Meisam Samadi; Abdolreza Bahremand; Ali Salajegheh; Majid Ownegh; Mohsen Hoseializade; Abolhasan Fathabadi
Abstract
In order to develop management plans for water and soil conservation, it is necessary to determine the sources of sediment production in watersheds. During the past three decades fingerprinting technique has been used extensively in determining the contribution of different sources of sediment. In this ...
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In order to develop management plans for water and soil conservation, it is necessary to determine the sources of sediment production in watersheds. During the past three decades fingerprinting technique has been used extensively in determining the contribution of different sources of sediment. In this study, was carried out sediments fingerprinting and determine the contribution of each source to sediment production of the Toulbane watershed in Golestan province. To this end, 44 source samples were collected from forest, pasture, agriculture and bank erosion. Also 8 sediment samples were collected using Philips time-integrated sediment sampler. Afterward, the concentration of 34 geochemical properties was examined in the laboratory using the ICP device. Next, the optimal composite tracers were determined to discriminate sediment sources by using statistical tests including mass conservation test and Kruskal-Wallis. The contribution of different sources to sediment production was determined using the multivariate mixing model. Finally, the uncertainty in the case of a low number of data, was examined using the Monte Carlo method. As a result, after statistical tests, 12 tracers were selected as the optimal composite fingerprints. The bank erosion was main source to sediment production with 52.18% and the forest had the lowest contribution to sediment production with 4.39%. The contribution of agriculture and pasture was 33.23% and 10.21%, respectively. According to the uncertainty analysis, bank erosion is the most significant source to sediment production. Also, the high difference between the upper and the lower boundaries in different sources indicates high uncertainty.
Suma Mohamadpur; Hamed Rouhani; Hojat Ghorbani Vaghei; Seyed Morteza Seyedian; Abulhasan Fath Abadi
Abstract
In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed ...
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In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed when rainstorms occur on steep slopes and sediment transport in rill flows exhibits the characteristics of non-equilibrium transport. In this paper, sediment concentration of rill flow is estimated by adaptive neuro-fuzzy inference system (ANFIS). A series of mathematical equations and parameters affecting rill hydrodynamics and soil detachment were used for well-defined rill sediment concentration. A series of filed experiments were performed to evaluate the model. The stepwise method was used to select the most important and effective input variables from measured input parameters of soil properties, topographic and vegetation attributes affecting sediment concentration of rill flow. Based on the stepwise procedure, the most significant parameters in the model predications were steep slope, vegetation percentage, clay percentage, and shear stress parameters. The values of sediment concentration simulated by the model were in agreement with observed values with Coefficient of Correlation (R2), Root Mean Square Error (RMSE) and Mean Bias Error (MBE) of 0.697, 30.5 and 1.0, respectively. The results of the investigation shows that the data-driven ANFIS modeling approach can be a powerful alternative technique for correctly estimating rill sediment concentration.
