Hydrogeomorphology - Articles in Press

Determine Appropriate Sites of Urban Development Based on Geomorphologic Elements (Case Study Urmia)

Sayyad Asghari; Batool Zeinali; Saleh Asghari

Volume 3, Issue 7 , October 2016, , Pages 39-57

Abstract

Sayyad Asghari[1]* Batool Zeinali[2] Saleh Asghari[3] Abstract The location of human settlements and other facilities created by human are affected by Environmental factors, particularly geomorphology and geology. Today, as a result of population growth, development of construction is inevitable and ... Read More

The Evaluation of the Spatial Structure of Mashhad Plain Precipitation in the Last Half-Century

Gholamabbas Fallah Ghalhari; Elham Kadkhoda

Volume 4, Issue 11 , September 2017, , Pages 39-57

Abstract

Introduction The meaningful, complex, and ongoing connection between the rainfall and other climatic elements causes diversity in space and time. A new approach in climatology is to describe the spatial variability of the rainfall based on the spatial statistics. Unlike the classical statistics, the ... Read More Introduction The meaningful, complex, and ongoing connection between the rainfall and other climatic elements causes diversity in space and time. A new approach in climatology is to describe the spatial variability of the rainfall based on the spatial statistics. Unlike the classical statistics, the spatial statistics shows the statistical data on a map. Therefore, the attention and emphasis on the spatial differences and the identification of the specific and unique points or homogeneous regions will be provided. Modeling of the rainfall behavior is one of the main foundations in any climate research. In this regard, two major efforts are of interest to climatologists. One of them is the precipitation zoning. The other one is the analysis of the spatial temporal variations of the precipitation. This analysis is important for weather forecasting and a wide range of decision makers, including hydrologists, farmers, and industrialists. Methodology Using statistical methods, the present study aimed to introduce the fundamentals of the spatial data and the general precipitation behavior of Mashhad’s plain along the space. In this regard, the study used the daily precipitation data of 34 synoptic stations, climatology, and rain gauge during the survey period, 1963-2013. The study initially analyzed the spatial and temporal distributions of the precipitation based on the classical statistical methods. Then, it focused on the central average, standard distance, and directional distribution. In this research, the universal Moran method was used to calculate the spatial autocorrelation data. In addition, the central mean method was used to calculate the basin rainfall gradient. Finally, directional distribution was used to calculate the trend and direction of the precipitation distribution. Discussion The results showed that the gravity center, the centroid, of the annual rainfall during the last half-century sustains a displacement of 3.83 km where the distribution arrow demonstrates the magnitude of the tilt and orientation on the amount of the precipitation. Also, the standard distance of the precipitation in Mashhad, in the fifth decade (2003-2013) compared to the first decade (1963-1973), changed to 1254.57. This change can be one of the reasons of the instability of the linear relationships of the spatial factors and the rainfall in the plains of Mashhad. Conclusion The results showed that the center of the rainfall gravity of Mashhad plain was displaced over 3.3 km during the 50-year period. In addition, there was a change of 0.269 degrees in the direction of the distribution of the precipitation in the fifth period compared to the first period. Since, this shift was negative to areas with spatial dependence, it indicated a general drop in the rainfall in the Mashhad plain. The results also showed that the roughness and height might be two important factors affecting the spatial patterns of the rainfall in Mashhad Plain.

The Estimation of Evaporation from Reservoirs Using the ANN Model (Case Study: Minab Dam)

Mohammad Hossein Jahangir; Ahmad Nohegar; Keyvan Soltani

Volume 6, Issue 18 , June 2019, , Pages 39-56

Abstract

IntroductionThe impact of drought on different parts is not the same. In a situation where different regions of the country have experienced a significant decline in rainfall, its impact on water resources is still unclear or the decline of surface water resources has no effect on agricultural production ... Read More IntroductionThe impact of drought on different parts is not the same. In a situation where different regions of the country have experienced a significant decline in rainfall, its impact on water resources is still unclear or the decline of surface water resources has no effect on agricultural production (Satari et al., 1395).Increasing or decreasing in hydrological time series can be described by changes in precipitation factors, evaporation, temperature, and the like (Nourani, 1395). Evaporation modeling from the reservoir level is important to predict the evaporation rate from the surface and the amount of water lost through evaporation and evacuated water and to have a proper planning to reduce the amount of this evaporation and its economic estimation. The heavy volume of computations and their time-consuming performance, especially in phenomena such as sudden floods, cause many financial losses and annoyances every year. One of these utilized and intelligent tools is artificial neural network which reaches acceptable output by establishing appropriate relationships between input variables in the shortest possible time and establishes the relationship with the output tool and provides the best possible result to experts. (Rajaei et al., 2010). In this regard, studies have been conducted in the world, including the study of the effect of different compounds of climatic parameters on the evaporation losses of the dam reservoir (Deswal & Pal, 2008).Methodology - Meteorological data routing nonlinearBefore proceeding to discuss the modeling and selecting the optimal model for the regions under discussion, the best nonlinear fittings are [1]obtained from the parameters affecting evaporation. For the study area, the fitting diagram for temperature data (oC), rainfall (mm), wind speed (Km/h), lake surface area (Km2) and evaporation (mm) were used, which resulted in the results and relationships for each of them.- Introducing Artificial Neural NetworkAn artificial neural network consists of three main layers of the input, the hidden (middle layer) and the output layers. The layer where the results of the model analysis are generated and the modeling is done is the output layer of the model (Fig. 1). The middle layer acts as the processor of the model and the processor nodes are at this stage (Traore et al., 2010). Fig.1 Artificial Neural Network structure with input, output and intermediate (hidden) layersResults- Artificial Neural Network Modeling for Minab DamIn order to use the artificial neural network, data from the Minab Dam was estimated from the data of the years 1998 to 2014 in MATLAB software. The best structures for the neural network are given in Table 1:Table.1 Error and correlation coefficient obtained by artificial neural networkNocorrelation coefficientMSE (Test)MSE (Learn)Neural network structure10.88490.0010.0016ANN(3,7,1)20.88490.000920.0014ANN(4,10,1)30.890.000880.0015ANN(4,11,1)- Training dataFor modeling of the neural network, 80% of the data was randomly selected by the MATLAB software. One of the most important diagrams used in neural network modeling is the actual values graph and evapotranspiration values using artificial neural network for training data (Fig. 2). Fig.2 Diagram of observation data and modeling at training stage, ANN [5,5,1]- Test dataThe remaining 20% of the data was also used to test the model obtained by the artificial neural network (Fig. 3). Fig.3 Diagram of observation data and modeling at testing stage, ANN [5,5,1] Discussion and conclusionEvaporation, as one of the natural parameters, has always been of interest to experts and researchers due to the high role that human has in reaching the outflow of water. In this research, we tried to evaluate the accuracy of this model by using the artificial neural network model in estimating evaporation from the lake level of the Minab Dam. In order to investigate the evolution of the evaporation parameters for the 19-year data, the best-fit nonlinear regression was drawn and the general trend of evolution of the effective parameters was studied. For modeling of the evaporation using artificial neural network, 19-year-old statistics between the years 1995 and 2013 were used.The best structure for estimating evaporation from the level of Minab Dam is selected in this paper. In this structure, the first and second layers have 5 neurons with 1000 replications to get the best result. The statistical coefficients obtained from the analysis using artificial neural network were considered in selecting the best structure. In this structure, the correlation coefficient with the value of 0.8941 had the highest value and the error values of training and testing the data were respectively 0.0011 and 0.0082. After this structures, ANN (3, 7,1), ANN (4,10,1), ANN (4,11,1), ANN (5,3,1) had acceptable correlation coefficient values and error in determining the amount of evaporation from the Minab dam.[1]- MSc Student, Faculty of New Sciences and Technologies, University of Tehran.

Investigation Effects of Landuse and Land Cover Changes on Flooding and Runoff Disc Harge (Case Study: Alavyan Dam Basin)

Volume 1, Issue 1 , January 2015, , Pages 41-57

Abstract

Landuse and land cover changes have direct effect on hydrological regime of basin.Iincreasing bare lands and similar landuses make flooding and increasing of orghard landuse area has effective role on reduce of runoff discharge. In this study using of soil hydrolic group map that indicat sensitive lands, ... Read More

Determining Factors in the Evolution of Geomorphology Fan and the Basin of Usko Chay

Volume 2, Issue 2 , January 2015, , Pages 41-66

Abstract

The rivers are sensitive to tectonic movements and there is a close relationship between the river landforms and such movements. Geomorphic indexes are used as tools to specify the new constructions and active workings of such movements. Due to active tectonic movements in Usku Chay’s basin ... Read More

Spatial Evaluation of Morphotectonic Indicators of Valleys Considering their Geological Structures (Case Study: Part of the Relief of Zanjan)

Gholam Hassan Jafari

Volume 2, Issue 5 , January 2017, , Pages 41-61

Abstract

There are many valleys in the city of Zanjan and Eijrood and parts of Abhar, Tarom and Mahneshan Zanjan province. The valleys are very different in terms of geology, lithology and physiographic factors and they are dispersed in different directions geographically with different forms. First the valleys ... Read More

The Evaluation of the Sustainability of Watershed Using Watershed Sustainability Index

Tahereh Mohamadi; Mohammad Taghi Dastorani

Volume 4, Issue 10 , June 2017, , Pages 41-64

Abstract

Healthy watersheds provide many ecosystem based services in different fields such as social and economic welfares. Hence, there is an urgent need to develop ways to determine the degree of health and sustainability of watersheds. One of the indices which is used to assess sustainability is the ... Read More

Investigation of Karst Development in Kurdistan Province; Geomorphology Mechanism of the Caves and Hydrogeology of Karstic Springs

Abdorreza Vaezihir; Nasser Jabraili andaryan; Shoaib Bakhtiyari

Volume 6, Issue 20 , December 2019, , Pages 41-56

Abstract

1- IntroductionThe rocks that can be karstified are divided into two categories of carbonate rocks (limestone and dolomite) and evaporates (salt and gypsum). One of the karstic landforms are caves. Hydrogeologically, caves are dissolved cavities with diameter larger than 5 to 15 mm. This is the threshold ... Read More 1- IntroductionThe rocks that can be karstified are divided into two categories of carbonate rocks (limestone and dolomite) and evaporates (salt and gypsum). One of the karstic landforms are caves. Hydrogeologically, caves are dissolved cavities with diameter larger than 5 to 15 mm. This is the threshold of turbulent flow. One of the important characteristics of the caves is the cross-sectional pattern and this parameter is a controlling factor of the cave development mechanism. Caves are developed in general due to tectonic factors are structure control with irregular cross section. These types of caves are developed along faults, joints and bedding. On the other hand, caves which are caused by hydraulic phenomena (hydraulic control), have a circular or ellipsoidal cross-section. However, the first factor in the creation of such caves may also be tectonic structures, but the most important factor for their development was the flow of water (Karimi, 2010).The karstic flow model was divided into two types of diffuse and conduit types according to velocity and governor flow equations. In diffuse flow systems, the water moves linearly through the connected fractures less than a centimeter. In this type of flow, the output of the springs is numerous and with low discharge. While in conduit system, water moves through the joints and channels larger than one centimeter and is usually exposed as a large discharge spring. Due to the high distribution of carbonate rocks in Kurdistan province and extensive exploitation of karst springs, recognizing these resources and identification of the role of karstification in supplying the water resources of the province is a necessity. Karstic water sources are strategic water supply sources specially in crisis of drought period. In this research, after determining the karst areas of the province, caves and karstic springs as two important indicators of karst development were studied to evaluate the characteristics of caves and springs of the province in terms of development model and flow system. 2- MethodologyIn order to investigate the development of karst and formation of caves and karstic springs, at the first, comprehensive knowledge of the region's petrology is necessary. The formation and structural conditions of the region should also be identified in order to recognize areas with high potential of karstification. Understanding the types of karstic units also helps to identify the level of karstification in different parts of the areas. Using plan of the caves, it is possible to compare the passage strike of the caves with fault's rose diagram. Study of spring density and the discharge rate of karstic springs in different parts of the area can be done to find out whether the karst development system is conduit or diffuse type. Also, to study the relation between the water quality of the springs with the geological formations, hydraulic conductivity (EC) was focused on. The lower electrical conductivity (EC) values are belonged to springs originated from hard rock and karstic units of the area. One of the ways that can be used to evaluate the permeability of hard and carbonate formations is Special Discharge assessment for each formation.4-Discussion and conclusionAbout 29% of the province area is formed of karstic units including impure and pure lime, lime with volcanic layers, and dolomite with the most coverage percent. The results of this study showed that all caves of the area are located on the karstic units of the province and their dominant strikes are concordant with dominant strike of the faults. By comparing Rose diagrams of fault lines around several caves with that’s of cave passage the effect of water or structure on controlling the development pattern of these caves was determined. The total discharge of the springs is 13.7 m3/s which 9.5 m3/s (about 70%) of that belongs karstic springs. Most springs with discharge above 5 L/S originated from karstic formations are located especially in limestone, impure limestone and limestone with volcanic layers. Average discharge of total karstic springs are 0.4 liters per second, showing poor development of karst or development of diffuse model of flow system. However, karst development system seems to be conduit dominated in the southern parts of the province and diffuse dominated at the other areas. The minimum electrical conductivity (EC) of the study area belongs to springs that discharge fractured and karstic units.The results of this study showed that all caves of the area are located on the karstic units and their dominant length is consistent with the dominant length of the faults. By comparing the Rose diagrams of fault lines around several caves with the cave passage strike revealed that structure and tectonics have the main control on development, pattern and profile of the caves. About 69.5 % of the total discharge of the springs of study area are originated from the karstic units. Karstic springs provide 79.32% of the water use in agriculture and 4% of urban and rural drinking water in Kurdistan province. The karst development system seems to be conduit dominated in the southern parts of the province and diffuse dominated at the other parts. The minimum electrical conductivity (EC) is belongs to springs which discharge fractured rock and karstic units. The most specific discharge rates are related to carbonate rocks and lowest discharge rates are related to crystalline rocks of the province.

A Survey on the Quality of Drinking Water in the Populated Areas of Ardabil Plain Using a Combination of Multi Criteria Decision Making Models and Geostatistics in the GIS Environment

Hashem Rostamzadeh; Mohammad Reza Nikjoo; Ismaeil Asadi; Jafar Jafarzadeh

Volume 2, Issue 3 , January 2017, , Pages 43-60

Abstract

Ardabil Plain is an intermountain area of approximately 820 square kilometers in northwestern Iran, located in the eastern plateau of Azerbaijan within the province of Ardabil. Plain water needed for agriculture, industry and drinking are provided from rivers, deep and semi-deep wells and springs in ... Read More

Surrounding Environment Change during the Past 60 Years by Means of Remote Sensing Geobased Analyses

Abbass Malian; Ali Mohammadi; Abbass Alimohammadi; Jalal Valiallahi

Volume 3, Issue 9 , March 2017, , Pages 43-62

Abstract

Gradual drying of Urmia Lake has become a national and international challenge. In recent decades, unsustainable agricultural and industrial development together with uncontrolled exploitation of aquifers are major causes of Urmia Lake drying. In this study, the change detection and monitoring of Urmia ... Read More

Morphometry and Studying of the Changes of Geometrical Parameters in the Qaresou River between 1959-2014

Mohamad Sharifi Paichoon; Fatemeh Parnoon

Volume 4, Issue 13 , March 2018, , Pages 43-62

Abstract

Introduction Rivers' channels tend to change due to different factors such as lithology, discharges, floods, sedimentation, and humans. Of all factors, understanding the morphological features of a river and its major controlling factors is importance in its control and regulation. The morphological ... Read More Introduction Rivers' channels tend to change due to different factors such as lithology, discharges, floods, sedimentation, and humans. Of all factors, understanding the morphological features of a river and its major controlling factors is importance in its control and regulation. The morphological features and geometric characteristics such as change in the length and width of the river, wavelength, arc length, arc angle, and sinuosity are the most important factors that should be considered to study and mange a river. Many investigations have been done to study the changes in the morphology of the rivers and the geometrical parameters. For example, Leopold and Wolman (1957), based on structural viewpoint, divided rivers to three groups including braiding, meandering, and relatively straight channels. Singh (2014) studied morphological changes of the Ganga River during 10 years using GIS. Iranian researchers (Ahmadian,2001; Ghafari et al., 2004; Hafezi Moghadas et al., 2012 ; Maghsoudi et al., 2010; Shahbazi et al., 2009) have also investigated the morphological and geometrical parameters of different rivers. In the current research, the changes in the geometrical parameters of the Qaresou River between 1959 and 2015 were evaluated. This river is located between latitude of 34° 30’ N to 34° 54’ and Longtitude of 46° 22’ E to 47° 22’ within Kermanshah’ Province in the west of Iran. It is one of the sub catchments of the Karkhe basin. It has a land extended about 5278 km2, with a maximum of 3360 m and a minimum of 1270 m height. The average precipitation of the basin fluctuates between 300 to 800 mm in a year. Three rivers which flow into the Qaresou includes Mereg, Qaresou, and Razavar rivers. Methodology In this research, to study the morphology of the Qaresou River and its geometric parameters and changes, the aerial photos (1955) and satellite images IRS (2015) were used. In fact, the photos were the best tools to compare the rate of changes in the morphology of the channel for about 60 years. First, the photos and images were scanned and georeferenced. Then, the river was digitized with high exactness. Next, using AutoCAD Software, the geometric parameters such as wavelength, arc length, arc angle, amplitude, sinuosity, central angle, and meandering were evaluated. Finally, through comparing geometric parameters between two periods, the rate of changes for about 60 years was calculated. Results and Discussion Evaluation of the geometric parameters of the Qaresou River was based on using drawing circles tangent with meanders of the river for both periods. The findings showed that the pattern of the river and some geomorphological and geometrical characteristics were changed during the time. For example, the number of meanders had reduced from 535 in 1959 to 379 in 2014. Also, the central angle, as a criteria to divide and determine the development of the meander in a river, was evaluated. This criteria showed a reduction in the average of the central angles in all segments of the river except its first segment with an increase in its width because of higher erosion. In addition, during this period, 60 years, the river has tended to a straight pattern. The changes in the radius of the meanders also reduced in all segments except the first one. However, at first, the pattern of the river changed to develop in the meanders. Besides, the curvature coefficient of the river reduced in all segments except the third one. Finally, there was an increase in the wavelength and the length of the channel between the years 1959 and 2015. Conclusion The result showed that although the frequencies of the arc in all segments reduced during the statistical periods, the geometric parameters fluctuated. For example, in the first segment, the wavelength increased 96 m and the length of the channel increased 31 m. Also, the curvature coefficient reached from 1/83 to 2/38. The radius of the meander increased about 38%. The central angle increased 40%. The increase in the geometric parameters showed grubbing in the river bed and increasing in the curvature of the river. In the fifth segment, in contrast, all geometric parameters declined. For example, the wavelength and the length of the channel respectively declined 31 and 15 m. In addition, the curvature coefficient was also decreased 3%. There was also a decline in the flow water of the channel. Therefore, the river follows a natural process of digging in the upstream and sedimentation in the downstream. Near Kermanshah city, the slope of the channel tends to the least and the river starts to leave all sediments, which leads to displacement in the channel.

Urmia Lake Salinity Mapping Using Support Vector Regression and Landsat-8 Imagery

Mahdi Hasanlou; Meysam Jamshidi; Mohammad Taghi Sattari

Volume 5, Issue 14 , June 2018, , Pages 43-65

Abstract

Introduction Urmia Lake is located in the North West of Iran and its area between 4750 to 6100 square kilometers at an altitude of 1250 meters above sea level. This lake is a permanent lake in Iran. In fact, Urmia Lake is one of the lowest parts of the catchment area North West of Iran. The total surface ... Read More Introduction Urmia Lake is located in the North West of Iran and its area between 4750 to 6100 square kilometers at an altitude of 1250 meters above sea level. This lake is a permanent lake in Iran. In fact, Urmia Lake is one of the lowest parts of the catchment area North West of Iran. The total surface area of Urmia Lake is 51,876 km square, which is 3.15% of the total area of Iran and 7% of all the water’s surface in the country. The depth varies between 6 and 16 meters, the length of the lake is 50 km and its width varies between 128 km to 140 km. In the catchment area of the lake, there is the main river with annual input about 2 billion cubic meters. Annual rainfall in the catchment area is variable between 200 and 300 mm. Air temperature the area around the lake in winter to 20°C and 40°C in summer increases. Urmia Lake is important in terms of economically, transport, exploitation of the mineral wealth of biodiversity, mitigating climate, and tourism. This unique Lake addition to the previous is habitat for kind of native artemia its name is urumiana artemia that this artemia is unique to this lake. Also, Urmia Lake is the world's second largest habitat for Artemia. According to the research, the main elements in the Urmia Lake include Cl-, Na +, Ca2+, Mg2+, HCo3-, K+, Li, So42- and F. Methodology In this study, newly launched Landsat series (Landsat-8) was used for monitoring Urmia Lake salinity and retrieving the salinity map. By incorporating the Landsat-8 datasets, this study determined the salinity changes and created a model to estimate the salinity in Urmia Lake with processing Landsat-8 satellite images as a result; we can obtain salinity map regularly without ground operations. We can also monitor the health of the habitat in terms of salinity and examine the impact of increasing salinity on the plants, animals, and ecosystems of the region. This study applied remote sensing techniques to develop a salinity prediction model for Urmia Lake. In this study, we use Landsat-8 satellite images radiances of Urmia Lake and some salinity indices and in-situ data so we have 17 features to make water surface salinity model with support vector regression (SVR) with all features. After that, we use two algorithms; GA and SFS for selecting suitable features and make models with those features. Result Results with all features model show RMSE=24.55 and R2=41% and result with GA feature selection model shows RMSE=21.97 and R2=54% and results with SFS feature selection model shows RMSE=21.93 and R2=53%. Discussion and Conclusion Satellite images show that from 1995 to 2003, the lake water surface dropped and proportionate to the dropping water salinity increased to 220 to 300 grams per liter. Also although Artemia is resistant to salt, appropriate salinity is below 100 grams per liter. When water salt is more than 100 grams per liter contents of his tiny body lost and die. Now because of reduction in salinity, the lake has arrived at about 300 grams per liter. Dissolved salt in water has a direct effect on the electrical conductivity of water. In this regard, incorporating high spatial resolution satellite like Landsat-8 images is inevitable. Also, the proposed modeling methods show these changes in multi-data and in widespread Urmia Lake very well.

The Impact of Land Use Changes on Water Quality in Pishkooh-Taft Basin in Yazd Province

Manijeh Ghahroudi Talli; Taher Valipoor; Lughman Shirzadi

Volume 5, Issue 16 , December 2018, , Pages 43-59

Abstract

Abstract Introduction One of the most important factors in the development of a region is the availability of sufficient water resources and its quality status for various uses. Due to Iran's arid and semi-arid climate and water shortage, it is essential to pay attention to the water quality of its ... Read More Abstract Introduction One of the most important factors in the development of a region is the availability of sufficient water resources and its quality status for various uses. Due to Iran's arid and semi-arid climate and water shortage, it is essential to pay attention to the water quality of its rivers. Methodology Pishkooh-Taft Basin is an important watershed inthe east of Yazd province and located between 54º 15’- 53º 40’ east and 31º 50’- 31º 30’ north. Tezerjan fault of Taft Continues to the west with the northsouth trend. There are 3 gauging hydrometric stations in this area. TM images13 October 1991 and ETM+ 15 October 2011 of Landsat 7 were used to change the detection of the land use. In addition, to examine the physical and chemical properties of water in Taft, Islamiyah and Feyz-abad stations during 1361 to 1390 years were used. First of all, the method which was performed included geometric correction for fixing errors and adapting the images by the Digital topographic map scale of 1: 25,000. Then, land use was extracted by supervised classification, training samples, and maximum likelihood techniques. Next, to report the physical and chemical properties of anions and cations, TDS, conductivity and pH of the water, the stations were investigated during 1361 to 1370 and 1371 to 1390 years, and the data was evaluated using Mann-Whitney statistic. Result The finding of land use showed that the wasteland and pastures were dominated and there was an increase in pastures compared to 1370, but there was a decline in covered meadows in 1390. To examine the relationship between land use changes and water quality, the elements of the water was dealt with. For this purpose, the physical and chemical properties of anions and cations, TDS, conductivity, and pH of the water between the years 1361 and 1370 and 1371 and 1390 were studied. The results of Mann-Whitney statistic in different elements and three stations showed that the difference between these two periods was significant in Taft station. Discussion and conclusion The effect of land use on water quality change over the period of 30 years showed a relative decline of water quality in Taft station and to some extent in Islamiyah station. This can be attributed to the increase in land occupancy and the existence of agricultural lands in Taft and Islamiyah Stations. However, in Feyz-abad station, due to the lack of habitat and agricultural lands and an increase in water flow, there was no decrease in water quality. On the other hand, the examination of the changes in the height of the stations showed that Taft station was in the outlet of the basin and had the minimum height which could affect the quality of the water. Several researchers (Townsend & Popcorn, 2009; Kazi et al., 2009; Travka, 2004; Orion, 1390) have studied the effect of reduction of water quality on increasing agricultural lands and urban areas which are in line with Taft station.

Morphological Classification and Channel Instability of Kaleybarchai River

Somaiyeh Khaleghi; MohammadMahdi Hosseinzadeh; Payam Fathollah Atikandi

Volume 6, Issue 21 , March 2020, , Pages 43-64

Abstract

1-IntroductionOne of the methods used in river surveys is river classification. The main aim of the classification of the river is simplify the processes of hydrology and sedimentation, and ultimately predict river behavior. So far, rivers have been categorized from different perspectives and the basics ... Read More 1-IntroductionOne of the methods used in river surveys is river classification. The main aim of the classification of the river is simplify the processes of hydrology and sedimentation, and ultimately predict river behavior. So far, rivers have been categorized from different perspectives and the basics of these categories are including topography, slope, flow discharge, river age, and pattern in the plan. The first classification Recognized by Davis in 1899. Davis classified the rivers according to their evolution and modification into three groups of young, mature, and old. Leopold and Welman (1957) divided the form of alluvial rivers based on the sinuosity coefficient and the ratio of width to Depth into three straight, meandering and braided groups. A descriptive classification by Shumm (1963) presented based on two factors of river stability and sediment transport. The objectives of this research are to identify the factors affecting the bank erosion of the Kaleybarchai River, identifying the damages incurred in the construction and banks of the river, runoff and preventing possible floods. In this research, the river classification system is based on the Rosgen method, which is presented by the American researcher Rosgen (1994) to the river engineering community. The Rosgen method is the most complete and comprehensive method provided so far and includes many of the features of previous systems. Rivers are living beings that constantly change their beds and banks, and this causes the river to undergo major changes over time. In addition, human activities, such as the utilization of riverine material and river modification, will cause the river to be moved.2-MethodologyTo evaluate the classification of the flow pattern in the Kaleybarchai River, the Rosgen model has been used at levels I, II, III. A reach of 3 km between the two villages of Pazhagh and Gheshlag was determined, and then 8 cross sections were selected in this reach. To simulate the river and extract the required parameters from geological maps, topography, land use and ARC GIS software was used. After determining the river reaches, based on field observations and topographic maps, classification in level I and level II were carried out in 8 cross-sections at the Kaleybarchai River, which are based on the slope, curvature coefficient, bankfull width, mean flood plain depth, flood plain width and bed material.3-ResultsAfter crossing the river route with field observations and then analyzing data and general calculations, 8 cross sections from the entire river course were extracted in all of the studied river and all the parameters required for classification and geometrical identification of the channel wrer calculated.In order to obtain the average size of channel material, 16 samples were taken at river in different reaches and were analzed in the laboratory (Table 2). According to the obtained data, the highest percentage of particles along the river were average sand with 26.6% and cobble up to 14.7%, which were evaluated for the Rosgen classification, according to the results, the total of river is in groups B and C.To determine the channel type at level I, after obtaining the slope of the Kaleybarchai River in the study area, four sections of the river were in type B and four sections in type C.4-Discussion and conclusionBased on morphological indices, sediment content and flow conditions, two different types of channels including B and C were identified in the study area and evaluated level according to the Rosgen in level I, II and III.Morphological study of type B in relation to the evaluation of the correspondence and efficiency of the Rosgen model showed that their dominant morphology consisted of narrow valleys with relatively low widths and moderate slopes and relatively stable banks. Type C has meandering and high sinuosity, valleys with floodplain and point bars in low slope.The high instability of the river bed in the reaches of 3, 5, 7, is a threat to the agriculture land land and surrounding buildings. Due to the fact that the braided rivers are not stable and the flow and position of the sedimentary islands and the width of this rivers are constantly changing, it is necessary to manage and organize the operations in this section with regard to the morphological variables and Flow conditions. The results of the Kaleybarchai River assessment based on the Rasgen classification system at level I, II and III showed that the Rosgen system present good the patterns of the channel in the Kaleybarchai River and, consequently, the effective parameters in the classification and separation of the channels. In this way, there are differences in the quantities and the parameters due to the specific conditions of the factors affecting in the locality.

Evaluation of Groundwater Potential of Sufi Chay basin Using Frequency Ratio models and GIS

leila Biabani; Arash Malekian; Behrooz Akbarpoor

Volume 7, Issue 22 , June 2020, , Pages 43-65

https://doi.org/10.22034/hyd.2020.14189

Abstract

The management of groundwater resources is a major for identifying areas with high potentials for groundwater. In this study, we tried to identify areas with the potentials for groundwater in Sufi chay Watershed by using frequency ratio model. Conditioning Factors used in this study include: elevation, ... Read More

Performance Evaluation of Geo-Statistical Methods to Estimate the Spatial Distribution of Snow Depth and Density in Mountainous Areas (Case Study: Gush Bala Watershed, Mashhad)

Ali Haghizadeh; Arman Kiani; Milad Kiani

Volume 4, Issue 12 , December 2017, , Pages 45-66

Abstract

Introduction One form of precipitation is snow. Due to the long-lasting process of its transformation into runoff, it is different from other ingredients of the water budget. In most permanent rivers whose basins are covered with snow, it plays a little role in water resources' studies. This case study ... Read More Introduction One form of precipitation is snow. Due to the long-lasting process of its transformation into runoff, it is different from other ingredients of the water budget. In most permanent rivers whose basins are covered with snow, it plays a little role in water resources' studies. This case study is the Gush Bala mountain watershed, which is located in the eastern part of Mashhad in Khorasan-Razavi province. Materials and methods In this study, 11 measured samples were used to map the depth and density of snow. Using Minitab software, the normality of the gathered data of snow was assessed through Kolmogorov-Smirnov test. The probability of more than 0.05 was considered as a criteria for the normality of the distribution of the data. If it does not have a normal distribution, they are normalized, through using modified shapes, in regard to their skewness. After testing the normality, the point data was transformed to regional data through Geo-Statistics such as Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Kriging, and Cokriging. Geo-statistical estimation consists of two phases. Its first phase involves identifying and modeling spatial structure that can be studied by means of half-changing facade and estimation which can be the best linear unbiased estimation. The mutual assessment method was utilized to choose the most appropriate Geo-Statistical method. In this method, for each step, one observed point was crossed out and its value was estimated. After that, the estimated value was compared with the observed one. Results and discussion The most vital criterion was Root Mean Square Error (RMSE).The comparison of the RMSE of different methods like IDW, RBF, Kriging, and Cokriging showed that the most and the least values were for simple cokriging and simple kiriging methods whose values were respectively 0.518 and 0.023. Therefore, the simple kriging revealed better results than the other methods. Overall, less values of RMSE led to a better performance of a spatial semi-variogram for depth and density. Because the values of RMSE for 11 functions including Circular, Spherical, Tetra spherical, Penta spherical, Exponential, Gaussian, Quadratic Rational, Hole Effect, K-Bessel, J-Bessel, and Stable for simple Cokriging for depth and simple kriging for density were respectively 0.518 and 0.023, the interpretation Variogram for 11 functions was needed in the case of simple cokriging for depth and simple kriging for density. The criteria which were used were Nugget and Nugget to sill ratio. Generraly, if they are less, their results are better super structure. The value with simple cokriging method for the depth of the snow for J-Bessel and sill ratio were respectively 0.795 and 0.95 which experienced better super structure. The value with simple kriging method for the density of the snow for J-Bessel and sill ratio were respectively 0.806 and 0.9 which showed an optimum method compared to other methods. All values that are obtained from interpolating kriging and cokriging methods must be evaluated with variogram structure, especially Nugget and Nugget to sill ratio. If the values of Nugget and Nugget to sill ratio increases, the predictability of the variogram decreases. In the variogram which was related to the depth and density data, the piece effect showed high levels. Furthermore, the ratio of nugget to sill was more than 0.75, which revealed a weak super structure between values in various distances. These findings demonstrated the heterogeneity of the data. On the other hand, considerable oscillations between nugget in depth data and density was shown on high values for nugget. Conclusion The results of the predictions assessment done with simple cokriging for depth and simple kriging for density showed the higher accuracy of the aforementioned methods to other methods. One reason for this high accuracy can be ascribed to the influence of these parameters. In general, when the environment is more homogeneous, the Mardr small scale's results will be better than conventional statistical analysis. Thus, it seems that the sampling method with homogeneous units using satellite and aerial images could result in the homogeneity of the data. In addition, as a result, the spatial vacillations of the data went down and the ability of Geo-Statistics to predict and estimate will improve.

Using Hybrid Wavelet-Support Vector Machine and Wavelet-Neural Network Models for Groundwater Level Prediction in Ardabil Plain

Farnaz Daneshvar Vousoughi; Vahid Manafianazar

Volume 5, Issue 17 , March 2019, , Pages 45-64

Abstract

Abstract Groundwater has played an important role in the urban and rural water supply and agriculture. In order to manage water resources, an accurate and reliable groundwater level forecasting is needed. In this research, 15 piezometers in Ardabil plain were used. SVM was applied for a prediction method ... Read More Abstract Groundwater has played an important role in the urban and rural water supply and agriculture. In order to manage water resources, an accurate and reliable groundwater level forecasting is needed. In this research, 15 piezometers in Ardabil plain were used. SVM was applied for a prediction method in one month-step-ahead. Clustering tool and Wavelet Transform (WT) as spatial and temporal pre-processing and an artificial neural system for modeling were also used. The results showed that the values of R2 coefficients in calibration and verification of prediction were respectively 0.94 and 0.89. On the other hand, the application of the WT to groundwater level data increased the performance of the model up to 3% and 5% for calibration and verification parts. The performance of the SVM model was compared to the proposed combined WT–ANN and ANN models. The results showed that the values of R2 coefficients in calibration and verification of prediction were respectively 0.94 and 0.88. The application of the WT to groundwater level data increased the performance of the model up to 3% and 7% for calibration and verification parts. The results obtained by the SVM model showed the improved performance of modeling and its combination with WT showed the best performance in the pre-processing of the modeling. Also the results of the ANN and hybrid WT-ANN models yielded good performance. Also, the results of the hybrid WT-ANN models showed slightly better results than the ANN model in some clusters. Introduction Recently, Artificial Intelligence (AI) approach, as a new generation of robust tools, has been developed for time series forecasting purpose. As such forecasting tools, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been extensively employed at different engineering fields. Among such AI models, the capability of the commonly used ANN models to approximate nonlinear mappings between inputs and outputs makes it a useful tool for modeling hydrological phenomena. However, ANN-based modeling may include some shortcomings, such as over fitting, convergence to local minima and slow training, which make it difficult to achieve adequate efficiency when dealing with complex hydrological processes [12]. Support Vector Machine (SVM), proposed in [13], is one of the most persuasive forecasting tools as an alternative method to ANN. SVM is based on the structural risk minimization principle and Vapnik–Chervonenkis theory, and involves solving a quadratic programming problem; thus, it can theoretically get the global best consequence of the primal problem. In recent decades, SVMs have been implemented in several hydrological fields and in groundwater levels. In this paper, the conjunction of SVM and the wavelet-based data pre-processing was examined by proposed Wavelet-SVM (WSVM) in modeling groundwater level for one month ahead. The proposed models were also compared with single SVM, ANN and Wavelet-ANN (WANN) models. The plain of Ardabil (38 – 38 N and 47 – 48 E), located in the north-west of Iran, covers an area of about 990 km2. In this plain, 15 piezometers (wells) are operated to measure the GWLs. The data sampling has been reported in one-month intervals for all of the piezometers. The plain is equipped with one runoff gauge at the outlet and 6 rain gauges within the watershed. Fig. 2 shows the position of piezometers as well as rainfall and runoff gauging stations. The monthly rainfall, runoff, and GWL data were available from 1988 to 2012 and used in this study. About 18 years of data were used for the training, and the remaining 7 years for the validation. Support Vector Machine SVM as a powerful methodology was used for solving problems in non-linear classification, function estimation, and density estimation. Via SVM, a non-linear function can be shown as: (1) where f indicates the relationship between the input and output, w is the m-dimensional weight vector, φ is the mapping f unction that maps x into the m-dimensional feature vector and u is the bias term. Artificial Neural Network (ANN) ANN is widely applied in hydrology and water resource studies as a forecasting tool. In ANN, feed– forward back–propagation (BP) network models are common to engineers. The Feed forward neural network (FFNN) is widely applied in hydrology and water resource studies as a forecasting tool. Three-layered FFNNs, which have usually been used in forecasting hydrologic time series, provide a general framework for representing nonlinear functional mapping between a set of input and output variables. The explicit expression for an output value of a three layered FFNN is given by (Kim and Valdes, 2003): (2) where i, j and k respectively denote the input layer, hidden layer and output layer neurons. wji is a weight in the hidden layer connecting the i th neuron in the input layer and the j th neuron in the hidden layer, wjo is the bias for the j th hidden neuron, fh is the activation function of the hidden neuron, wkj is a weight in the output layer connecting the j th neuron in the hidden layer and the k th neuron in the output layer, wko is the bias for the k th output neuron, fo is the activation function for the output neuron, xi is i th input variable for input layer and k and y are computed and observed output variables, respectively. NN and MN are respectively the number of the neurons in the input and hidden layers. The weights are different in the hidden and output layers, and their values can be changed during the network training process. Wavelet transform (WT) The WT has enlarged in occupation and popularity in recent years since its inception in the early 1980s, but the widespread usage of the Fourier transform has yet to occur (Grossman and Morlet, 1984). In real hydrological problems, the time series are usually in the discrete format rather continues and, therefore, the discrete WT in the following form is usually used (Mallat, 1998): (3) where m and n are integers that respectively control the wavelet dilation and translation; a0 is a specified fined dilation step greater than 1; and b0 is the location parameter and must be greater than zero. The most common and simplest choice for parameters are a0 = 2 and b0 = 1. This power-of-two logarithmic scaling of the dilation and translation is known as the dyadic grid arrangement. Self Organizing Map (SOM) SOM is an effective software tool for the visualization of high-dimensional data. It implements an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. Thereby, it is able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display while preserving the topology structure of the data (Kohonen, 1997). The way SOMs go about reducing dimensions is by producing a map of usually 1 or 2 dimensions which plot the similarities of the data by grouping similar data items together. The SOM is trained iteratively: initially the weights are randomly assigned. When the n-dimensional input vector x is sent through the network, the distance between the weight w neurons of SOM and the inputs is computed. The most common criterion to compute the distance is the Euclidean distance (Kohonen, 1997): (4) Results and Discussion The results of the proposed one-step-ahead GWL modeling using pre-processed data by SVM and WT-SVM were given. The SVM-based results were also compared with those of the ANN-based model. Results of clustering Due to the existence of various piezometers over the Ardabil plain and the importance of managing groundwater resources, it is a necessity to unite the adequate information about GWLs in various regions of the plain and identify the dominant piezometers to predict GWL conditions of the plain in the future. In order to accomplish the spatial clustering, an SOM was utilized to identify similar and predominant piezometers. The SOM classifies the similar piezometers (with similar temporal patterns and seasonalities) into the same classes. The clustering results of piezometers into 5 clusters are shown in Table 1. It is clear that clustering was achieved in the direction of main stream flow and probably groundwater flow regime was parallel with the surface water toward the outlet in the northwest of the plain. To evaluate the performance of the clustering results produced by SOM, the Silhouette coefficient was used as a measure of cluster validity. The Euclidean distance was then utilized to select the centroid piezometer of each cluster, which was the best representation of the GWL pattern of the cluster. Table (1) The results of clustering Cluster NO. Piezometers Silhouette Coefficient Central Piezometer Cluster 1 P4, P9 0.42, 0.34 P4 Cluster 2 P2, P12 0.46, 0.72 P12 Cluster 3 P1, P8, P11 0.45, 0.58, 0.11 P8 Cluster 4 P6, P7, P10, P14 0.41, 0.62, 0.40, 0.54 P7 Cluster 5 P3, P5, P13, P15 0.65, 0.71, 0.53, 0.51 P5 Results of SVM and ANN The results of one-step-ahead for all 5 central piezometers of clusters are shown in Table 2. As mentioned previously, for each ANN, the dominant input variables (column 2, Table 2) were determined by linear correlation, in which Pi(t) and Ij(t) respectively indicate the GWL and rainfall time series of central piezometer i and rainfall gauge of j. Q(t) is the outflow time series from the outlet of basin. The results of one-step-ahead indicated that all of the models produced acceptable outcomes, and confirm the appropriate identification of the representative GWL patterns over the watershed. Cluster 1 did not show reliable results because the Silhouette coefficient of P4 had a lower value than 0.5, which shows that cluster 1 had a weak structure. Piezometers in cluster 3 showed better results than cluster 1, despite the large utilization in the region which was due to being close to the outlet of the plain and accumulation of water of other regions near the outlet area. Other clusters showed superior results since they were near the supplying and recharging resources and in the highlands of plain. Therefore, the spatial clustering not only can enhance the modeling performance by grouping the similar time series within the same clusters but also it can identify the piezometers and regions with irrelevant data due to artificial and/or external impacts on the system. Table 2 Results of ANN and SVM models for one-step-ahead predictions Cluster NO. Input variable Output variable Model Type R2 RMSE (Normalized) Calibration Verification Calibration Verification Cluster 1 P4(t), P4(t-1), I4(t), Q(t) P4(t+1) SVM ANN 0.977 0.977 0.958 0.951 0.006 0.006 0.005 0.005 Cluster 2 P12(t), P12(t-1), Q(t) P12(t+1) SVM ANN 0.944 0.935 0.86 0.869 0.041 0.044 0.035 0.034 Cluster 3 P8(t), P8(t-1), I3(t-1), Q(t-2) P8(t+1) SVM ANN 0.99 0.996 0.99 0.992 0.023 0.015 0.015 0.014 Cluster 4 P7(t), P7(t-1), I4(t-1), Q(t-1) P7(t+1) SVM ANN 0.819 0.832 0.667 0.677 0.038 0.037 0.023 0.022 Cluster 5 P5(t), P5(t-1), Q(t-1) P5(t+1) SVM ANN 0.955 0.97 0.94 0.94 0.006 0.005 0.004 0.004 Results of WANN and WSVM models In addition to spatial patterns, some temporal features may also exist in the GWL process due to highly non-stationary fluctuations of the time series. To handle such features, wavelet-based temporal pre-processed data were entered into the ANNs or SVM in order to improve the modeling accuracy. The hybrid model, Wavelet-ANN (WANN) and Wavelet-SVM (WSVM), were simultaneously designed to catch the non-linear GWL modeling. Due to the structure of the Daubechies-4(db4) mother wavelet which is almost similar to the GWL signal, it could capture the signal’s features, especially peak values, and was selected as the mother wavelet for the decomposition of the GWL time series in this study. The decomposition of the main GWL time series at level L yields L+1 sub-signals (one approximation sub-signal, Pa(t) and L detailed sub-signals, Pdi(t) (i=1, 2, …, L)). The decomposition level 3 was considered as the optimum decomposition level. The decomposed sub-series of GWL (each resolution demonstrating a specific seasonal feature of the process) accompanied by the rainfall and runoff data of each cluster were used in the FFNN and SVM models in order to predict one-month-ahead GWL values. The results of WANN and WSVM models for one-step-ahead forecasting are presented in Table 3. The WANN and WSVM results of one-step-ahead showed that the performance of models for all clusters were accurate during both training and verification periods. According to Table 3, the results obtained by the WANN model show the improved performance of modeling in comparison to the ANN modeling. It is clear from the performance criteria that all WSVM yielded slightly better results than the WANN (except for clusters 1 and 5 in scenario 2). Table 3 Results of WANN and WSVM models for one-step-ahead predictions Cluster NO. Input variable Output variable Model Type R2 RMSE (Normalized) Calibration Verification Calibration Verification Cluster 1 Pi4(t), I4(t), Q(t) P4(t+1) WSVM WANN 0.993 0.988 0.973 0.975 0.003 0.005 0.004 0.004 Cluster 2 Pi12(t), Q(t) P12(t+1) WSVM WANN 0.962 0.968 0.901 0.916 0.033 0.031 0.029 0.027 Cluster 3 Pi8(t), I3(t-1), Q(t-2) P8(t+1) WSVM WANN 0.997 0.997 0.995 0.995 0.013 0.013 0.011 0.011 Cluster 4 Pi7(t), I4(t-1), Q(t-1) P7(t+1) WSVM WANN 0.898 0.922 0.822 0.861 0.028 0.025 0.017 0.015 Cluster 5 Pi5(t), Q(t-1) P5(t+1) WSVM WANN 0.979 0.971 0.967 0.963 0.004 0.005 0.003 0.003 Concluding Remarks In this paper, ANN based models were developed for GWL forecasting over the plain of Ardabil, in the north-west of Iran. The inputs of the AI models were monthly rainfall, runoff, and GWL at 15 piezometers over the study area. Data pre-processing via SOM and WT were shown to be useful tools in improving AI based GWL forecasting models. The proposed methodology was applied to Ardabil plain data to find one-month-ahead forecasts of GWL. As a result, the entire study area was divided into five clusters with SOM clustering scheme and then AI modeling was performed separately for each cluster. In order to improve model efficiency and consider seasonality effects, the WT which can capture the multi-scale features of a signal, was used to decompose GWL time series into different sub-signals at different levels. The sub-signals were then used as inputs of the AI models to predict GWLs. Overall, the results of this study provide promising evidence for combining spatial and temporal data pre-processing methods, and more specifically SOM and WT methods, to forecast GWL values using the AI method. One of the advantages of the proposed method is that by using a clustering method it is possible to identify piezometers and regions with good and bad data quality. In order to complete the current study, it is recommended to use the presented methodology to forecast the GWL by adding other hydrological time series and variables (e.g., temperature and/or evapotranspiration) to the input layer of the model. Moreover, due to the uncertainty of the rainfall process and the ability of the Fuzzy concept to handle uncertainties, the combination of the ANN and fuzzy inference system (FIS) models as an adaptive neural-fuzzy inference system (ANFIS) model, could provide useful results. It would also be useful to apply the proposed methodology in other heterogeneous groundwater systems in order to investigate the overall effect of the climatic conditions on the performance of the proposed model.

The Application of the Geo-statistical Methods in Optimizing and Strengthening the Network of the Rain Gauge Stations in Kordestan Province

Davood Mokhtari; Arash Zandkarimi; Sheida Zandkarimi

Volume 3, Issue 8 , December 2016, , Pages 53-72

Abstract

Received: 2015.07.09 Accepted: 2016.10.19 Davood Mokhtari[1]* Arash Zandkarimi[2] Sheida Zandkarimi[3] Abstract Rainfall is counted as the main entrance in hydrologic modeling. Efficient network of the rain gauge stations are the ones having an appropriate density and favorable estimations of rainfall ... Read More Received: 2015.07.09 Accepted: 2016.10.19 Davood Mokhtari[1]* Arash Zandkarimi[2] Sheida Zandkarimi[3] Abstract Rainfall is counted as the main entrance in hydrologic modeling. Efficient network of the rain gauge stations are the ones having an appropriate density and favorable estimations of rainfall in locations without any station. In order to optimize the position of the rain gauge stations, different methods have been proposed, among which the geo-statistical methods are widely used. The present study aimed to assess the status of the rain gauge stations of Kordestan Province, and to optimize their position based on geo statistical methods. In this study, in order to evaluate the accuracy of various interpolation methods, the Ordinary Kriging method with circular function was detected to be more credible compared to other models and that it was the most suitable interpolation method in the distribution of rainfall in the province. Furthermore, in order to optimize and estimate the errors of the current stations, the precipitation data from 145 meteorological stations were used, and given the sheer size of the study area and great changes to rainfall data, area segmentation or clustering of the stations was done, and the whole area was divided into 8 clusters. The results of the optimization based on the Kriging coefficient of variation indicated that, by the addition of new 17 proposed stations to the rain gauge network in the province, the values of spatial coefficient of variation of annual rainfall has decreased between 0.21 to 6.67 percent, and close to 12% from the central to the south, and in western areas, respectively. The results of this study have a great importance on the use of geo-statistical methods in optimization, and the generated maps are of high practical value for the executive agencies (Ministry of Energy, the National Weather Service, etc.). [1]- Associate Professor Professor Department of Geomorphology, University of Tabriz (Corresponding Autor), Email:d_mokhtari@tabrizu.ac.ir. [2]- Master Student Remote Sensing, University of Tabriz . [3]- M.A. Land Use-Environmenta, University of Payam Noor Tehran East.

Optimization of the Best Model to Estimate Suspended Sediment Load of Abshine River Dam Hamedan

mehran mohammadpanah moghadam

Volume 8, Issue 27 , August 2021, , Pages 57-37

https://doi.org/10.22034/hyd.2021.41680.1546

Abstract

1-IntroductionAt present, it is important to accurately predict the hydraulic parameters of flow and sediment for better operation and management of rivers.Today, quasi-two-dimensional mathematical models have been widely used as an optimal and efficient solution in the hydraulics of river flow and sedimentation ... Read More 1-IntroductionAt present, it is important to accurately predict the hydraulic parameters of flow and sediment for better operation and management of rivers.Today, quasi-two-dimensional mathematical models have been widely used as an optimal and efficient solution in the hydraulics of river flow and sedimentation Zahiri et al. (2018). Mathematical models are one of the most important and accurate tools for predicting the amount of sedimentation in riverbeds and dam reservoirs, which are based on the equations governing the phenomena affecting the transfer, sediment accumulation. Gholami,et al. (2017). Raeisi et al. (2019), in examining the temporal phenomena of the sediment measurement curve and comparing it with several statistical methods to estimate the suspended sediment load of Gamasiab watershed, showed that the time series model of the transfer function compared to other models used has higher performance. Lai et al. (2019) in a study of the capacity of current and sediment transfer with 3D model for open surface channels showed that the model of good simulation between flow and sediment for forecasting is presented and is well matched with experimental data.In the present study, an attempt has been made to examine and determine the most appropriate models for estimating the suspended sediment load of the watershed of Hamedan River by using different models.2-MethodologyAbshineh basin is located in the southeast of Hamedan city and its river regime is under the semi-humid cold mountainous snow-rainy and permanent climate.In this study, the amount of sediment yield of Hamedan Abashineh dam watershed using USBR models, the middle curve of the categories Seasonal measurement curve and FAO method were estimated and while direct sampling of suspended load, the selected model by logarithmic conversion error correction method (CF1, CF2) and GS + model were statistically analyzed and evaluated.3-Results and DiscussionExamination of CF1 and CF2 correction coefficients shows that the FAO method has evaluated the amount of suspended sediment over time better than the two linear USBR methods and the intermediate method with the least amount of error.In the model without data segmentation, the FAO method with the lowest relative error percentage and the mean power of the second error was selected as the optimal method, and the similar hydrological period method was selected as the most inappropriate method for estimating suspended sediment in the basin. The results show that the USBR method follows the normal distribution to some extent and the FAO method follows the perfectly normal distribution.The results of data analysis in Kriging method show that the regression line fitted in USBR method is not well adapted and could not provide a complete analysis of sediment observation data. However, in the FAO method, it is observed that the computational data have a high and good agreement with the fitted regression line.The results showed that FAO method, due to considering more parameters in boundary conditions and the lowest amount of correction coefficients of CF1 and CF2, estimates the amount of suspended sediment with the least amount of error compared to other methods over time with more acceptable accuracy and efficiency.Also, the results of the Gs + model show that the FAO method calculates suspended sediments more accurately in terms of tons per day and has more fit and consistency with the observed sediment values, and the USBR method has the least fit.4- ConclusionThe output results of Gs + model and comparison of suspended load estimation in models show that FAO method due to more compatibility and accuracy, more accurate sedimentary fit with observational data and with least error, as the best model and USBR method with matching and fitting Less with observational data in the next degree and similar hydrological period with the highest relative error percentage of 100.34 and low correlation coefficient were selected as the most inappropriate method for estimating suspended sediment in the basin.Reviews show that hydrological models had different results than each other. So that in the model without data segmentation, FAO method compared to other methods with the lowest percentage of relative error and the average power of the second error as the optimal and appropriate method of moreaccuracy and efficiency for estimating suspended sediment in a modified form in the Abshineh basin Hamedan.

The Study of the Effects of the Future Climate Change on Discharge Variation of the Herochay River using SWAT and LARS-WG

Reza Ghazavi; Maysam Nadimi; Ebrahim Omidvar; Rasul Imani

Volume 5, Issue 15 , October 2018, , Pages 54-79

Abstract

Abstract Introduction Information about river flow change and subsequent changes in water quality characteristics can help to manage and plan water resources. The environmental and socio-economic impacts of river flow changes are very important in an environmental water management. Climate change is an ... Read More Abstract Introduction Information about river flow change and subsequent changes in water quality characteristics can help to manage and plan water resources. The environmental and socio-economic impacts of river flow changes are very important in an environmental water management. Climate change is an important challenge that should influence different parts of human life on earth such as rivers and lakes. The Evaluation of the impact of climate change phenomenon on the hydrological processes of rivers can decrease the challenges of managers and planners of water resources in the next period. The selection of suitable models is important for evaluation and prediction of the effects of climate changes on rivers and watershed discharge. Several hydrological models were used to evaluate the effects of climate change on a hydrological cycle. The Soil and Water Assessment Tool (SWAT) has been extensively used, mainly by hydrologists for watershed hydrology related subjects, since 1993. SWAT model should include both a forecasting model and weather generating model. This means that the generated weather data of the future should be presented to SWAT model for forecasting future rainfall and temperature. This is a new possibility for future river and watershed hydrology studies. The main aim of this study was to evaluate the effect of the future climate change on river discharge of the Heruchay River in Ardebil using SWAT model. Methodology In this study SWAT2009 model was used to in investigate and predict the quantitative changes of the discharge of the Heruchay River. For the period of 2014-2041, the daily rainfall and temperature data were predicted under three scenarios of A2, B1, and A1B, using LARS_WG climate model. The simulated data was used as the entered information of SWAT model and the model was implemented for 2014-2041 period. SWAT is a river basin scale model that should work on a daily time-step. It was developed to predict the effect of the management decisions and climate change on the water cycle. In this study, SWAT model was used for its ability to simulate and forecast stream flow and evaluate the effect of climate change on river discharge. A topographical map (Digital Elevation Model), climate data (daily rainfall, Maximum and minimum temperature), and soil and land use maps were prepared using GIS and measured data. As the precipitation is an important key input that influences flow and mass transport of the rivers, 5 rainfall gauging stations and 2 weather stations located in the study watershed were used. Result and Discussion The results of this study showed that SWAT model had an acceptable performance for discharge simulation during calibration and validation periods with coefficients of variation of 0.81 and 0.8 respectively for calibration and validation. Based on the results of A2 and B1 scenarios, the flow rate of the study river increased, whereas a decrease in the flow rate was predicated based on the results of the A1B scenario. The results of the climatic model indicated that the pattern of the rainfall should change in the prediction periods as the rainfall decreases in the winter and spring, while it increases in the summer. Conclusion This study offered a methodology for flow simulation and forecasting of future discharge via SWAT model. The effects of future climate change on flow quantity were examined. In this study, SWAT model was used to predict the impact of the future climate changes on river discharge. Model evaluation was done via Nash and Sutcliffe (NS), coefficients of determination (R2), P-factor, and R-factor. After model calibration, the predicted data under several climatic scenario were presented to the model. The results showed that the average of discharge will increase based on the A2 and B1 scenarios, while it will decrease under the A1B scenario. Therefore, it can be concluded that SWAT is a suitable model for discharge simulation in semi-arid areas. The results of this study also indicated that the combination of the results of LARS-WG and SWAT model should lead to an acceptable prediction of hydrological behavior of the rivers. It is important to notice that in this study only the effects of climate change on river discharge was evaluated. For a sustainable management strategy, other aspects of the watershed such as population pattern changes, land use change, and industrial development should be considered. The impact of the climate and land use change on water quality and soil erosion should also be investigated in the future studies.

Hydro-geomorphology Effect of Nurabad Mamasani Plain Aquifer on the Region’s Water Resources Using GIS

Hossein Negarash; Najmeh Shafiei; Mohammad Sadegh Doraninejad

Volume 3, Issue 6 , January 2017, , Pages 55-73

Abstract

Hossein Negarash[1] Najmeh Shafiei[2] Mohammad Sadegh Doraninejad[3] Abstract Hydro-geomorphology is a branch of physical geography (Physiography) that studies roughness forms caused by water. The study area includes Nurabad plain’s aquifer within its catchment area under catchments of Hendijan ... Read More

Analyzing the Relationship between Precipitation and Sedimentation and Sedimentation Discharge in Ligvan Chay Drainage Basin

Mejid Rezaee Banafshe; Reza Abedi

Volume 2, Issue 4 , January 2017, , Pages 57-77

Abstract

Majid Rezaee Banafshe[1]* Reza Abedi[2] Abstract The process of deposition of sediment in the drainage basin which is the basic problems in the management of the drainage basin, has the nature of complex and needs to consider certain factors in the occurrence that can be To this end, in the present investigation ... Read More Majid Rezaee Banafshe[1]* Reza Abedi[2] Abstract The process of deposition of sediment in the drainage basin which is the basic problems in the management of the drainage basin, has the nature of complex and needs to consider certain factors in the occurrence that can be To this end, in the present investigation ,initially suspended sediment discharge for days that data was not done, estimated by using flow discharge and sediment in genetic programming. And with the use of the criteria of assessment, genetic programming the most accurate method for estimating suspended sediment in the watershed case study towards the fitting curve exponent of the discharge of sediment had selected. Then fitting regression functions between the parameters of precipitation and an average discharge of flow with an average discharge of sediment in the program of SPSS 16 and modelling in genetic programming at seasonal studied. The results showed an average discharge of variable flow and suspended sediment discharge caused significant along with solidarity over 90% in most of the time intervals exist about study And between the variable precipitation and sediment discharge of suspended is established effect meaningful together with low correlation toward the hydrometric parameter (an average flow). The highest amount of correlation between precipitation and discharge of suspended sediment has been in the spring and the lowest amount of correlation between precipitation and suspended sediment discharge has been for the seasons of fall and winter. In planning the genetics3rd Brigade was used to model And according to the criteria of evaluation of the second model (The average flow of the discharge and suspended sediment discharge) Was shown The most accurate model than the models of the first (Precipitation and sediment discharge) and the third (The average flow of the discharge and rainfall with Discharge of suspended sediment). in most cases, Enter the variable rainfall combined with an average discharge of flow in model The accuracy of the model is to reduce. [1]- Associate Prof.; Faculty of Climatology; University of Tabriz (Corresponding author), Email:mrbanafsheh@yahoo.com. [2]- M.A Student; Department of Geography; University of Tabriz.

The Analysis of the Changes of the Hydrological Flow Indices Affected by Dam Construction in Zarrinehrood and Saruqchai Rivers of West Azerbaijan Province

Fariba Esfandyari Darabad; Raoof Mostafazadeh; Reza Shahmoradi; Ali Nasiri Khiavi

Volume 6, Issue 18 , June 2019, , Pages 57-77

Abstract

IntroductionThe ease of the use of river water resources has led to an extensive exploitation and, thus, the alteration flow regime. Although human manipulation on the river flows has social benefits, it alters natural ecosystems and threatens biodiversity by changing natural flow regimes. Hydrological ... Read More IntroductionThe ease of the use of river water resources has led to an extensive exploitation and, thus, the alteration flow regime. Although human manipulation on the river flows has social benefits, it alters natural ecosystems and threatens biodiversity by changing natural flow regimes. Hydrological changes caused by dams and their related environmental problems have excited many concerns for hydrologists, ecologists, and policy-makers. The high number of constructed dams, the diversion of water, the exploitation of groundwater, the canalization of waterways and, the transfer of water into basins in the world have led to large-scale hydrological changes in the environment and aquatic ecosystems. The assessment of flow changes is important to understand and modify the considerable effects of dams on river systems. Therefore, the purpose of this study was to investigate the quantitative changes in hydrological parameters of the flow in four main groups including Low flows, Peak flows, Flow duration, and Flow variability in Zarrinehrood and Saruqchai Rivers in West Azerbaijan province.MethodologyIn this study, the percentage of changes of the Zarrinehrood and Saruqchai river flow regime, affected by the construction of dams, were evaluated. In addition, the daily discharge data from hydrometric stations were obtained. The recorded discharge data in the time periods of 1955 to 2012 were analyzed in this study. The values of 18 hydrological indicators categorized in four main groups including Peak flows, Low flows, Flow duration and Flow variability were calculated. In this regard, the percentage difference of each hydrologic index was calculated. Next, the hydrologic indices were plotted in the pre and post periods of the dam construction, and the results of the Sariqamish hydrometric station was presented as an example. Finally, the triple diagram model and the Surfer software were used to determine the variations of the percentage of difference in indicators against the mean discharge values over the study period.DiscussionAccording to the results, the Min and Q10 indices with values of 287.42 and -45.57%, had respectively the highest and the least changes The Q95 index and the rate of falling indicator showed an upward trend in the downside of the Miandoab hydrometric station. The highest percentage of difference of low flow group was related to the Miandoab hydrometric station, which indicated the increase of the minimum flow. The lowest percentage of difference was observed at the Alasaqqal-Chap hydrometric station. The Miandoab and Safakhaneh stations showed the highest and lowest percentage of differences in Peak flow group after dam construction. The changes of all hydrologic indices were small in low flow discharge and increased with greater amounts of river flow discharge in the Sariqamish hydrometric station, especially in discharge values of 0 to 20 cubic meters per second.The Miandoab hydrometric station also confirmed the previous results, which showed a decrease in the hydrological indexes of the arrinehrood River flow regime in 0-40 cubic meters per second over the study period, while the changes in the river flow regime had increased in the discharges intervals of more than 140 cubic meters per second.ConclusionTo summarize, the studied hydrological indices have been altered due to the dam construction. Indeed, they are decreasing or increasing based on the nature of indexes to characterize the flow variations. Also, according to the values of average difference percentage of indices in each main group, it can be said that the groups of Low flows, Peak flows, and Flow duration in the period after the construction of the dam compared to the period before the construction of the dam were respectively 303.37, 18.57 and 943.38% at the Miandoab river gauge station under the effect of Nowruzlu Dam. Also, triple diagram model confirmed that the difference in the flow regime indices were high in higher mean river discharge values. Considering the quantitative results related to the difference percentage in hydrologic indicators, the constructed dams considerably altered the natural flow regime of Zarrinehrood and Saruqchai Rivers. Therefore, it is necessary to consider the changes of hydrological regime resulting from the construction of dams to maintain the ecological flow requirements of the river ecosystem and ensure the use of surface water and healthy aquatic environmental condition.

Modeling of Mass Movements Using Data Mining Methods in the Southeast of Neyshabur City, Razavi Khorasan Province

Mahsa Ariapour; Mehdi Bashiri; Ali Golkarian

Volume 6, Issue 19 , September 2019, , Pages 57-77

Abstract

Introduction Mass movement, according to their nature, variety, hazards for human lives, and properties, have always been a matter of interest to various scholars. Considering that the occurrence of this phenomenon has a complex mechanism and complex factors and variables can affect it, extensive studies ... Read More Introduction Mass movement, according to their nature, variety, hazards for human lives, and properties, have always been a matter of interest to various scholars. Considering that the occurrence of this phenomenon has a complex mechanism and complex factors and variables can affect it, extensive studies to identify the effective factors, classification, zoning, and modeling of this process have been conducted. In this study, landslides of three watersheds in the southeast of Neishabour city were investigated and the hazard zonation map was prepared, using bivariate statistical methods of the information value and area density. There are few studies regarding the application of different data mining methods to determine the effective variables in the occurrence of landslides and most studies are based on other statistical methods. Data mining is called as knowledge discovery in databases and is a way to discover new and beneficial information through a lot of data. Some of the most important data mining algorithms include the decision tree, random forest, boosting aggregate demand, support vector machine, logistic regression, and neural network algorithm. The data mining extracts useful information from large volumes of data and has shown a good performance. Therefore, the aim of the present study was to prioritize Methodology The present study aimed to investigate the factors affecting the occurrence of a landslide and its zoning in three watersheds including Kharv, Harimabad and Grineh watersheds in the Razavi Khorasn province. First, 99 landslides were identified in the area and the landslide distribution map was prepared. Then, all effective factors on watershed landslides, in 15 information layers including the altitude, slope, aspect, climate, land use, pedology, vegetation cover, geology, evaporation, temperature, rainfall, land type, distance from road, distance from fault, and distance from river were digitized in the ArcGIS environment. Then, using data-mining algorithms in R software, the preferable algorithm and effective factors on landslide occurrence, were introduced. Finally, the landslide hazard zonation in the GIS software was done using bivariate statistical models. Results The results showed that the random forest algorithm with an accuracy of 92% is the best one and the variables of geology, climate, aspect, distance from road, altitude, pedology and land type are the most important variables in algorithms modeling. The most probability of occurrence of watershed landslides placed in areas with west and northwest directions, slopes higher than 30 degrees, dominant type of the environmental factors affecting the occurrence of a landslide including the altitude, slope, aspect, climate, land use, pedology, vegetation cover, geology, evaporation, temperature, rainfall, land type, distance from road, distance from fault, and distance from river using data mining algorithms, zoning its sensitivity, and bivariate statistical models of information value and area density in three watersheds including Kharv, Harimabad, Grineh watersheds in Razavi Khorasan province. mountains, the semi-humid climate, 1500 to 2000 mm evaporation class, entisols, dense vegetation, the gardens, bushes and shrubs land uses, being close to the roads and faults and being far from the rivers, and the altitudes of 2000 to 2500 m with the phyllite, boulders and sandstone formations. The results of the zoning map evaluation using the information value and density area methods showed that 45.45% and 55.55 % of landslides were respectively located at the high and very high risk zones and the rest were in very low, low, and moderate risk zones. As a result, in both methods, most of landslides were in the high and very high risk zones that indicated the suitable accuracy of the model. Discussion and Conclusions According to the results of this research, variables including the geology, climate, aspect, distance from road, altitude, soil science, and land type were considered as the most important factors in the occurrence of a landslide. In addition, factors such as slope, land use, vegetation cover, distance from fault and distance from river were identified as the most important factors influencing the development of landslide and classified as natural factors, which could be influenced by human factors. The comparison of two mentioned methods showed that the area density method was more appropriate than the information value method for the study area.

Statistical Distributions Analysis for Estimating of Climate Change Effects on Future Floods (Case Study: Azarshahrchay Basin)

Mohammadreza Goodarzi; Atiyeh Fatehifar

Volume 6, Issue 20 , December 2019, , Pages 57-78

Abstract

1-Introduction The assessment report fifth of the Intergovernmental Panel on Climate Change shows that global warming has led to a change in the water cycle due to increased greenhouse gas emissions. In the present time, with the increase of industrial activities and the neglected environmental issues, ... Read More 1-Introduction The assessment report fifth of the Intergovernmental Panel on Climate Change shows that global warming has led to a change in the water cycle due to increased greenhouse gas emissions. In the present time, with the increase of industrial activities and the neglected environmental issues, the effects of climate change have become more evident and poses this phenomenon as a global difficult. Increasing the probability of occurrence of extreme climatic events such as flood and increasing the frequency and intensity of the effects of climate change. Due to changes in climate and global warming, the probability of heavy rainfall and consequently the risk of flood due to incorrect drainage system and physical and environmental factors have increased. Therefore, the study of the region's climate is important given the new scenarios and flood frequency analysis with suitable statistical distributions for future planning. 2- Methodology In the present study, the effects of climate changes on the runoff of Azarshahrchay Basin with CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 release scenarios assessment report fifth (AR5) of the Intergovernmental Panel on Climate Change (IPCC), with Statistical down scaling model (SDSM), for the period 1976-2005 and 2059-2030 by the hydrologic model SWAT have been investigated. The accuracy of the simulation was evaluated with three indicators: Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Nash–Sutcliffe Efficiency (NSE). An analysis of the frequency of maximum annual flood for both base and future periods using their probability distribution function (PDF) and the Easyfit model. In this model, 5 types of probability distribution including Normal, Normal Log, Pearson, Log Pearson Type 3 and Weibull were used. The best distribution for each basic and future time series were ranked and selected by using three Chi-square, Kolmogorov–Smirnov, Anderson–Darling tests. In order to study how the maximum flood discharge regime changes in the base and future periods were used two indices: 1) The probability and the return period in the equal flows 2) Intensity of flow in the equal return periods 3- Results The obtained factors of the three RMSE, R2, and NSE indicators showed the good performance of the SDSM model in the down scaling the large-scale data. Investigating the performance of the SDSM model in the downscale of the Azarshahr station's climate data with a Coefficient of Determination and Nash–Sutcliffe of 0.99 and 0.98 for temperature for the period 1990-2001 and 0.86 and 0.83 for precipitation in the period 1976-2005. The simulation results showed a rise in temperature during the period 2030-2059 under scenarios and the highest increase was related to RCP8.5 (0.23°c). Also, rainfall at a station increased by 7.44 percent to RCP2.6 and at another station decrease by 7.57 percent to RCP8.5. The performance analysis of the SWAT model indicates a good accuracy of the model in runoff simulation with R2 and Nash 0.6 on average. The results of the 2.1% increase in runoff and the maximum flood peak and the probability of flood events in March and April (late winter and early spring) have been shown by the SWAT model. Results of the study of the regime of maximum annual flows (frequency and intensity) by fitting probabilistic distributions with the lowest error rate for the base distribution period of the Weibull, future period RCP2.6 distribution Log Pearson Type 3, RCP4.5 Log Normal and RCP8.5 Log Normal as best distribution are selected. Also, the frequency and intensity of flood have increased. In the return periods of constant, the maximum discharge increased, and in maximum discharge constant, with increasing return period (1000 years), the discharge rate significantly increased. So, in the 500-year return period is expected a 98% increase in maximum discharge RCP8.5 future period than base period. The most critical scenario is RCP8.5 scenario. 4- Discussion and conclusion The results indicate the impact of climate change on the basin in the future period. Therefore, knowing the increase in precipitation intensity, the flood risk increases. The occurrence of terrible floods due to climate change have caused many damages in different parts of the world in recent decades. The results of this study, like other previous studies, confirm that climate change is significant, especially with the increasing frequency of floods, governments, organizations, and educational centers need to take appropriate measures to eliminate or reduce the effects of climate change and adaptation to extreme events such as floods.

Volume 11 (2024)

Volume 10 (2023)

Volume 9 (2022)

Volume 8 (2021)

Volume 7 (2020)

Volume 6 (2019)

Volume 5 (2018)

Volume 4 (2017)

Volume 3 (2016)

Volume 2 (2015)

Volume 1 (2015)

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