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E.E.R. 2022, 12(3): 57-77 |
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Soleimanpour S M, Gholami H, Rahmati O, Shadfar S. Investigating the Contribution of Sheet, Rill, Gully, and Stream Bank Erosions in Sediment Production Using Sediment Fingerprinting Method in Neyriz Watershed, Fars Province. E.E.R. 2022; 12 (3) :57-77
URL: http://magazine.hormozgan.ac.ir/article-1-718-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-718-en.html
Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran , m.soleimanpour@areeo.ac.ir
Abstract: (1499 Views)
1- Introduction
Severe soil erosion is a serious threat to the sustainable management of land and the use of water and soil resources in many parts of the world. In order to control erosion of sheet, rill, gully, and stream bank erosions and to reduce the resulting sediment at the outlet of watersheds, it is necessary to identify the share of sources that produce their sediment to make protective measures more successful. One of the most common methods that has been used in recent years to determine the share of different sources of sediment is the sediment fingerprinting method.
2- Methodology
The purpose of this study is to investigate contribution of sheet, rill, gully and stream bank erosions in sediment production by using sediment fingerprinting method in Neyriz watershed, located in East of Fars province, with the help of sampling of sediment deposited in the bed. From each type of sediments, sheet, rill, gully and stream bank erosions, the main waterway within the basin and the outlet area of the watershed, 10 samples (60 samples in total) were collected. In order to determine the optimal tracers, two tests of "domain" test and "multivariate detection analysis" were used. Furthermore, by using the model of Collins et al., the share of each of the different sources of sediment was obtained. Then, the uncertainty related to the share of potential sources of sediments was calculated using the Monte Carlo simulation method with 95% confidence in MATLAB software. In order to evaluate the results of the hybrid multivariate model, the Goodness of Fit (GOF) proposed by Collins et al. was used.
3- Results
Based on the range test, among the 51 tracers measured in the samples, twelve tracers (Ag, Ba, Be, Eu, Mn, Ni, Ta, Tb, Th, Tm, W, and Zn) are found as tracer’s non-conservative variables were identified, and these detectors were discarded in other statistical tests such as Kruskal-Wallis and discriminant analysis function. The results of the Kruskal-Wallis test showed that among the 39 tracers that passed the range test, sixteen tracers (Al, Ca, Co, Cr, Er, Fe, Gd, Lu, Mo, Na, Nd, Pb, Pr, S , Sc and Zr) with significance at one percent level (p ≤ 0.01), and 9 tracers (Cu, Ga, Hf, Ho, La, Sn, Sr, Y and Yb) with significance at five percent level (p ≤ 0.05) is that in total, these 25 detectors had a significant level and could separate sources; while fourteen tracers (As, Ce, Cs, Dy, K, Li, Mg, Nb, P, Rb, Sm, Te, Ti and V) were not statistically significant, these tracers were deleted from the DFA statistical test. In the first step of the DFA test, the Zr detector, the second step of Zr and Al detectors (with Wilkes lambda from 0.717 to 0.244), the third step of Al, Zr and Fe detectors (with Wilks lambda from 0.39 to 0.057), the fourth step of Zr, Al, Fe and Sn detectors (with Wilkes-lambda 0.362 to 0.04), the fifth step of Zr, Al, Fe, Sn and Lu detectors (with Wilkes-lambda 0.233 to 0.03) and the sixth step Zr, Al, Sn and Lu tracers (with Wilks lambda 0.289 to 0.045) were entered into the model. Based on the obtained results, among the 25 tracers that passed the Kruskal-Wallis test, five tracers (Al, Fe, Lu, Sn and Zr) were entered into the DFA test step by step. In the third stage, iron tracer (Fe) was added to the model and in the sixth stage, it was removed from the DFA test. In general, four Zr, Al, Sn and Lu tracers were selected as the final optimum tracers. These four detectors were able to correctly classify 95% of sediment sources. The findings of this research, which were obtained by using Monte Carlo simulation and the combined multivariable model and evaluating their results using GOF, showed the contribution of gully, sheet, rill and stream bank erosion to the order is equal to 45.21, 3.07, 16 and 35.72% of the total erosions that have occurred in this watershed. Also, considering the GOF value of 0.8869 and mentioning that the closer this value is to one, the more accurate the results of the model is true in this research and this analysis also confirms the high accuracy of the model.
4- Discussion & Conclusions
In this study, the efficiency of sediment fingerprinting method was proved as a successful and effective method to determine sediment sources because the first and most important stage of the sediment source method is to choose a suitable combination of tracers that can isolate sediment sources, and this was done correctly in this research. Also, Monte Carlo uncertainty confidence levels showed that the scope of this uncertainty is large (0.8869) and therefore, it shows a greater lack of certainty on different sources of sediment production. Determining the share of four types of erosion in the Neyriz watershed and placing the share of gully erosion as the most important type of erosion in the production of productive sediments in it shows the importance of controlling erosions, especially gully erosion, with emphasis on biological plans.
Severe soil erosion is a serious threat to the sustainable management of land and the use of water and soil resources in many parts of the world. In order to control erosion of sheet, rill, gully, and stream bank erosions and to reduce the resulting sediment at the outlet of watersheds, it is necessary to identify the share of sources that produce their sediment to make protective measures more successful. One of the most common methods that has been used in recent years to determine the share of different sources of sediment is the sediment fingerprinting method.
2- Methodology
The purpose of this study is to investigate contribution of sheet, rill, gully and stream bank erosions in sediment production by using sediment fingerprinting method in Neyriz watershed, located in East of Fars province, with the help of sampling of sediment deposited in the bed. From each type of sediments, sheet, rill, gully and stream bank erosions, the main waterway within the basin and the outlet area of the watershed, 10 samples (60 samples in total) were collected. In order to determine the optimal tracers, two tests of "domain" test and "multivariate detection analysis" were used. Furthermore, by using the model of Collins et al., the share of each of the different sources of sediment was obtained. Then, the uncertainty related to the share of potential sources of sediments was calculated using the Monte Carlo simulation method with 95% confidence in MATLAB software. In order to evaluate the results of the hybrid multivariate model, the Goodness of Fit (GOF) proposed by Collins et al. was used.
3- Results
Based on the range test, among the 51 tracers measured in the samples, twelve tracers (Ag, Ba, Be, Eu, Mn, Ni, Ta, Tb, Th, Tm, W, and Zn) are found as tracer’s non-conservative variables were identified, and these detectors were discarded in other statistical tests such as Kruskal-Wallis and discriminant analysis function. The results of the Kruskal-Wallis test showed that among the 39 tracers that passed the range test, sixteen tracers (Al, Ca, Co, Cr, Er, Fe, Gd, Lu, Mo, Na, Nd, Pb, Pr, S , Sc and Zr) with significance at one percent level (p ≤ 0.01), and 9 tracers (Cu, Ga, Hf, Ho, La, Sn, Sr, Y and Yb) with significance at five percent level (p ≤ 0.05) is that in total, these 25 detectors had a significant level and could separate sources; while fourteen tracers (As, Ce, Cs, Dy, K, Li, Mg, Nb, P, Rb, Sm, Te, Ti and V) were not statistically significant, these tracers were deleted from the DFA statistical test. In the first step of the DFA test, the Zr detector, the second step of Zr and Al detectors (with Wilkes lambda from 0.717 to 0.244), the third step of Al, Zr and Fe detectors (with Wilks lambda from 0.39 to 0.057), the fourth step of Zr, Al, Fe and Sn detectors (with Wilkes-lambda 0.362 to 0.04), the fifth step of Zr, Al, Fe, Sn and Lu detectors (with Wilkes-lambda 0.233 to 0.03) and the sixth step Zr, Al, Sn and Lu tracers (with Wilks lambda 0.289 to 0.045) were entered into the model. Based on the obtained results, among the 25 tracers that passed the Kruskal-Wallis test, five tracers (Al, Fe, Lu, Sn and Zr) were entered into the DFA test step by step. In the third stage, iron tracer (Fe) was added to the model and in the sixth stage, it was removed from the DFA test. In general, four Zr, Al, Sn and Lu tracers were selected as the final optimum tracers. These four detectors were able to correctly classify 95% of sediment sources. The findings of this research, which were obtained by using Monte Carlo simulation and the combined multivariable model and evaluating their results using GOF, showed the contribution of gully, sheet, rill and stream bank erosion to the order is equal to 45.21, 3.07, 16 and 35.72% of the total erosions that have occurred in this watershed. Also, considering the GOF value of 0.8869 and mentioning that the closer this value is to one, the more accurate the results of the model is true in this research and this analysis also confirms the high accuracy of the model.
4- Discussion & Conclusions
In this study, the efficiency of sediment fingerprinting method was proved as a successful and effective method to determine sediment sources because the first and most important stage of the sediment source method is to choose a suitable combination of tracers that can isolate sediment sources, and this was done correctly in this research. Also, Monte Carlo uncertainty confidence levels showed that the scope of this uncertainty is large (0.8869) and therefore, it shows a greater lack of certainty on different sources of sediment production. Determining the share of four types of erosion in the Neyriz watershed and placing the share of gully erosion as the most important type of erosion in the production of productive sediments in it shows the importance of controlling erosions, especially gully erosion, with emphasis on biological plans.
Received: 2022/02/28 | Published: 2022/09/21
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