پژوهشی
Hamed Gholamian; Alireza Ildoromi
Abstract
1-IntroductionIn the catchment areas without statistics or incomplete statistics, the extraction of flood characteristics and the provision of water resources and sediment transport analysis are appropriate using empirical methods or models based on the watershed characteristics. One of these methods ...
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1-IntroductionIn the catchment areas without statistics or incomplete statistics, the extraction of flood characteristics and the provision of water resources and sediment transport analysis are appropriate using empirical methods or models based on the watershed characteristics. One of these methods is the use of the capabilities and capabilities of hydrological models in simulating the hydrological processes (Valderz et al., 1979). Rainfall-runoff models, most notably the GIUH hydrograph model and the WinTR-55 hydrologic model, are suitable tools for the study and estimation of maximum hydrograph discharge using geomorphologic parameters of the region (Ghorbani et al., 2015). The purpose of this study was to estimate maximum flood discharge, transfer capacity, and sediment yield of the Kermanshah River using the GIUH and WinTR-55 models. 2-MethodologySanqor basin, with an area of 6317 hectares and minimum and maximum heights of 1500 and 3300 m, respectively, is located in the northeast of Kermanshah province and part of Karkheh watershed. Average values of annual rainfall and temperature are 586.9 mm and 12.9 °C. The WinTR-55 model uses parameters, such as main channel length (flow length), channel gradient (flow gradient), manning roughness coefficient, width of waterway floor, and the slope of margins to determine the effects of flow type and velocity on the discharge peak output and water and sediment transport capacity in the basin. To this end, the area was divided into eight hydrological sub-basins after registration of the basin situation. After estimating the geomorphologic and hydraulic parameters of the canal, the discharge was estimated with different return periods using the WinTR-55 and GIUH models. Geomorphologic proportions include length ratio, branching ratio, area ratio, drainage network, and ranking of riverbeds in the basin (Valderz et al., 1979).3-Results and DiscussionThe estimation results of velocity types in each sub-basin with the WinTR-55 model indicate that the laminar flow in the flood basin were on the surface and not inside the channel, but flow frequency was low with high water content. However, centralized and channelized flows were flooded and concentrated, flowing through the canal or small or large drains. Peak discharge values estimated by the GIUH model were, on average, 6.52% higher than those estimated by the WinTR-55 model. The S3 and S4 sub-basins with low gradients and high roughness coefficients had low flow velocities. In S2, S6, and S8 sub-basins, on the other hand, the discharge and flow rate increased due to a high slope. Estimated peak discharge values by the GIUH model showed increases in all sub-basins other than S1 sub-basin and in the outlet relative to the peaks calculated using the WinTR-55 model. Discharge changes obtained from the GIUH increased on average by 76.1% and 7.1% in the outlet and in the S1 sub-basin, respectively, compared to that calculated by the WinTR-55 model. In the S2, S3, S4, S5, S6, S7, and S8 sub-basins, average increases were 7.31, 5.13, 5.98, 6.3, 6.9, 5.8, and 6.67 percent, respectively. The model calibration d and the sensitivity analysis of the flow parameter were done using the canal slope and the results were investigated at the basin output. The results of the model for a change in the slope of the waterway showed a low effect of the slope on the outlet flow variations. The evaluation results of GIUH and WinTR-55 models in peak discharge estimation with observational data by correlation coefficient (R) and root mean square error (RMSE) indicate good efficiency of both models. R values of 0.90 and 0.97 were obtained between observational and calculated data by the GIUH model by the WinTR-55 model, respectively. The RMSE values were very insignificant in the estimation of observed discharge and those estimated by the WinTR-55 model and the geomorphologic hydrograph unit method.4- ConclusionIn this study, the efficiency of WinTR-55 and GIUH models was investigated in peak discharge estimation. The results showed that there was a high flow rate in S1, S2, S6, and S8 sub-basins due to the high mountainous nature, along increased erosion and sediment transport capacity. In S3, S4, S5, and S7 sub-basins, transfer capacity and sedimentation dropped due to low slope and slower flow rate. The estimated discharge values of S8 and S6 sub-basins by the GIUH method increased by 8.31 and 6.67 percent, respectively, compared to those estimated by the WinTR-55 model, which is due to the increased gradient and its role in discharges calculated by both models. The discharge rate in the area outlet estimated by the GIUH method increased by 1.76% compared to that obtained by the WinTR-55 model, indicating the effect of geomorphologic parameters on the calculation of peak discharge in the basin. Assessments of R2 and RMSE showed that the efficiency of the WinTR-55 model was high at maximum average discharge rate for all return periods, with average RMSE values of 0.66 and 0.32 for the GIUH and WinTR-55 models, respectively. The results showed a high correlation between observational and calculated data obtained from both models. Additionally, the calculated RMSE values showed that the GIUH and WinTR-55 models had high and acceptable performance in peak discharge estimation and could well analyze the erosion and sedimentation conditions.Keywords: Hydrograph, Erosion and Deposition, Flow Velocity, Manning Roughness Coefficient, Sonqor Watershed5-References Ghorbani, M., Asadi, A., Jabari, H., & Farsadizadeh, D. (2015). Extraction of Instantaneous Unit Graph Hydrocopy (IUH) Using Shannon Entropy Theory, Journal of Watershed Management, 5(10).Valders, J.B., Fialloand, Y., & Rodriguez-Iturbe, I. (1979). A rainfall–runoff analysis of the geomorphologic IUH. Water Resources, Res, 15(6), 1421–1434.
پژوهشی
Hassan Khavarian; Maryam Aghaie; Raoof Mostafazadeh
Abstract
1-IntroductionLand use change has significant effects on hydrological and ecological processes at different temporal and spatial scales. Many hydrological models have been developed based on the characteristics of the basin, available data and purpose of the study. To predict the characteristics of river ...
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1-IntroductionLand use change has significant effects on hydrological and ecological processes at different temporal and spatial scales. Many hydrological models have been developed based on the characteristics of the basin, available data and purpose of the study. To predict the characteristics of river flow, we need to develop the rainfall-runoff model to predict the flow for a long period of time. This study has been carried out for the modeling of monthly runoff using Temez model and then the effects of the different land use change scenarios on runoff components have been assessed.2-MethodologyIn this study the OLI-Landsat 8 satellite imageries, a digital elevation model (DEM) as well as meteorological and hydrological data were used for the modelling purpose. The land use classification was carried out using a support vector machine (SVM) method to create a map with 6 land use classes: dry farming, forest land, water body, pasture, built-up and irrigated agriculture. Then, the 10 management scenarios have been developed based on the field observations and taking into account the field characteristics, changes trend in the land use pattern, and the suitability of the study area for different land uses. In order to simulate the runoff, the Temez monthly hydrological model was employed. A 10-year (2002 to 2012) daily precipitation, temperature and runoff data were aggregated to monthly time scales. The calibration and validation steps were performed based on observed data. For calibration of the model, the first 6 years data and for model validation 4 years data were used. The parameters of the Temez model were calibrated based on the values obtained from the literature. First, the appropriate coefficients were found for each land use in the watershed and then the area of land uses in all scenarios were computed. Finally, the weighted average was calculated for the coefficients and appointment in Temez model. 3-Results and DiscussionThe accuracy of the land use map was quite high. A Kappa coefficient of 0.95 and an overall accuracy of 0.975 was obtained. The accuracy of the modeled runoff was presented using R2 coefficient, which was 0.77 and 0.65, for calibration and validation stages, respectively. The results of considering the land use change scenarios on the monthly runoff showed that land use reclamation scenarios of 3, 4 and 5 had a decreasing effect on the runoff by 3.4, 3.3, and 4.1 percent, respectively. Also the land use scenarios of degradation condition, 9 and 10 scenarios, caused an increasing effect on the monthly runoff to 15.24 and 4.5 percent, respectively.4- ConclusionThe monthly hydrological Temez model showed relatively good performance in estimating monthly runoff values based on the data used. The results can be considered in predicting the development and degradation conditions in the study area. Keywords: Land Reclamation, Land Degradation, Kouzehtopraghi Watershed, Land use Change Scenario, Monthly Runoff Feature, Temez Model5-ReferencesArceo, M.G.A.S., Cruz, R.V.O., TiburanJr, C.L., & Balatibat, J.B (2018). Modelling the hydrologic responses to land cover and climate changes of selected watershed in the Philippines using soil and water assessment tool (SWAT) model, DLSU Business & Economics Review, 28, 84-101.Andrade Abe, C., Lucialobo, F.O., Berhan Dibike, Y., Farias Costa, M.P.D., Dos Santos, V., & L.M Novo, E.M (2019). Modelling the effects of historical and future land cover changes on the hydrology of an Amazonian basin, Water, 10(932), 1-19.Feki, M. R., G. Gepple, A. Mille, G. Mancini, M (2018), Impact of infiltration process modelling on soil moisture content simulations for irrigation management, Water, 10(850), 1-20.Garg, V., Nikam, B.R., Thakur, P.K., Aggarwal, S.P., Gupta, P.K., & Srivastav, S.K. (2019). Human-induced land use land cover change and its impact on hydrology, HydroResearch, 1, 48-56.Gumindoga, W., Rwasoka, D.T., Ncube, N., Kaseke, E., & Dube, T (2018). Effect of land cover/land-use changes on water availability in around Ruti dam in Nyazvidzi catchment, Zimbabwe, Water, 44(1), 136-145.Hyandye, C.B. Worqul, A., Martz, L.W., & Muzuka, A.N.N. (2018). The impact of future climate and land use/cover change on water resources in the Ndembera watershed and their mitigation and adaptation strategies, Environmental System Research, 7(7), 1-24.Jain, S.K. (1993). Calibration of conceptual models for rainfall-runoff simulation, Hydrological Sciences Journal, 38(5), 431-441.Onate-Valdivieso, F., Bosque-Sendra, J., Sastre-Merline, A., & Ponce, V.M. (2016). Calibration, validation and evaluation of a lumped hydrologic model in a montain area in Southern Ecuador, Agrociencia, 50(8), 945-963.Temez, J.R (1977(. Modelo matematico de transformacion. Precipitacion. Aportacion. Asociacion de Investigacion Industrial Electrica ASINEL, 1-10.
پژوهشی
shahram roostaei; davood mokhtari; christin jananeh
Abstract
1-IntroductionMass movements of the earth's surficial materials downward the slopes is called slope instability, which is affected by the earth gravity, while the rate of material mobility increases by the presence of water in the sediments. Each year, slope instabilities cause enormous economic damages ...
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1-IntroductionMass movements of the earth's surficial materials downward the slopes is called slope instability, which is affected by the earth gravity, while the rate of material mobility increases by the presence of water in the sediments. Each year, slope instabilities cause enormous economic damages to roads, railways, power transmission and communication lines, irrigation and watering canals, ore extraction, as well as oil and gas refining installations, infrastructures in cities, factories and industrial centers, dams, artificial and natural lakes, forests, pastures and natural resources, farms, residential areas and villages or threaten them. Nowadays, many instabilities are resulted by human intervention and manipulations. One of the human factors effective in the instability occurrence is the construction of roads. Road construction, especially in mountainous areas, increases the probability of occurrence of various types of instabilities, as it changes the natural balance of the slopes and causes deformations in the land. Each year, lots of casualties and financial losses are imposed by the occurrence of various types of instabilities in the slopes overlooking the roads, which also cause the destruction of many natural resources in the country. However, the construction of roads, highways and freeways is necessary and unavoidable in today’s life.The Karaj-Chaloos road and the Tehran-North highway are two routes that connect Tehran as Iran’s capital, with the southern shores of the Caspian Sea, although suffering frequent slope instabilities.2-Methodology This contribution aimed to study slope instabilities along these roads using logistic regression method. In this regard, layers of 14 effective factors were identified, comprised of elevation classes, slope, aspect, geology, land use, precipitation, distance from fault, river and road, normalized difference vegetation index (NDVI), climate, slope length (LS), stream power index (SPI) and topographic wetness index (TWI). Consequently, maps of the factors responsible for instabilities were prepared as separate layers in the GIS environment and transferred into the Idrisi software. The whole procedure included: (1) preparation of digital elevation model (DEM), river and fault layers based on the 1:25,000 topographic map of the area, as well as distance maps from rivers and faults, (2) creating slope and aspect maps from DEM, (3) preparation of land use and NDVI maps of the region based on unmatched classification of Landsat 8 image of OLI sensor, (4) preparation of geological map, (5) preparation of precipitation and climate layers based on the information obtained from the meteorological organization, (6) creating LS, SPI and TWI layers based on the DEM, (7) conversion of the distribution data of the regional instabilities using Landsat satellite and Google Earth images, (8) correlating the information layers with the regional instability map and calculating their density per unit area, and (9) performing the logistic regression model using Idrisi software.3-Results and Discussion Results obtained by applying logistic regression model showed that the most important factors affecting slope instabilities in the Karaj-Gachsar road area were the distance from river, climate and SPI, while those for the Tehran-Soleghan road area were the distance from fault and road and climate. 34.95 percent of the lands in the Karaj road area had medium to high potential for instability occurrence; 54.87 percent of the occurred instabilities corresponded to these areas. Moreover, 4.97% of the Karaj road area had a very high potential for instabilities, which correlated with almost 9% of the occurred instabilities. This was while 27.14% of the Soleghan road area possessed medium to high potential for instabilities, within which 86.26% of the instabilities have occurred. Furthermore, 4.57% of the Soleghan road area showed very high risk in terms of instability occurrence, encompassing 61% of the occurred instabilities. According to the prepared maps, the southern and middle parts of the Karaj-Gachsar road, as well as another part in the northwest of the study area had the highest potential for the occurrence of instabilities, whereas in the Tehran-Soleghan road area, the middle and southern parts and a small section in the north of the area had the highest potential for instability occurrence. By comparing these two areas, it was conceived that areas with medium to high potential of instability in the Soleghan road area were less than those of the Karaj road area (27.24% and 34.95%, respectively). However, the percentage of instabilities occurred in the Soleghan road area was much higher (86.26%) than the Karaj road area (54.87%). The high value of the ROC index and its proximity to the end value of 1 in both areas indicated that instabilities strongly correlated with the probability values derived from the logistic regression model. Additionally, the assessment of the instability potential map by the SCAI index showed that there was a high correlation between the prepared risk maps and the occurred instabilities, which have been confirmed by field surveys. The obtained results were in a good agreement with the general opinion that SCAI decreases especially in high and very high risk classes indicating a high correlation between the prepared risk maps and the occurred instabilities and field surveys in both areas.4-ConclusionThe results of this investigation showed that the logistic regression model was suitable for preparing the zonation of the probability of instability occurrence along the edges of the studied roads. Moreover, in addition to natural factors, the human-made factors and particularly unsystematic road construction can play an important role in the instability occurrences on the slopes overlooking the roads. In order to reduce the relative risks and increase the stability of the slopes, it is necessary to avoid manipulating the ecosystem and changing the current land use as much as possible, in addition to policy making for constructions in accordance with geomorphological and geological features of the area.Keywords:Instability, Logistic Regression, Tehran-North highway, Karaj-Chaloos road, Risk zonation.
پژوهشی
asadollah hejazi; mohammadhossein rezaeimoghaddam; adnan naseri
Abstract
1-IntroductionThe purpose of this study is to select the best model and identify landslide risk areas in the downstream basins of Sanandaj Dam. Every year, mass movements in the region cause damage to roads, natural resources, farms and residential areas, and increase soil erosion. Kurdistan province, ...
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1-IntroductionThe purpose of this study is to select the best model and identify landslide risk areas in the downstream basins of Sanandaj Dam. Every year, mass movements in the region cause damage to roads, natural resources, farms and residential areas, and increase soil erosion. Kurdistan province, with its mostly mountainous topography, high tectonic activity, diverse geological and climatic conditions, has the most natural conditions for mass movements. According to the available statistics, this province is the third province in terms of landslides after Mazandaran and Golestan. (Naeri & Karami, 2018). The Gheshlagh River Basin is a mountainous region with a north-south trend. In terms of construction land, it is located on the structural zone of Sanandaj-Sirjan. The study area with an area of 970.7 square kilometers is located downstream of Sanandaj Dam. The city of Sanandaj is located within the basin. Effective parameters for landslides according to the type of climate and morphological processes are provide in the geography of the region.2-MethodologyThe present study includes five stages of research background and data collection, preparation of information layers, implementation of artificial neural network and TOPSIS models, preparation of landslide Hazard zoning map in gheshlagh basin with the mentioned models and validation test of the models. In this study, nine effective factors for landslides, including slope, slope direction, fault distance, road distance, waterway distance, lithology, land use and precipitation were used .Using Google Landsat 8 ETM satellite imagery, Google Earth software identified 237 slip points. Then, the coordinates of the slip points transferred to the Arc GIS software and a map of the landslide distribution area in this environment was prepared. In addition, in this study, 89 non-slip points were prepared for use in the training and testing stages of Persephone neural network inside slopes less than 5 degrees. Artificial neural networks are made of a large number of interconnected processing elements called neurons that act to solve a coordinated problem and transmit information through synapses. Neural networks begin to learn using the pattern of data entered into them. Learning models, which is actually determining their internal parameters, based on the law of error correction. In this method, by correcting the error regularly, the best weights that create the most correct output for the network identified. The neurons are in the form of an input layer, an output layer, and an intermediate layer. TOPSIS is a very technical and powerful decision-making model for prioritizing options by simulating the ideal answer. In this method, the selected option should be the shortest distance from the ideal answer and the farthest distance from the most inefficient answer,) Dong, 2016). In the artificial neural network model, the middle layer selected by default. Percentage70 of the landslides occurred for neural network training and the remaining 30% as reference data used to test and calibrate the model. Data trained using a multilayer perceptron network with Adam learning algorithm. The final structure of the network has nine neurons in the input layer, 30 neurons in the middle layer and 1 neuron in the output layer. In the TOPSIS model, after scaling the decision matrix, Shannon entropy method used to weight the criteria and to determine the relative distance between the positive and negative ideals of the Euclidean distance.3-Results and DiscussionThe final structure of the network has nine neurons in the input layer, 30 neurons in the middle layer and 1 neuron in the output layer. In the TOPSIS model, after scaling the decision matrix, Shannon entropy method used to weight the criteria and to determine the relative distance between the positive and negative ideals of the Euclidean distance. After creating the raster layers of each index in the TOPSIS model, a vector-point layer created that has one row per pixel and one column per index, thus creating a matrix with dimensions of 9 by 1078555. The operation of Salavatabad fault in the east of the basin has caused Horst and Graben in the region. The significant difference between the height of the mountain unit and the riverbed has caused hazards and the transformation of landforms in the region. In both models, the western part of the basin is in a very high-risk zone, and housing and mass movements threaten agricultural land in these areas. The western outskirts of Sanandaj, which is located in the center of the basin, also affected by numerous landslides and classified in the high and very high danger zone.4- ConclusionThe downstream area of Sanandaj Dam is one of the most active areas of Kurdistan province and the west of the country in terms of human activities. Out of a total of 970 square kilometers, the area under study, according to the neural network model, is about 31 percent and the TOPSIS model is 30 percent of the area within the optimal areas for human activities. In addition, according to the neural network model, about 39% and the TOPSIS model 42% of the region are in the range of undesirable and very undesirable areas. The results show that the study area in general has a high potential for landslides. Dangerous areas are located mainly in the west and southwest of the constituency. These areas correspond to the mountain unit, rainfall of more than 385 mm and high slope. Rainfed agriculture and rangeland with medium-sized canopy are widespread in this area. These areas are also located on the k8, kp1 and PE geological units. Comparison of the results of risk zoning validation in the model shows that in this area, the perceptron neural network model has a better accuracy than the TOPSIS model.Keywords: Hazard zoning, Landslide, Neural network, TOPSIS, Sanandaj Gheshlagh Watershed5-References Dong, S. (2016). Comparisons between Different Multi-Criteria Decision Analysis techniques for Disease Susceptibility Mapping. Student Thesis Series INES. Department of Physical Geography and Ecosystem Science Lund University Sölvegatan. Sweden 12 S-223 62.Geological Map Description Sanandaj 1: 1000000. (1990). Geological Survey if Iran. Tehran. IranNaeri, R. Karami, M. (2018). Integration of Analytical Zoning Risk of Bijar Lanslade Occurrence, Journal of Engineering Geology, 12(1), 153-182.
پژوهشی
Mohammad Hossein Rezaei Moghaddam; asadollah hejazi; Khalil Valizadeh kamran; Tohid Rahimpour
Abstract
1- Introduction Floods are one of the major natural hazards that annually cause extensive damage worldwide. There are numerous floods in the northwest of the country with the beginning of spring and the start of spring rains, which in most cases results in heavy damages. Aland chai catchment suffers ...
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1- Introduction Floods are one of the major natural hazards that annually cause extensive damage worldwide. There are numerous floods in the northwest of the country with the beginning of spring and the start of spring rains, which in most cases results in heavy damages. Aland chai catchment suffers from destructive floods every year since the beginning of spring. The purpose of this study was to examine and analyze the role of hydrogeomorphic indices in flood sensitivity in this basin. Hydrogeomorphic parameters of sub-basins were studied from three aspects of drainage network characteristics (including order of stream, number of streams, length of streams, frequency of stream, bifurcation ratio, length of overland flow, drainage density, drainage texture, texture ratio, infiltration number, constant of channel maintenance, and Rho coefficient), shape characteristics (Including basin area, compactness coefficient, circulatory ratio, elongation ratio, form factor, and shape factor) and relief properties (relief, relief ratio, ruggedness number, and gradient). 2- Methodology With an area of 1,147.30 km2, Aland Chai basin is located in the Northwest of Iran and in the Western Azerbaijan province. This basin is located between 38°- 30¢-14² and 38°- 48¢-22² N and between 44°- 15¢- 13² and 45°- 01¢-02² E. The minimum elevation of the area is 1093 meters and the maximum elevation is 3638 meters. This basin is one of the sub-basins of the Aras basin, which flows into the Aras River after joining the grand Qotour River. SWARA multi-criteria decision analysis model was used to weight the parameters. The Step-wise weight assessment ratio analysis (SWARA) model was developed by Keršuliene et al (2010). WASPAS multi-criteria decision-making model was used to prioritize sub-basins in terms of flood sensitivity. The weighted aggregated sum product assessment (WASPAS) method was proposed by Zavadskas et al in 2012. The WASPAS method consists of two aggregated parts, namely (1) The weighted sum model (WSM) and, (2) The weighted product model (WPM). 3- Results and Discussion Hydrogeomorphic analysis is significantly involved in the analysis of hydrological behavior of the basins. In the present study, 22 hydrogeomorphic parameters were analyzed from three aspects of drainage network characteristics, shape parameters and relief properties with the purpose of examining the effect of these parameters on the flood sensitivity of the Aland Chai basin. In the first step, the study area was divided into 15 sub-basins based on topographic and drainage characteristics using a digital elevation model (DEM) with a 12.5m spatial resolution. In the next step, the information of each sub-basin was provided based on 22 hydrogeomorphic parameters using the geomorphological laws of Horton, Schumm, and Strahler in ArcGIS software. According to the comparison among 22 parameters using the SWARA method, drainage texture, texture ratio, and drainage density (weighted as 0.273, 0.273 and 0.156) had the highest impacts on the occurrence of floods in study area respectively. On the contrary, Rho coefficient, constant of channel maintenance, infiltration number, and length of overland flow exhibited the lowest weights respectively. 4-Conclusion The purpose of the current study was to examine and evaluate the role of hydrogeomorphic indices in flood sensitivity of Aland Chai basin, for which SWARA and WASPAS multi-criteria decision-making models were employed. The results of prioritization of sub-basins using WASPAS model indicated that sub-basin 1 with a coefficient of 0.907, sub-basin 3 with a coefficient of 0.858 and sub-basin 2 with a coefficient of 0.818 had respectively the highest sensitivity to flooding. The results also revealed that sub-basins 4, 7, 11 and 15 in are placed in the high level category, sub-basins 8 and 9 are categorized in moderate-level category class, sub-basins 5, 10, 12 and 14 are classified in the low-level class and sub-basins 6 and 13 are situated in the very low level category in terms of flood sensitivity. The total area of sub-basins in the high and very high class of flood sensitivity is 656.72 km2, which comprises 57.24% of the total Aland Chai basin. Therefore, according to the findings of the study, which indicate that the study area has high flooding, it is necessary to adopt protective measures such as watershed planning and dam construction in highly sensitive sub-basins to prevent flooding and mitigate potential damages in cases of severe flooding. Keywords: Flood, Hydrogeomorphic Indices, GIS, WASPAS Model, Aland Chai Basin 5- References Keršuliene, V., Zavadskas, E. K., Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA), Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12. Zavadskas, E.K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Electronics and electrical engineering, 122(6), 3-6. http://dx.doi.org/10.5755/j01.eee.122.6.1810
پژوهشی
Amin Navidtalab; Ghasem Askari; Farahnaz Ahmadpour; Maryam Tahmasebi
Abstract
1-IntroductionOne of the most important issues, facing the human society and environment, is water resources management. Regarding the drought, this issue turns to a serious challenge for decision makers, and affect the the people more than other natural hazards (Hagman, 1984). Normally, drought occurs ...
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1-IntroductionOne of the most important issues, facing the human society and environment, is water resources management. Regarding the drought, this issue turns to a serious challenge for decision makers, and affect the the people more than other natural hazards (Hagman, 1984). Normally, drought occurs in all climatic conditions (Dai, 2010). Through the current research, we try to investigate drought in Lurestan Province using Percent of Normal precipitation Index (PNI) which evaluates meteorological drought (Hayes, 2006; Zargar et al., 2011). Lurestan Province located in the western Iran, and has an area of about 29,308 Km2. Geographically, it sits between northern latitudes of 32֯ 38' 39" and 34֯ 24' 17" and between eastern longitudes of 46֯ 52' 14" and 50֯ 01' 59". Climatic differences has led to the emergence of three conspicuous climates: (1) mountainous cold climate in the northern and eastern parts, (2) temperate climate in central parts, and (3) warm climate in the south and southeastern parts.2-MethodologyThe meteorological drought intensity is evaluated through different methods including Standardized Precipitation Index (SPI), Percent of Normal Index (PNI), Deciles Index (DI), Effective Drought Index (EDI), China-Z (CZI), Modified China-Z (MCZI), Rainfall Anomaly Index (RAI), Z-Score Index (ZSI), Palmer Drought Severity Index (PDSI), (Willeke et al., 1994; Byun and Wilhite, 1999; Hayes, 2006; Salehnia et al., 2017). To evaluate drought, a period of thirty-year (1988 – 2017) data were adopted from nine synoptic weather stations including Khorramabad, Borujerd, Aligudarz, Aleshtar, Noorabad, Poldokhtar, Kohdasht, Azna, and Dorud. For calculating PNI, the following equation has been applied (equ.1): PNI=P/P ̅ *100 (1)where PNI stands for Percent of Normal precipitation Index, P for annual precipitation (mm), P ̅ for average precipitation of the thirty-year period. PNI (%) ≤110 represents Moderately to Extremely wet climate, 80-110 Normal, 55-80 Moderately dry, 40-55 Very dry, and 40≥ Extremely dry (Morid et al., 2006).3-Resultsand DiscussionConsidering 67 years recorded data for Khorramabad, 32 years for Aligudarz, and 30 years for borujerd, these stations are considered as milestones to reconstruct the data for stations with lack of data for the thirty-year period of study. For other stations, 13 to 17 years of data were reconstructed (Table 1). To find the best reference station for incomplete stations, geographic and climatic resemblance with the stations of complete thirty-year period data was considered. Temperature, precipitation, De Martonne aridity index, and climatic classification by Iran Meteorological Organization (IMO) were evaluated for all stations to find similarities.Table (1): Reconstructed years of data for each station based on geographic and climatic resemblance with the stations of complete thirty-year period data. De Martonne classificat-ionIMO classificationAvail-able yearsReconstr-ucted yearsStation21.3Semi-aridModerately wet, warm summer, moderately cold winter670Khorramabad17.8Semi-aridModerately wet, temperate summer, very cold winter1713Azna18.4Semi-aridModerately wet, temperate summer, very cold winter320Aligudarz7.55Dry or AridModerately wet, warm summer, cold winter1713Dorud18.4Semi-aridModerately wet, warm summer, moderately cold winter300Borujerd18.6Semi-aridModerately wet, temperate summer, cold winter2010Aleshtar19.5Semi-aridModerately wet, temperate summer, very cold winter1713Noorabad14.8Semi-arid to AridModerately wet, warm summer, cold winter2010Kuhdasht10.9Dry or AridModerately wet, very warm summer, moderately cold winter1911Poldokhtar 4-ConclusionNone of stations show Extreme drought. Severe drought is observed in 6 stations with little percentages (3.3-6.6%). Weak droughts has been recorded between 6.6 to 30% in all stations (Table 2). Therefore, dried 80% of springs and rivers in Lurestan could not be solely resulted from meteorological drought in Lurestan. The role of water management in creating this crisis should not be neglected.Table (2): Percentage of different intensities of drought in the studied stationsModerately to Extremely dryNormalModerately dryVery dryExtermely dryStation23.343.3303.30Khorramabad3040236.60Azna26.643.323.36.60Aligudarz2066.36.66.60Dorud30502000Borujerd36.646.616.600Aleshtar36.636.626.600Noorabad33.340206.60Kuhdasht26.65016.66.60Poldokhtar Keywords: Meteorological drought, Drought intensity, drought prediction, Lurestan5- References Byun, H. R., Wilhite, D. A. 1999. Objective quantification of drought severity and duration. Journal of Climate, 12(9): 2747–2756.Dai, A. (2011), Drought under global warming: a review. WIREs Clim Change, 2: 45-65. doi:10.1002/wcc.81De Martonne, E. (1926). Aerisme, et índices d’aridite. Comptesrendus de L’Academie des Sciences, 182: 1395– 1398.Hagman, G. (1984). Prevention Better than Cure: Report on Human and Natural Disasters in the Third World, Stockholm: Swedish Red Cross.Hayes MJ. Drought indices. What Is Drought? Lincoln, Nebraska: National Drought Mitigation Center, 2006. Available at: http://drought.unl.edu/whatis/indices.htm. Salehnia, N., Alizadeh, A., Sanaeinejad, H., Bannayan, M., Zarrin, A., & Hoogenboom, G. (2017). Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land, 9(6): 797-809.Willeke, G., Hosking, J. R. M., Wallis, J. R. (1994). The national drought atlas. In: Institute for Water Resources Report 94-NDS-4. U.S Army Corp of Engineers, CD-ROM. Norfolk, VA.Zargar, A., Sadiq, R., Naser, B., & Khan, F. I. (2011). A review of drought indices. Environmental Reviews, 19(NA): 333-349.
پژوهشی
maryam bayatikhatibi
Abstract
1-IntroductionIn the drainage basins of arid and semi-arid areas where the ecosystem is not able to recover quickly, extreme care should be taken with land use. The hydrological effects of changes in land use are manifested in the form of changes in runoff depth, minimum flow, maximum flow, soil moisture, ...
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1-IntroductionIn the drainage basins of arid and semi-arid areas where the ecosystem is not able to recover quickly, extreme care should be taken with land use. The hydrological effects of changes in land use are manifested in the form of changes in runoff depth, minimum flow, maximum flow, soil moisture, and evapotranspiration. Increasing runoff production in a particular area, in addition to increasing the potential for flooding, has other effects. Due to the type of soil and the topographic and climatic characteristics, the hydrogeomorphological changes caused by human encroachment on slopes and land use changes have been significant in Ojan Chai area (from the sub-basins located on the eastern slopes of Sahand Mountain). Due to erosion in the slopes of Ojan Chay area, it seems that the changes in the amount of runoff are very significant due to land use changes in the area. The study area is one of the rangelands of the country and unfortunately, cultivation is done in an unprincipled manner in the slopes that are not suitable for cultivation. In the coming days, the turbulence of the slopes will be intensified, the amount of runoff will increase, and the number of destructive floods will increase. Often, the soil of the slopes is severely eroded by runoff due to the extreme cultivation in the rangelands.2-MethodologyTo simulate the effects of land use change in a region or watershed, there are many hydrological models, one of which is the L-THIA. This model is a way to evaluate the long-term hydrological effects in a basin by which relative changes that occurred due to a change of use in the runoff can be simulated.The above model is a good tool to help measure the potential effects of land use change on surface runoff. This model is based on the Curve Number (CN) method of the US Soil Conservation Organization (SCS). Expresses CN values range between 0-100, where high values are for urban uses and low values are for areas with high permeability, such as wetlands and pastures with high vegetation density. One of the benefits of L-THIA is that it does not require calibrating the model with real area data. Model calibration is performed automatically using various default CN combinations available in L-THIA GIS. In this paper, to use the L-THIA model, station precipitation was prepared and Landsat satellite images (TM and ETM sensors) and specialized L-THIA software and Arc Map were used. In addition, the probability of a pixel being placed in a particular class is calculated, then the probability of its placement in other classes is estimated and classified according to the maximum similarity (maximum probability) in one of the classes. The above process is expressed based on Equation 1. (Eq.1). Where P (X) is the probability of the presence of the class wᵢ in the image, / x) wᵢ P (probability of each pixel with the spectral characteristic x belonging to the class wᵢ and p (wᵢ / x) the probability of belonging of each pixel with the spectral characteristic x appearing in the image Class wᵢ and p (X) is the probability of the presence of a pixel with a spectral characteristic. The error matrix, kappa coefficient and overall accuracy are used to evaluate the classification accuracy of the images using Equation 2.(Eq. 2). Where OA is overall accuracy, N is the number of experimental pixels, Pii∑ is the sum of the elements of the original diameter of the error matrix.The kappa index is calculated from Equation 3.(Eq. 3). Where po correctly observed, pc shows the expected agreement. The error matrix shows the interference or conversion of uses to each other. Land use maps have been prepared for two periods (1988, 2018) as well as land use change maps. 3-Results and DiscussionIn this research, using THIA L- model, the type of soil was determined according to the available soil maps, prepared samples, soil reports of studies of other organizations and field experiences, soil hydrological group in the study area as the basis of the model used. In the prepared map, it is clear that the range of hydrological group A is observed in the southern and southwestern parts. The area related to hydrological group B is mostly scattered in the northern, northeastern, and central parts. Hydrological group C is spread around the flood plains in the central part of the basin, and finally hydrological group D, which is the largest part of the basin surrounding Ojan largely.According to the land use map of 1988, the largest area is related to rangeland use with an area of 544.6575181 square kilometers and the smallest area is related to water use equal to 0.189899975 square kilometers. According to the land use map of the year 2018, the largest area is related to agricultural use with an area of 510.5889519 square kilometers and the smallest area is related to road use equal to 0.5715 square kilometers. Examination of runoff depth maps for 1988 and 2018 shows that significant changes have been made in terms of quantity and location. Examining the height of runoffs and comparing two different periods in a specific use in relation to changing the rainfall parameter shows that a change in the rainfall parameter can significantly increase runoff in agricultural areas. This situation in relation to the range of the gardens is different, especially in recent years, showing a complex situation. In the case of pastures between 2018 and 1988, there is no significant difference in the height of runoff. Runoff depth in different land uses and rainfall shows that in areas with low rainfall, the highest runoff height is seen in lands under agricultural use. With increasing rainfall, pastures produce the most runoff and again with increasing rainfall, the highest runoff production is related to agricultural lands. In agricultural lands, the amount of runoff has increased in three decades and decreased in pastures.4-ConclusionThe results show that over the past three decades, many rangelands have been cultivated. The area of agricultural lands has increased from 368.4917957 square kilometers in 1988 to 510.5889519 square kilometers in 2018. The results of calculations in such lands show that the height and volume of runoff has doubled from 1988 to 2018. In fact, increasing the area of cultivated land and land use changes from pasture to agricultural land has increased the amount of runoff. The results of studies on soils located on slopes show that the hydrological group of soils in this area is impermeable and with maximum daily rainfall that has increased in recent years, they can produce high-volume deep surface runoff in a short time. These slopes were considered pastures in 1988 (about 90 square kilometers of pastures have been converted into agricultural land). This has caused row crops to produce more runoff in these areas. The results of the studies with the model used and the result of this research in the area of Ojan Chay basin show that the main reason for the increase in height and volume of runoff was land use changes.Keywords: Land use changes, Runoff, Erosion, Flood, L-THIA model, Ojan Chay basin5-ReferencesKhaligi, B., Mahdavi, M., Sagafiyan, B. (2005). Investigating the effect of land use change on flooding using NRCS model, Natural Resources of Iran,vol,58,No,4,p 41-58.Razvizadeh, S., Salajegehe, A., Khaligi, S., Gafari, M. (2014). Investigating the effect of land use change on flooding using, HEC-HMS model (case study: Taleghan watershed) Journal of Rangeland and Watershed Management, Vol. 66, No.3, pp 373-386.Sadati, H, Golami, S., Sharifi, F., Ayobzadeh, A. (2008). Investigating the effect of land use change on runoff, Journal of Rangeland and Watershed Management, Vol. 4. No. 3, pp 301-315.-Hentati, A., Akira Kawamura, Hideo Amaguchi, Yoshihiko Iseri. (2010).Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map, Geomorphology, No. 122, 56–64-Kakembo,V., W.W. Xanga, K. Rowntree.(2009).Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa, Geomorphology, No. 110.188–194-Khairulmaini Osman Salleh and Fatemeh Mousazadeh.(2011).Gully erosion in semiarid regions,Procedia Social and Behavioral Sciences No.19, 651–661.Vahidi, Mohammadjavad; Rasoul Mirabbasi Najafabadi; Mohsen Ahmadi. (2020). Analysis and ranking of soil erosion prevention methods using multi-criteria decision-making methods in rural areas of Darmian County, South Khorasan, Hydrogeomorphology, Vol. 6, No, 23.209-233.Yamani, Mojtaba, Hamid Ganjaeian; Lila Garoso; Mahnaz Javedan. (2020). Identification of susceptible areas for the development of agricultural lands based on parameters Hydro geomorphology (Case study: Sanandaj city), Hydrogeomorphology, Vol. 6, No, 23.1-20.
پژوهشی
Abazar Esmali Ouri; Fatemeh Kateb
Abstract
1- IntroductionIn recent years, concerns regarding the impact of changing patterns of land-use owing to deforestation and agricultural development or elimination have led to numerous crises in the quality of water and soil resources (Lam et al., 2018). Alterations in land use resulting from human activities, ...
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1- IntroductionIn recent years, concerns regarding the impact of changing patterns of land-use owing to deforestation and agricultural development or elimination have led to numerous crises in the quality of water and soil resources (Lam et al., 2018). Alterations in land use resulting from human activities, such as deforestation, agriculture and urban growth among other activities, can have far-reaching and long-term consequences such as reduced biodiversity, increased surface runoff, soil erosion, increased greenhouse gases, global warming and energy imbalance on the surface of the ground (Mokhtari et al., 2020). Undoubtedly, all human activities in nature eventually lead to change of land use. Over the past three decades, the evolvement of human needs has led to a significant increase in change of land-use and damage (Hazbavi et al., 2018). Landscape is defined by focusing on the role that humans play in creating and influencing ecological patterns and processes. Therefore, it should be noted that man has always sought to change the appearance of environments where he feels dominant, hence replacing natural spaces with artificial ones in the process, itself leading to environmental instability (Nazarnejad et al., 2017). Landscape measurement criteria is the best way to compare the landscape of a land and different land uses (Akin et al., 2013; Wang et al., 2014). According to the Food and Agriculture Organization (FAO) of the United Nations, 3 million hectares of agricultural lands are lost annually due to erosion. The total annual sediment volume of basins should be evaluated for soil conservation projects, erosion control and sediment reduction methods, as well as the volume of reservoir dams. Estimation of erosion, annual sedimentation and subsequent preparation of landscape for soil erosions are of paramount importance in controlling soil erosion and maintaining mechanical and biological performance. Direct and indirect methods are two general tools employed for measuring soil erosion. In the direct method, the rate of erosion and sedimentation of different instruments is measured. In indirect methods, the rate of erosion and the level of sediments are measured based on experimental models and other parameters. It is difficult to prepare models with detailed information on local watersheds due to the lack of sediment measuring stations in most watersheds. Therefore, the use of experimental models is inevitable, but the main problem with the experimental models is the inaccuracies in processing and large amounts of data that must be first digitized by the GIS system and analyzed by mathematical models.The purpose of this study was to evaluate the changes in land-use measures at the level of class and landscape in Sharif Beiglou watershed for the development of the catchment area in line with the needs of the region. Due to the existence of Sharif Beiglou Reservoir Dam in the above basin, identification of sensitive and critical areas of erosion is necessary to carry out further conservation activities. Also, one of the obvious problems in this basin is the presence of excessive sediment due to the lack of suitable vegetation upstream of the basin. Examining this relationship can provide a good tool for monitoring land change and decision making in management.2- MethodologyIn this research, land-use map was prepared using images from Google Earth 2020, mostly owing to its high resolution and appropriate interpretation of the watershed. Image analysis was performed using ArcGIS 10.3 software. After preparing the land-use map for the area in ArcGIS 10.3 and converting it to raster format, Fragstats 4.2 was used to quantify the land-use measurements at the scope and class level for the watershed. Soil erosion sensitivity coefficient was calculated with EPM model for Shari Beiglou watershed. Then, the data from calculating the measures and the severity of soil erosion were inputted SPSS, determining a significant relationship there between.3- Results and DiscussionThe basis for calculating land-use metrics is land-use map at the level of landscape and class. The analysis of quantitative measures of land-use were performed at two levels of class (the level of each class being unique) and the landscape. According to the results, the maximum number of spots was witnessed in agricultural use, the minimum of which was related to the water body. This finding is not consistent with that of Madadi and Ashrafzadeh (2015), in which the most destruction was reported in the water body. The average spot density of the study area was 0.23, and the maximum value of the spot distance was related to agriculture and pasture, garden, residential and water body, respectively. This shows that human manipulation and interference in this use has been high over time. The value for the index of the largest spot in the study area was 80.65 for the rangeland, and the smallest value was zero assigned to the water body. Increasing the shape of the spot is associated with increasing the irregularity of the shape of the spots. In this regard, Karami et al. (2012) studied and compare the use of North and South Zagros lands with the ecological approach of the land of Kurdistan, Kohgilooyeh and Boyer-Ahmad provinces, and reported that the most and the least are related to agricultural lands and water bodies, respectively. The maximum and minimum total margins for Sharif Beiglou watershed at the level of class, were 68080.023 and 1345.224, respectively. The average total margin for the studied watershed was 50338.672 meters. Similar results have were previously by Kiani and Fiqhi (2015) for northern Iran, in which the margin density was the highest for the rangeland and the lowest for the water body. The spot shape index for all uses was more than 1, indicating the irregularity of the spots at the field level. The maximum and minimum values of this index were witnessed in agriculture (8) and water body (1.27) respectively. The maximum and minimum values for the average spot size were respectively 313.66 and 6.92, (Mokhtari et al, 2020). Moreover, the results showed that the average size of forest spots has increased from 1987 to 2018. The minimum and maximum value for the landscape rupture of the studied watershed was equal to 1 and 0.34, respectively pertaining to residential use, and water body and rangeland. The maximum and minimum values for fragmentation rate was determined to 376.889 and 1.53, respectively. Also, on the surface of Sharif Beiglou watershed 49 spots were identified, with spot density of 1.15, largest spot index of 80.64, total margin of 7558.008, average margin of 17.75, spot shape index of 4.56, and average spot size of 86.79, while the landscape rupture was calculated to be 0.34 and fragmentation rate was calculated to be 1.52. According to the results obtained from the study basin (Esmali and Abdollahi, 2011), agricultural land-use has high erosion, rangeland and garden land uses have moderate erosion, residential land-use has low erosion and water body has partial erosion. In the southwestern part of the basin, owing to agricultural activities, the intensity of erosion is high, while the intensity of soil erosion in the northeastern part is moderate. Owang et al. (2010) examined the watershed upstream of the Yellow China River from 1977 to 2006 and reported that factors including the continuous expansion of bare lands, water areas and agricultural lands has significantly increased soil erosion. Analysis of the results at the spot level confirmed that the amount of sediment transport from the edge of the spot has also increased due to the increase in the margin of the spot. Obtaining and employing such information will definitely help curb regional and local environmental pollution. Also, the landscape of Koozeh Topraqi watershed is composed of pastures, agriculture, rocky outcrops and residential areas with the shares of 29.13%, 64.77%, 3.50% and 0.80%, respectively (Alaei et al., 2019). The data from calculating the measures and the intensity of soil erosion were inputted to SPSS software and the significant relationship there between was determined according to Pearson correlation test. The results indicated that only the fragmentation index (SPLIT) is statistically significant with a negative correlation and is thus an effective measure that can be employed in methods of reducing the severity of soil erosion.4- ConclusionThe results showed that in the study basin, agricultural applications have high erosion, rangeland and garden applications have moderate erosion, residential applications have low erosion while water body has partial erosion. At the level of landscape, for Sharif Beiglou basin, the number of spots was 49, the spot density was 1.15, the largest spot index is 80.64, the total margin was 75508.008, the margin density was 17.75, the spot shape index was 4.56, the average spot size was 86.79, and landscape rupture was 0.34 and fragmentation was determined to be 1.52. At the class level, the amount of fragmentation in the water body was the highest. Therefore, it can be concluded that its relationship with their assemblies has been severed. Finally, with the intervention of changes in land features, the study of soil erosion was optimally performed, in that the erosion potential map indicated that areas with high erosion are affected by the measurement indicators and the hydro-geomorphological properties used.Keywords: Land use, Land degradation, Landscape, EPM, Sharif Beiglou. 5- References Akın, A., Erdoğan, A., Berberoğluc, S. (2013). The Spatiotemporal Land use/cover Change of Adana City. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 7, 11-17.Alaei, N., Mostafazadeh, R., Esmali Ouri, A., Sharari, M., & Hazbavi, Z. (2019). Assessing and comparing the continuity of the landscape in the Koozeh-e-Topraqi watershed, Ardabil province. Applied ecology, 8 (4): 34-19.Esmali, A., & Abdullahi, K. (2011). Watershed management and soil protection. Mohaghegh Ardabili Publications, 574 p.Hazbavi, Z., Jantiene, B., Nunes, J.P., Keesstra, S.D., & Sadeghi, S.H.R. (2018). Changeability of reliability, resilience and vulnerability indicators with respect to drought patterns. Ecological Indicators, 87, 196-208.Kiani, W., Jurisprudence, J. (2015). Investigation of the cover / use structure of Sefidrood watershed using ecological criteria of the land feature. Environmental Science and Technology, 17, 141-133.Karami, A., Fiqh, J. (2012). Monitoring and comparing the use of North and South Zagros lands with the ecological approach of the land landscape (Case study: Kurdistan, Kohgiluyeh and Boyer-Ahmad provinces). Land Management, 4 (6), 34-5. Lam, N.S., Cheng, W., Zou, L., & Cai, H. (2018). Effects of landscape fragmentation on land loss. Remote Sensing of Environment, 209, 253–262.Madadi, H., Ashrafzadeh, M. (2010). Investigation of land cover changes in the area of Bamdaj wetland with the ecological approach of land appearance. Journal of Science and Technology, 9 (1): 51-61.Mokhtari, M., Abedian, S., & Qolpour, M. (2020). Detection and modeling of forest land use change trends in Qarahsu watershed using land features. Applied ecology, 8 (4): 18-1.Nazarnejad, H., Hosseini, M., & Irani, T. (2018). Using Landscape Measurements in Assessing Landscape Structure Changes in Qarahsoo Watershed in Kermanshah. Geography and Environmental Hazards, 26, 36-23.Wang, X., Blanchet, G.B., & Koper, N. (2014). Measuring habitat fragmentation: An evaluation of landscape pattern metrics. Methods in Ecology and Evolution, 5, 634–646.
پژوهشی
soghra andaryani; Vahid Nourani
Abstract
1-IntroductionMining industry has almost negative and destructive effects on the environment and ecosystems of regions and can affect the health of humans and other living organisms including animals, plants, soil, water, and the entire biosystem of the region on a local and regional scale.2-MethodologyThe ...
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1-IntroductionMining industry has almost negative and destructive effects on the environment and ecosystems of regions and can affect the health of humans and other living organisms including animals, plants, soil, water, and the entire biosystem of the region on a local and regional scale.2-MethodologyThe study area is located in East Azerbaijan and it belongs to the sub-basins of the western part of the Aras Basin.2.1-DataLandsat 5, 8 data available in July (1984-2019), monthly and annually value of temperature and precipitation data (1998-2017) in Varzeqan station.2.2-Method In the present study, land-cover density using Normalized Differential vegetation Index (NDVI) was extracted from satellite imagery of Landsat (Thematic Mapper and Operational Land Imager) as time series for the period of June 1984-2019. 27 images were analyzed after correction and NDVI extraction. See question 1: (1)Where, NIR is reflectance value of near infrared and RED is reflectance value of red band.To determine the trend in vegetation, first, the land-cover density extracted from satellite images were pre-whited in time series and then the trend analysis was done by Mann-Kendall (MK) method in each of the pixels, and also beginning of the trends were analyzed by Mann-Kendall sequence (SMK) in per case studies. SMK was used in MATLAB software (Ye et al., 2013; Moraes et al., 1998). 3-Results and DiscussionThe average values of land-cover in all three case studies (i.e., case 1, case 2, and case 3) have increased in the period 1984-2019 and have almost had the same fluctuations over the period under study. Therefore, that linear regression was derived between the land-cover of cases 1 and 2, 1 and 3, and finally 2 and 3 with the average correlation coefficient of 96%, 96%, and 98%, respectively. The highest vegetation peak was in 1992, 2004, 2013, and 2018 to 2019, however, such an increase in the average occurred in all three study units. The peak of average land-cover density in different years is consistent with the peak of precipitation and decrease in temperature on an annual scale. According to the results, in studying the trend of vegetation changes, it is not possible to generalize the numerical average of vegetation for the whole region or analyze the trend. By emphasizing this result in each of the pixels as a time series, trend analysis was performed by MK method. Case 1 experienced the most fluctuation and case 3 (downstream of the mined area) experienced the least fluctuation trends. The significant decreasing trend in both levels of reliability, 95% and 99% has the highest level of the mining area (Fig. 1). Fig. (1): Classification of trend analysis at 95% and 99% confidence levels, (a) case 1, (b) case 2 and (c) case 3. Figures 1-3, which have been resulted from Fig. 6 (a), indicate areas with significant decrease. Figs. 1 and 2 are the mining areas and Fig. 3 is the Andaryan village0.48% of the case 1 area is under the significant decreasing trend, which is 0.18% and 0.22% in case 2 and 3, respectively. Therefore, there is a significant decrease in all three case studies. Approximately, 5%, 2%, and 3% of the area of cases 1, 2, and 3 have a decreasing trend, respectively. The percentage of areas with a significant increasing trend at both 95% and 99% confidence levels are equal to 35.5%, 54%, and 36.5% for each of the 1-3 case studies, respectively. According to Varzeqan station data, these areas have received good rainfall in the last decade, so the area of vegetation has increased significantly. The existence of 88% correlation between the area where the mining took place and the area that is untouched in terms of exploration operations shows the insignificant impact of exploration operations and smelting services on the vegetation of the area. Although most of land-cover of about 51 hectares has been lost due to road construction on steep slopes for mining and smelting services, based on sustainable development goals, the affected vegetation can be restored to the original state and at the same time to make the best use of existing minerals and consider future generations (Thenepalli et al., 2019). 4- ConclusionThe results show fluctuations in land-cover density; however, in general, high dense areas in terms of vegetation are observed in all three areas. The case studies 1- 2, 1 - 3, and 2 -3 have a correlation of 96%, 96%, and 98% with each other, respectively. Therefore, using the Mann- Kendall statistical model, NDVI values were analyzed pixel by pixel as a time series. The results show a significant decrease in the vegetation of regions 1-3 equal to 0.48%, 0.19% of the area in all three regions, respectively. The results of the Mann-Kendall sequence and correlation in the areas with a significant reduction in vegetation and the considered various hypotheses showed no chemical leakage to downstream of the basin.Keywords: Impact of mining, Land-cover, NDVI, Mann-Kendall Test, Varzegan, Northwestern Iran 5-References Ye, X., Zhang, Q., Liu, J., Li, X., & Xu, C.Y. (2013). Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. Journal of Hydrology, 494, 83–95.Moraes, J.M., Pellegrino, H.Q., Ballester, M.V., Martinelli, L.A., Victoria, R.L., & Krusche, A.V. (1998). Trends in hydrological parameters of southern Brazilian watershed and its relation to human induced changes. Water Resources Management, 12, 295–311.Thenepalli, T., Chilakala, R., Habte, L., Quang Tuan, L., & Sik Kim, C. (2019). A Brief Note on the Heap Leaching Technologies for the Recovery of Valuable Metals. Sustainability, 11, 334.
پژوهشی
hojatolah younesi; ahmad godarzi; behzad javadi
Abstract
1-IntroductionFlood is a natural phenomenon that human societies have accepted as an unavoidable event caused by a number of factors, depending on the climatic and natural conditions of the region. It is believed that the relationship between rainfall and runoff is significantly different from one basin ...
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1-IntroductionFlood is a natural phenomenon that human societies have accepted as an unavoidable event caused by a number of factors, depending on the climatic and natural conditions of the region. It is believed that the relationship between rainfall and runoff is significantly different from one basin to another. Given that, in order to prevent the occurrence of such harmful phenomena, it is not possible at present to make changes in the factors and elements of the atmosphere. Therefore, any principled and useful solution should be sought on the ground, especially in watersheds. From this point of view, areas with "high potential" for flood production should be identified. Accordingly, the first measure to reduce the risk of floods for the sustainable settlement of the population is to control floods at their source, namely, the watershed sub-basins. Thus, it is essential to identify floodplains within the basin; however, due to the large size and scope of the watersheds, is not possible to carry out modeling, implementation and remediation operations throughout the basin. Thus, it is advised to use various computerized models to prevent floods.2-MethodologyIn this study, it has been attempted to combine GIS and multi-criteria decision-making systems (MCDM) to identify areas with different degrees of flood risk for sustainable settlement of the population in each of the cities of Khorasan Razavi Province, Iran. For this purpose, first the data of 6 effective parameters including Maximum discharge with 2, 3, 5, 10, 25, 50, 100 and 200 year return periods obtained from HEC-HMS software output, drainage density, land use and vegetation, CN, slope and permeability of the study area were prepared in GIS software environment. Then, using ANP method and pairwise comparison, the weight of each criterion and the weight of the classes of each layer were calculated in Super Decision software, respectively. Then, using GIS software analysis functions, the whole range was zoned for each of the specified criteria. Ultimately, through combination of the zoned maps and based on the weights of the ANP, the final map was prepared in five classes of low-risk flooding and high-risk flooding areas.3-Results and DiscussionThe results area of the cities exposed to floods with a very high degree as well as flood risk zoning with a return period of 2 years in the entire Khorasan Razavi province show more than 86% of areas with low and very low flooding, 12.2% of medium areas and 1.8% with high. While the results of flood zoning in the 200-year return period show 41.3% low flooding, 31.4% moderate flooding, 13.3% high flooding and 14.1% very high flooding in the entire province.4-ConclusionsThe results of this study were analyzed using field visits and ground control, which indicates that all selection criteria are met revealing the usefulness of combining MCDM methods with GIS in identifying areas with different degrees of flood risk.Keywords: Flood risk, Population settlement, Couple comparison, Khorasan Razavi
