpur, H. R. Motaghian, Sh. Ghorbani,
Volume 22, Issue 1 (6-2018)
Abstract
The study of the kinetics of non-exchangeable potassium (NEK) release is very important for a better understanding of K availability for plants in different soils. Moreover, aggregates with different sizes have different effects on the release of nutrients. Therefore, the aim of this study was to examine the release of NEK in 5 calcareous soils of chaharmahal-va- bakhtiari province, and small and large aggregates (<250 μm and >250 μm) using CaCl2 0.01 M at 25±1ºc for 2-2017 h. The results showed that cumulative released NEK in soils, and small and large aggregates was 173.5-372.7, 215.1-426.1 and 178.9-381.5 mg kg-1, respectively. The results revealed that coefficients of the cumulative released NEK in small aggregates was lower than those of the soils and large aggregates. Based on the coefficient of determination (R2) and standard error (SE), the released NEK was well described by the first order, the power function, parabolic diffusion, and simplified Elovich equations. The rate coefficients of the release of K were different in different soils. The cumulative released amount of K and its rate of release in a solution of calcium chloride in small aggregates was more than those of large aggregates.
S. Shakeri,
Volume 22, Issue 1 (6-2018)
Abstract
Potassium fixation is one of the most important factors influencing the availability of this ion for plants. This research was carried out to evaluate the relationship between potassium (K) fixation with some physical and chemical characteristics of soils and clay minerals and to investigate the effect of the dry and wet cycle on potassium fixation in Kakan Plain, in Kohgilouye & Boyerahmad Province. To measure the amount of Potassium fixation, four levels of K were added to the samples and the samples were shaken for 24 h and then dried in the oven at 50°C for 24 h. The drying and wetting cycle was repeated three times. Another set of soil samples was similarly incubated for a period similar to the previous treatment, but drying was performed at room temperature in an equilibrium state. The results showed that potassium fixation was increased with the potassium concentration increment, whereas K fixation percentage was reduced. Also, potassium fixation showed a positive significant relationship with cation exchange capacity (CEC) as well as clay content, in both normal and dry and wet treatments, and a negative significant relationship with organic carbon. Moreover, potassium fixation was enhanced with the increase of smectite content in both normal and dry and wet treatments. Besides, due to more organic carbon and less smectite, surface horizons fixed K less than the subsurface horizons.
H. Faghih, J. Behmanesh, K. Khalili,
Volume 22, Issue 1 (6-2018)
Abstract
Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The percentages of the model in estimating the region's annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.
C. Tofighi, R. A. Khavari-Nejad, F. Najafi, Kh. Razavi, F. Rejali,
Volume 22, Issue 2 (9-2018)
Abstract
Salinity adversely affects crops metabolism and yield. The present work was conducted to evaluate the singular and interaction influences of Arbuscular mycorrhizal (AM) fungi and brassinolide, as an active group of (brassinosteroids) BRs, on some physiological parameters of wheat plants to cope with salt stress14-day old mycorrhizal (Glomus mosseae) and non- mycorrhizal wheat (Triticum aestivum L.). Plants were foliar sprayed with 0 and 5 µM epibrassinolide 3 times once every two days. Then, each group was treated with 0 and 150 mM NaCl once every 3 days for 10 days. After salt treatment, some plants were harvested to estimate the leaf reducing sugar and glycine betaine contents. After the final growth, all wheat plants were harvested to measure some yield parameters. Synergistic influence of brassinolide and AM fungi was observed in protein and 1000-grain weight. It seemed that this was rooted in the increased accumulation of reducing sugars and glycine betaine, both helping to maintain osmotic potential in cells under high salinity in soil.
Z. Mollaee, J. Zahiri, S. Jalili, M. R. Ansari, A. Taghizadeh,
Volume 22, Issue 2 (9-2018)
Abstract
Spectral Reflectance of suspended sediment concentration (SSC) remotely sensed by satellite images is an alternative and economically efficient method to measure SSC in inland waters such as rivers and lakes, coastal waters, and oceans. This paper retrieved SSC from satellite remote sensing imagery using radial basis function networks (RBF). In-situ measurement of SSC, water flow data, as well as MODIS band 1 and band ratio of band 2 to 1 were the inputs of the RBF. A multi-regression method was also used to make a relationship between the in-situ data and the water reflectance data retrieved from MODIS bands. The results showed that RBF had the best SSC prediction error (RMSE=0.19), as compared to the multi-regression and sediment rating curve methods, with the RMSE of 0.29 and 0.21, respectively.
A. Mansouri, B. Aminnejad, H. Ahmadi,
Volume 22, Issue 2 (9-2018)
Abstract
In the present paper, fluctuations of inflow into the Karun-4 Dam under different scenarios of the climate change for the future period of 2021-2050 were investigated. For this purpose, the outputs of the HadCM3 model under the scenarios of B1 (optimistic) and A2 (pessimistic) were utilized for the fourth report; additionally, the outputs of the ensemble model under RCP 2.6 (optimistic) and RCP 8.5 (pessimistic) scenarios were used for the fifth report. Moreover, in order to estimate runoff in the future period, the artificial neural network was considered as a rainfall-runoff model. The results indicated that the average annual precipitation in the five study stations under B1 and RCP 2.6 scenarios was increased by 15 and 5%, respectively, while it showed a decrease equal to 8 and 6%, respectively under the scenarios A2 and RCP 8.5. Furthermore, the average annual temperature in all scenarios showed increase, which was at least 1.06 ⁰C under the scenario B1 and 1.89 ⁰C under scenario RCP 8.5. Examining the input inflow into the Karun-4 dam showed that under both B1 and RCP 2.6 scenarios, the annual inflow will be increased by 1.8 and 1.5%, respectively; under the two scenarios A2 and RCP of 8.5, the annual inflow will be decreased to 10.4 and 9.8%, respectively.
V. Rahdari, A. R. Soffianian, S. Pourmanafi, H. Ghaiumi Mohammadi,
Volume 22, Issue 3 (11-2018)
Abstract
Determining the cultivation crops area is important for properly supplying crops. The aim of this study was mapping the cultivation area crops in Chadian city for spring and summer during 2015 by using the time series data of the Landsat 8 satellite of OLI imagery. At first, the under cultivation area was determined by setting a low threshold in the marginal pixels of the agricultural rain fed in the spring image NDVI index. The area cultivated with wheat and alfalfa was prepared by subtracting spring and summer NDVI values. Cultivation maps, which were cultivated with potatoes, corn and orchards, were prepared using the supervised classification with the FISHER method in a step by step manner. Spring and summer cultivation maps were combined; finally, the major cultivation crops maps were produced by the hybrid classification method. Map accuracy assessment was done by producing error matrix and calculating kappa coefficient, total accuracy, commission and omission error, producer, and use accuracy; in all indices, they had an acceptable value, showing the capability of OLI and the used methods in separating each cultivation.
B. Farid Giglou, R. Ghazavi,
Volume 22, Issue 3 (11-2018)
Abstract
In this research, a regression model was introduced to study the mechanisms of the formation of gullies in the Quri Chay watershed, northern Ardebil province (Moghan Plain); this was done through investigating the effective factors of geo-environment and soil characteristics on the gully erosion. For this purpose, 17 gullies were randomly assigned through field surveys. Mapping and recording the morphometric of the selected gullies were performed by GPS positioning after seven rainfall events. The catchment-upper area of each gully was determined and its related physical parameters were calculated in order to investigate the effect of the physical characteristics of the catchment. Soil sampling was also done at the head of each gully at two different depths (30-30 and 60-30 cm) in order to determine the physical and chemical characteristics of the soil. According to measurement of the morphometric characteristics of the gully and soil characteristics through multivariate analysis of the data, a suitable regression model was developed for the longitudinal development of erosion after determining and calculating environmental factors related to the upper catchment of the gullies. The results of the correlation matrix between the longitudinal extension of the gully and the factors investigated indicated that the factors related to the physical characteristics of the beside watershed (area, perimeter, main stream length and average width of the catchment, main stream slope), gully morphometric characteristics (mean of gully cross section, the gully expansion area, and the gully average width) and soil characteristics (geometric mean of the aggregates diameter, lime, organic matter percentage) affected the formation and expansion of gully erosion in the Quri chay catchment. The results of regression analysis showed that the longitudinal expansion of the gully was mostly influenced by the area around each gully and the percentage of organic matter, which resulted in pressure on the rangeland and the loss of vegetation, which increased runoff and accelerated the lengthwise expansion of the gully. Also, the increase in the area of the beside catchment the gullies is known as one of the factors influencing the length of the gully, due to the high volume of runoff entering the head cut section; so it is necessary to manage runoff in the gully with the large beside catchment.
K. Nosrati, M. Heydari, M. Hoseinzadeh, S. Emadoddin,
Volume 22, Issue 3 (11-2018)
Abstract
Ziarat drainage basin, in the southern part of Gorgan city, is exposed to mass movement, especially landslide occurrence, due to geologic, geomorphologic, and anthropogenic reasons. The objectives of this study were to predict landslide susceptibility and to analyze the effective factors using rare events logistic regression. In view of this, the map layers of the variables including geology, land use, slope, slope aspect, distance of road, distance of fault and distance of river were prepared using topographic and geologic maps and aerial photo interpretation. In addition, the map layers of the soil variables including the percent of clay, silt, sand, and saturation water as well as plasticity limit index were determined based on the laboratory analysis of 32 soil samples collected from landslide sites and 32 soil samples obtained from non-occurrence landslide sites. The controlling factors of landslide were determined using rare events logistic regression analysis; then based on their coefficients, the landslide risk zoning map was prepared and validated. The landslide risk zoning map was classified in five different hazard classes ranging from very low risk to very high risk; the very high risk class with 16.8 km2 was assigned as the having the highest percent of the catchment area. The results of the model validation showed that the rare events logistic regression model with the receiver operating characteristic (ROC) of 0.69 could be a suitable prediction model for the study area. The results of this study could be, therefore, useful for corrective actions and watershed management landslide high-risk zones.
S. Shakeri, S. A. Abtahi,
Volume 22, Issue 4 (3-2019)
Abstract
This research was carried out to assess the origin and clay minerals characteristics and their relationship with potassium forms in the calcareous soil of this region, with the humid climate conditions. Based on aerial photos and topographic maps, physiographic units were separated and soil sampling was done in each diagnostic horizon. The results showed that smectite was the main and dominant clay mineral in the study area. In well-drained pedons, the convincing process for smectite abundance seemed to be mainly the transformation of palygorskite and mica. According to the results, the exchangeable potassium in the surface horizon was higher than that of the subsurface horizons. The main reason for the higher level of exchangeable K in the soil surface, was more smectite and organic carbon. The results revealed that unlike exchangeable and non-exchangeable K, because of the suitable conditions like temperature and humidity in surface horizons, the relative mean of structural K in the surface soils was less than that in the subsurface. Also, since an increase in calcium carbonate resulted in a decrease in amount of clay and the amount of relative clay minerals (dilution effect), the amounts of exchangeable, non- exchangeable and structural K were decreased.
F. Jahanbakhshi, M. R. Ekhtesasi,
Volume 22, Issue 4 (3-2019)
Abstract
Land use/cover maps are the basic inputs for most of the environmental simulation models; hence, the accuracy of the maps derived from the classification of the satellite images reduces the uncertainty in modeling. The aim of this study was to assess the accuracy of the maps produced by machine learning based on classification methods (Random Forest and Support Vector Machine) and to compare them with a common classification method (Maximum Likelihood). For this purpose, the image of the OLI sensor of Landsat 8 for the study area (Sattarkhan Dam’s basin in the Eastern Azerbaijan) was used after the initial corrections. Five land uses including urban, irrigated and rain-fed agriculture, range and water body were considered. For conducting the supervised classification, ground truth data were used in two sets of educational (70% of the total) and test (30%) data.
Accuracy indexes were used and the McNemar test was employed to show the significant statistical difference between the performances of the methods. The results indicates that the overall accuracy of Support Vector Machine, Random Forest, and Maximum Likelihood methods was 96.6, 90.8, and 90.8 %, respectively; also the Kappa coefficient for these methods was 0.93, 0.81 and 0.83, respectively. The
existence of a significant statistical difference at the 95% confidence between the performances of the Support Vector Machine algorithm and the other two algorithms was confirmed by the McNemar test.
H. Asakereh, A. Shahbaee Kotenaee, M. Foroumadi,
Volume 23, Issue 1 (6-2019)
Abstract
In the vast majority parts of the Earth, a prospect now visible is the mostly synthetic thinking and fabrication by the human hand. Collision and impact of humans on the natural environment in the short and long-term courses for obvious geographical features have changed a variety of spaces. One of the consequences of human impact on the natural environment during the current period is the phenomenon of climate change. One of the climatic parameters that plays an important role in agriculture, energy, urban, tourism and road transport is the minimum temperature. In this study, an attempt was made using the minimum temperature data from 5 meteorological stations in the West Mazandaran province, as well as HADCM3 model data, to show how to change this parameter in the future periods based on simulation by the SDSM model. Accordingly, after selection of the suitable climate variables and model calibration, the accuracy of the created model in the base period was evaluated; after ensuring the sufficient accuracy of the model according to A2 and B2 scenario, data minimum temperature in 2100 was simulated. Based on the simulation results showed that the values of minimum temperature in the region over the coming years would increase. This parameter was such that the average seasonal periods 2016 to 2039, 2040 to 2069 and 2070 to 2099, as compared to the baseline period would increase, on average, by 1.8, 3.5 and 6 percent. The largest increases in the minimum temperature in the western and southern parts of the region could occur. It was also found that unlike other months of the year, the minimum temperature in January would be a decreasing trend.
