Journal of Range and Watershed Managment
Year:2018 | Volume:71 | Issue:3 |Papers Count:23

In this study, the effect of land use changes on groundwater resources as well as monitoring of spatial and temporal changes of groundwater quantitative and qualitative parameters were assessed in west basin of Jazmoryan Wetland. Landsat satellite images of TM 2002 and OLI 2015 sensors by applying of Maximum Likelihood Method were used to investigate land use changes trend. Also, information related to wells in years of 2002 to 2015 was used to assess groundwater quantitative and qualitative parameters. To do this, zoning maps of spatial and temporal changes of groundwater quantitative and qualitative parameters were prepared using the best interpolation method in ArcGIS software. The results related to evaluation of the best interpolation method showed that Kriging method had the least error. According to the results of this study, the area of agricultural and urban land uses has been increased, while the area of ephemeral stream, dam, rangelands, bare and mountain land uses has been decreased in 1394 compared to 1381. These changes indicate the increased degradation as well as unstable conditions of the region that adversely affect groundwater resources. As a result of these changes, groundwater quality in southern parts of the study area has been declined. Among the factor causing this declined groundwater quality are construction of Jiroft dam on Halil-rood permanent river, lack of water right and expanded urban and agricultural lands which have caused decrease in groundwater quality over time.

Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the most important hydrological variables in agricultural research and water and soil conservation. In recent decades, artificial intelligence techniques have proven high capability and flexibility to estimate and predict nonlinear phenomena. In this study, three important data mining techniques including Artificial Neural Network, Active Neuro-Fuzzy Inference System and Regression Decision Tree were used for predicting evaporation. For this purpose, 8 climatic variables (Minimum average temperature, average maximum temperature, average temperature, sunshine hours, wind speed, wind direction, relative humidity and evaporation averages) were employed in this study. The results showed three models are able to predict evaporation for 12 months after. Finally among the used models, ANN showed better performance with coefficient efficiency of 0. 97 and RMSE of 5. 1and ME of 0. 48. Also, The results showed that there is not significant difference in simulation results to predict the evaporation between two scenario, original data and normalized data.

There are many methods for estimating the maximum flood discharge including frequency analysis methods and risk study of hydraulic structures based on flood frequency analysis is often sensitive to the observations and selected statistical distribution that cause errors in design. Since heavy rainfalls are the main cause of floods and the rainfall records are longer than flow records, hence long-term records of rainfall at rain gauge stations of Bakhtiary basin in a 66-year period and the 58-year records of daily maximum discharge were used in this study. In this research, peak and maximum daily flows were estimated by using hydro-climatic methods of Agregee and Gradex. Then, the results obtained from the simulation based on hydro-climatic approach for the different return periods were compared with those of classical statistical techniques of Gumbel and Generalized Extreme Values (GEV). The results showed that using additional information like rainfall data plus hydrometric data in hydro-climatic methods gives better estimates rather than frequency analysis methods. Because each three evaluation criteria of Root Mean Squared Error (RMSE), Nash– Sutcliffe efficiency (NSE) coefficient, Kling-Gupta efficiency (KGE) coefficient confirm performance of hydro-climatic methods in comparison with Gumbel and Generalized Extreme Values (GEV) distributions. Finally, a peak to volume ratio extracted from the 26 major flood events detected at Tang-e panj hydrometric station within the hourly discharge records was used to transform the cumulative distribution function of daily discharge into peak discharge.

Soil heterotrophic respiration, which is the result of soil organic matter decomposition, is affected by environmental factors, especially temperature. A variety of models have been proposed to understanding the respiration response of the soil to temperature and respiration sensitivity to temperature (Q10). The aim of this study was to evaluate the respiration response of soil to temperature variations using incubation technique and to examine variety of models in two different management systems. For this purpose, Intact soil samples were collected from a grazing and grazing exclosure in Fandoghlo Ardebil, incubated for 4 weeks at 10, 20 and 30 ° C temperature. Soil respiration was measured by alkaline adsorption method. Nonlinear regression method and The Levenberg-Marquardt algorithm were used to determine the parameters of models. Both ecosystem showed an exponential increase in Soil heterotrophic respiration with temperature. The rate of respiration in soil of grazing, at all three temperature levels, was higher than grazing exclosure. Most models describing the relationship between soil respiration and temperature showed a good fit to the experimental data, especially in the grazing exclosure. Q10 in the grazing (1. 21) was higher than the grazing exclosure (0. 97). In general, based on the coefficients of the models and the Q10 analysis, the Arrhenius model can be better than the others of model for expressing the relationship between soil respiration with temperature, as well as good numerical estimation for Q10 of soil.

The purpose of this study is to investigate the potential of some plants for Phytoremediation of soil contaminated with lead, zinc, cadmium and copper. To do this, four plant species of Achillea millefolium, Agropyron elongatum, Bouteloua dactyloides and Artemisia sieberi were cultivated and plant samples were irrigated with refinery wastewater. The results showed that in terms of plants, Bouteloua dactyloides, Artemisia sieberi and Achillea millefolium are good transfer of metals to their aerial parts which is suitable for plant extraction (the most important technique of Phytoremediation). Agropyron elongatum mostly accumulates copper and lead metals in the root. This is a proper feature of phytostabilization. Also the capability of these four plants species for Phytoremediation is as follows: Bouteloua dactyloides> of Achillea millefolium> Artemisia sieberi = Agropyron elongatum. Bouteloua dactyloides is proper for Phytoremediation of all heavy metals. For Bouteloua dactyloides, plant transfer factor (TF) in metals: zinc, copper, lead and cadmium is 1. 17 and 1. 9, 1. 12 and 1. 41, respectively, and bio concentration factor (BCF) for that in these metals are 1. 77 and 1. 22, 0. 95 and 1. 37, respectively. Since Bouteloua dactyloides has high tolerance to drought and high temperatures and is also suitable for lawn making, it is suggested that this type of species is used for Phytoremediation of soils contaminated with heavy metals in polluted areas as well as lawn making so that in addition to soil cleansing from heavy metals and being proper with native climates in many parts of Iran and having low irrigation requirements.

Water quality is always one of the major challenges for managers and decision makers in water resource management. However, the problems of water quality are more important than quantity. One of the main ways in thorough review and assessment of water quality using multivariate statistical techniques are, the majority of changes to a system, in order to identify important factors that influence could explain. This study classified zone Groundwater Quality Mashhad plain terms of agricultural potential and its quality review process has been carried out in recent decades. Therefore, the quality of groundwater for agricultural use was studied and maps of quality classification for 2001-2011 years were prepared. Then, using geological map, the effect of geological formations on degradation of groundwater quality was assessed. For water quality analyses, 10 important water quality variables in 39 selected wells were measured and they were analyzed using multivariate statistical techniques. The statistical analyses which were used are: Factor analysis for determining the most important variables, cluster analysis for determination of variables homogenous groups and Pearson Correlation for investigation of relationships between variables. The results have shown the best relationship between geological formation and quality factors. Also, the results of Factor Analysis also showed that EC and TDS 71. 02 of the total variance explained by factor loading 0. 98 and pH 14. 91 of the total variance explained by factor loading 0. 93 are the most important variables affecting the quality of groundwater in the study area.

Preference evaluation of four species of perennial forage grasses including Alopecurus textilis L., Festuca ovina L. and F. sulcata L. and Agropyron trichophorum. In order to use for improvement and restoration of rangelands, seeds of species were collected from of Ardebil province. After seed germination test and preparing of seed bed. This eyperiment was done based on Randomized completely block design with 3 replicates in dryland conditions from 2010-2014 in Ardabil station. After plants establishment and end of growing, crown cover Total height, seedling vigority, forage and seed production. Analysis variance of data was done using SAS software and the mean comparison with Duncan method at 5% level. The results showed that, the species in term of studied traits, had significant difference exept survival, The highest height, crown cover and forage production. Be longes to Agropyron trichophorum, The highest seedling vigority belodes to Agropyron trichophorum and Festuca sulcata finally, Therefore these con be used in improvement and developmend procces of pasture in arid and semi arid region.

Steam condensation system in soil is a new and developing method that is capable of regenerating land using renewable energy. In this method, the supply of water and moisture, is obtained from the air steam transferred directly to the root zone. The method is used to increase the moisture content of different soils improved with organic matter. The results of this study showed that steam condensation has a significant role in increasing soil moisture and has a promising prospect. The moisture content stored in organic soils improved by more than 11% during the experiment. Statistical evaluation of measured data showed that with a 99 percent chance, the method can help to increase soil moisture. Also, with a 99% chance of changing these factors, it's best to increase soil moisture. By changing soil texture with a 95% probability, the best effect can be achieved in increasing moisture content. The results showed that soil with moderate texture, 35% organic matter and steam 45 ° C with coated conducting tube had better conditions than moisture and temperature in comparison with other treatments.

The purpose of this study is the effects of the morphometric factors on peak discharge in 108 hydrometric stations in the southern watersheds, Iran. After homogeneous tests and random data, a time period (from 1983-1984 to 2013-2014 was chosen and used to choose the best probability distribution function. Overall, the 84 morphometric and geometric parameters were calculated in ARC GIS software. In this research, the structural equation modeling with the least approach in smart – PLS software was used to check the most effective factors on the annual maximum discharge. 18 variables were identified as effective factors on the maximum discharge. between more than 84 structures, the effect of the focus time structures, positive height ratio, miller slenderness ratio structures, the main river-slope characteristics, elevation number and the main river-slope height properties are negative than can predict overall the %46 of the annual maximum discharge changes in the watershed areas of Iran s southern parts. These factors affect directly on the flood in the total focus time about %38 thus, the most effective factor on the flood discharge is the focus time factor that should be considered in the flood management in Iran s southern areas.

Land cover mapping is essential for natural resource management. Satellite imagery can be used for mapping land cover. Several methods are available for land cover mapping whilst choosing the best method is one of the most important issue in this context. To compare pros and cons of three methods of classification including maximum likelihood, object-based segmentation and artificial neural network, first, the efficiency of these three methods were evaluated. Then the trend of land cover changes in Shahrekord basin was investigated for 1999, 2009 and 2015 using Landsat images of TM, ETM+ and OLI sensors, respectively. After geometric and radiometric corrections, the land cover map of 2015 was prepared based on the three methods. The results of the validation mapping methods revealed that object-based method was more promising than the others with 93 and 90% for total accuracy and Kappa coefficients of agreement, respectively. So, the object-based segmentation method is recommended for monitoring of land cover changes. The results of the land cover change indicated that residential areas increased from 1. 727% in 1999 to 2. 98% in 2015 and agricultural lands increased from 5. 73% to 12. 60% but rangelands were decreased by 9. 05 in total. Moreover, bare-lands were increased from 1999 to 2009 by 6. 19% but decreased from 2009 to 2015 by 5. 27%. The result of this study showed that the object-based method is superior to pixel based method of Maximum-liklihood and neural network. So, object-based segmentation is recommended for estimating land cover changes.

Mulching on the ground surface is one of the common approaches for fixing the sandy dunes and controlling dust. Despite the frequent use of oil mulch, due to its heavy costs and environmental problems, researches are focused on producing new environmentally friendly mulches. In this research, different treatments of cement, lime, wind sands and steel-slag, as cement-slag mulch, have been used to fix the sand dunes samples prepared from Robat-Karim region. Steel-slag is added to the mulch composition because of the benefits of waste reduction from environment and replacement of a part of the cement in the mulch composition because of Steel-slag pozzolanic property. To reduce the number of tests and find the optimal composition of the mulch, statistical mixture design approach was used. Furthermore, the effect of different percentages of the ingredients on shear, compression and impact strength and moisture content of cement-slag treatments were investigated. Results show that the addition of steel-slag increases the compressive and shear strength as well as the ability to maintain soil moisture in the proposed mulch. Finally, developing the mathematical model of the experiments, six different objective functions were considered for optimizing the composition of the proposed cement-slage mulch. As a result combination of 68. 731% sand, 27% cement, 2. 979% steel-slag and 1. 3% lime were the best mulch considering all the objective functions. To validate the results of the optimization, six optimal compositions were re-constructed and the tested again. Obtained results showed an acceptable consistency between the predicted and tested values.

The use of saline and lands low-yield lands for forage production is an important step towards optimal utilization of low quality soil and water resources and supplying livestock forage as well as conservation of soil and water resources. Therefore, this research was aimed to investigate the establishment potential, canopy cover percentage, height growth and yield. Garmasar Desert Research Station with a soil salinity of 30-35 ds/m and Atriplex canescens, Atriplex leucoclada, Atriplex verrocifera, Aeluropus lagopoides and Aeluropus littoralis were selected and the species were cultivated in three replications. In each replication, 15 individuals of the study species were cultivated on each row. According to the results, the establishment percentage of At. ca, At. le, At. ve, Ae. la and Ae. li was recorded to be 95. 5%, 95. 5%, 100%, 97. 7%, and 100%, respectively, indicating high establishment potential. The results of analysis of variance for annual yield showed that all the study species were able to produce forage in saline lands with a soil salinity of 35 ds/m. Among the study species, At. ca and At. le had the highest canopy cover and height. The highest forage production (more than 2 kg ha-1) was recorded for At. le, compared with other study species. Our results clearly showed that yield and canopy cover superior to other traits in showing the differences among the study species.

This study aimed to effect of converting rangeland to afforestation on carbon and nitrogen storage was done in forested areas of Hassan Abad in Sanandaj. Masses of studied was include Cupressus arizonica, Pinus eldarica, Fraxinus rotundifolia, Robinia psedoacacia and Thuja orientalis whit an average age of 20 years and adjacent rangeland that converting was not created was selected as control. Sampling of soil was done in afforestation mass and control and characteristics of organic carbon, nitrogen, phosphorus, potassium, bulk density, pH, electrical conductivity, percent clay, silt and sand was measured. To compare the effect of forestry corrective operations on carbon sequestration was used independent t-test analysis and to compare the effects of different types of afforestation on soil carbon and nitrogen sequestration rates was used one-way ANOVA. Results shows that Robinia psedoacacia have a significant effect on increase of carbon and nitrogen sequestered and amount of phosphorus and potassium in soil and in total in Robinia psedoacacia mass amount of cabon sequestered and nitrogen stored in soil was calculated 80. 62 and 5. 42 ton/ha and in control 47. 05 and 3. 08 ton/ha and Robinia psedoacacia mass cause to increase of soil carbon and nitrogen amount of 71 and 75 percent and lowest amount of carbon and nitrogen was obtained in soil of Cupressus arizonica mass. Stepwise regression results showed that nitrogen, phosphorus and potassium, respectively are most important components affecting in soil of investigated mass.

Soil is one of the most important production factors that has a great impact on human socio-economic life and the process of soil erosion is one of the environmental issues that threatens the environment, natural resources and agriculture. Spatial and temporal information of the soil loss and soil erosion on the land has a significant role in influencing management practices, soil erosion control and watershed management. Therefore, this study was conducted with the aim of studying the spatial and temporal estimation of soil erosion during 1994, 1999, 2008 and 2015 in the sub-basin of Menderjan with an area of 21100 hectares located in the west of Isfahan province using RS and GIS. In the present study, while conducting field studies, various data and information including the digital elevation model, satellite images, soil, and statistics on rain gauge stations were used as a research tool. Estimation of soil erosion in the study area was carried out using RUSLE Model. The results of this study showed that the amount of soil erosion in 1994, 1999, 2008 and 2015 was 0. 001 to 233, 0. 001 to 297, 0. 001 to 231 and 0. 001 to 215 "ton/” ha. year” . The topography factor in the study area with the correlation coefficient of 80% had the greatest effect on the estimation of annual soil erosion by the RUSLE model. This research corroborate the effectiveness of modern GIS technologies and remote sensing in temporal simulation for quantitative, exact, and point-to-point estimates in the whole area to obtain soil erosion content.

Drought is a natural and repetitive phenomenon. In this study, using SDI and GRI indicators, the hydrological drought condition was evaluated and compared with meteorological drought indicators. In the MATLAB environment, the GRI index was determined. According to the results, the GRI index during the statistical period (1981-2015), the maximum drought severity was-73. 25, and the maximum duration of drought was 79 months. These values are higher than the SDI index in the same time scale and the common statistical period. The Frequency of different groups of GRI indicator in the 35-year statistical period showed that normal drought had the highest percentage of abundance. The SDI index has the highest maximum continuity in the low-level time scales compared to the drought indicators of the meteorology. Frequency of drought groups was calculated based on an SDI index for the statistical period for different time scales. The results of this study showed that SPEI on 24 and 48 months scale with 3 month delay have the highest correlation with GRI which showed the impact of meteorological drought after two years has more effect on groundwater resources. Knowing the time interval between the occurrence of meteorological drought as main cause of coming drought that could help planners and managers to take the necessary management measures to cope with the drought caused by a shortage of water resources including surface and groundwater.

By identifying the removal areas the reasons can be identified instead of addressing the causes, And focused on executive activities in the harvesting areas And for this, the identification of sediments is particular importance In this paper, with the aim of better understanding and interpreting sedimentary samples, Investigating and analyzing the distribution of sediments in the facies of the study area has been investigated. For this purpose, after sampling of surface soil and preparation of samples, The drying procedure was performed according to A. S. T. M standard in 8 classes, which was smaller than 64 microns to 4000 microns. By entering the data into Gradistat software, statistical parameters such as mid-diameter, skewness and sample sorting were calculated based on the Fulc's comprehensive drawing method. The results of the study showed that the particle sorting is between 0. 8 and 0. 3, which confirms the near-average spacing distance from the harvesting area to the sediment accumulation point. The results of the particle tilting index are in the median vein facies, agricultural lands, agriculture, and sandy areas with symmetrical pebble cover. In other facies, the index is tilted towards fine particles. In the facies of the Rigi plain, the puffy lands and the permafrost shells of the wear coefficient classes are between 0-200 and then fully angled and the particles are transported from a distance. In the rest of the facies, the wear coefficients are between 200 to 400, in which case the particles are semi-angled and the particles are transported from a relatively distant distance.

Functions of a natural resource, including rangeland ecosystems, can be divided into four groups of regulatory, habitat, production and information functions. Therefore, in this study, the economic valuation of forage production function and utilization of rhizome orchid were in terms of performance and utilization of orchid feed, studied in Trgvr grasslands of Urmia. For this purpose among, the distribution areas of orchids in the grasslands area, six locations with a total area of 120 hectares with different water levels that are representative of a wide range of grassland area, were selected. Then, The production of different vegetation forms and the amount of orchids production was measured in represetive sample, and the economic value was estimated by marketing method according to the results each hectare of grassland area is able to produce 1731. 78 kg forage and 4. 02 kg orchids in a growing season, whit it's economic value of each hectare of forage production and utilization of orchids in terms of function, respectively equal to 16. 88 and 44. 72 IRR. The results of the study are useful in determine the acceptable level of damages grassland area and to calculated and green national accounts.

Sediment yield caused by soil erosion process as the most important land degradation index is considered a main challenge in sustainable development and threats the ecosystems. It is therefore very important to estimate the reliable sediment discharge at watersheds outlets. The large river drainage basins and the lack of sediment gauges have led to apply regional analysis methods, to estimate suspended sediment load in the basins without gauges or the gauges with lack of data. The objective of this study was to estimate regional suspended sediment load using principal components regression in homogeneous regions of Sefidrood drainage basin with an area of 59273 km2as dependent variable and 18 physiographic and hydrologic factors in sediment load were recognized in each homogenous region based on principal components analysis (PCA). Finally, the relationship between suspended sediment load with different return periods and controlling factors were determined. The results showed that the stations located in the study area were clustered in two homogeneous groups. In the homogeneous region one, based on the PCA, 18 variables reduced into 5 factors accounting more than 87% of total variance and in the second homogenous region reduced into 3 factors accounting more than 92%. Using the principal component regression in the first homogeneous region, the first factor with the coefficient of determination of sediment discharge with 25-year return period, 0. 67, and in the second homogeneous region, the first and second factors with coefficient of determination 0. 32 were entered in model.

Groundwater system studies to understanding its behavior, requires the exploratory drilling wells, pumping test and geophysical experiments, which can carried out with most cost. For this reason, simulation of groundwater flows by mathematical and computer models, which is an indirect method to groundwater studies, is being spent a few costs. In this research, the efficiency of artificial neural network, fuzzy logic and random forest models has been investigated in groundwater level estimation of Boukan plain. Parameters of precipitation, temperature, flow rate and water level within time period of the previous month were used as input and the water table in each period were selected as output through monthly scale (2006-2017). To evaluating the performance of models, Correlation coefficient, root mean square error and coefficient of mean absolute error were used. The results showed that the Fuzzy Logic and Random Forest models are able to estimate water levels with acceptable accuracy. In terms of accuracy, fuzzy logic model with the highest correlation coefficient (0. 96), lowest root mean square error (0. 068 m0) and mean absolute error (0. 056 m) was recognized as a best the model in the groundwater level prediction.

To better managing of rangeland the vegetation map is one of major factors, because plant communities is planning units of rangeland management and vegetation map shows the current status of plant communities. This research was conducted to produce vegetation type's map using Landsat 8 image classification in Behbahan, Khuzestan province. Rangelands of the study region is warm semi steppe and winter grazing. Geometric correction of satellite image was performed by ground control points with an error of less than one pixel. Atmospheric correction of existing data using the dark object subtraction was done. Field visits for vegetation type's border controlling and sampling training area was conducted. Eight supervised classification algorithms included Parallelepiped (PP), Minimum Distance to mean (MD), Mahalanobis distance (MAH), Maximum Likelihood (ML), Neural Net (NN) and Support Vector Machine (SVM) was performed. The results showed that ML algorithm has the highest overall accuracy (87. 5 percent) and kappa (0. 867) and PP algorithm has the lowest overall accuracy (67. 1 percent) and kappa (0. 571). It is suggested that, along with digital methods of classification of satellite images, visual interpretation should be used to clarify the boundary of the obtained vegetation types map.