Department of Soil and Water, Estación Experimental de Aula Dei (EEAD-CSIC), Avda, Montañana 1005, Zaragoza, 50059, Spain.
Department of Soil and Water, Estación Experimental de Aula Dei (EEAD-CSIC), Avda, Montañana 1005, Zaragoza, 50059, Spain.
J Environ Manage. 2017 Jun 1;194:42-53. doi: 10.1016/j.jenvman.2016.07.058. Epub 2016 Aug 3.
Information on sediment contribution and transport dynamics from the contributing catchments is needed to develop management plans to tackle environmental problems related with effects of fine sediment as reservoir siltation. In this respect, the fingerprinting technique is an indirect technique known to be valuable and effective for sediment source identification in river catchments. Large variability in sediment delivery was found in previous studies in the Barasona catchment (1509 km, Central Spanish Pyrenees). Simulation results with SWAT and fingerprinting approaches identified badlands and agricultural uses as the main contributors to sediment supply in the reservoir. In this study the <63 μm sediment fraction from the surface reservoir sediments (2 cm) are investigated following the fingerprinting procedure to assess how the use of different statistical procedures affects the amounts of source contributions. Three optimum composite fingerprints were selected to discriminate between source contributions based in land uses/land covers from the same dataset by the application of (1) discriminant function analysis; and its combination (as second step) with (2) Kruskal-Wallis H-test and (3) principal components analysis. Source contribution results were different between assessed options with the greatest differences observed for option using #3, including the two step process: principal components analysis and discriminant function analysis. The characteristics of the solutions by the applied mixing model and the conceptual understanding of the catchment showed that the most reliable solution was achieved using #2, the two step process of Kruskal-Wallis H-test and discriminant function analysis. The assessment showed the importance of the statistical procedure used to define the optimum composite fingerprint for sediment fingerprinting applications.
需要有关汇水区域泥沙补给和输移动态的信息,以制定管理计划来解决与水库淤积有关的细颗粒泥沙影响等环境问题。在这方面,示踪技术是一种间接技术,已知对河流汇水区的泥沙源识别非常有价值和有效。在巴罗萨纳流域(西班牙中比利牛斯山脉,1509 平方公里)的先前研究中发现泥沙输送存在很大的可变性。SWAT 和示踪技术的模拟结果表明,荒地和农业用途是水库泥沙供应的主要来源。本研究采用示踪技术对水库表层沉积物(2cm)中的<63μm 泥沙进行了研究,以评估不同统计方法的应用如何影响源贡献的数量。选择了三个最佳综合指纹,通过应用(1)判别函数分析,基于土地利用/土地覆盖对来自同一数据集的源贡献进行区分;并将其与(2)Kruskal-Wallis H 检验和(3)主成分分析相结合。源贡献结果因评估选项而异,使用选项 3 的差异最大,其中包括两步过程:主成分分析和判别函数分析。应用混合模型的解决方案的特点和对汇水区的概念理解表明,使用 Kruskal-Wallis H 检验和判别函数分析的两步过程的选项 2 可以获得最可靠的解决方案。评估结果表明,在泥沙示踪应用中,为获得最佳综合指纹而采用的统计方法的重要性。