K. Nosrati, H. Ahmadi, F. Sharifi,
Volume 16, Issue 60 (7-2012)
Abstract
Sediment sources fingerprinting is needed as an autonomous tool for erosion prediction, validation of soil erosion models, monitoring of sediment budget and consequently for selecting soil conservation practices and sediment control methods at the catchment scale. Apportioning of eroded-soil into multiple sources using natural tracers is an integrated approach in soil erosion and sediment studies. The objectives of this study, as a first work, are to assess spatial variations of biochemical tracers and theirs validation in discriminating sediment sources under different land uses and water erosions at catchment scale and to apply them as fingerprints to determine relative contributions of sediment sources in Zidasht catchment, Iran. In view of this, 4 enzyme activities as biochemical tracers were measured in 42 different sampling sites from four sediment sources and 14 sediment samples. The results of discriminant function analysis (DFA) provided an optimum composite of two tracers, i.e. urease and dehydrogenase that afforded more than 92% correct assignations in discriminating between the sediment sources in the study area. Sediment source fingerprinting model was used based on optimum composite of two tracers resulting from DFA to explore the contributions of sediment from the four sources. The results showed that the relative contributions from rangeland/surface erosion, crop field/surface erosion, stream bank and dry-land farming/surface erosion sources were 11.3±5.3, 8.1±3.8, 75±8.5 and 3.6±2.5, respectively. Therefore, we can conclude that fingerprinting using biochemical tracers may help develop sediment fingerprinting models and as a first step facilitate a more complete tool for fingerprinting approach in the future.
K. , and M. R. Nosrati, M. Amini, A. Haddadchi, Zare3,
Volume 20, Issue 78 (1-2017)
Abstract
Accelerated soil erosion in Iran causes on-site and off-site effects and identifying of sediment sources and determination of their contribution in sediment yield is necessary for effective sediment control strategies in river basin. In spite of increasing sediment fingerprinting studies uncertainty associated with magnetic susceptibility properties has not been fully incorporated in models yet. The objective of this study is determination of the relative contribution of sediment sources using magnetic susceptibility properties (High frequency, Low frequency and Frequency dependence) incorporated in uncertainty mixing model. For this purpose, 25 bed sediment samples were collected from the outlet of drainage basin and outlet of sub-basins and their magnetic susceptibility was measured and calculated. The results of Kruskal–Wallis test and discriminant function analysis showed that magnetic susceptibility properties can be used as optimum set of tracers in the uncertainty mixing model. The results of Bayesian mixing model indicated that mean (uncertainty range) relative contribution of Sparan, Joyband and Boyoukchay are 92 (83.9-94.8), 2.8 (0.2-10.7), 5.7 (0.2- 10.5) percent, respectively. According to these results, the highest amount of sediment yield is related to Sparan sub-basin and these results could be used in soil conservation and management planning.
K. Seydinaureh, S. Ayoubi, K. Nosrati,
Volume 23, Issue 4 (12-2019)
Abstract
The purpose of this study was to determine the relative contribution of sub-basin resources to sediment production by using magnetic susceptibility data as the tracer in Chehelgazi catchment, Sanandaj. For this purpose, 20 samples of the output 5 sub-basins were measured by harvesting and magnetic susceptibility. Kruskal-Wallis test results showed that in all three trackings, frequency high, low and dependent, at least two sources had the ability to differentiate. In the second step, the three tracers were tested on the discriminant analysis by the sub-basin source and two tracers with different power splitters showed the high frequency of 88% and the frequency dependence of 12%; power splitters both tracers together in the sub-basin splitters was 90%, so they were selected as the optimal combination; therefore, they have the capability to determine the relative contribution model of sediment. The results of Bayesian uncertainty model also indicated Todarsamadi sub-basin with 44.4% of the largest contribution and Doveyseh, Chatan and Cherendo sub-basins with 35.5, 7.9 and 4.5, respectively, and Madian Dol sub-basin with 4/4 percent had the lowest contribution to sediment production. Based on the available results, Todarsamadi and Doveyseh sub-basins had the highest amount of sediment production; so these results could be used in soil conservation and management planning.
