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What Are the Key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?

机译:影响河流水质空间差异的主要集水特征是什么?

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摘要

This study uses water-quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to further our understanding of spatial variability in riverine water quality. We focus on concentrations of total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrateitrite (NO,), and electrical conductivity. We used an exhaustive search approach to identify the linear models that best link catchment characteristics to time-averaged constituent concentrations. We ran additional analyses to (1) assess the performance of these models under drought conditions, and (2) understand the key drivers of site-level variability (standard deviations) of constituent concentrations. Natural catchment characteristics appear to have a greater effect on spatial differences in average constituent concentrations. Performance of the statistical models of time-averaged constituent concentrations varied, and spatial variability in mean electrical conductivity levels could be more readily explained by catchment characteristics compared to more reactive nutrients. Notwithstanding, the models performed relatively well under varying hydrologic conditions for most constituents. As such, these models provide an insight into the key factors affecting spatial variability in average stream water-quality conditions. We also identified that hydrologic, climatic, and topographic characteristics of the catchment helped explain the spatial variability in temporal changes in constituents. After calibration and validation, these models of both average water quality and variability in water quality could be used to forecast stream water-quality responses to future land use, climate, or soil and land management changes.
机译:这项研究使用了20年来收集的水质数据,这些数据是从澳大利亚维多利亚州的102个主要为农村的地点收集的,以加深我们对河流水质空间变异性的理解。我们专注于总悬浮固体,总磷,可过滤的活性磷,凯氏氮,硝酸盐/亚硝酸盐(NO)和电导率的浓度。我们使用了详尽的搜索方法来确定将流域特征与时间平均成分浓度最佳关联的线性模型。我们进行了其他分析,以(1)评估这些模型在干旱条件下的性能,并且(2)了解成分浓度的场地水平变异性(标准差)的关键驱动因素。自然流域特征似乎对平均成分浓度的空间差异具有更大的影响。时间平均成分浓度统计模型的性能各不相同,与反应性更高的养分相比,集水特征更容易解释平均电导率水平的空间变异性。尽管如此,该模型在大多数成分变化的水文条件下表现相对较好。因此,这些模型提供了对影响平均溪流水质状况中空间变异性的关键因素的见解。我们还确定流域的水文,气候和地形特征有助于解释组分随时间变化的空间变异性。经过校准和验证后,这些平均水质和水质变异性模型可用于预测溪流水质对未来土地利用,气候或土壤和土地管理变化的响应。

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  • 来源
    《Water resources research》 |2018年第10期|7252-7272|共21页
  • 作者单位

    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia|Monash Univ, Dept Civil Engn, Clayton, Vic, Australia;

    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia;

    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia;

    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia;

    Queensland Dept Nat Resources Mines & Energy, Toowoomba, Qld, Australia;

    EPA Victoria, Appl Sci Directorate, Macleod, Vic, Australia;

    Bur Meteorol, Canberra, ACT, Australia;

    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia;

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