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运用指纹识别程序,结合不同的多元统计技术和贝叶斯混合分解模型,研究山区集水区休闲道路作为沉积物来源的重要性。

Investigating the importance of recreational roads as a sediment source in a mountainous catchment using a fingerprinting procedure with different multivariate statistical techniques and a Bayesian un-mixing model.

作者信息

Nosrati Kazem, Collins Adrian L

机构信息

Department of Physical Geography, School of Earth Sciences, Shahid Beheshti University, 1983969411 Tehran, Iran.

Sustainable Agriculture Sciences Department, Rothamsted Research, North Wyke, Okehampton EX20 2SB, UK.

出版信息

J Hydrol (Amst). 2019 Feb;569:506-518. doi: 10.1016/j.jhydrol.2018.12.019.

Abstract

Road construction associated with land development generally increases erosion and sediment yields. Construction of unpaved roads has the potential to alter hydro-sedimentological behavior and catchment sediment source dynamics and, to date, this has largely been investigated in forested environments. The objective of this study, therefore, was to assess the relative importance of unpaved recreational roads as a sediment source alongside hillslope surface soils and stream channel banks in a non-forested mountainous catchment in northern Tehran, Iran, using a fingerprinting procedure. Eleven geochemical tracers were measured on 27 samples collected to characterise the sediment sources and five suspended sediment samples collected at the study catchment outlet. The statistical analysis employed to select three different composite fingerprints for discriminating the sediment sources comprised: (1) the Kruskal-Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), and (3) a combination of KW-H and principal components & classification analysis (PCCA). A Bayesian un-mixing model was used to ascribe sediment source contributions using the three composite fingerprints. Using the KW-H composite signature, the respective relative contributions (with uncertainty ranges) from recreational roads, hillslope surface soils and channel banks were estimated as 64.5% (57.7-73.1), 1.1% (0.1-4.9), and 33.9% (24.9-41.0), compared to 55.3% (45.5-68.5), 1.9% (0.1-7.9) and 42.1% (27.8-52.4) using a composite signature selected using a combination of KW-H and DFA, or 82.0% (69.7-93.8), 8.2% (0.7-22.7) and 7.3% (0.7-21.0) using a fingerprint selected using KW-H and PCCA. The root mean square difference between the apportionment results using the fingerprints identified on the basis of the three different statistical approaches ranged from 5.5% to 25.7%, highlighting the sensitivity of source estimates to the tracers used. Regardless, the different composite signatures all suggested that unpaved recreational roads were the dominant source of the suspended sediment samples, underscoring the need for mitigation measures targeting these anthropogenic features of the catchment system, including closure to permit re-vegetation, surface ripping and/or mulching to improve infiltration or gravel re-surfacing to reduce exposure of bare surfaces to sediment mobilisation.

摘要

与土地开发相关的道路建设通常会增加侵蚀和沉积物产量。未铺砌道路的建设有可能改变水沙动力学行为和集水区沉积物源动态,迄今为止,这方面的研究主要集中在森林环境中。因此,本研究的目的是在伊朗德黑兰北部一个非森林山区流域,采用指纹识别方法,评估未铺砌的休闲道路作为沉积物源相对于山坡表层土壤和河道河岸的相对重要性。对采集的27个样本测量了11种地球化学示踪剂,以表征沉积物源,并在研究集水区出口采集了5个悬浮沉积物样本。用于选择三种不同综合指纹以区分沉积物源的统计分析包括:(1)Kruskal-Wallis H检验(KW-H);(2)KW-H和判别函数分析(DFA)的组合;(3)KW-H与主成分及分类分析(PCCA)的组合。使用贝叶斯混合模型,利用这三种综合指纹来确定沉积物源的贡献。使用KW-H综合特征,休闲道路、山坡表层土壤和河道河岸的相对贡献(含不确定范围)分别估计为64.5%(57.7-73.1)、1.1%(0.1-4.9)和33.9%(24.9-41.0),而使用KW-H和DFA组合选择的综合特征时,相对贡献分别为55.3%(45.5-68.5)、1.9%(0.1-7.9)和42.1%(27.8-52.4),使用KW-H和PCCA选择指纹时,相对贡献分别为82.0%(69.7-93.8)、8.2%(0.7-22.7)和7.3%(0.7-21.0)。基于三种不同统计方法确定的指纹得出的分配结果之间的均方根差异在5.5%至25.7%之间,突出了源估计对所用示踪剂的敏感性。尽管如此,不同的综合特征均表明未铺砌的休闲道路是悬浮沉积物样本的主要来源,这凸显了针对集水区系统这些人为特征采取缓解措施的必要性,包括封闭道路以促进重新植被、表面翻耕和/或覆盖以改善入渗,或重新铺设砾石以减少裸露表面受沉积物移动的影响。

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