جستجوی مقالات مرتبط با کلیدواژه "آذربایجان شرقی" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Covering an area of 45490 square kilometers, the East Azerbaijan province is located in the northwest corner of the Iranian plateau at the range of 45 degrees 7 minutes to 48 degrees 20 minutes’ east longitude and 36 degrees 45 minutes to 39 degrees 26 minutes north latitude, being considered the 10th largest Iranian province. In general, the East Azerbaijan province is a mountainous region, about 40% of whose surface is mountainous, 2.28% of its surface is hilly, and 8.31% of its surface comprises of plains (including mountainous plains). Moreover, the province generally enjoys a cold and dry climate. However, the region has different climates due to its diverse topography. Affected by the cold northern and Siberian winds and the Mediterranean and Atlantic seas' humid winds, the province is a cold and mountainous region that is classified as a semi-arid region in terms of climate, whose average annual precipitation rate is 250-300 mm.

Introduction of SDSM Exponential Micro Scale Model  Weibel et al. (2005) used a multivariate regression model called SDSM to examine the effects of climate change on statistical downscaling, in which the station's daily forecast data (predicted), large-scale NCEP variables (predictor), and large-scale variables of general circulation models under various diffusion scenarios serve as inputs for future study periods. The predictor outputs have many variables. This study used the meteorological data collected from synoptic stations, NCEP data, and CANESM2 data under two RCP 2.6 and RCP 4.5 scenarios. The calibration of the model was performed using the NCEP data. Moreover, the temperature and precipitation rates of Tabriz, Mianeh, and Sarab stations were predicted for the three periods of 2020-2050, 2051-2080, and 2080-2100, which were then compared with the base period. It should be noted that the SDSM model performs better than other models because it combines both regression and probabilistic methods to produce meteorological data, and it is, therefore, one of the best models in this regard compared to other models. Application of the SDSM model to the study basin This study used the SDSM 5/3 model for statistical downscaling. It also used the data regarding the temperature and precipitation collected from three synoptic stations (Tabriz, Mianeh, Sarab), each of which contained 31 statistical years. The SDSM model is performed in several stages, including selecting predictor variables; calibration and validation; model performance review; developing climate scenarios for RCP 2/6 and RCP 4/5 and calibration. Assessing the Performance of the SDSM Model There are various statistical indices for evaluating the performance of observational data, prediction, and error values, including the Index of Agreement (d), Nash-Sutcliffe Performance Index (NSE), Second Root Mean Square Error (RMSE), and Mean Error Average (MAE). In Nash-Sutcliffe Index (NSE) whose variations range from infinite to minus one, the closer the data are to 1, the higher their accuracy would be (Nash et.al, 1970). On the other hand, the value of d ranges from one to zero; Accordingly, the closer the d values are to one, the higher the accuracy and agreement of the predicted and observed data would be.  The average error values ​​between the predicted and observed data are shown by RMSE and MAE. Each of the aforementioned variables is calculated through the following equations. represents monthly precipitation data of Cordex (Ghonchepour et. al., 2019).                                                                                                                                                       

The results obtained from the three meteorological stations indicated that compared to the observational period, the precipitation rate would increase in Tabriz and Mianeh stations and decrease in Sarab station in all three periods under both RCP 2.6 and RCP 4.5 scenarios. Moreover, the temperatures would decrease in Tabriz and Sarab stations in all three periods under both the RCP 2.6 and RCP 4.5 scenarios compared to the observational period, while it would increase in Mianeh station during the same period and under the same scenarios.

The results of this study obtained from the SDSM outputs can be used in hydrological and environmental sectors. They can also be used for predicting climate parameters and assessing climate change, considering the fact that climate change affects water resources, human health, food, and agriculture. Therefore, the study's results could also be used in all of these sectors. On the other hand, as any decline in precipitation rate and increase in temperature would lead to quantitative and qualitative changes in water resources, the water uses plans need to be revised so that they can minimize the harmful effects of such variations.

Droughts are intrinsically regional. The critical conditions in the dry periods occur when there is a severe shortage of water resources for a long period on a wide area. Therefore, an investigation of the regional and not of a local drought offers better understanding of this phenomenon. This study aims at a regional analysis of the hydrological drought in East Azarbaijan Province for a period of 45 years (1972– 2017) using the data collected at 25 hydrometric stations with proper spatial distribution across the province. Homogeneous hydrological areas was identified based on hierarchical analysis method in the SPSS software using the following independent variables: Area, Perimeter, Length of the main basin, Main channel gradient, Drainage density, Basin shape factor, Total hydrographic network, Weighted average slope of the basin, Maximum, Minimum and Median height of the basin, and its length. Frequency analysis and probability distribution of the dependent variables (duration and intensity of hydrological drought spells) was performed on the basis of the two year return period for each hydrometric station. The factor analysis was performed using the PCA method in the SPSS software. The suitability of the data for the factor analysis was evaluated using the KMO coefficient. The value of this coefficient was estimated as 0.546, which indicates that the data were suitable for factor analysis. The result of factor analysis showed that the contribution of perimeter, height and shape of the basin were 40.13, 26.42 and 13.12% (Sum 79.68 % ) respectively in explaining the variance of the variables (i.e. the most important factors in zoning). Results of regional analysis using multivariable regression (stepwise method) indicated that perimeter, avrage height and the basin shape coefficient have a decisive role in estimating the drought severity and its duration with two-year return period in East Azarbijan. In the scarcity of hydrometric stations in most of the watersheds of the East Azarbaijan province, the regional equation presented in this study can be used to estimate hydrological drought characteristics of watersheds without observed surface runoff data.

Watershed resident's benefit increasing and soil erosion controlling are the main goals of agricultural sustainable development. Landuse optimization operation is an effective solution in order to maximizing the benefits, as well as minimizing soil erosion damages. Current research was executed in southern parts of Ahar Chaey Watershed in East Azerbijan Province. In this research, Multi-Objective linear programing based on Simplex method applied for optimizing landuse in form of three different scenarios: current landuses, implementation of management into current landuses and performance of the standard landuses by WinQSB software. Results showed that due to landuse optimization, irrigated orchards could be increased from 1.736 to 6.629 km2. In addition, based on suitable soils and adequate rainfalls, it is expected to expand the rainfed orchards up to 9.72 km2. Also, the weighted average of soil erosion reduces from 16.3 to 14.8 and from 10.11 to 9.5 tha-1year-1 within the first and second scenarios, respectively. This rate will reduce up to 6.8 tha-1year-1 in the standard landuse situation. On the other hand, maximizing results showed that the annual net benefits increases from 6.6 to 12.9 and 8.46 to 14 million Rails per unit area in the first and second scenarios, respectively. This rate increases up to 15.67 million Rails per unit area for standard landuse situation. Sensitivity analysis results showed that irrigated and rainfed orchards are of high sensitivity in maximizing profit functions due to high profitability of these landuse in unit area. In contrast, the rangelands, forests and drylands are of high sensitivity in soil erosion minimization, so that with increasing their area, soil erosion will increase significantly in the region.

Snow and snowmelt are important factors controlling flow regimes in mountainous basins and provide most of the water. As a result, snow hydrology is of great value in mountainous areas. Estimation, simulation and prediction of flow resulting from rain and snowmelt has applications in provision of drinking and irrigation water, water control in rivers, flood control and flood warning systems, and estimations of flood damage in a basin. In this study, flow resulting from snowmelt was simulated using the MLP neural network and support vector machine (SVM) models for the Baladarreh Kandovan, Marand ski resortand, and Sandoghlu ski resort stations in the East Azerbaijan Province in the 2006-2013 period. The coefficient of correlation, root-mean-square error (RMSE), and mean absolute error (MAE) were used to evaluate accuracy. The results indicate that the neural network model had better accuracy compared to the SVM model. Among the different arrangements of neural networks, the 3-6-1 arrangement with snow density, length of snow sample, and snow depth had the highest accuracy.

Understanding the spatial distribution and variability of erodibility and soil properties is essential for planning of water conservation methods, controlling of flood and runoff and managing of soil erosion or watershed. Selecting and using appropriate interpolation techniques for soil properties and erodibility mapping by erosion models such as WEPP is essential. The objective of this study was regionalization of interrill erodibility and effective factors like clay, organic matter and lime using kriging and cokriging and remote sensing data (Landsat 7). For this purpose, 100 soil samples were selected randomly from 0-15 cm depth of Selin watershed in Kaleibar region of East Azerbaijan. Interrill erodibility of WEPP model and some soil properties as clay, organic matter and lime were measured. Correlation analysis between soil properties and digital number (DN) ETM image showed that clay, organic matter, lime and interrill erodibility had the highest correlation with DN of Band 7, 1, 1 and 3 ETM image (−0.406, -0.431, 0.291 and 0.299), respectively. Therefore, the DN of these bands used as auxiliary data for cokriging estimator. The spherical model was fitted the best model to calculate variogram of interrill erodibility, clay, organic matter and lime. No significant difference were noted between kriging and cokriging despite using remote sensing data as auxiliary data. This can be attributed no strong correlation between interrill erodibility, clay, organic matter and lime and remote sensing data.

Floods are the main natural disasters that produce serious agricultural, environmental, and socioeconomical damages in many parts of the world. Accurate estimation of river flow in streams can have a significant role in water resources management and in protection from possible damages. This study aims to compare the abilities of Support Vector Machine (SVM), M5 model trees and Linear Regression (LR) methods in forecasting hourly discharge flow of Aharchay River. The hourly water level-discharge and 14 flood events data of Aharchay River measured at the Tazekand hydrometric station was used for modeling. The results showed that the SVM method gives more accurate results than the M5 model and LR method in forecasting river flow for next one and two hours with the R2=0.96 and RMSE=0.0472 (m3s-1) and the R2=0.90 and RMSE=0.1596 (m3s-1), respectively. Comparing the performance of SVR and M5 models indicated that, however the SVR approach may present more accurate results than the M5 model tree, but the M5 model provides more understandable, applicable and simple linear relation in forecasting hourly discharge. Thus, the M5 model tree can be used as an alternative method in forecasting hourly discharge.

Differential synthetic aperture radar interferometry (D-InSAR) has become a useful technique for monitoring ground movement. The technique enables the analysis of very small ground movements in continuous and large areas and has the advantages of high accuracy, high resolution, all-weather adaptability, low cost and inaccessible area coverage. Thus, D-InSAR has been widely used for investigation of geologic hazards, such as subsidence, landslide, earthquake, and volcanic activity. In this research, D-InSAR technique was applied for detection of unstable slopes and determining moving displacement rate. For this purpose, eight SAR images of PALSAR sensor of ALOS satellite were selected for processing based on D-InSAR approach. Obtained results were validated by field observations prates. This paper is only represented results related to image pair processing of 5th July to 5th October 2007 with 92 days interval. Garm Chay watershed with 940 km2 area is located in North Eastern and 40 km far from Miyaneh in Eastern Azerbyjan, Iran. This region with 380 landslides is considered as one of the unstable landslide proven area in Eastern Azerbyjan. Occurrence of these slides caused heavy damages to rural properties and arable lands. As a result, Sovin village in north western part of this watershed was moved to other safesite. The results clearly showed that some old stabilized landslides were still active. Because of their proximity to local stream networks (check the possibility for stream displacement), they can be considered as important sources of sediment yield. Before mentioned period, the maximum displacement was calculated 5.8 cm in landslide surface near to Avin, Atajan and Benavaran villages. The results from this method should be validated by geodetic methods and field obsevations.