Leila Avazpour; Mehdi Ghorbani; Hossein Azarnivand; Hamed Rafiee
Abstract
Evaluate non-market functions and services of the environment, including tourism for many reasons, including the recognition and understanding of environmental and ecological benefits by humans, presenting the country's environmental issues to decision makers and planners, adjusting and modifying the ...
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Evaluate non-market functions and services of the environment, including tourism for many reasons, including the recognition and understanding of environmental and ecological benefits by humans, presenting the country's environmental issues to decision makers and planners, adjusting and modifying the set of national calculations such as GDP It is important to nationalize and prevent the indiscriminate destruction and exploitation of natural resources.The purpose of this study is to estimate the economic value of rangeland tourism function of 4 villages of Maneh and Samolghan counties of North Khorasan province, using conditional valuation method. the logit model was estimated using the maximum likelihood method. The required data were collected by completing 133 questionnaires and face-to-face interviews with visitors from all four areas. The sample size was obtained using Cochran's formula and the sampling method used is random sampling. Based on the results of the model used to determine the factors affecting the value of tourism in the region, the variables of marital status, employment status, proposed amount and rangeland attractiveness are significant at 5% probability level and effective factors in the WTP rate of visitors to use rangelands Are studied. the average WTP as input price for each visitor to use the rangelands of the region was 5880 Tomans and the recreational value of each hectare of rangelands in the region was 49556 Tomans per year. the rangelands of the studied villages have significant tourism value, which can help plannersand executives in planning, protection and sustainable use of rangelands in the region.
Nasrin Beiranvand; Alireza Sepahvand; Ali Haghizadeh
Abstract
In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge ...
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In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge and solute load (SL) of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE). Results suggest that GP-PUK and GP-RBF models works well than other modeling approaches in estimating the SL in low and high water-periods. The result showed that, In the high-water period, in Cham Anjir, Sarab Said Ali and Kaka Reza stations the GP-RBF model and in the Bahram Joo station the GP-PUK model with the highest C.C and the lowest error were selected the optimal models in estimating the SL. Also, in the low water period, result shown that in Cham Anjir, Sarab Said Ali and Bahram Joo stations the GP-RBF model and in the Kaka Reza station the GP-PUK model were the best models in estimating the SL. Therefore, these models can be used to estimate the solute load of nearby rivers by/without hydrometry station for the management of the quantity and quality of surface water.
Seyed Majid Reza Hosseini Mofrad; Hassan Ahmadi; Ali Akbar Mehrabi; Baharak Motamedvaziri
Abstract
For this purpose, according to Cochran's equation, from Hashtgerd-Taleghan asphalt road 26 km long and Hashtgerd-Taleghan dirt road 9 km long, road sedimentation was calculated with 17 and 11 samples, respectively, and finally, using statistical relationships, the erosion rate was calculated. And sedimentation ...
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For this purpose, according to Cochran's equation, from Hashtgerd-Taleghan asphalt road 26 km long and Hashtgerd-Taleghan dirt road 9 km long, road sedimentation was calculated with 17 and 11 samples, respectively, and finally, using statistical relationships, the erosion rate was calculated. And sedimentation was estimated in the entire route. By direct measurement of erosion, the total sediments remaining along the route were equal to 17259.32 tons per year, of which 6241.45 tons per year are related to the excavation wall and 11017.87 tons per year are related to the embankment wall. Using the WARSEM model, the total amount of sedimentation from asphalt and dirt roads was estimated as 15172.67 tons per year, equivalent to 52.14 tons per hectare per year, of which 9464.53 tons per year, equivalent to 48.03 tons. Per hectare per year, the contribution of the excavation wall and the embankment wall of the asphalt road is 14.5708 tons per year, which is equivalent to 22.63 tons per hectare per year, the contribution of the excavation wall and the embankment wall of the dirt road. The sensitivity of the WARSEM model for the standardized parameters for the asphalt road showed that the slope and geological factors are important factors in the sedimentation of the embankment wall and the height factor of the embankment wall is a very important parameter in the sedimentation of the embankment wall. The sensitivity of the model on the dirt road according to this score of the parameters was very close to each other.
Moselm Rostampour; Effat Akbari; Mohammad Saghari
Abstract
The seed planting depth is one of the most important factors affecting the uniform plant emergence and the success of planting. This research was designed to determine proper seed planting depth for Atriplex canescens . in the nursery of the General Department of Natural Resources and Watershed Management ...
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The seed planting depth is one of the most important factors affecting the uniform plant emergence and the success of planting. This research was designed to determine proper seed planting depth for Atriplex canescens . in the nursery of the General Department of Natural Resources and Watershed Management of South Khorasan , allowing desirable seedlings with better vegetative properties to spend less cost in nurseries can be produced.For this purpose, after the collection and preparation of Atriplex canescens, seeds and pots, 15 seeds were selected and planted at 5 depths: 1, 2, 3, 4 and 5 cm with 5 repetitions. After the planting , the emergence percentage and vegetative traits of the seedling were measured. The data was analyzed by ANOVA and t LSD test. The results showed that with increasing seed planting depth, all the properties of the study were significantly reduced , at a minimum depth of 4 and 5 cm. The highest emergence percentage (34.7 % and 34.3 %) was observed at the depth of 1 and 2 cm and the lowest seedlings emergence percentage was observed at the depths of 4 and 5 cm (2.4 %). Since there is no significant difference between the emergence percentage, vigor index, and seedling stem length and weight, and the moisture content of the Atriplex canescens at the depths of 1 cm and 3 cm, Due to greater seed losses at the soil's surface depth by granivores such as insects and birds, as well as rapid drying of soil due to severe evaporation of water in the pot, A. canescens seed planting is recommended at a depth of 3 cm.
Maryam Asadi; Arash Malekian; Ali Salajegheh
Abstract
GCM models are widely used to assess climate change on a global scale, but outputs of these models are not sufficient and accurate to assess climate change at local and regional levels. Therefore, in this study, SDSM model was used for micro-scaling of CanESM2 model data and climate conditions of Semirom ...
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GCM models are widely used to assess climate change on a global scale, but outputs of these models are not sufficient and accurate to assess climate change at local and regional levels. Therefore, in this study, SDSM model was used for micro-scaling of CanESM2 model data and climate conditions of Semirom region based on three scenarios of RCP2.6, RCP4.5 and RCP8.5 in the period 2020 to 2100. The results of model evaluation based on NCEP database showed that the model was more accurate in estimating and predicting temperature data especially mean temperature. Comparison of observation and simulated data of temperature and precipitation of GCMs in the baseline period (1980 to 2005) based on NCEP predictor variables showed the mean correlation of precipitation data of 0.52, mean temperature of 0.88, maximum temperature of 0.80 and minimum temperature of 0.70 for validation and verification periods. The results of the estimation of precipitation variations in different scenarios also predicted a decrease of at least 7.24% and a maximum of 18.55% for the time period of 2020 to 2100 compared to the baseline period (1980-2005). The results of precipitation prediction also show the changes of precipitation pattern. The comparison of the scenarios also shows that the RCP2.6 scenario as the most optimistic scenario has the least rainfall while the RCP8.5 scenario predicts the highest rainfall reduction. Examination of the predicted changes in temperature also shows an increase for the mean, minimum and maximum temperatures,
Maryam sadat Jaafarzadeh; Ali Haghizadeh; Iraj Vayskarami
Abstract
Agriculture is not only the largest user of groundwater resources throughout the world but also its economy is highly dependent on these sources. Thanks to having more effective parameters and subsequently more accurate results, the classification methods in many fields, such as sustainable agriculture ...
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Agriculture is not only the largest user of groundwater resources throughout the world but also its economy is highly dependent on these sources. Thanks to having more effective parameters and subsequently more accurate results, the classification methods in many fields, such as sustainable agriculture has been taken into consideration. Discriminant analysis models are more complex, more accurate and more efficient in comparison to modern methods. In current study, the areas with infiltration potential located in some parts of Khomein, Shazand, Azna, Aligudarz and Durood areas (Marboreh watershed) were went under investigation using the mixture discriminant analysis (MDA) model. For this purpose, the infiltration samples gathered by double ring test, with the environment-effecting layers on infiltration, were prepared and then introduced to R_studio, employed to run MDA. In order to assess the results, validation indices (ROC curve, CCI, TSS, Recall and Precision indices) were used. According to the results, 6.2, 6.1, 12.7, 13.3 and 15.9% of areas of Shazand, Khomein, Durood, Azna and Aligodarz respectively lie in highly potential infiltration, whereas 1.1 16.5, 14.3, 19.6 and 10.8% of those areas were found to have extremely potential infiltration. Most of these areas have sandy soil texture and Quaternary formations with agricultural and range land uses. The accuracy indices that obtained as 0.89%, 76.66, 0.53, 0.91% and 0.73%, witnessing the acceptance and excellence of model performance. The results of this study can be useful in the decision-making for managers and planners regarding to the groundwater recharge in accordance with urban and agricultural needs, because groundwater resources and ensuring their stability are the main factors for sustainable agriculture.
Hamidreza Keshtkar; Hassan Yeganeh; Omid Kavoosi
Abstract
Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six ...
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Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six predictive models: Artificial Neural Networks, Random Forest, Classification Tree Analysis, Surface Range Envelope, Generalized Boosting Machines, and Generalized Linear Models. To evaluate the interactions between topographic factors and other variables, two environmental datasets were quantified and used for model calibration. The first dataset includes eleven factors covering topographic, climatic, edaphic, and remote sensing variables. Meanwhile, the second dataset contains six factors, focusing on climatic, edaphic, and remote-sensing variables. Model accuracy was evaluated using the True Skill Statistic (TSS), the area under the curve of the Receiver Operating Characteristics (ROC), and the Accuracy Index. The evaluation indices indicate that the Generalized Boosting Machine (GBM) model predicted the ecological niche of F. gummosa more accurately than the other methods. Additionally, the results showed that removing topographical variables reduced the model accuracy by 11 to 25%. The slope, NDVI, wetness, and soil groups were found to be the most important factors in mapping potentially suitable habitats for the target plant. According to the results obtained from the GBM model, approximately 45% of the Ghorkhoud area is in excellent condition. This knowledge can aid in the selection of predictors for practical Species Distribution Model (SDM) applications and provide information on which modeling techniques are most useful for a group of species.
