University of TehranPhysical Geography Research2008-630X50220180622The Role of Correction Factors in Sediment Source Fingerprinting of the Lake Urmia Sand DunesThe Role of Correction Factors in Sediment Source Fingerprinting of the Lake Urmia Sand Dunes2933056849310.22059/jphgr.2018.240078.1007107FAHesam Ahmady-BirganiAssistant Professor of Natural Resources, Faculty of Natural Resources, Urmia University, IranJournal Article20170819Introduction
Over the last decades, sediment fingerprinting technique relative to the experimental models for erosion and deposition processes is now used for its higher reliability and lower uncertainties. Its reliable information give the best indication of sediment yield produced by spatial sources of a catchment and let authorities know how take conservative operations and proper actions across the catchment to stop the soil erosion. Therefore, identification of the dominant processes and sources generating the sediment within its catchment are vital. The western shore of the Lake Urmia, NW Iran, the world’s second largest hyper-saline lake has now retreated more than 7 Km from the shoreAs a result, sand dunes and sand ridges are appearing across its western margin. We made an exploration of the geomorphological/lithological units as the sediment feeders out of its western catchment using geochemical data. As the main aim of the present research, we need to correct the contributing factors including particle size, organic matter and tracer discriminatory weighting in recognition of potential changes in ﬁngerprint properties during sediment delivery.
Material and methods
A mixing model algorithm was used to estimate the relative contributions from the potential sediment sources by minimizing the sum of squares of the weighted relative errors.
S<sub>Sink</sub>: concentration of ﬁngerprint property (i) in the sediment was collected from the outlet;
P<sub>S</sub>: percentage contribution from source category (s);
S<sub>Source</sub>: mean concentration of ﬁngerprint property (i) in source category (s);
Z<sub>S</sub>: particle size correction factor for source category (s);
O<sub>S</sub>: organic matter content correction factor for source category (s);
W<sub>i</sub>: tracer discriminatory weighting;
n: number of ﬁngerprint properties comprising the composite ﬁngerprint;
m: number of sediment source categories;
The above algorithm has incorporated three correction factors to reflect the impact of element concentration in given sediment load size. The effects of the correction factors into the fluvial and alluvial sediment loads have been approved, what has not been well understood for Aeolian sediments and desert environments. Therefore, the role of the correction factors is to estimate the proportion of each potentially sediment source. Paired t-student statistical method was applied to find out whether there are differences between being correction factors and not being the correction factors.
Results and discussion
As the paired t-student method results show, there is not significant differences between the source contribution before using the correction factors and after using them. However, it is a statistical result and objective function results have another story. According to Table 2, before using the correction factors, Qmf and Qt geomorphological/lithological units with 47.76% and 52.24%, respectively, have the highest proportion in generating the sediment load of the catchment. After implementation of the correction factor, Qf and Klshi geomorphological/lithological units with 67.5% and 32.5%, respectively, have also the highest contribution. Thus, different source proportion was seen with no significant statistic results.
Conclusion
The present research successfully interpreted the impact of correction factors on sediment source contribution of the sand dunes of Lake Urmia. These correction factors are now widely used into the mixing model or objective function to improve the comparability of source and sediment samples. It is inferred that the organic matter correction factor can be used while mineral-magnetism properties of samples are put as the tracers. The particle size correction factor due to its strong inﬂuence on many tracers used for ﬁngerprinting is applied, as the relation of grain size to each tracer's concentration is tested. With generating a scatter plot of particle size or organic matter content against tracer concentration for each source group, necessity of correction factor is evaluated. Generally, it is interpreted that applying the correction factors is vital when some other parameters including sediment environments, tracer properties, chronology of sediments, particle size of sediment loads and etc. are preliminary evaluated.Introduction
Over the last decades, sediment fingerprinting technique relative to the experimental models for erosion and deposition processes is now used for its higher reliability and lower uncertainties. Its reliable information give the best indication of sediment yield produced by spatial sources of a catchment and let authorities know how take conservative operations and proper actions across the catchment to stop the soil erosion. Therefore, identification of the dominant processes and sources generating the sediment within its catchment are vital. The western shore of the Lake Urmia, NW Iran, the world’s second largest hyper-saline lake has now retreated more than 7 Km from the shoreAs a result, sand dunes and sand ridges are appearing across its western margin. We made an exploration of the geomorphological/lithological units as the sediment feeders out of its western catchment using geochemical data. As the main aim of the present research, we need to correct the contributing factors including particle size, organic matter and tracer discriminatory weighting in recognition of potential changes in ﬁngerprint properties during sediment delivery.
Material and methods
A mixing model algorithm was used to estimate the relative contributions from the potential sediment sources by minimizing the sum of squares of the weighted relative errors.
S<sub>Sink</sub>: concentration of ﬁngerprint property (i) in the sediment was collected from the outlet;
P<sub>S</sub>: percentage contribution from source category (s);
S<sub>Source</sub>: mean concentration of ﬁngerprint property (i) in source category (s);
Z<sub>S</sub>: particle size correction factor for source category (s);
O<sub>S</sub>: organic matter content correction factor for source category (s);
W<sub>i</sub>: tracer discriminatory weighting;
n: number of ﬁngerprint properties comprising the composite ﬁngerprint;
m: number of sediment source categories;
The above algorithm has incorporated three correction factors to reflect the impact of element concentration in given sediment load size. The effects of the correction factors into the fluvial and alluvial sediment loads have been approved, what has not been well understood for Aeolian sediments and desert environments. Therefore, the role of the correction factors is to estimate the proportion of each potentially sediment source. Paired t-student statistical method was applied to find out whether there are differences between being correction factors and not being the correction factors.
Results and discussion
As the paired t-student method results show, there is not significant differences between the source contribution before using the correction factors and after using them. However, it is a statistical result and objective function results have another story. According to Table 2, before using the correction factors, Qmf and Qt geomorphological/lithological units with 47.76% and 52.24%, respectively, have the highest proportion in generating the sediment load of the catchment. After implementation of the correction factor, Qf and Klshi geomorphological/lithological units with 67.5% and 32.5%, respectively, have also the highest contribution. Thus, different source proportion was seen with no significant statistic results.
Conclusion
The present research successfully interpreted the impact of correction factors on sediment source contribution of the sand dunes of Lake Urmia. These correction factors are now widely used into the mixing model or objective function to improve the comparability of source and sediment samples. It is inferred that the organic matter correction factor can be used while mineral-magnetism properties of samples are put as the tracers. The particle size correction factor due to its strong inﬂuence on many tracers used for ﬁngerprinting is applied, as the relation of grain size to each tracer's concentration is tested. With generating a scatter plot of particle size or organic matter content against tracer concentration for each source group, necessity of correction factor is evaluated. Generally, it is interpreted that applying the correction factors is vital when some other parameters including sediment environments, tracer properties, chronology of sediments, particle size of sediment loads and etc. are preliminary evaluated.https://jphgr.ut.ac.ir/article_68493_57d01380e6612a86cb571a8e8fc10972.pdf