پژوهشی
Leila Goli Mokhtari; Najme Hshafiei; Abolalfazl Rahmani
Volume 5, Issue 17 , March 2019, Pages 1-21
Abstract
Introduction
Soil erosion is a phenomenon that typically occurs in a large part of the earth and the exacerbation of this process, as a limiting factor, can be an obstacle to the management of the land. It reduces the soil fertility and results in the desertification of the fields and the sediment deposited ...
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Introduction
Soil erosion is a phenomenon that typically occurs in a large part of the earth and the exacerbation of this process, as a limiting factor, can be an obstacle to the management of the land. It reduces the soil fertility and results in the desertification of the fields and the sediment deposited in the drains and reservoirs of sediment droplets reduces their intake capacity. Soil contamination is one of the environmental problems that threatens natural resources, agriculture, and the environment. Soil erosion's time and space data plays an important role in management measures, erosion control, and management of catchment areas. Therefore, in order to protect effectively and prevent undesirable effects of erosion, it is necessary to identify the factors involved in erosion to provide an appropriate estimation of the amount of erosion in the area. So far, several methods have been proposed to estimate erosion in areas with different characteristics. The models presented in three categories are empirical, conceptual, and physical models. The empirical models have always been considered for ease of use and availability of the data, and there have been significant advances in their development. The Global Soil Erosion Equation (USLE) is one of the experimental models that has been proposed to predict mortality on grazed lands, but the modified Global Soil Deterioration Equation (RUSLE) has expanded for various uses, including forest, pasture, crop, and bayer lands. Similar to the USLE, the RUSLE model has six factors, but more accurate estimates of rainfall erosion, soil erosion, vegetation, and conservation operations are used to predict soil losses in wider areas and in different conditions such as crops, forests, grassland, and damaged forests. This model estimates soil erosion as a combination of six factors that indicate the rainfall erosivity, soil erodibility, length and gradient, cropping system, and management operations.
Methodology
In this research, the erosion of the Nourabad Mamassani basin using the RUSLE model was studied. The method was descriptive. To prepare the studied basin maps, the topographic map of 1: 50000, the geological map of 1: 100000, Google Earth images, Landsat 8 satellite images, soil layers, monthly and annual precipitation data of synoptic stations were used. In addition, Kriging zoning method was used to prepare the rainfall erosion layer. A regression analysis was used to determine the relationship between the dependent and the independent variables as well as the effect of the most important factor on the annual waste of soil. The annual regression model of the soil was the dependent variable. The rainfall erosivity factors, soil erosion, topography and vegetation were considered as independent variables. As previously mentioned, the model used in this study was the RUSLE global erosion model. It consists of 6 factors as follows
Relationship (1) R.K.L.S.C.P = A
A: soil erosion per unit area. R: rainfall erosion factor. K: soil erodibility factor. L: slope factor. S: slope factor. C: covering agent. P: a protective operation
Results
The annual mean erosion of the soil was determined using the coefficient of erosivity (R), soil erodibility factor (K), topographic factor (LS), vegetation cover factor (C) and conservation factor (P), and the ArcGIS software. The map obtained from this equation is shown in Fig. 7. The erosion values in the studied basin vary from 6 to 75 tons per hectare per year at the pixel level. According to table (3), about 48% of the area is a low erosion class, which mainly includes a large part of the basin. About 28% of the range is in average erosion, and about 23% of the basin is under severe erosion, which is located in the southern part of the basin.
Discussion and Conclusion
Investigating the rainfall erosivity map at the basin level showed that the values of this factor varied from 11 to 31. The erosivity values from the central parts of the basin to the northern part of the trend were decreasing and in the southern parts where rainfall was higher, erosivity has increased. Soil erosion rate varied from 0.25 to 0.48. The results of the vegetation analysis showed that the values of this factor varied from 0.7 to 1.35. The major part of the role of destructive factors on soil erosion was in rain and pasture lands related to the human factors. The study of the soil erosion risk map, which was produced from the combination of erosivity layers, soil erosion, topography, and vegetation, showed that the soil erosion risk level in the basin was variable from 8 to 75 per hectare per year. According to the map of the soil erosion risk, areas with high erosion risk were mainly in uneven areas of the region. Also, the effect of the rainfall erosion on the increase of erosion in the southern parts of the basin had a medium to high erosion risk. Also, areas with a high erosion risk included areas that had a rugged area. The results of this study showed the high capability of GIS and remote sensing to generate the data needed to generate RUSLE factors, resulting in Output data high quality. Therefore, GIS and RS can be effectively used to develop managerial solutions and provide selected choices for managers to solve the erosion problem.
پژوهشی
Raoof Mostafazadeh; Ali Nasiri khiavi
Volume 5, Issue 17 , March 2019, Pages 23-44
Abstract
Abstract
Introduction
The analysis of the temporal and spatial variations of surface runoff is one of the important issues in hydrology, water and soil resources management, and environmental science. Variability is an intrinsic component of environmental factors and elements. Today, the study of changes ...
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Abstract
Introduction
The analysis of the temporal and spatial variations of surface runoff is one of the important issues in hydrology, water and soil resources management, and environmental science. Variability is an intrinsic component of environmental factors and elements. Today, the study of changes in hydrological patterns and processes is one of the most important requirements for water and soil resources management. The temporal and spatial variations of runoff flow and discharge in terms of water use and exploitation have many economic effects, and this variability is the main cause of floods and droughts at different scales. In addition, hydrological processes, by changing the spatial and temporal scales, provide different responses that limit the repeatability of hydrological observations. In general, the tools of assessing changes in hydro-climatic time series include information theory and dominance measure. The first category indices include the Shannon Index and the Brillouin Index. While, the dominance measures include the Simpson index, the McIntosh index, and the Berger-Parger index, the indices of information theory measures have the best parameters. The Shannon index has a better distribution than Simpson or Berger Parker. While the Braillein index has a similar distribution to the Shannon index, it limits ranges from zero to one.
Therefore, the main purpose of this research was to evaluate the spatial and temporal changes of the Discharge Variability Indices (DVI) of surface runoff in some watersheds of Ardabil Province.
Methodology
Toward this attempt, the discharge variability indices of river flow fluctuations were calculated on a monthly time-scale including Shannon, Brillouin, Simpson, McIntosh, Berger-Parker, Index of Variability, Rainfall Anomaly Index, and Discharge Variability Index. For this puprpose, 22 river gauge stations in Ardabil Province were selected. First, the index values were calculated in the Excel software. After calculating these indices, their spatial variations were investigated in the studied area using the distance mapping method in ArcGIS 10.1. The spatial variations of the indices in the studied area were evaluated. In addition, the Triple Diagram Models were used to determine the temporal variation of the DVIs in relation to flow changes over the study time periods using Surfer software. Next, the Pearson correlation coefficient between the discharge variability indices were performed using R software.
Results and Discussion
The results showed that the variability of the DVIs were higher in the upstream regions than the downstream regions, which can be related to the less changes in river flow regimes and the limited interference caused by human utilizations. The highest and lowest values of the coefficient of variation were observed in the Macintosh and DAI+ indices respectively with the values of 195.55% and -567.06%. The results of the triple diagram models indicated that the variability of DVIs were higher in low river flow values. According to the interpolation results, the upstream stations were less variable, while in the downstream stations, the degree of variability was greater due to different human interactions. Based on the results of the triple diagram models, it can be said that the variability of Shannon, Simpson, Berger-Parker, McIntosh, and DVI indices was lower in low discharge values. Also, the DAI+ and DAI- indices were more variable in lower discharge values. The results also showed that there was a significant correlation between the Brillouin index and Index of Variability (-0.42), while the Berger index -Parker and Index of Variability had a positive correlation (0.91). Also, there was a significant positive correlation between RAI+ and RAI- indices (0.62) and the correlation between RAI- and DVI was significant (0.64). In addition, the degree of variability had decreased in recent years. Also, the correlation relationship of DVIs were tested using the R software.
Conclusion
Based on the results, in the upstream regions, the flow rate of the rivers was much lower than the downstream river gauge stations, which can be explained by the condition of the flow near the natural flow of the river. However, in the downstream stations on the main river (such as Samian and Arbab Kandi stations), the existence of Yamchi and Sabalan dams have been caused by a disruption and through the regulatory effect of the dam. In general, the Shannon index as an information-based index and Simpson (dominance-based index) yielded different results from other indicators. Most of the indices showed that the rate of variability in the low flow was higher than the high discharge values. In addition, in recent periods, the degree of variability of the flow has decreased based on most indices, although determining the cause of reducing the variability caused by climate change or human activities requires further studies. The assessment of the variability of the flow at the watershed scale allows the optimal utilization of surface water in the proper seasons and determines the effect of human activities on the river regime.
پژوهشی
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 ...
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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.
پژوهشی
Nezam Asgharipour Dasht Bozorg; Mohammad Reza Servati; Pervez Kardavani; Siavash Shayan
Volume 5, Issue 17 , March 2019, Pages 65-84
Abstract
Introduction
Alluvial fans have a great importance in terms of their high efficiency to create natural aquifer and groundwater storage. Increasing the rate of water demand and relying on groundwater has caused a remarkable decline in groundwater resource and aquifer level. On the other hand, flood spreading ...
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Introduction
Alluvial fans have a great importance in terms of their high efficiency to create natural aquifer and groundwater storage. Increasing the rate of water demand and relying on groundwater has caused a remarkable decline in groundwater resource and aquifer level. On the other hand, flood spreading is known as an applicable and an effective method for artificial aquifers recharging in arid and semiarid regions. Sabzab and Gotvand plains (fig. 1), in Khuzestan Province, have experienced vivid decline in groundwater-level due to over pumping of aquifer resources, since last decades. Therefore, these plains have been selected to implement methods of artificial recharging of groundwater, especially flood spreading.
Methodology
The research methodology included comprehensive methods of field observations, application of ArcGIS, 10.3 tools, and modeling. Accordingly, a geographical information system was used for the zoning of the suitable areas to implement artificial recharging by a flood spreading method based on the fuzzy logic model. Imported data for zoning included Landsat ETM+ satellite images (2010, 28.5 m resolution), topographic maps of Gotvand and Sarbishe regions (1:25000 scale), geological map of Dezfol region (1:100000 scale), precipitation data, soil permeability data, and the measurement of the electrical conductivity of floods region. The zoning procedures provided 6 GIS-ready map layers including quaternary deposits of the region, slope, infiltration, electrical conductivity, thickness of alluvium, transmissivity, and drainage density. In the second step, the effective factors were formulated in a fuzzy manner and GIS-Ready layers were overlapped using Sum, and, OR, Product, and Gamma operators. Finally, the zones with high suitability for flood spreading were overlaid over the alluvial fans outcrops.
Results and discussion
Several thematic maps were produced on the basis of the fuzzy method. The suitability zoning as the main objectives of the research was obtained in four classes ranged (fig. 5) from high suitable to unsuitable (table 1). The results showed that high-suitable areas were often at the bottom of the Bakhtiari conglomerate formations and alluvial fans (Fig. 7). In addition, alluvial fans which had mainly composed of coarse size sediments had close genetic relationship with Bakhtiari conglomerate formation and represented the remarkable matching with two high-suitable and suitable classes. The mean rate of 83 % of the different fuzzy operators showed the most comparability with high suitable and suitable areas.
Conclusion
The research hypotheses were successfully confirmed by the resultant data. The present research indicates the importance of geomorphological landforms in terms of artificial groundwater recharge and it should be protected as a source of water. Therefore, incorrect changes to this lands form should be avoided. Furthermore, the fuzzy method has represented a useful manner to find suitable zones for flood spreading. The research method is also recommended to be used in other similar geological conditions in Khuzestan Province.
پژوهشی
Aghil Madadi; Elnaz Piroozi; Leila Aghayary
Volume 5, Issue 17 , March 2019, Pages 85-102
Abstract
Introduction
One of the most striking natural hazards in the world is flood which generates a lot of financial and human losses every year. It can be said that in comparison with other natural hazards, it occurs with high abundance and in vast expanses. Some of its causes can be severe or prolonged ...
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Introduction
One of the most striking natural hazards in the world is flood which generates a lot of financial and human losses every year. It can be said that in comparison with other natural hazards, it occurs with high abundance and in vast expanses. Some of its causes can be severe or prolonged rainfalls, melting, breaking the dam and landslide, high waves, channel closure, rainfall intensity, type of rainfall, time and volume of rainfall, previous river conditions, drainage basin, inappropriate use, and falling of forest trees in the sources of the rivers. Knowing susceptible areas to floods is one of the basic measures in natural resource management and development planning. One of the most important flood management methods is flood zoning. The zoning of potential flooding is to identify and describe areas with potential for surface runoff. The Khiyav Chay Watershed Basin, with an area of 318 km2, is located in Meshgin shahr. Due to the specific circumstances of the region, such as topography, slope, and climatic conditions (sudden precipitation and spring precipitation, melting, flooding of rivers in the spring), there is a high potential for flood occurrence. Therefore, the purpose of this research was to study the area's potential for flood occurrence.
Methodology
In this study, ten factors of slope, height, rainfall, CN, runoff height, distance from the river, soil, lithology, vegetation, and user-use were identified as effective factors for flood formation in the region. Using Landsat 8 images including OLI and TIRS sensors and the Maximum Likelihood supervised classification method, in the ENVI 5.3 environment, the land use map was obtained. Then the user map was compared with the index table and integrated with the hydrologic group data, and the CN curve number was prepared. In the next stage, with mean precipitation and CN, and by using SCS method, ARC GIS software and Arc-Hydro and Arc CN-Runoff subtraction, the runoff height of the range was calculated. Also, the NDVI index, one of the most widely used indices for vegetation monitoring, was undertaken to prepare a vegetation map of the basin. Then, the other layers of information were provided in the GIS environment. The weights of the layers using the Critical method based on the correlation, interference, and standard deviation of the factors were determined. The final analysis and modeling was done using the WLC model as one of the methods of multi-criteria analysis techniques.
Discussion
By studying the zoning of the potential flood area of the study area and comparing it with each of the standard maps, it was concluded that the high risk areas were mainly in the hilly and mountainous areas of the area (slope over60%). Due to the slope and elevation of the area, the main role was with runoff, flood discharge, penetration, precipitation losses, and flow and water velocity. In these high risk areas, due to the fact that most of the formations belong to the formation of volcanic activity in the late third and early fourth centuries, the degree of permeability was very low but the runoff and CN amount were high. Secondly, areas with potential hazard were located within the urban boundaries of Meshkinshahr. In the city of Meshgin Shahr, on the east side, is the deep valley of khiyave chay, where the khiyave chay River flows. Two other radial valleys in the natural pathway formed the surface water stream, along which residential neighborhoods were developed that were subject to flood and extreme flow of surface water. Due to the fact that most of the city is made up of asphalt and residential surfaces, the permeability was very low, in contrast to the amount of runoff (99%) and CN (curve above 8).
Conclusion
According to the results of weighing, height factors with weight coefficients (0.173), lithology with weight coefficients (0.163), slope with weight coefficient (0.139) and rainfall with weight (133/0) were the most important factors on flood formation in the region. The results of the study showed that 13.33% and 22.88% of the study area were in high risk and high class. According to the final map, high-risk areas, in the first priority, were mainly in the hilly and mountainous regions of the region, but in the second priority they were within the urban boundaries (especially in the central regions of the city due to lower construction and permeability). The results of the study also indicated that due to the high potential of the study area in terms of the risk of flood, water protection and protection measures at the basin level should be considered. In addition, the simultaneous use of remote-sensing and GIS and using the SCS-CN model could be useful in preparing a flood zoning map.
پژوهشی
AtaAllah Nadiri; Esfandiar Abbas Novinpour; Rana Faalaghdam; Zahra Sedghi
Volume 5, Issue 17 , March 2019, Pages 103-123
Abstract
Introduction
Population growth and the development of the agriculture and industry and the excessive use of groundwater resources have caused a drop in the water level. In arid and semi-arid areas, aquifer water management plays an appropriate role within human health of river basins and, therefore, ...
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Introduction
Population growth and the development of the agriculture and industry and the excessive use of groundwater resources have caused a drop in the water level. In arid and semi-arid areas, aquifer water management plays an appropriate role within human health of river basins and, therefore, their protection from anthropogenic contamination sources can be managed by proactive tools based on the aquifer vulnerability indices. The groundwater system does not respond quickly to contaminants. The arrival and diffusion of pollutants to groundwater occurs over time. Groundwater contamination is identified using the aquifer to provide water. Consequently, complete elimination of pollution is a long and often impossible process.The concept of vulnerability was first introduced in the late 1960s in France to provide information on groundwater contamination. The SINTACS framework is a suitable prescriptive approach but despite its popularity, it is susceptible to the need for expert judgment on assigning weights and rates for each parameter, which expose the output vulnerability maps to uncertainties in the same study area. Among different AI techniques, the current study was based on Mamdani Fuzzy Logic (MFL) to remove the expert opinion applied to SINTACS indices.
Materials and Methods
The Bilverdi sub-basin, with an area of 289 km2, is located approximately in 65 km of Tabriz city, East Azerbaijan, Iran. There is a vallilu arsenic mine to the north of Bilverdi plain. There are 208 wells, 7 springs, and 17 qanats in the study area.There is a possibility that the mine drainage leaks into the water resources and also extensive agricultural activities in the region increase the need to evaluate the vulnerability of the Bilverdi plain. In this study, SINTACS methods were used for the assessment of the inherent vulnerability of the Bilverdi plain aquifer. The SINTACS method is a PCMS which was developed by Civita and De Maio(2004) in order to assess the intrinsic vulnerability of groundwater with an increasing weight parameters and the wider range of ratings than the DRASTIC method. The acronym SINTACS originates from Italian words. The SINTACS method uses seven effective environmental parameters including Soggiacenza (depth of water), Infiltrazione efficace (effective infiltration), Non saturo (vadose zone), Tipologia della copertura (soil cover), Acquifero (aquifer), Conducibilità idraulica (hydraulic conductivity), and Superficie topografica (slope of topographic surface) to assess the vulnerability of the aquifer. After assigning weight and rate in the ArcGIS software, it was prepared as raster layers. Then SINTACS optimization was performed using Mamdani Fuzzy Logic (MFL). In this research, for the first time, the SINTACS method was optimized with artificial intelligence methods. Seven layers of the SINTACS method as an input and the SINTACS index corrected with nitrate were selected as the output model.
Results and Discussion
The SINTACS vulnerability Index Obtain by overlaying these seven layers and the Mamdani Fuzzy Logic (MFL) were used to optimize the SINTACS method and the data was divided into two categories of train and test. After model training, the model results were evaluated by the nitrate concentration through coefficient of determination (R2) and correlation index (CI) criteria. The results are as follows: The SINTACS Vulnerability Index was estimated to be between 70 and 169, of which 30, 67 and 3% of the study area were respectively located in low, medium, and high vulnerability zones.The results of the validation of the vulnerability maps with measured nitrate concentrations showed a correlation index (CI = 29). The results of the Mamdani Fuzzy Logic (MFL) were respectively R2 = 0.9, RMSE = 5.1 and R2 = 0.85, RMSE = 7.79 in the training and testing stages. The Vulnerability map of the numerical index is between 167.23 and 88.94 and the correlation index was (CI = 31).
Conclusion
This study used the SINTACS framework to assess groundwater vulnerability for Bilverdi basin, East Azerbaijan, Iran. The combined use of the SINTACS method and the geographical information system (GIS) produced a useful groundwater vulnerability map. The SINTACS index was calculated from 70 to 169. The poor determination coefficient calculated by the basic SINTACS framework made a research case for the application of Mamdani Fuzzy Logic. The results showed that Mamdani Fuzzy Logic (MFL) model showed high capability to improve the results of the general SINTACS and reduced the subjectivity of the model. The most vulnerable areas were in the northeast and southwest plain. The high vulnerability area needed to adopt strategic plans and policies to prevent the pollution of aquifers.
پژوهشی
Maryam Azarakhshi; Majid Aboutalebi; Ali Akbar Nazari Samani; Bahram Mohhamadi Golrang
Volume 5, Issue 17 , March 2019, Pages 125-144
Abstract
Introduction
In arid and semi-arid areas, due to the lack of proper management of renewable natural resources, not only the proper utilization of water and soil resources is not done, but also water becomes a natural disaster, and every year, floods cause many human and financial losses. One ...
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Introduction
In arid and semi-arid areas, due to the lack of proper management of renewable natural resources, not only the proper utilization of water and soil resources is not done, but also water becomes a natural disaster, and every year, floods cause many human and financial losses. One of the integrated methods of flood control operations is flood spreading. This method improves the status of utilization of water and soil resources, plant cover, and artificial recharge of groundwater. Sediments that are carried with the flood, deposit in flood spreading region and may change the physical and chemical properties of soil over time. The most serious danger which threatens the flood spreading networks and artificial recharge schemes is the reduction of the infiltration of soil due to sedimentation. The most important factor affecting the performance of flood spreading systems is the amount of input sediment into the spreading canals, its depositing on the surface and accumulation in the depth of soil, which can change the physical and chemical properties of the soil. In this regard, current research was conducted to investigate the role of input sediments into flood spreading field, determine the penetration depth of sediments and the spatial pattern of physical and chemical changes in soil.
Materials and Methods
Kashmar flood spreading station is located in 17 km east of Kashmar, in the longitude of 58° 38' to 58° 40' of the east and latitude of 35° 15' to 35° 39' of the north. This research was conducted in the first Phase of Kashmar flood spreading. In this research, the first five channels of dewatering were divided into three study networks (outset, middle, and end), and the upstream of the spreading flood arena was considered as the control sample. In each grid, three points were selected as repeat tests and soil profiles were drilled with a depth of 1 m. In each soil profile, the soil samples from 0-50 and 50-100 cm depths were provided. At the test site, soil infiltration was determined using double rings. After transferring soil samples to the lab, the soil texture (clay, silt and sand percentages) was determined by the hydrometer method. In the laboratory, the value of the chemical parameters of the soil, including soil acidity (PH), electrical conductivity (EC), bicarbonate (HCO3ˉ), sulfate (SO4-2), chlorine (Cl), potassium (K+), and sodium (Na+) were measured. The effects of flood spreading on the physical and chemical properties of soil in spreading filed were investigated with the factorial experiment in a completely randomized block design.
Results
The results of the analysis of variance showed that there was a significant difference between the soil infiltration in channels 1 to 5 and the control arena at the probability level of 1%. However, there was no significant difference (p=%5) in soil infiltration in the start, middle, and end sections of the channels. In channels 1 to 5 and in the control arena, there was no significant difference (p=%5) in the amount of sand, silt, and clay. Flood spreading increased the amount of the clay in the depths of the channels compared to the control area. The Analysis of the variance showed all chemical properties of the soil. Except potassium, there was a significant difference (p=%1) between the dewatering channel and the control field. The amount of the variables did not change in the second depth compared to the control arena. The interaction effect between the depth and channel was not significant at 5% probability level. Thus, flood spreading did not change the chemical characteristics of soil in depth.
Discussion and Conclusion
The rate of the soil infiltration in spreading channel reduced 4.3 times of soil infiltration in the control arena. The least infiltration was observed in channels 1 and 2 due to the proximity of these channels to the source of flood and deposition of more suspended load on the surface of the soil. Because the suspended load of floods increased the clay particles and reduced the macro porosity, it decreased the soil infiltration. Flood spreading caused an increase of 1.5% in the soil acidity of the channels compared to the control arena. A 30% reduction in electrical conductivity was observed in the first two and third channels, compared to the control arena. The amount of HCO3-, Cl-, SO4-2, and Na+ reduced in the first, second, and third channels but increased in the fourth and fifth channels. The amount of potassium in all channels decreased compared to the control samples but this decrease was not significant. In general, flood spreading in Kashmar site caused the diminution of soil infiltration, which had a negative effect on flood spreading system. Therefore, it is recommended that water spreading channels are plowed every year to increase soil permeability.
پژوهشی
Sanaz Daei; Meysam Salarijazi; Khalil Ghorbani; Mahdi Meftah Halaghi
Volume 5, Issue 17 , March 2019, Pages 145-163
Abstract
Introduction
There are many models for flood prediction that are based on different conceptual bases. The standard SCS-CN method was developed in 1954 and it is documented in Section 4 of the National Engineering Handbook (NEH-4) published by Soil Conservation Service (now called the Natural Resources ...
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Introduction
There are many models for flood prediction that are based on different conceptual bases. The standard SCS-CN method was developed in 1954 and it is documented in Section 4 of the National Engineering Handbook (NEH-4) published by Soil Conservation Service (now called the Natural Resources Conservation Service), U.S. Department of Agriculture in 1956. The document has been revised several times. It is one of the most popular methods for computing the volume of surface runoff for a given rainfall event from small agricultural, forest, and urban watersheds. The method is simple, easy to understand, and useful for ungauged watersheds. The method accounts for major runoff producing watershed characteristics, viz., soil type, land use/treatment, surface condition, and antecedent moisture condition. Recent researches focus on the improvement of this model and improve its efficiency but it is necessary to evaluate the improved models for Iran's watersheds. The purpose of this study was the comparison of standard SCS-CN and developed three parameter Mishra-Singh models for flood hydrograph and peak estimation using data of five watersheds in Golestan Province.
Methodology
Study Area and Used Data
Five watersheds (including Galikesh, Tamer, Kechik, Vatana, and Nodeh) located in Golestan Province were considered to evaluate different models for flood hydrograph estimation. The characteristics of the selected watersheds are different. For Tamer, Galikesh, Kechik, Nodeh, and Vatana watersheds, the areas are equal to (1527, 401, 36, 790 and 11 km2), the parameters are (289, 139, 26, 208 and 20 km), the mean altitudes are (1131, 1358, 928, 1540 and 899 m), the mean slope of the watersheds are (19, 27, 19, 28 and 33%), the length of the main channels are (94, 58, 10, 66 and 8 km), and the number of rainfall-runoff events are (10, 13, 3, 9, and 4 cases).
Descriptions of Models
The standard curve number (SCS-CN) model was based on the following basic equations:
(1)
(2)
P is total rainfall, Q is excess rainfall, CN is curve number, Ia is initial abstraction, and S is maximum retention.
Using the concept of the degree of saturation (C=Sr), where C is the runoff coefficient (= )), Mishra and Singh (2002) and Mishra et al. (2006) modified the original SCS-CN model after the introduction of antecedent moisture Mas:
(3)
The relationships developed by Mishra et al. (2006) for Mare:
(4)
(5)
P5 is prior 5-day rainfall depth.
Three model accuracy criteria including root mean square error (RMSE), Nash-Sutcliff efficiency (NSE) and percentage error in peak (PEP) were applied to compare the results of models (Adib et al., 2010-2011).
Results
There were 39 rainfall-runoff events, of which 25 and 14 events were respectively selected for the calibration and validation steps. The parameters of investigated models for different events and watersheds and related model accuracy criteria were calculated. The root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) criteria can be used for the analysis of the flood hydrograph simulation while percentage error in peak (PEP) criteria is suitable for the analysis of the flood peak discharge simulation. In the Gallikesh watershed, for the developed three parameter Mishra-Singh and standard SCS-CN models, the RMSE criteria values were (16, 11.05, 2.8, and 10.63) and (17.94, 14 , 6.56 and 13.56), the values of NSE values were (-0.88, -84.44, -0.9 and -4.77) and (-1.37-, -1.38, -9.7, and -8.4), and the PEP values were (0.4, -1.4, 0.55, -0.3) and (0.24, -2.11, -1.39 and -0.62). For the Nodeh watershed in different events, the RMSE criteria values were (13.22, 23.57, 79.53 and 68.15) and (11.83, 22.74, 88.96 and 69.92), the NSE values were (-6.88, -2.7, -0.17 and -66) and (-5.31, -2.46, -0.46 and -69.5), and the values of PEP were (-1.19, -1.98, 0.83, -2.48) and (-1,-2.4, 0.99 and -2.57) for the developed three parameter Mishra-Singh and standard SCS-CN models were calculated. In the Tamer watershed for two models of developed three parameter Mishra-Singh and standard SCS-CN, the values of different criteria estimated as the RMSE criteria values were (13.04, 26.85, 5.9 and 19.26) and (12.04, 92.62, 5.26 and 48.81), the values of NSE criteria were (-0.92, -20.3, -4.9 and -0.14) and (-0.73, -252.5, -3.75 and -6.37), and the PEP criteria values were (0.52, -0.2, -0.8, and 0.62) and (0.62, -5.14, -0.74 and 1.09). In Vatana and Kechik watersheds for the developed three parameter Mishra-Singh model different criteria were calculated as the RMSE values (2.5) and (1.5), the NSE criteria values (0.51) and (-0.07), the PEP criteria values (0.45) and (-0.3). However, in these two watersheds for the SCS-CN standard model, the RMSE criteria values were (4.8) and (2.91), the NSE criteria values were (-0.82) and (-2.93) and the PEP criteria values were (0.95) and (0.6).
Discussion and Conclusion
The values of root mean square error (RMSE), Nash-Sutcliff efficiency (NSE) showed that the developed three parameter Mishra-Singh model improved the accuracy of the flood hydrograph estimation relative to the standard SCS-CN model for 71% of the studied events and the difference between two models for remaining 29% event was negligible. Also, the values of percentage error in peak (PEP) revealed that the three parameter Mishra-Singh model led to a decline equal to 78% in flood peak estimation in comparison with standard SCS-CN model application. In addition, the standard SCS-CN and the three parameter Mishra-Singh models were respectively 64% of and 57% of the studied cases. In this study, the accuracy of the standard SCS-CN andthedeveloped three parameter Mishra-Singh models compared the flood hydrograph and peak estimation considering data of five watersheds in Golestan Province. The investigation of the model accuracy criteria revealed that the developed model led to a considerable improvement of flood estimation in studied watersheds.
پژوهشی
Reza esmaili; Maryam Ghorayashvandi; Esa Jokar Sarhangi
Volume 5, Issue 17 , March 2019, Pages 163-183
Abstract
Extended Abstract
Introduction
Flood is one of the most important hazards in the alluvial fans and its analysis is associated with many complications. The term alluvial fan flooding refers to only a specific type of flood hazards that occurs only on alluvial fans (NRC, 1996). These floods in alluvial ...
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Extended Abstract
Introduction
Flood is one of the most important hazards in the alluvial fans and its analysis is associated with many complications. The term alluvial fan flooding refers to only a specific type of flood hazards that occurs only on alluvial fans (NRC, 1996). These floods in alluvial fans are characterized as high flow velocity, different flow paths, very active erosion, transport, and deposition processes. Many of the rural and urban areas of Iran which are located in areas of alluvial fans are potentially at risk of flood. Therefore, the identification of high risk areas of flood at different scales can be useful in managing them. This research was conducted with the aims of estimating the flash flooding in the northern alluvial fan of the city of Izeh, identifying potentially hazardous areas in terms of flooding, and prioritizing them for management purposes. The study area with an area of 75 km2 is located in the north of the city of Izeh and the Lake Miangaran. The mean elevation is 1470 m. According to the Izeh meteorological station, the average rainfall in the region is 637 mm and the average annual temperature is 23 ° C.
Methodology
The boundaries of the watersheds and alluvial fans were separated and mapped using Google Earth Images of 2016. The potential of flood hazards was studied in three main steps: (1) Identifying active and passive zones of alluvial fans; the geomorphic indices including intersect point of alluvial fan, braided drainage pattern, and alluvial fan topography profile were used to identify the active and passive zones of alluvial fans. (2) The estimation of runoff and discharge with SCS method; the Ghahreman and Abkhezr method (2004) was used to calculate the amount of rainfall during different return periods. The information layers of the soil, land use, and vegetation cover were prepared from maps of the Natural Resources Administration of Khuzestan province. By combining the information layers, the curve number (CN) values for different basins and weighted average were calculated. Using the data such as catchment area, rainfall and its duration, number of the curve, length of stream and its slope, peak of flood discharge and time to peak were calculated. (3) Ranking of the risk areas by TOPSIS method; in the TOPSIS method, the n×m matrix is evaluated for a decision that has m option and n criterion. The basis of this technique is based on the notion that a selective choice should have the least distance with the positive ideal solution (best possible) and the greatest distance with the negative ideal solution (the worst possible condition).
Result
In the study area, twelve catchment- alluvial fan systems were identified. The active areas of the alluvial fans were 5 to 100% of their total areas. Based on soil characteristics and surface coverage, the average relative weight of CN varied from 78 to 90. The rainfall was calculated at 30, 60, 120, 180, and 360 minutes at the return periods of 2, 5, 10, 20, 50, and 100 years. Due to the small size of the basins and the short duration of concentration, the rainfall of 120 minutes with a return period of 10 years, which was 36.77 mm, was considered as the peak estimation criterion. To assess the ranking areas in the TOPSIS method, four criterion including flood peak due to precipitation of 120 minutes with a 10-year return period, time to peak of discharge, active alluvial fan area, and area of villages located in active alluvial fan were used. Twelve studied alluvial fans also appeared as options in the matrix. The results of the flood hazards ranking in the alluvial fans of the study area showed that fans of 2 and 5 were the villages of Perchestan Gurii with a population of 1168 people and the village of Pershestan Ali Hossein Mola with a population of 317 which had respectively had proximity coefficients of 1 and 0.4481 in rankings 1 and 2.
Discussion and conclusion
Within the studied area, the active areas of the alluvial fans are considered to be a major contributor to the flood hazard ranking so that the variables of the alluvial fans area and the area of villages based on them account for 77% of the weight of the ranking. Hence, the determination of the active regions of the alluvial fans can be used on a regional scale using Google Earth satellite 3D images. Using multi-criteria decision-making methods such as TOPSIS can rank the flood hazards in the alluvial fans of north Izeh with regard to the influential variables. This regional-level ranking can show areas at risk and, if necessary, detailed geomorphological studies and field studies will be needed.
پژوهشی
Shirin Mohammad Khan; Fatemeh Moradi Pour; Anvar Moradi
Volume 5, Issue 17 , March 2019, Pages 185-203
Abstract
Introduction
Lake Urmia in the northwest of Iran has faced a steady decline over recent decades. This has led to an increase in the level of dry lands and, consequently, has caused many environmental and social problems.There are many ecological effects on the biodiversity and growth of Artemia, the ...
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Introduction
Lake Urmia in the northwest of Iran has faced a steady decline over recent decades. This has led to an increase in the level of dry lands and, consequently, has caused many environmental and social problems.There are many ecological effects on the biodiversity and growth of Artemia, the Zooplankton, a native of Lake Urmia and major source of food for large birds. Consequently, the monitoring of coastal areas and the extract of changes in these areas in different time intervals have great importance because the nature of the coastline is dynamic. For this purpose, the remote sensing technology has a unique application in the acquisition of information about these phenomena because multi-spectral satellite images have advantages including their availability and digital interpretation.The purpose of this research was to investigate the development of the North West salt land in Lake Urmia using field data, satellite imagery, and geomorphic faces of the region.
Methodology
This research was an applied type and the data was collected through library, laboratory, and field studies. The statistical population was the water Retreat Zone of Lake Urmia Which suffered from wind erosion. This research was carried out using satellite imagery and multiple observations. Also, test sediments were obtained from this area using standard methods. Using remote sensing technology, when combined with land surveys and ground-based results, can provide better and more reliable results. For this purpose, in the present research, along with the use of remote sensing methods, field studies were also used. To carry out this research, samples of lake sediment were taken in different transects from a specific area in the northwest of the lake. The saturated extract of each sample was measured by EC meter. In the next step, using satellite imagery and field survey, the salinity, the expansion of salty and geomorphic faces were extracted. Finally, the results of the field and laboratory data, satellite imagery and geomorphic faces were compared and validated.
Results
The results showed that the surface area of the lake water was very low and spread to vast amounts of salt. Accordingly, the area of the salt land has significantly increased since 2000, especially over the past decade. According to the results of the field studies, areas with higher salt density matched the salt lines obtained from satellite imagery and geomorphic faces. The study area had 12 faces. The harvest area with a total area of 2,641 ha in the central part of the range and sandy areas with a total area of 14 ha in the northwestern part of the area had respectively the highest and lowest extent of outcrops in the studied area. Consequently, there was a close correlation between the field data and satellite imagery and salt land areas in satellite imagery and results of field studies. Due to the increase of the salinity levels in the area around the lake, local winds, and time lapse, the Agrarian lands of Azerbaijan general zone moved towards salinization and desertification. If this trend continues at the same speed, undoubtedly, in the long run, we have to wait for the enormous environmental crisis in the region.
Discussion and conclusion
The strongest indicator to extract the salt is the SI2 indicator with an accuracy of 97% and the weakest indicator is NDSI indicator with a precision of 52%. The EC value in 22 soil samples ranging from 0.9 to 78.37 indicates the high salinity variability in the region. There is the highest correlation with visible bands and infrared bands 1, 2, 3, and 4, which indicates that the saline soils in the visible and infrared areas have a higher reflection. Between 2006-2015, the range of salt land has been expanded. Altogether, with the decreasing trend of the size of Lake Urmia, in 1976 regular domain faces, in 1985 sediment removal, in 1990 harvested area, in 2000 wet area, harvested area, and salt deposits, in 2006 bar salt and salt zone, and between 2011 and 2015 salt zone were formed and expanded.
