Geomorphology
Fariba Esfandyari Darabad; Ghobad Rostami; Raoof Mostafazadeh; Mousa Abedini
Abstract
In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including ...
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In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.
hydrogeology
Fariba Esfandyari Darabad; Zeinab Pourganji; Raoof Mostafazadeh; Maryam Aghaie
Abstract
Floods as destructive natural hazards need to be predicted in accurate way through evaluation of the hydrological response of watersheds to the effective input rainfall. Due to the variety of rainfall-runoff models, it is very important to choose a suitable model that can simulate the hydrological behavior ...
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Floods as destructive natural hazards need to be predicted in accurate way through evaluation of the hydrological response of watersheds to the effective input rainfall. Due to the variety of rainfall-runoff models, it is very important to choose a suitable model that can simulate the hydrological behavior of the watershed. In this study, various rainfall-runoff transformation methods have been evaluated, including triangular, broken triangular, variable triangular and SCS-curvilinear unit hydrograph methods in Nenekaran watershed, Ardabil province. In this regard, the Wildcat5 hydrological model have been used to this purpose. The precipitation amount at the 25-year return period was calculated using Cumfreq software. After preparing the land use map of the study area using satellite images, the area of each land use in the area has been calculated using ArcGIS software. The precipitation value and the time of concentration were considered constant during the simulation procedure. The results showed that the SCS method had the highest runoff of 44.50 cubic meters per second. The minimum time to the peak was 2.19 hours and the variable triangular method had the lowest peak flow rate. The simple triangular method has a maximum time to peak of 4.51 hours, which shows the great difference between the hydrograph of the SCS method and the other three methods. The difference in the nature of the methods, the watershed condition, and the suitability of estimating tc and CN parameters should be considered in rainfall-runoff transformation methods.
hydrogeology
Rasool Hasan zadeh; Friba Esfandyari; sayyad Asghari saraskanrood; Zahra Miri
Abstract
the object-oriented method in preparing the land use map of Darre Rood catchment area using Landsat 5 and Landsat 8 images in a period of 30 years, from 1990 to 2019 and its effects on changes in Darre rood river discharge it placed. The images were classified into fourteen classes and the changes in ...
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the object-oriented method in preparing the land use map of Darre Rood catchment area using Landsat 5 and Landsat 8 images in a period of 30 years, from 1990 to 2019 and its effects on changes in Darre rood river discharge it placed. The images were classified into fourteen classes and the changes in the area of the classes revealed that the classes of irrigated agriculture, rainfed agriculture, rocky areas, residential areas, gardens and lakes with increased area and barren lands, pastures, forest lands and riverbeds decreased They were. To find out the changes in the river flow trend, SCS method was used which was implemented in SWAT model and according to land use in 1990 and 2019 in SWAT model was determined according to the digital elevation layer of the basin and all the necessary parameters to the model. Which included soil layers and land use changes and climate data were called into the model and two separate scenarios for 1990 and 2019 were used. The results showed that with the change of land use, the amount of CN in the second scenario compared to the first scenario increased by 5% and increased from 02.70 to 5.73, which due to the change in land use in favor of the basin becomes more impermeable to rain. Compared to 1990. Also, due to the increase in the type of vegetation, the amount of deep penetration has decreased from the first scenario to the second scenario from 257.09 to 97.9.
Fariba Esfandiyari Darabad; Rasoul Bakhshandeh; Masoud Rahimi; Khadijeh Haji; Raoof Mostafazadeh
Abstract
1-Introduction The changes in river processes due to river discharge and sedimentation as a primary principle driving force can affect the geometry of rivers. Determining the amount of sediment and floodplain and water quality study are prerequisites for river management operations. Any change in the ...
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1-Introduction The changes in river processes due to river discharge and sedimentation as a primary principle driving force can affect the geometry of rivers. Determining the amount of sediment and floodplain and water quality study are prerequisites for river management operations. Any change in the steady-state of the rivers will result in physical changes in the rivers and a new reaction to the rivers' behavior. Morphological studies to determine the quantity and quality of river response will predict future river behavior. The downstream river reaches of the Hamzekhanloo River basin is one of the most important agricultural areas of Germi city due to its fertile flood plains and sufficient water availability, which has undergone many changes in recent years. In this study, the Hamzekhanloo River was investigated based on the Rosgen stream classification scheme. 2-Methodology The Hec-Ras hydrodynamic model was used to simulate the Hamzekhanloo River cross-sections and floodwater capacity. The processing of the required data for modeling purposes was performed at the ArcGIS software; the classification of stream reaches was done using the Rosgen stream classification system. Rosgen classification system predicts river behavior based on morphology and hydraulic relationship and flow sediment with specific morphology. Based on Rosgen's method, morphological characteristics of rivers are investigated at four different levels but focuses more on two levels of general geomorphic properties and morphological description. Level 1 (General Classification): Describes the morphological characteristics of the river obtained by combining information on catchment, landform, and valley morphology. Level two (descriptive classification) of the river. 3-Results and Discussion The results of the Rosgen classification scheme showed that the studied river had been classified at the C class in some river reaches, which had high flood sensitivity, high vegetation control, high sediment recovery, and sediment supply potential. Also, these reaches had narrow to wide valleys, constructed from alluvial deposition with a well-developed floodplain. Meanwhile, some sections of the study river fall in the B class according to the Rosgen classification. These reaches exist primarily on moderately steep to gently sloped terrain, resulting in narrow valleys that limit the development of a wide floodplain. These streams display a low channel sinuosity, and streambank erosion rates are normally low. The sensitivity to flooding and sediment supply is high; the influence of moderate vegetation control and recovering potential is excellent. Moreover, the cross-section patterns in the river and the parameters affecting the classification and segmentation of reaches are consistent with the overall pattern on the Rosgen classification model. 4-Conclusions The river bed of the Hamzekhanloo River is a combination of rubble, gravel, and sand. Farmers and gardeners dig the riverbed and store water to irrigate the orchard fields and gardens during the summer, and crop cultivation is observed in the river bed and floodplain. Sand mining is a common activity in the river bed to carry out the development and construction purposes of the area. Sand removal from the riverbed has led to the formation of ponds within the basin, and such alterations have altered the bed and morphology of the Hamzekhanloo River. Thus, Rosgen's model can predict the geomorphic quantification of the Hamzekhanloo River and rivers with similar conditions. This type of river channel morphological classification can be used to develop engineering designs and management implications and river restoration.
Ebrahim Beheshti Javid; Fariba Esfandiyari Darabad; Shahram Rostei
Volume 5, Issue 16 , December 2018, , Pages 177-197
Abstract
Abstract
Introduction
Geomorphometry is the science of quantitative land-surface analysis (Pike, 1995, 2000a; Raseman et al., 2004). It is an interdisciplinary field that has evolved from mathematics, the Earth sciences, and most recently computer sciences (Pike et al, 2008, 3). It is ...
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Abstract
Introduction
Geomorphometry is the science of quantitative land-surface analysis (Pike, 1995, 2000a; Raseman et al., 2004). It is an interdisciplinary field that has evolved from mathematics, the Earth sciences, and most recently computer sciences (Pike et al, 2008, 3). It is well to keep in mind the two overarching modes of geomorphometric analysis first distinguished by Evans (1972) as specific, addressing discrete surface features (i.e. Landforms), and general, treating the continuous land surface. The morphometry of landforms per se, with or without the use of digital data, is more correctly considered part of the quantitative geomorphology (Thorn, 1988; Scheidegger, 1991; Leopold et al., 1995; Rhoads &Thorn, 1996). The shape of terrain, i.e. landforms, influences flow of surface water, transport of sediments, and soil production, and determines climate on local and regional scales. Furthermore, natural phenomena like vegetation are directly influenced by landform patterns and their relative position across the landscape (Blaszczynski 1997; Blaschke & Strobl, 2003).
The Earth’s surface is structured into landforms as a result of the cumulative influence of geomorphic, geological, hydrological, ecological, and soil forming processes that have acted on over time. Landforms define boundary conditions for processes operative in the fields of geomorphology, hydrology, ecology, pedology and others (Dikau, 1989; Dikau et al., 1995; Pike, 1995, 2000a; Dehn et al., 2001). In this study, using MRS algorithms and Ecognition software, landforms in the northern slopes of Mount Sabalan have been extracted and the effects of Landform morphometry on its hydrology have been investigated
Methodology
The semi-automated methods refer to the automatic procedures of extracting a landform based-process. This is mainly relying on unsupervised isodata classification, pixel-based classification (supervised /subpixel classifier based on training material), the analysis of digital elevation models (DEM), algorithms, hydrological modelling, and object oriented analysis (Nabil and Moawad, 2014:42).
In this study object-oriented methods and Ecognition software were used for the classification and the extraction of landforms. The object-oriented classification was used as an alternative to traditional pixel-based classifications, to cluster grid cells into homogeneous objects, which can be classified as geomorphological features (Seijmonsbergen, 2012). In addition, the DEM and its derivation (Slope, Profile and plan curvatures, maximum and minimum curvatures), were used in order to extract landforms. Then, using fuzzy logic method, the landform, land use, NDVI index , precipitation, density of river, and lithology layers were Overlaid and the potential flooding area was obtained.
Results and Discussion
In the object-oriented method, determining the scale parameter is a very important factor in the separation of different objects in an image. Scale parameter is a crucial threshold that determines the maximum allowed heterogeneity for segmentation and has a direct influence on the size of the objects to be obtained. The scale parameter, after a trial and error process, is recognized to be within a particular range (Gerçek, 2010:115). A novel method that was introduced by Dragut et al. (2010) and the ‘Estimation of Scale Parameter (ESP) that built on the idea of ‘Local Variance’ (LV) were employed to obtain the optimum scale out of a range of scales. By interpreting thresholds and prominent peaks in the ROC-LV graph, characteristic scales relative to data properties at the scene level could be found. This curve in 100 scale level was produced for the study area by using the ESP software and with respect to curve, the scale of 25 was selected for the segmentation. After segmentation, using the morphometric differences between the landforms, the landforms were extracted. After this stage, the landforms along with three layers of NDVI index, land use, and lithology was fuzzy. Finally, using gamma 0.8, they were combined and the zoning map of the potential flooding was estimated. Flood zoning map was classified into 5 classes and the percentage of each zone risk was calculated in each landform.
Conclusion
In this research, using an object-oriented model, landforms were extracted as plain, peak, pit, ridge, channel, nose, shoulder slope, hollow shoulder, spur, planar slope, hollow, spur foot slope, and hollow foot. An assessment of the effect of landforms on the hydrology of the area revealed that three landforms of hollow, shoulder and planar slope which were respectively 67.3%, 62.9%, and 53.2% had the greatest impact on flooding and their area were zoned as high and very high flooding. On the other hand, plain and pit landforms were zoned in the form of low and very low flooding areas.
Fariba Esfandyari; Morteza Garachorlou
Volume 2, Issue 4 , January 2017, , Pages 125-142
Abstract
Fariba Esfandyari[1]* Morteza Garachorlou[2] Abstract This study seeks to identify and determine the spatial-temporal variations of sediment yield in Qarahsu watershed situated in Ardabil province. To do so, the relations between sediment yield and precipitation were examined in term of temporal-spatial ...
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Fariba Esfandyari[1]* Morteza Garachorlou[2] Abstract This study seeks to identify and determine the spatial-temporal variations of sediment yield in Qarahsu watershed situated in Ardabil province. To do so, the relations between sediment yield and precipitation were examined in term of temporal-spatial variations in order to provide the estimate model of sediment load in the subwatersheds. Data gathered from six water and rain gauges of the same name over a 22-years period was used. The method followed regression analysis between precipitation and sediment yield by SPSS software and analysis of temporal variations of precipitation and sediment yield by Excel software. Analysis of temporal relations between sediment load and precipitation indicated a higher correlation in intra-annual than in inter-annual scale. In terms of intra-annual variations, except for Hir water gauge, that underwent an increasing trend, other stations had decreasing trends in sediment load. Nonetheless, the increasing trend in annual precipitation of 4 rain gauges was considerable. Results of regression analysis, on one hand, indicated weak correlation between precipitation and sediment load in intra-annual scale, but on the other hand, indicated high correlation in inter-annual scale. Meanwhile, the Fournier index, as seasonal precipitation index, can explain 65% variance of specific sediment yield in the studied watershed. Hence, the index, as indicator of precipitation erosive power, can be effectively used to estimate specific sediment yield in the watershed. [1]- Associate Professor; Department of Physical Geography; University of Mohaghegh Ardebili, Iran (Corrosponding author), Email:Esfandyari@uma.ac.ir. [2]- Ph.D Student in Feomorphology; at University of Mohaghegh Ardebili; Iran.
Volume 1, Issue 1 , January 2015, , Pages 59-73
Abstract
Catchment land use changing in an area is one of the most important factors in hydrology. As a model, L-THIA was designed to assess the long-term impacts on the hydrology of a catchment for researchers who wanted to determine the relative changes in the runoffs from one land-use condition to another. ...
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Catchment land use changing in an area is one of the most important factors in hydrology. As a model, L-THIA was designed to assess the long-term impacts on the hydrology of a catchment for researchers who wanted to determine the relative changes in the runoffs from one land-use condition to another. In this regard, upstream section of Gharasoo catchment in Ardabil province has been evaluated in this study in terms of land use change (1987-2012) and the impacts on runoff production. To accomplish this end the research has used daily precipitation data from four stations, Landsat Satellite images (TM and ETM sensor), L-THIA extension software and Arc Map software. Modeling results indicate that during the 25 years, the land use change has caused an average of 1.8 mm increase in the runoff in this catchment. Land use changes mainly increase the expansion of residential areas, and loss of woodland and pastures. In some areas, such as Ardabil plain, due to conversion of pastures into farmland, land use changes have operated in a positive direction increasing the permeability of the ground and have mutually reduced runoff in this part of the catchment. Due to its capabilities in providing a zonation map, the volume and depth of the runoff, the model used in this study has the required ability to show the hydrologic impacts of land use change and demonstrate susceptible areas and flooding in the basin.
