Desert
Volume:20 Issue: 1, Winter - Spring 2015

The objective of this research was to determine the best model and compare performances in terms of producing land use maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance of mean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral angle mapper (SAM), and support vector machine (SVM) were considered in three areas of Iran''s dry climate. The selected study areas for dry climates were Shahreza, Taft and Zarand in Isfahan, Yazd, and Kerman Provinces, respectively. Three Landsat ETM+ images and topographical maps of 1:25,000-scale were used in the present study. In addition, training samples for each land use were constructed using GPS and extensive field surveys. The training sites were divided into two categories; one category was used for image classification and the other for classification accuracy assessment. Results show that for the dry climate areas, Maximum Likelihood and Support Vector Machine algorithms with averages of 0.9409 and 0.9315 Kappa coefficients are the best algorithms for land use mapping. The ANOVA test was performed on Kappa coefficients, and the result shows that there are significant differences at the 1% level, between the different algorithms for the dry climate zones. These results can be used for land use planning, as well as environmental and natural resources purposes in study areas.

Rainfall-runoff relationship is very important in many fields of hydrology such as water supply and water resource management and there are many models in this field. Among these models, the Artificial Neural Network (ANN) was found suitable for processing rainfall-runoff and opened various approaches in hydrological modeling. In addition, ANNs are quick and flexible approaches which provide very promising results, and are cheaper and simpler to implement than their physically based models. Therefore, this study evaluated the use of ANN models to forecast daily flows in Bar watershed, a semi-arid region in the northwest Razavi Khorasan Province of Iran. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBF), were developed and their abilities to predict run off were compared for a period of fifty-five years from 1951 to 2006. The best performance was achieved based on statistical criteria such as RMSE, RE and SSE. It was found that MLP showed a good generalization of the rainfall-runoff relationship and is better than RBF. In addition, 1 day antecedent runoff affected river flow, such that the statistical criteria decreased but the 5-day antecedent rainfall remained unaffected. Furthermore, considering MLP, RE and RMSE, the best model produced the values 46.21 and 0.75 while the RBF model recorded 177.60 and 0.82, respectively.

Thallium is widely found in nature, but the only inorganic stones full of this element are crookesite and lorandit. It is also found in pyrites of copper, lead and inorganic stones. The element and its compositions are toxic and harmful to the environment; therefore, its application requires caution and further research. It is important to develop sensitive and accurate analytical methods to determine trace levels of thallium in environmental and real samples. In this research, dispersive liquid-liquid microextraction based on solidification of floating organic drop as a sample preparation method was used for separation and preconcentration of ultra-trace amounts of thallium in soil samples prior to graphite-furnace atomic-absorption spectrometry. Investigated effective parameters on extraction include pH, the amount of chelating agent, type and volume of extraction solvent and extraction time. Under optimum conditions, the calibration curve was linear in the range of 0.2-10.0 ng mL−1 of thallium in the original solution, with limit of detection of 0.03 ng mL−1. The relative standard deviation (RSD) for ten replicated determinations of thallium ion at 5.0 ng mL−1 concentration level was calculated as 3.3%. The proposed method was successfully applied to the determination of thallium in soil samples.

Water salinity and drought are the major abiotic stresses limiting turf grass growth. On the other hand, shortage of water resources and salinity of water and soil in the arid and semi-arid zones such as Iran, are the restricting factors in developing lawn turf grasses. An experiment was conducted to evaluate the combined effects of water salinity and deficit irrigation on tall fescue (Festuca arundinacea Schreb.). This study was conducted under outdoor conditions in a completely randomized design with factorial arrangements. Treatments included four water salinity levels (0.5, 3, 6 and 9 dS.m-1) and three deficit irrigation regimes (50%, 75% and 100% FC) with five replicates under outdoor conditions. Results indicated a rise in the ion leakage, and soluble sugar and proline concentration and a decrease in visual quality, shoot length, leaf area and fresh and dry weights of shoot, leaf relative water content (RWC), leaf chlorophyll content and photosynthetic rate and starch content with an increase in the levels of both stresses. Antioxidant enzymes, superoxide dismutase (SOD, EC 1.15.1.1), and catalase (CAT, EC 1.11.1.6) showed higher activity under moderate drought or water salinity conditions; however, this parameter decreased at higher levels of these salinity stresses. Practically, based on the results of the present study, tall fescue could be grown under moderate levels of the combined stresses of water shortage and salinity without considerable damage to the plant at the physiological and/or biochemical level. This is the first report on applying the combined stresses of water and salinity on an important agricultural crop.

The main limiting factors in saline-sodic soils are high amounts of salts, low soil organic matter (SOM) and low availability of macro and micro-nutrients. The effect of different amounts of nano iron oxide powder and urban solid waste compost coated sulfur (USWCS) on the chemical properties of a saline-sodic soil was investigated. The experiment was conducted using a randomized complete block design with factorial arrangement and three replications, in a farm near Qom city. Treatments used in this study included USWCS (0 and 15 ton/ha) and nano iron oxide powder (0 and 20 mg/kg); treatments were applied to treated plots of 4 m2 and sunflower seeds were sown. The results show that 20 mg/kg of nano-iron oxide, significantly (P<0.05) increased electrical conductivity of saturation extract (ECe), availability of nutrient elements (5.26, 30, 18.18% for phosphorous (P), iron (Fe), and copper (Cu), respectively) in soil, while nitrogen (N), pH and sodium adsorption ratio (SAR) in soil decreased significantly (P < 0.05). Fifteen (15) tons/ha of USWCS significantly increased soil ECe (6.1%), micro and macro-nutrients amounts (8.3, 21.1, 46.6, 45. 5, and 14.9% for N, P, Fe, Cu, and Manganese (Mn) respectively), but significantly (P < 005) decreased the soil pH and SAR, compared to the control. The combined effect of 20 mg/kg of nano iron oxide and 15 ton/ha of USWCS increased micro-nutrient availability, but decreased soil pH and SAR significantly. It was concluded that the combined application of USWCS and nano iron oxide increased the availability of nutrients in saline-sodic soil.

One of the most concerning issues for researchers is to predict the shape and dimensions of the scour pit near hydraulic structures such as the base of bridges, weirs, valves and stilling basins due to both financial and human hazards induced by destruction of the structure. As the scour issue has its own complexity in relation to the multiplicity of effecting factors on it, in this study therefore, the results of laboratory analysis of local scour due to submerged horizontal jets were compared with numerical simulation results from Flow-3D three-dimensional model to test the potency of the numerical model. As a result, the model is proposed in place of the experimental model which has its own drawbacks and high costs. In this study, we measure maximum scour depth in relative equilibrium in 2 states and 6 test modes with different valve openings and tail water depth per different discharges. Comparison of the results indicates about 11% error for Flow-3D numerical model in relation to the experimental model which by considering the complexity of scour and deposition phenomena, is considered a good result.

Geomorphosites or special geomorphologic sites are new concepts which have entered tourism literature with an emphasis on special sites. Basically, the goal for discussing such concepts is to identify landforms with special importance on understanding the geomorphologic structure of a region and their scientific, ecological, cultural, aesthetic, and economical values. Generally, they are used for comprehending and exploiting human tourism. The tourism industry is, however, multidimensional and has economic, social, cultural and environmental (ecotourism, geotourism) aspects. As a green and clean industry, ecotourism plays a major role on national tourism development planning in Iran as well as attracting nature’s tourists which is a fundamental necessity forthis industry. Due to high natural tourism capacities such as caves and diapirism, unique geological and geomorphological attractions along with social and historical attractions, Kerman province is among the five historical and superior provinces for tourism. This studyattempts to evaluate the geomorphosites of Kerman Province through Prolong approach and field studies. Quadruple alloys studied in terms of their potential ability of geomorphosites in this research include external, scientific, historical, cultural, social and economical beauty alloys. Two variables, exploitation value and quality were taken into consideration. According to the results, Loot field desert geosite had the highest score. As regards the values for exploitation level and quality, Meymand village obtained the highest score requiring greater attention from the authorities.

Groundwater quality management is the most important issue in many arid and semi-arid countries, including Iran.Artificial neural network (ANN) has an extensive range of applications in water resources management. In this study, artificial neural network was developed using MATLAB R2013 software package, and Cl, EC, SO4 and NO3 qualitative parameters were estimated and compared with the measured values, in order to evaluate the influence of key input parameters. The number of neurons in the hidden layer was obtained by the trial-and error method. For this purpose, data from 260 water samples of 13 wells in Bahabad plain were collected during 2003- 2013. The results show that the performance of ANN model was more accurate for Cl (R=0.96), EC(R=0.98), and SO4(R 0.95), using back-propagation algorithms according to the best chosen input parameters. It was observed that the use of ANN model for NO3 was not very accurate, perhaps this was because of the different water sources or the impact of other parameters; thus, this result is in contrast with the study of Diamantopoulou et al. (2005). However, this study confirms that the number of neurons in the hidden layer cannot be found using a specific formula (double the number of inputs plus one) for all parameters but can be obtained using a trial-and-error method.

Expansive soils can be found in many parts of the world particularly in arid and semi-arid regions. These soils pose a significant hazard to civil engineering structures due to its high swelling and shrinkage potential. This paper presents the results of an experimental program developed to investigate the effect of cyclic drying and wetting on the swelling potential of expansive soils with various pore water qualities. Soil samples were prepared by static compaction with distilled and saline pore water solutions consisting of sodium chloride (NaCl) with 50 and 250 g/L concentrations. Soil samples were subjected to drying and wetting cycles using a modified oedometer apparatus, under a surcharge pressure of 10 kPa. Axial deformations caused by drying and wetting during various cycles were measured until shrink-swell equilibrium condition was attained. The results indicated that conducting consecutive drying and wetting causes a considerable reduction in the swelling potential of soil samples prepared with different qualities of pore water. Shrink-swell equilibrium in soil samples prepared with distilled water and 50 g/L NaCl solution was achieved after 5 consecutive cycles while soil samples with 250 g/L NaCl solution as pore water, reached equilibrium condition after approximately 3 or 4 cycles. Furthermore, the overall swelling potential for soil samples prepared with 250 g/L NaCl solution was seen to be greater compared to distilled water and 50 g/L NaCl solution respectively.

To investigate the influence of salinity and calcium (Ca) on some properties of Plantago ovata, a pot factorial experiment based on randomized complete block design with three replicates was conducted in Jiroft, south of Kerman Province, Iran. Three salinity levels of 0, 100 and 200 mM NaCl and Ca levels of 0, 5 and 10 mM Calcium nitrate were applied as the first and second factors, respectively. Results showed that salt levels significantly affected plant height, dry weight, number of spikes per plant, spike length, number of seeds per spike, mucilage and 1000 seed weights. Maximum values for the measured variables were obtained in control and the minimum values in 10 mmol sodium chloride. The effect of Ca on these traits was significant as well; it diminished the adverse effects of salinity. In addition, the interactive effects of salt and Ca on all the above traits were remarkable except for mucilage and spike length on which no significant effect was observed.