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采用一种结合统计分析和数值模拟的改良程序对英格兰南部一个低地农业流域进行泥沙来源示踪研究。

Sediment source tracing in a lowland agricultural catchment in southern England using a modified procedure combining statistical analysis and numerical modelling.

机构信息

ADAS, Woodthorne, Wergs Road, Wolverhampton WV6 8TQ, UK.

出版信息

Sci Total Environ. 2012 Jan 1;414:301-17. doi: 10.1016/j.scitotenv.2011.10.062. Epub 2011 Nov 25.

Abstract

Catchment erosion, soil losses and resulting sediment pressures continue to represent cause for concern with respect to the ecological vitality and amenity value of riverine systems, including those in the agricultural catchments of southern England. Given that the sources of fine-grained sediment are typically diffuse in nature, it is essential to adopt a catchment-wide perspective to corresponding management strategies and sediment source tracing procedures have proved useful in assisting such planning. There remains, however, scope for further refining sediment sourcing procedures and on that basis, a recent study in the upper River Kennet (~214 km(2)) catchment in southern England, provided an opportunity for designing and testing a refined statistical procedure for sediment source discrimination with composite fingerprints using Genetic Algorithm (GA)-driven Discriminant Function Analysis, the Kruskal-Wallis H-test and Principal Components Analysis. The revised statistical verification of composite signatures was combined with numerical mass balance modelling using recent refinements including a range of tracer weightings and both local and GA optimisation. Comparison of the local and global optimisation increased confidence in the outputs of local optimisation and the goodness-of-fit for the predicted source contributions using the optimum composite signatures selected from the revised statistical testing ranged from 0.914 to 0.965. Overall relative frequency-weighted average median source type contributions were estimated to be 4% (agricultural topsoils; predicted deviate median inputs 1-19%), 55% (unmetalled farm track surfaces; predicted deviate median inputs 9-91%), 6% (damaged road verges; predicted deviate median inputs 4-42%), 31% (channel banks/subsurface sources; predicted deviate median inputs 5-41%) and 4% (urban street dust; predicted deviate median inputs 0-20%). The study provides further evidence of the importance of eroding farm tacks as a catchment scale sediment source and confirms the utility of tracing for assembling information on sediment inputs from both the agricultural and urban sectors.

摘要

集水区侵蚀、土壤流失以及由此产生的泥沙压力,仍然是河流系统(包括英格兰南部农业集水区的河流系统)生态活力和宜人价值的关注原因。鉴于细颗粒泥沙的来源通常具有弥散性,因此必须采用集水区整体的观点来制定相应的管理策略,而泥沙源示踪技术已被证明有助于此类规划。然而,在细化泥沙源示踪技术方面仍有进一步的空间,在此基础上,最近在英格兰南部的肯尼特河上游(~214km²)流域进行的一项研究为设计和测试一种使用遗传算法(GA)驱动的判别函数分析、克朗-瓦利斯 H 检验和主成分分析的复合指纹识别泥沙源的细化统计程序提供了机会。修订后的复合指纹统计验证与最近的改进相结合,包括一系列示踪剂权重以及局部和 GA 优化,用于数值质量平衡建模。局部和全局优化的比较提高了对局部优化输出的信心,并且使用从修订后的统计测试中选择的最佳复合指纹预测源贡献的拟合优度从 0.914 到 0.965 不等。整体相对频率加权平均中位数源类型贡献估计为 4%(农业表土;预测偏离中位数输入 1-19%)、55%(未铺面的农场车道表面;预测偏离中位数输入 9-91%)、6%(受损道路路肩;预测偏离中位数输入 4-42%)、31%(渠道堤岸/地下源;预测偏离中位数输入 5-41%)和 4%(城市街道尘土;预测偏离中位数输入 0-20%)。该研究进一步证明了侵蚀性农场车道作为集水区规模泥沙源的重要性,并证实了示踪技术在收集农业和城市部门泥沙输入信息方面的实用性。

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