Comparative analysis of discriminant ability of quantitative discriminant models for reservoir sediment sources

doi:10.7522/j.issn.1000-694X.2021.00110

Journal of Desert Research ›› 2022, Vol. 42 ›› Issue (2): 36-43.DOI: 10.7522/j.issn.1000-694X.2021.00110

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Comparative analysis of discriminant ability of quantitative discriminant models for reservoir sediment sources

Baicheng Niu¹^,²(), Fenggui Liu², Qiang Zhou¹^,²(), Qiong Chen¹^,², Benli Liu³

^1.College of Geographical Sciences，Qinghai Normal University，Xining 810008，China
^2.Academy of Plateau Science and Sustainability，Xining 810008，China
^3.Dunhuang Gobi Desert Research Station，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China

Received:2021-06-28 Revised:2021-08-30 Online:2022-03-20 Published:2022-03-30
Contact: Qiang Zhou

Abstract

Abstract:

Reservoir siltation is one of the main environmental problems in arid and semi-arid sandy areas. The sediment source fingerprinting is a new method to identify reservoir sediment sources， and screening an optimal discriminant model for sediment sources in a certain area is an important prerequisite for accurate identification of sediment source. In this study， Danghe Reservoir was selected as the research object. Based on field investigation， indoor analysis， model simulation and other means， the applicability of multiple composite fingerprints， optimal composite fingerprint， and distance weight method in the identification process of sediment source were compared and analyzed. The results showed that both multiple composite fingerprints and optimal composite fingerprint could obtain reasonable contributions and were relatively close， which were suitable for the quantitative identification of sediment sources in this watershed. However， the distance weighting method was not satisfactory and needed to be further tested. Increasing the number of composite fingerprints （19） could narrow the 95% confidence interval， which has statistical significance. From a statistical point of view. Theoretically the multiple composite fingerprints perform better， which will improve the accuracy of quantitative discrimination results to a certain extent.

Key words: fingerprinting method, reservoir sedimentation, provenance identification, Danghe Reservoir

CLC Number:

P333.4

Baicheng Niu, Fenggui Liu, Qiang Zhou, Qiong Chen, Benli Liu. Comparative analysis of discriminant ability of quantitative discriminant models for reservoir sediment sources[J]. Journal of Desert Research, 2022, 42(2): 36-43.

Figures/Tables 8

Fig.1 Location of the study area， and sampling sites

Fig.2 Inferring sediment source contributions using DFA and calculating distances. Here， D1 is the absolute X-value （Function 1） of the source centroid for cultivated land to the X-value of the sediment centroid

Table 1 The fingerprints of average concentration， standard deviation， and the results f the Kruskal-Walls H test in each sediment sources and Danghe Reservoir

指纹因子	沙丘		戈壁		上游山区		党河水库		H值	P值
指纹因子	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	H值	P值
Ti	5 391.6	864.2	5 457.7	743.0	4 215.1	412.0	5 112.7	538.5	14.24	0.001
Mn	657.0	67.6	681.5	50.7	613.2	30.8	638.6	33.7	10.75	0.005
Co	74.4	30.6	39.1	7.1	33.7	4.1	44.2	23.6	23.21	0.000
Cu	18.9	1.2	22.0	2.4	24.3	1.6	22.9	1.0	20.59	0.000
Zn	28.7	6.0	42.8	3.8	46.3	4.6	39.5	8.8	21.79	0.000
Sr	212.4	1.9	239.8	33.1	218.0	7.0	222.1	5.4	18.50	0.000
Nd	34.0	7.0	38.1	8.7	24.4	4.1	35.4	7.2	16.74	0.000
Al₂O₃	8.0	0.5	8.7	0.4	9.5	0.4	8.9	0.5	24.31	0.000

Fig.3 The estimated proportions of 19 composite fingerprints

Fig.4 Relative frequency distributions of the contributions of each sediment source and their average proportion

Table 2 Using stepwise discriminant function analysis to select fingerprints for optimum composite fingerprint

步骤	选入的指纹因子变量	Wilks’lambda	累计判别力/%
1	Zn	0.290	66.7
2	Al₂O₃	0.116	86.1
3	Co	0.087	88.9
4	Cu	0.068	94.4
5	Mn	0.052	97.2
6	Ti	0.040	97.2

Fig.5 Quantitative identification the contributions of the three potential sediment sources using composite fingerprints and the distance weighting method

Fig.6 95% confidence interval of 19 groups of dunes， gobi and upstream mountains to the sediment contribution of danghe reservoir

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Comparative analysis of discriminant ability of quantitative discriminant models for reservoir sediment sources

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