Determining lithological units contribution and ranges of peak ground acceleration in sediment yield using the sediment fingerprinting technique (Talar drainage basin of Mazandaran province)
Document Type : Original Article
Authors
1
Faculty of Earth Sciences, Shahid Beheshti University, Tehran
2
Department of Environmental Planning and Design, Environmental Science Research Institute, Shahid Beheshti University,Tehran.
10.22034/gmpj.2023.413321.1452
Abstract
Extended Abstract
Introduction
Soil erosion is a global issue that causes environmental degradation.One of the factors that increase sedimentation is seismic activity and peak ground acceleration index (PGA). PGA index strongly depends on the frequency of large and small earthquakes and provides a correct measurement of the seismicity of a region. Among the results of seismic activity in rocks is the increase in weathering and sensitivity to erosion. Identifying the critical areas of the watershed in the PGA index and the role of these areas in sediment yield and soil erosion is one of the topics that has received less attention from researchers. Fingerprinting or Sediment tracing as a method with proper accuracy and high efficiency has been noticed by different researchers. In this method, the soil eroded from the sources is divided and discriminant based on the measured tracers and existing composite models. Therefore, the purpose of this research is to determine fingerprinting of sediment using uncertainty Bayesian model and to determine contribution of surface and subsurface erosion units and PGA in sediment yield in the Talar drainage basin of Mazandaran province.
Materials and Methods
The study area (2105 km^(2))is located at latitudes between 35° 44΄ and 36° 19΄ North, and at longitudes between 52° 35΄ and 53° 23΄ East. The main formations in the study area are Shemshak, Elika, Karaj, Lar. Sampling of the target sediment in each outlet of the sub-basins, 10 main drape sediments samples were taken at distances of 100 meters from the outlet of the sub-basin to the upstream side of the river. Sampling of sediment sources was done based on 4 dominant lithological units based on the largest area and sampling of the channel bank in sub-basins 1 and 2. Sampling was done from the depth of 0-5 cm in lithological units and channel bank that have suffered surface and subsurface erosion. So 140 soil samples in first approach and 80 samples in second approach collected respectively sub-basin 1 (77) and (47), sub-basin 2 (63) and (33) of lithological units and range of peak ground acceleration and 28 geochemical elements (Al-As-Ba-Be-Ca-Ce-Co-Cr-Cu-Fe-K-La-Li-Mg-Mn-Na-Ni-P-Pb-S-Sc-Sr-Ti-V-Y-Yb-Zr-Zn) measured as tracers in all samples in size fractions < 63 μm. In order to remove non-conservative tracers, standard bracket and average concentration tests were used. After identifying the conservative tracers, the significant level of each of the tracers was determined by the Kruskal-Wallis test, and finally, the composite fingerprint that have the highest power of discriminant of sediment sources was determined in the discriminant function analysis (DFA). Bayesian un-mixing model was used to estimate the relative contribution source sediment by composite fingerprints.
Findings and Discussion
The composite fingerprints in sub-basin 1, five tracer respectively, Zn, Co, Ba, Mn, Ni, and in the second approach, five tracers Ni, Zn, Co, Ba, Mn, Li were selected in DFA.
In sub-basin 2, six tracer respectively, Zn,Ba, K, P, Mn, Sc and in the second approach four tracers Ba, Pb, Ni, K were selected. The cumulative percentage of correctly classified samples in the first and second approach in sub basin 1 and 2 respectively , is (97.4% - 97.9% ), (96.8% - 100%). The relative contribution of sediment sources estimated in Bayesian un-mixing model in the corresponding uncertainty range (5-95%) in the first approach in sub-basin 1 for sandstone and conglomerate, limestone and dolomite, marl and shale, alluvial sediments and terraces , channel bank is respectively (59.1-59.1) 59.1%, (40.5-40.5) 40.5%, (0.1-0.1) 0.1%, (0.2-0.2) 0.2%, (0.1-0.1) 0.1% and for sub basin 2 (0.1-2.1) 0.7%, (5.3-7.5) 6.5%, (40.8-53.7) 47.2%, (1.4-19.3) 8.4%, (23.8-47.5) 37.2%.
In the second approach, for the PGA classes g(0.41-0.5), g(0.51-0.6) and channel bank, in sub basin 1 is (0.1-3.9) 0.9%, (46.5-54.4) 50.3% and (43.8-52.6) 48.4%, also in sub basin 2, is (0-1.4) 0.4%, (31.7-38.1) 35% and (61.3-67.6) 64.6% respectively.
In the first approach, in sub-basin 1 unit of sandstone and conglomerate with 59.1%, and in sub-basin 2 units of marl and shale with 47.2%, and in the second approach, in sub-basin 1 PGA (0.51-0.6)g with 50.3% and in sub-basin, 2 units channel bank with 48.4% have the highest relative contribution in sediment yield.
The size fractions < 63 μm show well the concentration of geochemical elements in soil and sediments. the influence of lithological units and acceleration levels on the distribution and control of geochemical elements in sediment yield is very important.
According to (Figure 1), the length of Firoozkoh and IRQ 112 faults have had a significant impact on the seismic trend in high and medium acceleration ranges.
PGA in (0.51-0.6) g (0.41-0.5) due to the frequency of large and small earthquakes, sediments and erodible rocks in sensitive slopes experience instability and displacement, which causes The conditions of mechanical and chemical weathering, crushing and weakening of rocks will eventually accelerate erosion processes and transfer sediment from the slopes to the streams over time.
Proximity to active faults such as Firouzkoh, PGA (0.51-0.6)g, proximity of erodible units especially marl and shale to the outlet of the sub basin, lateral stream erosion in PGA (0.51-0.6)g causes surface and subsurface soil erosion in the Talar drainage basin.
Conclusion
The results of this study showed that the range of PGA have a direct effect on the control of other sediment yield processes such as weathering and rock weakening. Also, the division of lithological units as sources of sediment yield based on the range of PGA, which have a great impact on sediment yield, as a new approach, can be of great help in understanding sediment yield processes.
Introduction
Soil erosion is a global issue that causes environmental degradation.One of the factors that increase sedimentation is seismic activity and peak ground acceleration index (PGA). PGA index strongly depends on the frequency of large and small earthquakes and provides a correct measurement of the seismicity of a region. Among the results of seismic activity in rocks is the increase in weathering and sensitivity to erosion. Identifying the critical areas of the watershed in the PGA index and the role of these areas in sediment yield and soil erosion is one of the topics that has received less attention from researchers. Fingerprinting or Sediment tracing as a method with proper accuracy and high efficiency has been noticed by different researchers. In this method, the soil eroded from the sources is divided and discriminant based on the measured tracers and existing composite models. Therefore, the purpose of this research is to determine fingerprinting of sediment using uncertainty Bayesian model and to determine contribution of surface and subsurface erosion units and PGA in sediment yield in the Talar drainage basin of Mazandaran province.
Materials and Methods
The study area (2105 km^(2))is located at latitudes between 35° 44΄ and 36° 19΄ North, and at longitudes between 52° 35΄ and 53° 23΄ East. The main formations in the study area are Shemshak, Elika, Karaj, Lar. Sampling of the target sediment in each outlet of the sub-basins, 10 main drape sediments samples were taken at distances of 100 meters from the outlet of the sub-basin to the upstream side of the river. Sampling of sediment sources was done based on 4 dominant lithological units based on the largest area and sampling of the channel bank in sub-basins 1 and 2. Sampling was done from the depth of 0-5 cm in lithological units and channel bank that have suffered surface and subsurface erosion. So 140 soil samples in first approach and 80 samples in second approach collected respectively sub-basin 1 (77) and (47), sub-basin 2 (63) and (33) of lithological units and range of peak ground acceleration and 28 geochemical elements (Al-As-Ba-Be-Ca-Ce-Co-Cr-Cu-Fe-K-La-Li-Mg-Mn-Na-Ni-P-Pb-S-Sc-Sr-Ti-V-Y-Yb-Zr-Zn) measured as tracers in all samples in size fractions < 63 μm. In order to remove non-conservative tracers, standard bracket and average concentration tests were used. After identifying the conservative tracers, the significant level of each of the tracers was determined by the Kruskal-Wallis test, and finally, the composite fingerprint that have the highest power of discriminant of sediment sources was determined in the discriminant function analysis (DFA). Bayesian un-mixing model was used to estimate the relative contribution source sediment by composite fingerprints.
Findings and Discussion
The composite fingerprints in sub-basin 1, five tracer respectively, Zn, Co, Ba, Mn, Ni, and in the second approach, five tracers Ni, Zn, Co, Ba, Mn, Li were selected in DFA.
In sub-basin 2, six tracer respectively, Zn,Ba, K, P, Mn, Sc and in the second approach four tracers Ba, Pb, Ni, K were selected. The cumulative percentage of correctly classified samples in the first and second approach in sub basin 1 and 2 respectively , is (97.4% - 97.9% ), (96.8% - 100%). The relative contribution of sediment sources estimated in Bayesian un-mixing model in the corresponding uncertainty range (5-95%) in the first approach in sub-basin 1 for sandstone and conglomerate, limestone and dolomite, marl and shale, alluvial sediments and terraces , channel bank is respectively (59.1-59.1) 59.1%, (40.5-40.5) 40.5%, (0.1-0.1) 0.1%, (0.2-0.2) 0.2%, (0.1-0.1) 0.1% and for sub basin 2 (0.1-2.1) 0.7%, (5.3-7.5) 6.5%, (40.8-53.7) 47.2%, (1.4-19.3) 8.4%, (23.8-47.5) 37.2%.
In the second approach, for the PGA classes g(0.41-0.5), g(0.51-0.6) and channel bank, in sub basin 1 is (0.1-3.9) 0.9%, (46.5-54.4) 50.3% and (43.8-52.6) 48.4%, also in sub basin 2, is (0-1.4) 0.4%, (31.7-38.1) 35% and (61.3-67.6) 64.6% respectively.
In the first approach, in sub-basin 1 unit of sandstone and conglomerate with 59.1%, and in sub-basin 2 units of marl and shale with 47.2%, and in the second approach, in sub-basin 1 PGA (0.51-0.6)g with 50.3% and in sub-basin, 2 units channel bank with 48.4% have the highest relative contribution in sediment yield.
The size fractions < 63 μm show well the concentration of geochemical elements in soil and sediments. the influence of lithological units and acceleration levels on the distribution and control of geochemical elements in sediment yield is very important.
According to (Figure 1), the length of Firoozkoh and IRQ 112 faults have had a significant impact on the seismic trend in high and medium acceleration ranges.
PGA in (0.51-0.6) g (0.41-0.5) due to the frequency of large and small earthquakes, sediments and erodible rocks in sensitive slopes experience instability and displacement, which causes The conditions of mechanical and chemical weathering, crushing and weakening of rocks will eventually accelerate erosion processes and transfer sediment from the slopes to the streams over time.
Proximity to active faults such as Firouzkoh, PGA (0.51-0.6)g, proximity of erodible units especially marl and shale to the outlet of the sub basin, lateral stream erosion in PGA (0.51-0.6)g causes surface and subsurface soil erosion in the Talar drainage basin.
Conclusion
The results of this study showed that the range of PGA have a direct effect on the control of other sediment yield processes such as weathering and rock weakening. Also, the division of lithological units as sources of sediment yield based on the range of PGA, which have a great impact on sediment yield, as a new approach, can be of great help in understanding sediment yield processes.
Keywords
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