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Determination of sediment sources in a mixed watershed within the Appalachian-St. Lawrence Lowland Regions of southern Quebec using sediment fingerprinting

机译:阿巴拉契亚-ST中混合流域中沉积物来源的测定。魁北克南部劳伦斯低地地区使用沉积物指纹识别

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This paper identifies the main sediment sources to the Beaudet Reservoir in Quebec (Canada) using sediment fingerprinting. The reservoir, which is built on the Bulstrode River and provides drinking water to Victoriaville, has decreased in capacity by 35% in the past 35 years. This study provides new data on fingerprinting in large and complex watersheds, a first in the province of Quebec. Nine sampling sites on the Bulstrode River and its three main tributaries were selected and five sampling campaigns were conducted. Samples from river bank profiles and adjacent fields, along with suspended sediments, were collected. All samples were sieved to 2 mm and analyzed for 137Caesium, 15 geochemical elements and sieved to 63 mu m for color analysis. Source classification, based on an ANOVA test to verify the independence hypothesis and iterative linear discriminant analysis to optimize the ratio of inter-group/within-group variability, resulted in four sample classes: agricultural soils, forested soils, stream bank bottom and stream bank top. A Kruskal-WallisHtest then identified 21 out of the 32 tracers withpvalue 0.05. The linear discriminant analysis led to a set of 14 tracers, namely 137Cs and 13 color coefficients with a discriminating result of 94%. That combination of 137Cs and color coefficients proved to be a cost-effective fingerprint. Based on MixSIAR modeling results, this sediment fingerprinting study has demonstrated that the main sediment sources varied within the watershed but, generally, forested soil particles dominated (33 to 49%), then agricultural soils (43 to 50%) reflecting the land use changes, followed by stream bank bottoms (82%) at the Beaudet Reservoir.
机译:本文使用沉积物指纹识别魁北克(加拿大)的Beaudet水库的主要沉积来源。储层建于金钱河上,为维多利亚维尔提供饮用水,在过去35年内以35%降低了35%。本研究提供了关于魁北克省的第一和复杂流域的指纹识别的新数据。选择九架河流上的九个采样网站及其三个主要支流,并进行了五项抽样活动。收集来自河流档案和邻近田地的样品以及悬浮沉积物。将所有样品筛分为2mM,分析137℃,15个地球化学元素,并筛分为63μm以进行颜色分析。基于ANOVA测试的源分类,验证独立假设和迭代线性判别分析,以优化组间/内部内变异性的比例,导致四种样品类别:农业土壤,森林土壤,流银行底部和流银行最佳。然后kruskal-wallishtest然后用Pvalue <0.05中的32个示踪剂中识别出21个。线性判别分析导致一组14个示踪剂,即137℃和13个颜色系数,辨别结果为94%。将137℃和颜色系数的组合证明是一种经济高效的指纹。基于MixSiar的模型结果,这种沉积物指纹研究表明,流域内的主要沉积物源各不相同,但通常,森林土壤颗粒占主导地位(33至49%),然后农业土壤(43至50%)反映土地利用变化,然后是Beaudet水库的流银行底部(82%)。

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