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

Soil erosion and sediment yield are one of the most destructive phenomena that was cause a lot of damages in different regions. In order to combat  with increasing sediment loads, informed of the nature and sediment sources in watershed scale is essential. Soil conservation projects and for effective sediment control strategies, need to knowledge of sediment sources throughout a catchment environment. The main aims of this study are to apply tracers to determine relative contributions of sediment sources with six mixing models and to use Monte Carlo -mixing model that estimates contributions of sources to associated the uncertainty estimation in sediment fingerprinting in the land uses(rangelands, forests, cultivation and gardens)and geology information(Razak, Kashkan, Bakhtyari, Quaternary, Pabedeh Gorpi and Asmary) of  Tange Bostanak catchment, in Fars province. Multivariate discriminant function analysis was subsequently used to identify the best composite fingerprint incorporating a number of these tracers. Four tracers(C, Cu, Si, Ti) verify  ability to discriminate between land use  source categories and four tracers(Nd143/144, Cu, Si, Ti) verify  ability to discriminate between geology information’s  source categories. The application of six commonly used mixing models(Collins, MCollins, Landwehr, MLandwehr, Motha, Slattery) to Monte Carlo Simulation identified all outputs remained in the acceptable range of contributions  defined by the Monte Carlo uncertainties. Mean relative contribution (percentile 5%, percentile 50% and percentile 95%, respectively) of sediment samples resulting from the Monte Carlo mixing model uncertainty approach. The best explanatory parameters provided by the Collins model based on GOF index(99.3)was best model for land uses and the best explanatory parameters provided by the Modified Collins model based on GOF index(99.98)was best model for geology information. The relative contributions from areas of range lands with 58.76%(42.5%-75.65%)  and Asmary inrormation with 53.63%(46.7%- 60.11%) are highest, respectively. Pabedeh Gorpi and Bakhtyari information’s with 0.24 and 0.27  are the lowest relative importance, also gardens  and range lands  with 3.73 and 3.5  are the highest  relative importance, respectively.

Extended abstract Soil erosion is a major environmental threat worldwide. This three-stage process including detachment, transportation and sedimentation of soil particle by runoff affects natural and agricultural areas of Iran. Soil erosion has many off-site and on-site effects such as sediment deposition in the lake of dam and channels, transportation of nutrients and contaminants including phosphorous, pesticides, heavy metals, pathogens and radionuclides (Horowitz, 2008). Therefore, understanding spatial variations of sediment sources can be useful for managing the supply of sediment and contaminants in river systems. Quantifying sediment sources can be important to target efficient management measures, reducing sediment supply in the catchments. Sediment fingerprinting techniques are therefore increasingly applied to determine sediment sources and pathways in catchments and thus inform management interventions (Walling, 2005). Many scientists applied sediment fingerprinting techniques for quantifying source contribution of fluvial (e.g., Owens et al., 2005; Russell et al., 2001; Walling et al., 1999; Zhang and Liu., 2016; Nosrati et al., 2018; Collin et al., 1997 and 2012) and aeolian sediments (Gholami et al., 2017a,b; Liu et al., 2016). The sediment fingerprinting approach has been used for a variety of different applications including agricultural, forest harvesting, wildfires and urbanization (Koiter et al., 2018).
Fingerprinting techniques have evolved from single-property fingerprints to multi-property composite fingerprints because reliance on a single property of sediment makes it difficult to accurately distinguish sediments from a variety of sources in large fluvial systems, such as catchments (Collins and Walling, 2004). Many different physicochemical properties have been successfully used to discriminate potential sediment sources, including mineralogy (Klages and Hsieh, 1975), geochemical elements (e.g. Martinez-Carreras et al. (2010b); Collins et al. (2013); Pulley et al. (2015); Chen et al. (2016)), elemental composition (e.g. Motha et al. (2003); Devereux et al. (2010)), biomarkers (Chen et al., 2016), and environmental radionuclides (Martínez-Carreras et al. (2010)). Sediment fingerprinting technique is principally based on statistical tests such as Kruskal-Wallis H test and discriminant function analysis; and mathematical mixing models. The main objective of this study is quantifying sub-basins contributions as potential sources for sediments deposited on the back of the dam in the Lavar watershed, Fin, Hormozgan province by fingerprinting technique. 2.1.Sampling and Laboratory analysis
In this study, spatially distributed source samples were taken from 23 sites, of which 9, 6 and 8 samples were taken from northern sub-basin, midlle sub-basin and southern sub-basin potential sources, respectively, and seventeen samples were collected from the deposited sediments in the lakes dam, Lavar watershed, Fin, Iran.Samples were collected from the upper 0–5 cm depth of potential sources and deposited sediments on the lake’s dam.All sediment samples and potential source samples were dry sieved in the laboratory. Concentration of 56 geochemical elements including Al,Ba,Be,Ca,Ce,Co,Cr, Cs,Cu,Dy,Er,Eu,Fe,Ga,Gd,Hf,Ho,K,La,Li,Lu,Mg,Mn,Mo,Na,Nb,Nd,Ni,P,Pb,Pr,Rb,Sb,Sc,Sm,Sn,Sr,Ta,Tb,Te,Th,Ti,Tl,Tm,U,V,W,Y,Yb,Zn,Ag,Zr,As,Bi, Sand Sbwere determined using ICP-OES and also, eight REE ratios (∑REE, Nd/Yb, Eu/Eu* (Europium Anomaly), (La/Lu)n, (La/Sm)n, (Gd/Yb)n, (La/Yb)n and δCe (Cerium Anomaly)) were calculated and assumed as tracers.
2.2. Disrcimination of sources and quantification of their contribution
A stepwise discriminant function analysis (DFA) applied to discriminate sources. A mathematical multivariate mixing model was used in conjunction with the composite fingerprint to quantify the relative contributions of each source type to the sediment samples collected from the back of dam. The results of the stepwise DFA, based on the minimization of Wilk’s lambda, for discriminating the three source types, on the basis of the individual geochemical properties, showed that six tracers includingLa/Yb,Nd/Yb, Th, Bi,Pr and Cr were selected as optimum fingerprints. A total of six properties were selected for the optimum composite fingerprint, which correctly discriminated 100% of the source type samples. The minimum and maximum of GOF were calculated 45 and 94%, respectively. The fact that a majority of the GOF values was well above 80% suggested that the mixing model performed well in assessing the sediment sources in our study area (Zhou et al., 2016; Haddadchi et al., 2013). Among of six optimum fingerprints for discriminating sources of sediment, three optimum fingerprints (La/Yb,Nd/Yb and Pr) were selected from rare earth elements and their indices. This indicates that rare earth elements (REE) and their indices have great potential to identify the provenance of aeolian sediments and their transport pathways, because they are less fractionated during weathering, transport and sedimentation (e.g. Rao et al. (2011); Hu & Yang, (2016)). According to the results, the contribution mean from northern sub-basin, middle sub-basin and southern sub-basin were estimated 18%, 16% and 66%, respectively. Therefore, southern sub-basin was recognized as the main source to supply material for sediments deposited on the back of the dam.

Many catchment erosion studies focus on formation and land use as the primary source of sediment. It is important to improve information on sediment sources, especially in large catchments and sediment source information which can support catchment management decisions. Erosion control projects need to be understood as the relative contributions of different sediment sources from catchments. Fingerprinting methods identify soil erosion sources where geologic variations or different land uses span watershed boundaries. Sediment fingerprinting studies often rely on the collection of sediment from different sources within a catchment. Few studies have focused on using the Hughes mixed model to identify sediment sources. This model can quickly process a large number of samples from the main samples based on Monte Carlo simulation. The main objectives of this research were to determine the contribution of sediment sources by applying a fingerprinting mixing model in a Tange Bostanak drainage catchment.
Material and Methods.Case Study Our study area was located in the Tange Bostanak catchment (30°16′ to 30°25′ N and 52°03′ to 52°13′ E),in the Southern Zagros Mountains, 80 km Northwest of Shiraz, Iran. The drainage area of the Tange Bostanak catchment is 81.73 km2.
Sediment source samples were collected throughout the study catchment from each of the three principal source types (cultivated land, pasture, forest, gardens and also six formations in catchment). 43 representative samples were collected from these potential sources at different locations within the study catchment. Samples were initially oven-dried to 40 °C in order to remove the bias associated with the grain-size effects, only the C, N, Cu, Ti, Si and Sr were identified by the Kruskal–Wallis test to discriminate the potential sediment sources in land use and Nd, Si, C, N, Ti and Nd144/Nd143 were identified by the Kruskal–Wallis test to discriminate the potential sediment sources in the formations. In stepwise multivariate discriminant function analysis, four tracers(C, Cu, Si, Ti) were capable of correctly distinguishing the land use source type. Four tracers (Nd143/144, Cu, Si, Ti) verified the ability to discriminate between geology information source categories. The results on geology information showed that the mean relative contributions related to the areas of Asmary (84.51%) and Quaternary (5.37%) were highest, respectively in Local optimization with 99.94 GOF index. For land uses, the results showed that the GOF index with 97.84 associated with GA optimization were the greatest. The relative contributions related to the areas of range lands (63.04%) and forest (31.81%) were the highest, respectively. Pabedeh Gorpi and Bakhtyari information with 0.24 and 0.27 were the lowest relative importance; also cultivation and forest land uses with 0.022 and 0.55 were the lowest relative importance, respectively. This study suggested that the future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the Hughes (relying on iterations involving all data, and not only their mean values) models with the optimization using genetic algorithms to best predict the relative contribution of sediment sources. Comparing the applications in this catchment, the Hughes mixed model appears a more robust method in Tange Bostanak catchment using the GA optimization method.

Recognition of origin of sand dunes is very important in wind erosion control projects. Because of many problems associated with traditional procedures for identifying sediment sources, fingerprinting techniques, based on physical, chemical and organic properties of sediment and source materials, are increasingly being used as a valuable and effective alternative approach to assembling such information. In this method, a suitable composite (set) of diagnostic properties and a multivariate mixing model are employed to estimate the relative contribution of sediment sources to sediments transported to basin outlet. In this study, using suitable composites of geochemical elements, capable of discriminating of the study basin,were used to determine contributions of that sub basins to sediment yield. For this aim, 20 surface samples from the 3 sources region facies and 5 samples from sand dunes were collected and Particles less than 62.5 as the target were tested. Elements were used as tracer. Then, Initial choice and optimal combination of tracers were done by Statistical methods including one-way Anova analysis of variance tests, Kruskal-Wallis, and Discrimination Analyze. the method using discriminant analysis 2 tracer(Manganese, Calcium) The tracers were used as the right combination. Finally, Contribution of the various land used Farmlands, Range and arid lands 50.52, 26.34, 23.14% respectively. The total highest sediment land used produce. Low absolute errors 3.6% and predicted properties. High model efficiencies 96%. the fingerprinting approach to source ascription has high efficiency to determine relative importance of sediment sources (subbasins) in the study basin.