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A Bayesian methodological framework for accommodating interannual variability of nutrient loading with the SPARROW model

机译：利用SPARROW模型适应养分负荷年际变化的贝叶斯方法框架

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摘要

[1] Regression-type, hybrid empirical/process-based models (e.g., SPARROW, PolFlow) have assumed a prominent role in efforts to estimate the sources and transport of nutrient pollution at river basin scales. However, almost no attempts have been made to explicitly accommodate interannual nutrient loading variability in their structure, despite empirical and theoretical evidence indicating that the associated source/sink processes are quite variable at annual timescales. In this study, we present two methodological approaches to accommodate interannual variability with the Spatially Referenced Regressions on Watershed attributes (SPARROW) nonlinear regression model. The first strategy uses the SPARROW model to estimate a static baseline load and climatic variables (e.g., precipitation) to drive the interannual variability. The second approach allows the source/sink processes within the SPARROW model to vary at annual timescales using dynamic parameter estimation techniques akin to those used in dynamic linear models. Model parameterization is founded upon Bayesian inference techniques that explicitly consider calibration data and model uncertainty. Our case study is the Hamilton Harbor watershed, a mixed agricultural and urban residential area located at the western end of Lake Ontario, Canada. Our analysis suggests that dynamic parameter estimation is the more parsimonious of the two strategies tested and can offer insights into the temporal structural changes associated with watershed functioning. Consistent with empirical and theoretical work, model estimated annual in-stream attenuation rates varied inversely with annual discharge. Estimated phosphorus source areas were concentrated near the receiving water body during years of high in-stream attenuation and dispersed along the main stems of the streams during years of low attenuation, suggesting that nutrient source areas are subject to interannual variability.
机译：[1]回归型，基于经验/过程的混合模型（例如，SPARROW，PolFlow）在估算流域尺度上的养分污染源和运移方面发挥了重要作用。然而，尽管经验和理论证据表明相关的源/汇过程在每年的时间尺度上变化很大，但是几乎没有尝试明确地在结构中适应年际养分负荷的变化。在这项研究中，我们提出了两种方法来适应年际可变性，即流域属性的空间参考回归（SPARROW）非线性回归模型。第一种策略是使用SPARROW模型估算静态基线负荷和气候变量（例如降水量）以驱动年际变化。第二种方法允许SPARROW模型中的源/接收过程使用类似于动态线性模型中使用的动态参数估计技术，在每年的时间尺度上变化。模型参数化基于贝叶斯推理技术，该技术明确考虑了校准数据和模型不确定性。我们的案例研究是汉密尔顿港流域，这是一个位于加拿大安大略湖西端的农业和城市混合居住区。我们的分析表明，动态参数估计是两种测试方法中最简单的方法，可以提供与分水岭功能相关的时间结构变化的见解。与经验和理论工作一致，模型估算的年度河内衰减率与年排放量成反比。估计的磷源区域在河道内高衰减的几年中集中在接收水体附近，并在衰减率低的年份内沿河流的主干散布，这表明养分源区域受到年际变化的影响。

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  • 来源
    《Water resources research》 |2012年第10期|W10505.1-W10505.22|共22页
  • 作者

    Christopher Wellen; George B. Arhonditsis; Tanya Labencki; Duncan Boyd;

  • 作者单位

    Ecological Modeling Laboratory, Department of Physical and Environmental Sciences, University of Toronto, Toronto, ON MIC 1A4, Canada;

    Ecological Modeling Laboratory, Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada;

    Great Lakes Unit, Water Monitoring and Reporting Section, Environmental Monitoring and Reporting Branch, Ontario Ministry of the Environment, Toronto, Ontario, Canada;

    Great Lakes Unit, Water Monitoring and Reporting Section, Environmental Monitoring and Reporting Branch, Ontario Ministry of the Environment, Toronto, Ontario, Canada;

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