Suppr超能文献

新西兰马纳瓦图河悬浮泥沙来源的特征描述与量化。

Characterization and quantification of suspended sediment sources to the Manawatu River, New Zealand.

机构信息

Institute of Agriculture and Environment, Massey University, Palmerston North, New Zealand; Soils and Landscapes, Landcare Research, Palmerston North, New Zealand.

Institute of Agriculture and Environment, Massey University, Palmerston North, New Zealand.

出版信息

Sci Total Environ. 2016 Feb 1;543(Pt A):171-186. doi: 10.1016/j.scitotenv.2015.11.003. Epub 2015 Nov 12.

Abstract

Knowledge of sediment movement throughout a catchment environment is essential due to its influence on the character and form of our landscape relating to agricultural productivity and ecological health. Sediment fingerprinting is a well-used tool for evaluating sediment sources within a fluvial catchment but still faces areas of uncertainty for applications to large catchments that have a complex arrangement of sources. Sediment fingerprinting was applied to the Manawatu River Catchment to differentiate 8 geological and geomorphological sources. The source categories were Mudstone, Hill Subsurface, Hill Surface, Channel Bank, Mountain Range, Gravel Terrace, Loess and Limestone. Geochemical analysis was conducted using XRF and LA-ICP-MS. Geochemical concentrations were analysed using Discriminant Function Analysis and sediment un-mixing models. Two mixing models were used in conjunction with GRG non-linear and Evolutionary optimization methods for comparison. Discriminant Function Analysis required 16 variables to correctly classify 92.6% of sediment sources. Geological explanations were achieved for some of the variables selected, although there is a need for mineralogical information to confirm causes for the geochemical signatures. Consistent source estimates were achieved between models with optimization techniques providing globally optimal solutions for sediment quantification. Sediment sources was attributed primarily to Mudstone, ≈38-46%; followed by the Mountain Range, ≈15-18%; Hill Surface, ≈12-16%; Hill Subsurface, ≈9-11%; Loess, ≈9-15%; Gravel Terrace, ≈0-4%; Channel Bank, ≈0-5%; and Limestone, ≈0%. Sediment source apportionment fits with the conceptual understanding of the catchment which has recognized soft sedimentary mudstone to be highly susceptible to erosion. Inference of the processes responsible for sediment generation can be made for processes where there is a clear relationship with the geomorphology, but is problematic for processes which occur within multiple terrains.

摘要

由于沉积物运动对农业生产力和生态健康相关的景观特征和形态具有重要影响,因此了解整个流域环境中的沉积物运动至关重要。沉积物示踪是评估河流流域内沉积物来源的常用工具,但在应用于具有复杂源区分布的大型流域时,仍存在不确定性。本文将沉积物示踪法应用于马纳瓦图河流域,以区分 8 种地质和地貌来源。源类别为泥岩、丘陵次表层、丘陵表层、河道岸、山脉、砾石阶地、黄土和石灰岩。使用 XRF 和 LA-ICP-MS 进行地球化学分析。使用判别函数分析和沉积物混合模型分析地球化学浓度。使用 GRG 非线性和进化优化方法结合两种混合模型进行比较。判别函数分析需要 16 个变量才能正确分类 92.6%的沉积物源。虽然需要矿物学信息来确认地球化学特征的原因,但一些选定的变量可以得到地质解释。优化技术的模型之间实现了一致的源估计,为沉积物定量提供了全局最优解。沉积物源主要归因于泥岩,约占 38-46%;其次是山脉,约占 15-18%;丘陵表层,约占 12-16%;丘陵次表层,约占 9-11%;黄土,约占 9-15%;砾石阶地,约占 0-4%;河道岸,约占 0-5%;石灰岩,约占 0%。沉积物源分配与流域的概念理解一致,该流域已认识到软沉积泥岩极易受到侵蚀。对于与地貌有明确关系的过程,可以推断出沉积物产生的过程,但对于发生在多个地形中的过程则存在问题。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验