جستجوی مقالات مرتبط با کلیدواژه « sediment » در نشریات گروه « جغرافیا »

Soil erosion is the process by which soil particles and components are separated from their main bed by an erosive agent and transported to another location. In the soil erosion process, there are three distinct phases: 1- separation of soil particles, 2- particle transfer and 3- sedimentation of transported materials. In water erosion, the erosive factors are rainfall and runoff. Erosion and the consequent reduction of soil fertility are among the issues that make it difficult to achieve sustainable agricultural development and environmental protection. It is important to study the quantity and quality of erosion in the country's watersheds and to prevent the loss of one of the richest and most valuable natural resources of the country, namely soil, and to fight against this process. (Tabatabai, 1392). Therefore, to calculate the rate of erosion and sediment production in most watersheds of the country that lack statistics or lack of statistics, the use of experimental models to estimate erosion and sediment is required. According to what has been said, the present study was conducted based on the following two main objectives 1- Estimation of erosion and sediment in Adineh Masjed watershed, which is one of the main sub-basins of Kamal Saleh Dam, using EPM and MPSIAC experimental models and 2- Investigation and comparing two models and choosing a better model for similar regions and climates.

Adineh Masjid watershed is one of the sub-basins of Dez and the main sub-basin of Kamal Saleh dam. Temperature, isotherm, geology of the area, slope and available information were performed and finally, by interpreting the photos, types, land units, current land use were determined and updated with field control. For a more detailed study, first, according to the condition of the main waterway and changes in the appearance of the land and vegetation and new land material, the ridges separating the basin were divided into 15 sub-basins. In EPM model, four watershed erosion coefficient (Ψ), land use coefficient (Xa), rock and soil susceptibility coefficient to erosion (Y) and average basin slope (I) and in MPSIAC model, nine geological, soil, climate factor (Climate), runoff, slope, vegetation, land use, current erosion status and waterway erosion are examined. Each model was scored according to data analysis and digital images and then placed in the relevant formula. Finally, the amount of erosion and sediment in the basin was estimated and the sedimentation class of the area was determined.

To determine the score of nine factors affecting soil erosion using MPSIAC method and the four factors of EPM model, each of the factors affecting erosion in units were analyzed. Finally, by weighting, the points of each factor in the models were calculated. The degree of R deposition from the sum of the nine factors of MPSIAC model and the degree of Z erosion was obtained by combining the four EPM factors. Then, the amount of sediment production and erosion in the field of relationships related to each model was calculated and compared and analyzed. In MPSIAC model, the amount of specific sediment (M3 / Km2 / year) was calculated as 112.713 and the specific erosion (M3 / Km2 / year) was calculated as 375.71. In the EPM model, the amount of specific sediment (M3 / Km2 / year) was calculated as 213.95 and Specific erosion (M3 / Km2 / year) was calculated to be 395.86.

The results of sediment and erosion estimation were estimated separately for each sub-area using two models and it was found that the two models are somewhat relatively compatible with each other. The results of MPSIAC model, have more accuracy and reliability, and therefore the results of the MPSIAC model can be used to estimate the amount of sediment entering the Kamal Saleh Dam. However, due to the small distance between the results of the two models, if we do not have access to MPSIAC model data in similar areas, the EPM model can be used with less data and more easily accessible. It was also observed that in the upper and entrance parts of the basin, where the slope is higher and the vegetation is less, the amount of sediment production and erosion is higher in these areas. So that the upper parts of the basin are in the medium erosion class and the rest of the basin is in the low erosion class.

The present study was conducted to investigate the trend of land use changes and its impact on erosion and sediment, using Landsat satellite images of 1987, 2010 and 2019 and EPM model in the Kal-Aji watershed of Golestan province. Land use map was prepared using the maximum likelihood method. Training data were used to evaluate the accuracy of the results. Then, the most important methods of accuracy assessment, including total accuracy and classification kappa coefficient, were extracted. Those for 1987, 2010 and 2019, the total accuracy were 95, 98 and 95 and kappa coefficient were 0.93, 0.96 and 0.93, respectively. Erosion and sedimentation zoning maps were performed using the resulted land use maps, and such factors as slope, lithology, soil and precipitation were performed using the EPM method. The results showed that one of the most important factors in the Kal-Aji watershed is the loss of rangeland vegetation and its conversion into agricultural land. According to the hypothesis, the role of land use changes in the amount and increase of erosion and sediment was significantly determined. That is, with the change of land use, erosion and sediment have also increased. The reasons for the increase in the amount of sediment in the Kal-Aji area are the development of agricultural land uses and the decrease in rangeland between 1987 and 2019 (16.83%). These changes have reduced the important role of rangelands and vegetation of Kal-Aji watershed and have increased the amount of sediment in the outflow of sub-watersheds. Based on the obtained results, the total amount of special sediment was related to the period of 1987 (4353 cubic meters), which increased to 5164 cubic meters in 2019, which indicates the changes in land use that occurred in the watershed.

Due to erosion, soil structure is destroyed, plant elements are removed from the soil by runoff and destroyed, soil fertility is reduced and, as a result, the production and productivity of the soil is reduced. The use of modifiers such as polyacrylamide while reducing runoff production and soil erosion can be useful in reducing the wastage of soil nutrients, especially in sloping lands. The aim of the present study was to investigate and compare the rate of sediment and runoff production and nutrient loss in three slope positions and four concentrations of polyacrylamide using a rain simulator. Direct measurement of runoff and sediment, efficiency and repeatability in different intensities, durations and amounts of rain are among the advantages of using a rain simulator in investigating surface runoff, erosion, and sediment (Jahanbakhshi et al., 2016). Therefore, in order to investigate and study soil erosion and direct measurement of runoff and sediment, rain simulators can be used.

The present research endeavor was conducted to investigate the effect of using different amounts of anionic polyacrylamide superabsorbent in controlling the erosion and wastage of soil nutrients in sloping lands. The experiment was performed as a factorial experiment in a completely randomized design with three replications. The first factor of the slope was determined in three positions (slope toe, slope foot, and shoulder slope) and the second factor was the concentration of polyacrylamide at five levels (0,0/4, 1, 3, and 6 g/m2). The test area was located on the campus of Mohaghegh Ardabili University being exposed to a 10-minute rainfall with an intensity of 35 mm/hour; it was checked that the intended rainfall was simulated by a rain simulator. A total of 45 experiments were performed by the rain simulator on plots measuring 2 m2. After conducting each rain simulator test in the plots, runoff and sediments were collected in a collection tank and the amount of sediment, runoff volume and amounts of sodium, potassium, calcium, magnesium, phosphorus and organic carbon were measured in the laboratory.

The effects of slope position on all soil characteristics were significant (P <0.01) increasing from 0.35 kg/ha to 0.55 kg/ha. The effect of slope position on organic carbon loss was significant at the 5% level and other elements at the 1% level. The effect of polyacrylamide concentration was significant only on phosphorus loss at the level of 1%. Also, the interaction effect of slope and concentration on calcium loss was significant at the level of 5%. By changing the position of the slope from the toe slope to the foot slope, the average runoff volume increased from 3.84 to 14.8 liters, and the average phosphorus loss increased from 0.035 kg/ha to 0.55 kg/ha. The effect of PAM in higher concentrations was greater than in lower concentrations in reducing runoff, sediment, and then the loss of nutrients in the soil. The results showed that the maximum production of sediment due to the simulated rain on the surface of the plot is 192.36 grams per square meter. This is the value of the control plot at the foot slope (S0) which is significantly different from other treatments. The lowest amounts of sediments were related to the treatment of 6 grams per square meter of PAM in the position of the toe slope (F6) with an average production sediment of zero, and the treatment of 6 grams per square meter of PAM in the position of the toe slope (F3) with an average sediment production of 1.69 grams per square meter.

The highest amount of sediment and runoff was produced at the foot slope. The greatest loss of nutrients was observed at the foot slope. It was observed that the effect of PAM in higher concentrations is greater than in lower concentrations in reducing runoff, sediment, and then the loss of nutrients in the soil. It can be concluded that the use of anionic polyacrylamide has an important effect on reducing soil erosion and consequently reducing the wastage of elements such as phosphorus and organic matter in sloping lands, because the release of these elements from the soil mainly happens attached to the soil particles. This research showed that the effect of PAM in reducing runoff, sediment and then the loss of nutrients of soil, in higher concentrations is greater than in lower concentrations but from a certain concentration onwards (3 grams per square meter) the impact of PAM does not increase.

Determining the type of relationship between different components of soil erosion can provide more complete information about the performance of these components in different watersheds and land uses. In this study, in order to determine the type of relationship between different components of soil erosion in different land uses of Gachsaran Formation deposits, a part of Kuhe Gach watershed with an area of 1202 hectares was selected. In this study, using univariate regression, the type of relationships between sediment - runoff, sediment - infiltration, sediment - runoff and erosion threshold, runoff - infiltration, runoff - runoff and erosion threshold, infiltration - runoff and erosion threshold were determined and also, the relationship between them was also examined. Sampling of erosion different components at 6 points with 3 replicates and at different rainfall intensities of 0.75, 1 and 1.25 mm/min in three land uses of the range, residential area and agricultural lands using a rain simulator was performed. SPSS and EXCEL software were used for statistical analysis. The results showed that in general in the intensity of 0.75 mm/min in all three land uses, range, agricultural and residential and in the relationship between all the different components of soil erosion in seven cases, there is a positive relationship and in eleven cases, there is a negative relationship. And at an intensity of 1 mm/min in all three rang, agricultural and residential land uses and in the relationship between all the different components of soil erosion in seven cases, there is a positive relationship and in eleven cases, there is a negative relationship. And at an intensity of 1.25 mm/min in all three uses of the range, agricultural and residential land uses and in the relationship between all the different components of soil erosion in eight cases, there is a positive relationship and in ten cases, a negative relationship.

Soil erosion and production of sediment load in the watershed has become one of the important environmental problems today, and therefore, Preventing its occurrence is one of the most important factors for protecting natural resources. Increasing soil loss in watersheds Watershed is a continuous challenge that is caused by the increase in population and the pressure on natural resources and unsustainable cultivation in soils and lands Slope causes a decrease in production in the land (Ahmad Abadi, 2017). One of the most important factors of soil destruction and fertility reduction is soil erosion, which is increasing nowadays and leads to Loss of good agricultural soil.

which is called the RUSLE relationship and is a function of six factors as follows:A=R*K*L*S*C*P (2) A: average soil loss per surface unit (tons per hectare per year) R: rain erosion factor, K: soil erodibility factor, L: slope length, S: slope degree, C: management factor Vegetation, P: Factor of soil protection measures This model is still the best available erosion prediction model that can be easily applied at the local level area to be used. In this method, using the DEM map of the basin in the GIS environment and having the factors RKLSCP factors, raster maps are prepared and finally, with the correlation of these maps, the soil erosion map is obtained. In the GIS environment, rain erosibility index maps (R), soil erodibility index (K), slope length index (L), Slope degree index (S) is prepared. Also, Landsat satellite bands 1 and 7 are used to prepare the NDVI map, Vegetation index (C) is prepared. - Third step: modified PSIAC

In 1968, the American Water Management Committee presented the Psiak method qualitatively. This model is based on the sum of 9 factors (X1-9) Effective on erosion, it gives the sedimentation of the basin, according to which the erosion class of the basin can be determined. In the year 1982 Johnson and Wegbhart took these factors numerically (Y1-9) so that the model takes on a quantitative state and its accuracy to increase Factors affecting erosion in this model include: lithology, soil, climate, runoff, topography, vegetation, Land use, the current state of erosion and erosion of Khanhai River. - Fourth step: preparing the necessary invoices:As stated, the revised global equation of soil erosion is an empirical model of soil erosion estimation based on the global equation. Soil erosion is designed. This model, in addition to being able to use data such as the physical characteristics of the basin and Meteorological stations can estimate the amount of soil erosion, it also enables the spatial distribution of soil erosion.

In this regard, in order to calculate the average loss of soil per surface unit (tons per hectare per year), the following factors were examined and calculated and prepared:- Slope degree: S=L* H/A S =541781.94*20/144538670=0.074 S=0.074*100=7.4 - Slope length:Basin slope length 16.03 square kilometers were calculated. Then, the erosive factors of precipitation, soil erodibility, slope length, slope degree, vegetation cover and protective measures were calculated and put in relation to the global equation as follows:R=8.86*0.376*0.72*7.17*1 R=17.197 Also, 3 factors involved in the modified Psiak model were calculated as follows and placed in the relevant section as follows:Q = 0253e^(0/036R) Q = 0.253*〖2.718〗^2.641 0.253×14.02=3.54

In this research, by preparing different layers of information, the state of sediment erosion and the amount of sediment production were determined. According Based on the amount of sediment production calculated and comparing this number with the sediment determination table, it can be said that the basin In terms of sedimentation and erosion, Kal Ismail Dara is in Class I and among the areas with very low intensity of sedimentation. The studied basin has a maximum height of 2000 meters and a minimum height of 1320 meters and in terms of topography (82.38) percent of the region includes, as well as the mountainous region with an area of 25.43 square kilometers, 17.61 percent of the basin includes In this research, using the Revised Universal Equation of Soil Erosion (RUSLE) (sediment erosion rate in the basin) 17.197 tons per hectare per year was obtained in Kal Ismail Valley, and other researchers used the same model for basins.They have calculated different figures. So that Artman et al. (2011) in Navroud, the rate of sediment erosionSediment erosion is 0-100 tons per hectare per year, Mousavi (2015) (in Shahroud-Miami, Mohammadi et al.) (2016) Taaler, Mahmoudi and Naqshbandi (2019) (in Gavshan dam, Arman et al.(2015) in TengSarkh dam, Mozbani et al.( 2021) In Sikan, the amount of sediment erosion was calculated as 2.31-68.185, 0-92.01, 35.2 and 62.17 tons per hectare respectively.Other researchers have also provided different figures for different basins using the PSIAC method. Rostaminia and Safarlaki (2017) In the Cham Gardalan basin, Eylamra 9.6, Alipour et al. (2016) in the Iver basin, 2.95 and Divasalari et al. (2013) in Solqan Qom, Nasiri et al. (2021) in Bakhtar, Qochan and Ghafari et al. (2015) in Cannes, the amount of sediment erosion They calculated 19.85, 4.5-84.2, 4.6-4.17 and 72.78 tons per hectare respectively. The basin of Kal Ismail Dara has an elongated shape

In recent years, with the change of use and development of agricultural lands in the country's basins, the rate of erosion and sediment production has increased. Given that in most sub-basins, the long term data of sedimentation stations have not been recorded, it is difficult to estimate the amount of sedimentation and erosion. The objectives of this study was to determine the factors influencing erosion and sedimentation and to determine the quantitative values of erosion in the Khorkhoreh watershed, Kurdistan, Iran. For this purpose, first, using topographic maps, geology and aerial photographs in GIS environment, type and shape maps of erosion were prepared and evaluated by field survey. Based on the MPSIAC model, the nine factors influencing erosion for all sub-basins were identified separately and the scores of each factor were determined. By summing the factors, the degree of sedimentation for each sub-basin was determined and the amount of sedimentation and special erosion and total erosion in each sub-basin were calculated. The results showed that the topographic factors and the current state of erosion have the most role and the weather factor has the least role in the sedimentation rate of the basin.Moreover, 92% of the total basin has a high degree of sedimentation in the fourth order erosion class. The amount of Sediment Delivery Ratio of the basin (SDR) varies between 32 and 50 percent. The lowest and highest specific erosion rates in different sub-basins were 10 and 35 ton/ha.yr, respectively. Also, the amount of special sediment and special erosion of the whole basin was 6.4 and 17.4 ton/ha.yr, respectively.

This article aims to analyze the trend of monthly, seasonal and annual changes in the flow and sediment of the Mordaghchai. located in East-Azerbaijan province. In this regard, using non-parametric methods, discharge and sediment data of Gheshlagh-Amir hydrometric station have been analyzed in three time scales: annual, seasonal and monthly. The modified Mann-Kendall test was used to analyze the trend of gradual changes in discharge and sediment data. Also, the Sen's slope estimator was used to estimate the slope of the trend line and the non-parametric Pettitt test was used to investigate the abrupt changes in the discharge and sediment time series. The modified Mann-Kendall test was used to analyze the trend of gradual changes in discharge and sediment, and the Sen' slope estimator test was used to estimate the slope of trend line. Also, Pettit test was used to investigate abrupt changes in the river discharge and sediment time series. The results show that annual, monthly and spring, summer and winter discharges significantly decrease at the level of 5%. The annual and all-season sediment load data significantly decreased by 5%. There is a significant decrease in sediment load in all months except March, April and October. The results of the Pettitt test show that the average annual discharge in the period after the breaking point (1998) has decreased by 45% compared to the period before the breaking point. Also, the average annual sediment load after the breaking point (1996) has decreased by about 52% compared to the previous period.

Severe soil erosion is a serious threat to the sustainable management of land and the use of water and soil resources in many parts of the world. In order to control erosion of sheet, rill, gully, and stream bank erosions and to reduce the resulting sediment at the outlet of watersheds, it is necessary to identify the share of sources that produce their sediment to make protective measures more successful. One of the most common methods that has been used in recent years to determine the share of different sources of sediment is the sediment fingerprinting method.

The purpose of this study is to investigate contribution of sheet, rill, gully and stream bank erosions in sediment production by using sediment fingerprinting method in Neyriz watershed, located in East of Fars province, with the help of sampling of sediment deposited in the bed. From each type of sediments, sheet, rill, gully and stream bank erosions, the main waterway within the basin and the outlet area of ​​the watershed, 10 samples (60 samples in total) were collected. In order to determine the optimal tracers, two tests of "domain" test and "multivariate detection analysis" were used. Furthermore, by using the model of Collins et al., the share of each of the different sources of sediment was obtained. Then, the uncertainty related to the share of potential sources of sediments was calculated using the Monte Carlo simulation method with 95% confidence in MATLAB software. In order to evaluate the results of the hybrid multivariate model, the Goodness of Fit (GOF) proposed by Collins et al. was used.

Based on the range test, among the 51 tracers measured in the samples, twelve tracers (Ag, Ba, Be, Eu, Mn, Ni, Ta, Tb, Th, Tm, W, and Zn) are found as tracer’s non-conservative variables were identified, and these detectors were discarded in other statistical tests such as Kruskal-Wallis and discriminant analysis function. The results of the Kruskal-Wallis test showed that among the 39 tracers that passed the range test, sixteen tracers (Al, Ca, Co, Cr, Er, Fe, Gd, Lu, Mo, Na, Nd, Pb, Pr, S , Sc and Zr) with significance at one percent level (p ≤ 0.01), and 9 tracers (Cu, Ga, Hf, Ho, La, Sn, Sr, Y and Yb) with significance at five percent level (p ≤ 0.05) is that in total, these 25 detectors had a significant level and could separate sources; while fourteen tracers (As, Ce, Cs, Dy, K, Li, Mg, Nb, P, Rb, Sm, Te, Ti and V) were not statistically significant, these tracers were deleted from the DFA statistical test. In the first step of the DFA test, the Zr detector, the second step of Zr and Al detectors (with Wilkes lambda from 0.717 to 0.244), the third step of Al, Zr and Fe detectors (with Wilks lambda from 0.39 to 0.057), the fourth step of Zr, Al, Fe and Sn detectors (with Wilkes-lambda 0.362 to 0.04), the fifth step of Zr, Al, Fe, Sn and Lu detectors (with Wilkes-lambda 0.233 to 0.03) and the sixth step Zr, Al, Sn and Lu tracers (with Wilks lambda 0.289 to 0.045) were entered into the model. Based on the obtained results, among the 25 tracers that passed the Kruskal-Wallis test, five tracers (Al, Fe, Lu, Sn and Zr) were entered into the DFA test step by step. In the third stage, iron tracer (Fe) was added to the model and in the sixth stage, it was removed from the DFA test. In general, four Zr, Al, Sn and Lu tracers were selected as the final optimum tracers. These four detectors were able to correctly classify 95% of sediment sources. The findings of this research, which were obtained by using Monte Carlo simulation and the combined multivariable model and evaluating their results using GOF, showed the contribution of gully, sheet, rill and stream bank erosion to the order is equal to 45.21, 3.07, 16 and 35.72% of the total erosions that have occurred in this watershed. Also, considering the GOF value of 0.8869 and mentioning that the closer this value is to one, the more accurate the results of the model is true in this research and this analysis also confirms the high accuracy of the model.

In this study, the efficiency of sediment fingerprinting method was proved as a successful and effective method to determine sediment sources because the first and most important stage of the sediment source method is to choose a suitable combination of tracers that can isolate sediment sources, and this was done correctly in this research. Also, Monte Carlo uncertainty confidence levels showed that the scope of this uncertainty is large (0.8869) and therefore, it shows a greater lack of certainty on different sources of sediment production. Determining the share of four types of erosion in the Neyriz watershed and placing the share of gully erosion as the most important type of erosion in the production of productive sediments in it shows the importance of controlling erosions, especially gully erosion, with emphasis on biological plans.

Dams are one of the most important human structures along rivers that are constructed with the aim of generating electricity, flood control, and providing water for agriculture and urban centers. Today, very few large and small rivers remain uncontrolled; what is important geomorphologically is the changes that occur in the performance of erosion processes downstream of the river after dam construction. These changes are not limited to after dam construction, but these morphological changes are the result of changes in the performance of erosion and deposition processes in drainage basins, completely transforming the face of the basin and canals in the area close to the constructed dam. Analyzing the geomorphological changes of rivers due to the creation of human facilities such as dams is one of the most important tasks of geomorphologists. This study is done with different models and methods. Among the common methods of the last decade are the GCD model and machine learning. The GCD method is the result of subtracting two digital elevation models at different times, which are produced by different methods of these dams. To analyze the geomorphological changes caused by the construction of the dams in downstream of the river, in addition to using historical dams, machine learning methods can be used for more accurate modeling by involving a variety of effective maps in detecting changes. The main purpose of this study is to apply the machine learning method using the data obtained from the GCD model to generate regression maps due to the impact of dam operation downstream of the river.

This study was carried out in the Sojasrood River and downstream of Golaber dam. Software and tools used in this research included the following: Arcgis, envi, sagagis software, R software, GCD software and extension, Google Earth Engine system, AutoCAD software, Excel, topographic maps of 1/50000, Elevation digital model and Garmin GPS. Machine learning methods were used to evaluate the effects of Golaber Dam in the period before and after the construction of the dam. In order to access the data required for this research, digital elevation models of stereo pair images of L1A series and L1B satellite ester were used as time series. First, through the GCD model, the volume changes of erosion and sediment downstream of the dam were calculated. Then, the data obtained from this model were used as a target variable along with nine layers of geomorphometry and precipitation and runoff as predictive data to implement machine learning algorithms in three methods of multiple linear regression, decision tree, and random forest. 70% of the data were used for modeling and 30% of the data were used for evaluation in R programming software.

Given that continuous data on erosion and sedimentation rates from the GCD model have been used for the machine learning method, naturally, a regression method (prediction) should be used for the output of the machine learning models. Three steps were taken to achieve the result of machine learning. First, the models were run one by one in R software and evaluated with 30% of the experimental data, and finally, the model maps and their correlation coefficient and RMSE error were calculated. Comparison of the output results of multiple linear regression models and decision tree and random forest showed differences in statistical data and time series maps before and after dam construction. Therefore, the output maps of the models before and after the construction of the dam were also different from each other. The main reason for this is a significant reduction in the runoff, land-use changes, increased vegetation of the bed and riverside, which has led to changes in the independent variable research data in the period after the construction of the dam. Although statistically in multiple linear regression, the p-value was less than 0.05, the output of maps of this model were associated with a large error. And the model did not predict the rate of erosion and sediment well. In the multiple linear regression model, the correlation coefficient of the map before the construction of the dam was higher than the period after the construction of the dam. CART method was used for decision tree modeling. The map produced by this method with a correlation coefficient above 0.6 showed better performance compared to the multiple linear regression model. The best method for modeling erosion and sedimentation rates in both periods was the random forest method. This model with a correlation coefficient above 0.7 provided the most accurate prediction in this study.

Various methods and models have been proposed to estimate the rate of erosion and sediment in rivers. In recent years, new and more accurate models have been formed, especially in the analysis of the time series of river developments. New methods include the GCD model and machine learning (ML). In this study, in order to observe the changes in erosion and sedimentation rate due to the construction of Golaber Dam in the period before and after its construction, first, the GCD model of volumetric erosion and sedimentation changes was estimated through the model of published errors and multiple time series maps were produced. Then, from the data obtained from this model, along with maps and geomorphometric layers and maximum rainfall and runoff data, in order to more accurately predict the impact of the dam on the riverbed in terms of erosion and sedimentation rates and geomorphological changes of the river, machine learning method was used. The results of modeling showed that dam utilization was strongly effective in erosion and sedimentation of river bed and Random Forest algorithm with a correlation coefficient above 70% and RMSE less than the other two models showed the best prediction for both periods before and after dam construction. The maps produced by the decision tree method also modeled the erosion and sedimentation process in the riverbed in both time series analyses well, but the output of the linear regression model was not accurate enough. For an overview of machine learning algorithms, in addition to evaluating the experimental data of the models themselves, the overall average results of some morphometric indices of the river such as number of meanders, center angle, channel length, and Sinuosity index were also evaluated. This comparison showed the accuracy of modeling decision tree and random forest algorithms applied in the present study.

Dry and ultra-dry conditions, prevailing in a large part of Iran with less than 710 mm of rainfall per year, have caused about 40 million hectares of the country to cover desert areas, sand dunes and areas with low vegetation (Refahi, 2004). Nebkas are very important in stabilizing mobile sands in arid and semi-arid areas and protecting human settlements and facilities to some extent from the onslaught of wind sands. They are found and usually formed in semi-arid, hot and dry and hot and humid areas (Amini et al., 2011, quoted by Thomas and Tousar). Nebka plays a very important role in the stability of ecosystems in arid and semi-arid areas. One of the species that has high resistance to wind and its roots and stems settle large volumes of aerosols and wind sediments and fights against wind erosion is Choug species that can stabilize quick sands in the south of the country. No studies have been conducted on this species. Therefore, this study aimed to investigate sediment changes in relation to vegetation in sediments and its comparative analysis against wind erosion to evaluate the process of stabilization of quick sands.

 In this research, one-dimensional sampling method and longitudinal unit have been used. This method allows random sampling of nebkas throughout the study area. After identifying the study areas and determining the scope of development of nebkas, by field reference, sampling and measurement of morphometric components of nebkas were performed. Sampling was done with 5 representative areas, 10 transects of 1000 meters with a distance of 500 meters from each other and perpendicular to each other. To determine the starting point of transect and the beginning of sampling, transects were selected so that they transversely cover the study areas. In each linear transect, 5 plots with dimensions of 4 * 4 meters were placed equal to 50 plots at each transect. The size of plot was determined by minimal area method. A total of 10 transects and 50 plots were used in each area; vegetation measurement was performed on totally 250 plots put at the region. Then, to start the sampling, the points were selected by GPS as an indicator at equal distances from the start of the Nebka landscape in the 5 areas under the study. Two Landsat satellite images from 1990 and 2020 were also used to determine a mount of morphometric changes in the sampled nebulae over a 30-year period. Then, using Envi 4.7 software, the region's nebkas and other existing uses were determined. Finally, after preparing land use maps, the area in the region was surveyed. 3-1: Measurement of morphometric properties of nebkas At this stage, the morphometric characteristics of Nebka such as sediment volume, dune height, dune base diameter, nebka slope, plant height and canopy diameter were measured. The basis for measuring the morphometric components of Nebka is as follows:In order to measure the height of Nebka, the height of Nebka peak to the level of its base and for the diameter of the base of Nebka, the average diameter of the base were measured by a tape measure. The slope of the Nebka cone is calculated by means of a slope gauge and the volume of the Nebka cone is calculated by Equation (1). Also, in order to obtain the average height of nebkas in the main and larger habitat, after many rounds of forest, the total height of nebkas was measured in all sample plots of one hectare that were systematically randomly distributed in the habitat (Zobeiri, 2009). Then, the ratio of canopy area to nebka area (in percentage) and the volume of each nebka were estimated using Equation (1).

 The relationship between plant traits and Nebka morphometric traits was estimated based on correlation analysis and multivariate regression analysis. In order to compare and evaluate the measured parameters of sediment and plant, to investigate the relationships between them and to perform the mean comparison test to evaluate the differences between the measured parameters in the study site, EXCEL 2013 and SPSS16.0 software were used. In order to compare the indicators studied in the study, Pearson correlation test with 95% confidence level was used to investigate the trend of their changes. In order to investigate the correlation coefficient and the explanatory relationship between plant morphological indices and sediment morphometric indices, after determining the non-normality of the data, the data were normalized by multiplying each data by 2. After normalizing the data, considering the indices measured from Nebka as a dependent variable and the indices measured from the plant as an independent variable, the regression relationship was examined and also the degree of correlation between the different measured indices was computed. 4- Findings (Results) 4-1 Results of correlation study between variables The results of correlation between sediment and plant variables in the Sirik region showed that there is a significant correlation between all measured variables except the canopy diameter of the plant at 95 and 99% levels. 4-2 Multivariate linear regression

The results of multivariate linear regression using independent variables of plant height and canopy diameter in the Sirik region showed a significant relationship for estimating dune height and sediment volume. Based on the results, plant height was the most important and effective independent variable in estimating the height and volume of dune sediment in the old Sirik region. 4-3: The rate morphometric changes of the measured nebulae during a period of 30 years The results of the study and classification of Nebka in the Sirik region during the 30-year period showed that the rangeland use has undergone the most changes and the least amount of changes were related to areas with Nebka. In addition, the study area has been affected by wind erosion over time, which has reduced the area of ​​nebkas in the study area by 8 hectares.

The results of multivariate linear regression using independent variables of plant height and plant canopy diameter in the study areas showed that there is a significant relationship between sediment height and nebka volume only in the Sirik region. Based on these results, plant height was the most important and effective independent variable in estimating the height of sand dune and volume of Nebka in the old Sirik region. These results were in line with the findings of Glaze et al. (2014), who studied sediment transport recovery in Nebka vegetation and uncovered Nebka vegetation and wind speed influences. Examination of sediment relations between flowing dunes and plant species in southern Thailand showed that sediment in windmills in the direction of wind and at the foot of phanrophite plant species was more than hemicryptophyte and criptophyte species and in the opposite direction of wind. Considering this fact, the sediments at the foot of Salvadora persica phanrophytic species in this study, along with the species, was more than Caparis decidua or Alhaji camelorum; this fidning confirmed a similarity in the results of these two studies. The results of Bobenzer (2020) in Egypt confirmed that the level of sand stabilization with a linear pattern has decreased, which is in line with the findings of this study. Valentini (2020) also stated that the changes in the use of Nebka habitat near the coast in the northern part are decreasing and in the southern part are increasing, so that the level of Nebka habitat is generally growing; these findings are not in line with the findings of this study. Also, the results of Hogan Holtz (2012) showed that the areas stabilized by quick sands, especially active nebulae, are in an increasing process and have deposited more sediment and quicksand than the last four decades; in fact, their findings contradicted the findings of this study.

Plant species that make up nebkas are important elements that stabilize quicksands and have the ability to survive under wind sedimentshe aim of this study was to statistically analyze the geomorphological characteristics and sediments of Nebka sediments in order to stabilize quicksands in the Sirik region of Hormozgan province. In the present study, 3 regions, in each region, 5 representative regions and in each representative region, 10 transects of 1000 meters with a distance of 500 meters from each other and perpendicular to each other were placed in each linear transect. In each nebka, nebka height, nebka length and base of nebka, canopy diameter, nebka volume, nebka base diameter and nebka sand stabilization area were measured. Also, to determine the amount of geomorphological changes of the sampled nebkas during the 30-year period, Landsat satellite images of OLI sensor from 1990 and 2020 were used. Then, using ENVI 5.3 software, the area's wells and other existing uses were determined. The results showed that there was a significant difference between the variables in the three regions at the level of 95%. With the increase of plant height in Sirik from 1.7 to 2.2 meters, the volume of sediment dune increases from 15 to 72 cubic meters and in Mishi region with increasing plant height from 1.65 to 3.5 meters, the volume of sediment from 15 to 45 Cubic meters has increased. Also, the study of changes in the area of nebkas in the region during the last 30 years using satellite image processing showed that the area of nebkas in the region has decreased from about 67 hectares in 1369 to about 59 hectares in 1399. Due to the importance of nebkas, efforts should be made to protect them and these destroyed areas should be rehabilitated with salvadora persica seedlings.

Soil infiltration situation indicates soil behavior against water reaching the soil surface. This phenomenon determines the amount of both the water reaching the soil surface and rainfall losses. Soil infiltration of a basin has unique parameters based on its climate, soil conditions, and buildings. Soils are a set of discontinuous particles among which pores exist so that water can move from a point with more energy to a point with less energy; this property is called the passage of water through continuous pores. Gachsaran marl formation has a thickness of about 1600 m and consists of salt, anhydrite, colorful lime marl, and some shale from a lithology point of view. The age of this formation is lower Miocene (Ahmadi, 1999: 714). Estimation of soil infiltration using various erosion components can be a useful method to determine soil infiltration in the shortest time and at the lowest cost.

In this study, soil infiltration was estimated using erosion different components in different land uses in deposits of Gachsaran formation by selecting a part of the Kuhe Gach watershed of Izeh city with an area of 1202 hectares. The relationship between soil infiltration and erosion different components, such as sediment rate, runoff rate, and runoff and erosion threshold, in different land uses of Gachsaran formation was determined by the multivariate regression. Then, different erosion components were sampled at six points with three replicates and different rainfall intensities of 0.75, 1, and 1.25 mm/min in three land uses of rangeland, residential area, and agricultural land using a rainfall simulator. SPSS and Excel software was used for statistical analysis. A portable Kamphorst rainfall simulator used in this study has a plot size of 625 cm2, which determines the characteristics of soil, erosion, and water infiltration, and is suitable for soil research. It is used as a standard method to determine the soil infiltration of surface deposits in the field. The experimental plot area was selected 625 cm2 with a smooth gradient. The preparation of the testing area was followed by installing and setting the rainfall simulator and then starting a chronometer upon observing the precipitation on the screen. The amount of plot infiltration was determined at 10-min intervals (Kamphorst, 1987: 407).

The estimation of soil infiltration was acceptable and appropriate in some models in this study, which have a lower regression coefficient. Therefore, it is not possible to make appropriate comments about the estimation of the models only using regression coefficients and other statistical coefficients nor the significance levels of observational and estimated data as well as the minimum square mean of errors (MMSEs); in some cases, the MMSEs are not sufficient and require more studies (Jain and Kumar, 2006: 272). Despite scientific advances and improvement of measuring equipment, regression models are still used by researchers in different fields due to simplicity.

The results showed that the most positive and negative effects of different erosion components on estimating soil infiltration were related to sediment rate, runoff, and erosion threshold in all three mentioned land uses in three precipitation intensities (0.75, 1, and 1.25 mm min). Meanwhile, the role of sediment rate in estimating soil infiltration was slightly higher than runoff, and erosion threshold and runoff rate had no role in estimating soil infiltration in this method due to a high correlation of data.

A bare surface on forest roads is created due to road construction. This surface is the main source of erosion and sediment yield to streams in forest areas. The increase of sediment in streams causes dramatic damage to the quality of water ecosystems and the life of aquatic organisms. Therefore, road engineers should pay attention not only to the cost of road construction but also to its environmental damage. So, it is necessary to reduce the amount of erosion and sediment produced by these roads. With the accurate prediction of erosion and sediment yield of roads, it is possible to mitigate the negative effects of sedimentation and manage the region, sustainability. In this regard, several models such as WARSEM and SEDMODEL have been introduced to estimate the sediment and to identify the sensitive erosion points. These models are often used to estimate road surface erosion in forest regions. Therefore, the purpose of this study was to evaluate different models in estimating forest road sediment and compare them to field measurements. The findings of the study are useful in finding a suitable and realistic model in the estimation of erosion and sediment of the forest road.

Two prediction models, including WARSEM and SEDMODEL, were used to estimate the amount of annual sediment production of forest roads. For this purpose, 2602 m of roads in compartments 202, 212, and 244 in the Rezaian forestry plan of ZarrinGol forests were selected. The total length of roads in the region was divided into homogeneous units, and then factors of road length and width, geological condition, road surface, traffic, longitudinal road slope, rainfall, and sediment transport were calculated using GIS maps (road layer, river, waterway, geology, edaphic and topography), forestry plan booklet and field measurements. A sediment trap and suitable container were installed at the end of each unit to measure the actual amount of road sediment after each rainfall. The volume of water was measured (in L) after the deposition of sediment in the container, and then the deposited sediment was taken out of the container and placed in an oven. After drying, the amount of sediment was calculated (in g/m2). In the laboratory, the sediment concentration was obtained by filtering the suspended load sample and passing the runoff sample through the Whatman 42 filter paper. The sediment samples were then placed in the oven at 105 °C for 24 hours. The samples were weighed in a Desiccator using a digital scale (one-thousandth accuracy), and the sediment concentration was obtained by dividing the sediment mass (g) by the runoff volume (L).

Results showed that the estimated sediment by SEDMODL and WARSEM for different roads and compartments were 23.87 and 20.07 tons per year, respectively. In addition, the longitudinal slope had a significant effect on the amount of sediment production estimated by the two models WARSEM and SEDMODL, at a probability level of 5%. While this factor has no effect on the amount of sediment measured in real conditions amount of sediment estimated by the models in the slope class of 5-10% was significantly higher than the slope class of 0-5%. There was no significant difference between the amount of sediment estimated by WARSEM and SEDMODL models; however, estimated values were significantly higher than the measured value at the probability level of 5%. Validation of WARSEM and SEDMODL models and their comparison with the field measured value showed a significant difference. Both models estimated the amount of sediment more than the field measured value. The results also showed that the sediment delivery potential estimated by WARSEM and SEDMODL was related to the part of the road located in parcel No. 202.

By calculating the amount of sediment production in the road and various parts using WARSEM and SEDMODL and comparing them with the measured value, it was found that the total amount of sediment estimated by WARSEM and SEDMODL was 20.07 and 23.87 tons per year, respectively. In general, there was no significant difference between the amounts of sediment estimated by WARSEM and SEDMODL, but these estimated values ​​were higher than the field measured value at the probability level of 5%. Moreover, the effect of the longitudinal slope of the road on sediment production was also studied. According to the factors measured by WARSEM and SEDMODL, it is necessary to design roads on geologically resistant formations, improve the pavement quality, decrease the level of sediment delivery and reduce the traffic.

The Soil surface characteristics have significant effects on the erosion process. The wide areas of watershed soil surfaces are covered with considerable amounts of rocks and pebbles. Surface rock fragments, resulting in water connectivity, change during the erosion process. Rock fragments on the soil surface may increase the infiltration and reduce soil losses because they can act as protective covers. When rainfall and runoff occur, this coating can has an important effect on soil erosion, sediment production, and infiltration processes. Surface rock fragments can protect the soil surface from raindrop impact, which further decrease the overland flow and its transport capacity, thus reducing erosion. But the effect of deployment pattern for these impermeable surfaces on the soil erosion phenomenon is not well known.

In the present study, using a rainfall simulator and soil erosion plots, the effect of impermeable rock surfaces include control (zero), 10, 15 and 20 percent of plot surface coverage, in three surface, semi-embedded and embedded situations and in three replications (30 plots in total) were studied. The experiments were performed in the rainfall and erosion simulation laboratory of the university of Torbat-Heydarieh. The erosion plots were smiliar to a  rectangular with a length of 1 m and a width of 0.5 m. The plots with a constant gradient of 9% for 10 minutes were exposed to rainfall with an intensity of 1.4 mm/min and after each rainfall event, the runoff volume, sediment production, sediment concentration, runoff coefficient, and infiltration rate were sampled and calculated at the plotchr('39')s outlet. Also, the spatial variations of sediment at the surface of plots were measured and compared using Laser roughness meter in different sections before and after each simulation.

The results showed that by increasing the percentage of rocks in the surface situation, the amount of sediment production increases, the volume of produced runoff volume decreases, and the infiltration rate increases. However, if the rock fragments are embedded, the sediment load and volume of produced runoff increase but the infiltration rate decreases. However, the main and interactive effects of treatments on soil erosion processes were not significant. Finally, according to the results of the comparison between pre-and post-roughness data in each treatment, there was a significant difference between the two groups. That, the difference between the groups, were caused by two factors: plot-level drop or the sectional transportation of sediments at the plot area.

In the current simulation study, the runoff and sediment yields were measured in soils with the different surface rock fragments in the laboratory utilizing a rainfall simulator and erosion plots. The most commonly utilized method for studying the influence of rock fragment cover on hydrological processes is to simulate rainfall on disturbed soils under laboratory conditions. The results showed that the difference between soil erosion and sediment production at the erosion plotchr('39')s level is also very considerable. So, the sediment delivery ratio should be seriously considered in erosion studies. It should be noted that the evidence of such differences between sediment analysis results and spatial changes of sediment, presents a new challenge for the erosion modeling and all of these inter-relationships should be perceived well enough that it would be possible to make them into effective erosion models.

Existing challenges in arid regions have caused climatic and environmental problems such as low rain, winds with high speed and intensity as well as lack of vegetation. These problems happen by destroying and transporting particles leading to the influx of sand flowing into agricultural lands and residential centers. This is one of the most concerns of residents in the arid and desert region of the country. Because, it causes a lot of life and financial losses, the move of sands, and the formation of sand dunes are influenced by interactions between wind flow, the site of deposition, and the morphology of the sedimentation site that gives rise to wind landforms. The vegetation cover plays an important role in determining the morphology and dynamicsby influencing transportation conditions and trapping the sand carried by the winds. This process takes the form of creating a wind vision during a natural reaction, with the creation of the Nebaka phenomenon. The phenomenon appears in desert areas to neutralize wind erosion stress. Accordingly, the presence of vegetation is a prerequisite for Nebakas and controlling the flow of sand flows in arid and desert areas due to the specific climatic conditions in these areas. Numerous studies have been conducted to investigate the effect of vegetation factors on Nebaka formation. The volume of Nebakas is influenced by the vegetative form. The volume of its constituents is different from each other. Vegetation factors have played an important role in the development of Nebakas. Studies have shown that the vegetation cover has the main role in the formation and development of Nebakas so that vegetation reduces sediment replacement and limits its source. Among the critical areas that are referred to as the main focus of wind erosion, the Sistan region has always been affected by wind erosion. The present study was conducted with the aim of investigating the effect of vegetation restoration on morphometric components of Nebaka and its effect on sand dunes stabilization in the Nimroz area of Sistan province.

To achieve the purpose of the present study, after floodwater spreading and forestry operations in the Nimroz area of Sistan during 2003, parameters of Nebaka including Nebaka high, Nebaka base diameter, Nebaka volume, vegetation cover, plant height, wind direction, and back to the wind in 45 Nebakas to the Tamarix species were measured at different time intervals in a 16-year period by restoration vegetation and installing 5 linear transects with a length of 50 m randomly in the area. Then, by measuring the morphometric properties of Nebakas, the correlation of morphometric components was investigated using correlation analysis and multivariate regression analysis.

In the correlation analysis regarding the morphometric characteristics of Nebaka, the findings showed that a significant correlation (at the level of 0.99) of the plant characteristics such as vegetation cover and high plant parameters such as Nebaka high, Nebaka base diameter, Nebaka volume, wind direction, and back to the wind. The multiple regression analysis approved 92.9 percentage of the volume changes of the Nebaka with the vegetation cover. Investigating the amount of sediment stabilized in the Nebaka also showed that increasing vegetation wills increased the volume of Nebaka in such a way that with increasing vegetation, the volume of Nebakas on average increased from .0.53m3 in 2008 to 15.69m3 in 2018 with the amount of stabilized sediments increased from 184.97 ton to 879.79 ton. The statistical comparison of the measured data showed that there is a significant difference (at the level of 0.01) between the mean stabilized sediments in Nebakas during the research process. According to the results of the study, the restoration of vegetation in the study area shows a good background for the formation of Nebakas. As a result, a considerable amount of sands has stabilized in these Nebakas.

In this study, the role of wind activity in the formation and development of Nebaka areas where wind power was low was confirmed. Based on the result of this research, a significant amount of wind sediments has been stabilized in Nebakas. As a result, the Sistan area is always affected by wind erosion and the problem of sand dunes. The method of restoring vegetation by doing flood and forestry plans in susceptible areas is effective to stabilize the sand with the creation of the Nebaka phenomenon in the study area. Re-vegetation in the study area has provided a good basis for the creation of Nebakas in the region.

The Soil and Water Assessment Tool – SWAT (Arnold et al. 1998; Arnold et al., 2012) is a well-established physically based, semi-distributed hydrological model for river basin scale application (Brighenti et al., 2019). is a hydrological model to assess sediment (Yesuf et al., 2015; Vigiak et al., 2017; Abdelwahab et al., 2018), impact of land use (Zeiger and Hubbart, 2016; Choto and Fetene, 2019; stream flow generation (Hallouz et al., 2018; in-stream water quality, climate change and, water quality and quantity variation (Jiao et al., 2014; Francesconi et al., 2016; Yang et al., 2018 ). A number of studies used laboratory, field scales and modeling studies to understand soil erosion and sediment dynamics in various regions. Several worldwide studies have been done using the SWAT model. The objective of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard in Badavar catchment and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The specific objective of the study is validation of the SWAT model to assess its capability in predicting sediment yield transport in Badavar catchment.

Study area The study area is Badavar catchment (58311 ha) in the west of Iran and the northern part of Lorestan Province. Badavar catchment is one of the sub-catchments of of the Karkheh River. The average height of the catchment is 1941 m. The annual average precipitation is 527 mm, and the annual average temperature is about 10.5 °C. The climate of study area is in the category of sub-humid, characterized by cold winters and temperate summers. Most of the study area has slope 0-5 degree. Rain farming and week rangeland are the dominate land use in this area.The soil erosion is critical in the Badavar catchment. soil erosion in addition to damages to the land, it will reduce the capacity of the downstream dam of this catchment (the Karkheh dam). So in this study, SWAT model is used to identify the risk of erosion in Badavar catchment and various factors affecting of the erosion.SWAT Model: Model parameters: For preparation of input data, the GIS "version10.3"and ArcMap "ArcSWAT2012" was used. The following basic data were selected as SWAT model inputs:1) Digital Elevation Model (DEM): it is extracted from 1:50000 topography map with a spatial resolution of 30 m. 2) Landuse: it was obtained by Forests, Range and Watershed Management Organization of Iran. 3) Slope: it is extracted from DEM. Slope map shows that the Badavar catchment consists mainly of the plain with low slope (0-5 degree). 4) Hydro-meteorological data: The data required includes rainfall, river discharge, and climate data such as temperatures, solar radiations, humidity, and wind speed. These hydrological and meteorological data are collected from two organizations; the Iran Meteorological Organization and the Ministry of Energy.5) Soil data: SWAT model requires different soil textural and physico-chemical properties. These include soil texture, available water content, hydraulic conductivity, bulk density, and organic carbon content for different layers of each soil type. All of these soil characteristics and the soil map of the catchment were prepared by field survey and 39 systematic random samples of soil and laboratory works. Also the soil texture data was used to extract hydrological soil groups that were linked with FAO’s texture classification. This was then linked with the SWAT database using the soil layers and soil type. 6) Delineation of sub-catchments and HRUs:SWAT uses two types of functional units: the subbasin and the Hydrologic Response Unit (HRU) (Neitsch et al., 2011). The sub-basin is a spatially defined region that comprises a main reach and its contributing area, which is composed by one or more HRUs (Vigiak et al., 2017). On the other hand, analysis the catchment is allowed by SWAT as a whole or by subdividing it into sub-basins containing the same portions called Hydrological Response Units (HRU) (Briak et al., 2016). The HRU is a land unit of homogeneous environmental properties (soil, land use/cover, management, and topography) and hydrologic behavior (Vigiak et al., 2017). 33 sub-catchments and 53 Hydrological Response Units (HRU) have been generated for Badavar catchment.After simulation, factor analysis was used to determine the most important factors in sediment production. Factor analysis attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. The purpose of factor analysis is to reduce the complexity within the similarity matrix of a multivariate data collection, transforming it into a simpler and more easily interpreted factor matrix.Finally, for comparisons between observed and simulated sediment loads, four model evaluation statistics were selected; Correlation coefficient (R2), Root Mean Squared Error (RMSE), Index of Agreement (D) and Correlation determination (r).

The most important inputs of the model are: soil information, land use, slope, elevation, geology, weather information (rainfall, background and temperature, relative humidity, dew point, solar radiation and wind speed). Also factor analysis was used to determine the most important factors in sediment production. The simulation results showed that amount of sediment output from the basin is 7170 tons per year. After implementation of the model, the amount of sediment simulated and observation sediment was compared. Correlation coefficient (R2), Root Mean Squared Error (RMSE), Index of Agreement (D) and Correlation determination (r ) were validated with 95%, 03%, 97% and 97%, respectively which indicated the high accuracy of the results. Also the results of factor analysis showed that the role of land use in sedimentation of the study area is more than other factors.

The comparisons between observed and simulated sediment loads showed that the model has fairly acceptable accuracy for Badavar catchment. A more accurate estimation of erosion and sediment yield can be made by providing accurate data and the best management practice is highly recommended for the dam sustainability, because of the proximity of Badavar cahchment erosion to the Karkheh dam.
Key word: Erosion, Sediment, Factor Analysis, SWAT, Badavar.

Shorelines are very dynamic geomorphic systems where continuous changes occur at differents spatial and temporal scales .Coastal sustainable management, need to know the trend of sea level changes, showing of shoreline changes can ensure coastal relative sustainable for performing plan coastal environmental management. Case study is shoreline of Karkanrod estuary that is located in the west of Gilan between eastern longitudes 48°-34ʹ to 48°-58ʹ and northern latitudes 37°- 42ʹ to 37°-57ʹ.The aim of this research is to extract shorelines from past to now, determine effective land and hydrodynamic processes in the morphologic changes of Karkanrod delta shoreline to assess the rate of shoreline changes and their reasons. Waves and wind data, hydrodynamic data such as sediment and water, sea level fluctuations ancient graphs, Landsat satellite images, Aerial photos were utilized to study shoreline changes that show progress and regress of karkanrod delta. The method used in this research, is a combinational of, descriptive and analytic method with field and library studies.In field observation, some Geomorphologic evidence in the study area such as: V-shaped valleys, fluvial terraces and loesses were identified, that rectify the effect of active tectonics and sea level changes in the region. DSAS method was used to extract shoreline changes in different times.The results of this study show that the shoreline of Karkanrod delta experienced the most erosion during 1985-2000 and the most accretion during 2008-2017. Relative sea level change plays an important role in the shoreline of karkanrod delta. Transects of 7, 8 and 9 that are almost in the center of delta, where the karakanrod estuary is located, have more accretion rate that it shows the effect of sediment rivers. The results and methods may be helpful in coastal management and understanding the evolution of the entire delta from the perspective of shoreline change.

Today soil erosion is considered a danger to human and life. In areas where erosion is not controlled, soils gradually erode and lose their fertility, so it is necessary to study the issue of erosion. According to a study conducted in the Masouleh watershed, there are two types of erosion intensity called type 3 or moderate erosion with an area of ​​28.15 km2, type 4 or high erosion with an area of ​​13.79 km2. The predominant forms of erosion in the region include mechanical, surface erosion, furrows, waterways and deposits. According to this study, the average amount of erosion based on Mpsiac method in this field is 4.81 tons per hectare per year, sediment delivery ratio (SDR) is 74.3 and the average amount of sediment in this field is 3.85 tons per hectare per year. It indicates moderate erosion and sedimentation in the whole area and high in some sub-fields. To control erosion and reduce production sediment in this area, various programs include holding training courses for people in dealing with nature, implementing management programs (including applying grazing systems, balancing livestock and pastures, development and medicinal plants), and conducting biological operations ( It has been suggested for rangelanding in the form of sowing, sowing, mulching and planting in a non-productive way) and mechanical operations (including the construction of mortar stone dams, gabions, dry land, etc.) have been proposed.

Soil erosion and sediment production is including fundamental limitations in the useing of soil and water resources. The sediment yield of watersheds in addition to the loss of soil and soil fertility decline, caused reducing water quality. Therefore, evaluation processes that govern their behavior to better understand and explain the systems Watershed management practices is essential. The aim of this study is to estimate the total sedimentation determine of the basin. Studies obtained in the sub-basin of Saman 1, Vrkbar 1, four level 2 including R and R2 provides the same results in other sub-basins and the results have changed slightly. After calculating the data in tables results were compared with geomorphology. The results obtained by Hydro physical indicated that the basin Hryqan 2 with scaling potential 9.724 (including R2) in km2 highest and sub-basin four level 2 scaling potential 1.147 have been severity of erosion, but the way geomorphology sub-basin fence Tea 1 with producing potential 07 / 6 highest and sub-basin Hryqan 1 with producing potential 226/1147 least erosion was in the case study. The results obtained in the method Hydro physical indicated the index R2 be the better results than the R. comparing the two models showed that for determining erosion potential watershed geomorphology method is more suitable from method of Hydro physical.

Nearly 2 billion tons of resource soil   is destroyed and massive damage equivalent to 18.5 × 1012 Rails enter the country. Therefore to prevent this damage, it is needed adopt suitable management for preventing erosion and sediment movement. Soil erosion is one form of soil degradation. While erosion is a natural process, human activities have increased by 10-40 times the rate at which erosion is occurring globally. Excessive (or accelerated) erosion causes both "on-site" and "off-site" problems. On-site impacts include decreases in agricultural productivity and (on natural landscapes) ecological collapse, both because of loss of the nutrient-rich upper soil layers. In some cases, the eventual end result is desertification. Off-site effects include sedimentation of waterways and eutrophication of water bodies, as well as sediment-related damage to roads and houses. Water and wind erosion are the two primary causes of land degradation; combined, they are responsible for about 84% of the global extent of degraded land, making excessive erosion one of the most significant environmental problems world-wideSoil erosion is a naturally occurring process on all land. The agents of soil erosion are water and wind, each contributing a significant amount of soil loss each year. Soil erosion may be a slow process that continues relatively unnoticed, or it may occur at an alarming rate causing serious loss of topsoil. The loss of soil from farmland may be reflected in reduced crop production potential, lower surface water quality and damaged drainage networks  To archive to the aim the accurate data about the erosion in the watershed is necessary (intended, H. 1389: 18). Since soil erosion can be studied qualitatively and quantitatively, there are many models and more models for evaluation and were not giving much information about the erosion of soil. Specify a successful strategy to prevent erosion and reduce soil erosion depends on the mutual understanding between soil quality and erosion.

In order to achieve the purpose of the research the case study be studied and identify based on the model of geomorphology and geo physical. In this study, to obtain the production of sediment and soil erosion the geomorphology and hydro physical models be used. In the research,   to prepare the database for estimation of sediment and erosion suitable map were produced, the including topography map, slop map, land use map, caver map,   the layer database in the environment GIS  software corrected and analysis.  In the research using hydro physical and geomorphology models was used to estimate erosion and sediment production. Many data were collected by field.

According to the results of study in Table 11 by Hydro physical model -sub-basin Hryqan 2 with producing potential 9.724 (including R2) highest and sub-basin four level 2 sby producing potential 1.147 least severe erosion was, but in the geomorphology model sub-basin fence Tea 1 by producing potential 07/6 was the highest rate producing erosion and sediment in case study and    Hryqan 1 basin with producing potential 226/1147 be least erosion in the case study. The results of table 4 showed then the hydro physic model including R2 have better results than R, because the results obtained with R2 in the geomorphology model showed the   closer result. After calculating the data in tables results were compared with geomorphology. The results obtained by Hydro physical indicated that the basin Hryqan 2 with scaling potential 9.724 (including R2) in km2 highest and sub-basin four level 2 scaling potential 1.147 have been severity of erosion, but the way geomorphology sub-basin fence Tea 1 with producing potential 07 / 6 highest and sub-basin Hryqan 1 with producing potential 226/1147 least erosion was in the case study. The results obtained in the method Hydro physical indicated the index R2 be the better results than the R. comparing the two models showed that for determining erosion potential watershed geomorphology method is more suitable from method of Hydro physical.

In this century, wind erosion as one of the most important processes in arid and semi-arid regions has affected about one-sixth of the world's land area. Wind erosion is usually attributed to the process of soil particles being removed and displaced by the wind. Wind is one of the dominant processes in arid, semi-arid, and sub-arid regions and is the result of wind dynamics, transport of soil or sediment particles, and the development and evolution of desert roughness based on erosion and sedimentation intensity.The risk of wind erosion and dust is also more severe in areas where the soil is loose, dry, and bare, with high winds and high repetition. This study was conducted in Jazmourian basin and its playa. Jazmurian Playa has been studied as the most important area affected by wind erosion and the source of dust hazard in the south-east of the country; because these two hazards are often found in closed basins of old lakes or playa that contain loose sediments, and they are disconnected, they are happening.

Data used are library data, meteorological statistics, maps, field, and laboratory data. At first, using the (Eriffer) model, nine effective factors in determining the wind erosion potential in the geomorphological facies of the basin were investigated, and their sedimentation rate was determined. The annual windroses were plotted with hourly data from the synoptic stations over the period (2009-2019) for six stations, and the wind situation of the area was analyzed. Then, 20 surface sediment samples were taken from the area to determine the effect of particle size on the performance of wind processes, and Granulometry was performed. And the position of the sand-dune as the end point of wind erosion and the areas affected by the dust were also identified.

According to the results of the implementation of the Eriffer model for determining the wind erosion potential in the Jazmourian basin of high and diverse sandstone facies - very high erosion class and seasonal litter lithofacies, seasonal muddy salt marsh, terraces lakes and alluvial plain. There is a lot of erosion in the classroom. Also, alluvial facies are in middle erosion grade and uneven mountain slopes in low erosion grade. In fact, in the Jazmourian basin, there are scattered sandy masses, but the highest distribution is in the southeast of Playa, with 44.49% of sedimentation of the whole basin. The wind Baft situation in the area indicates that the maximum velocity at the station is 2.10 to 3.60 m / s. The highest wind speed at this station is in the range of 5.70 to 8.80, which accounts for about 2 percent of all winds. The Bam Station Wind in different directions at speeds below 4.5 m / s. Khash Station is about 2 percent of the winds in the speed range of 8.80-70.70, and about 80 percent is between 0.50 to 3.60 m / s. Jiroft Station is about 1 percent of the winds in the speed range of 8.80-70. Nikshahr Station At this station, about 2% of the winds blow at a speed of 5.70 to 3.70 / 60 and the Other at lower speeds. Iranshahr Station about 80% of the winds in this station are northeast to southeast and are classified at speeds of 0.50 to 3.60 m / s. Annual windrose show at stations in the area Local winds can create local dust. The results of the analysis of the samples show that 74.75% of the eastern Playa sediments, 76.25% of the southern Playa sediments, 67.50% of the northern sediments, and 95% of the Playa center sediments are below 63 microns in size. Surface sediments are the environment for the production of dust.

The results show that the low and flat topography, the presence of fine particles in the sediments, and the dominant winds during the dry season, with the dominant northwest and west direction, caused the sediment to move suspended, then jump and creep. And the dust hazard has Extensive performance on the playa and its margins, and more importantly, the Jazmourian playa is one of the main sources of dust in the south-east of the country. Also, the areas covered by dust hazards are greater in the central, southern and, western margins of the hole than in other areas.