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Sci Total Environ. 2020 Sep 20;736:139362. doi: 10.1016/j.scitotenv.2020.139362. Epub 2020 May 14.
Prevention of excessive periphyton standing crop (quantified as chlorophyll a) is among primary objectives for river management. Defensible instream nutrient criteria to achieve periphyton chlorophyll a targets at the site scale require robust predictive models. Such models have proved elusive because peak chlorophyll a depends on multiple factors in addition to nutrients. A key predictor may be accrual period, which depends on river flow variability and the flow magnitudes (effective flows, EF) at which periphyton biomass removal is initiated. In this study we used a seven-year dataset from 44 gravel-bed river sites in the Manawatū-Whanganui region, New Zealand, to explore the relative importance of accrual period, nutrients, and other variables in explaining peak chlorophyll a, using a regression approach. We also assessed the effect of combining data from multiple years. Previous empirical studies have used a universal flow metric (3 × median flow) to define accrual period (Da3). We calculated site-specific EF, which varied from 2 × to 15 × median flow. Accrual period based on EF (DaEF) outperformed Da3 in models. However, in the study region, more variance in chlorophyll a was explained by conductivity (EC) and dissolved inorganic nitrogen (DIN) than by DaEF. The best models derived from multi-year datasets included EC, DIN and DaEF as predictors and accounted for up to 82% of the variance in peak chlorophyll a. Models from annual data were weaker and more variable in strength and predictors. The models indicated that EC and DaEF should be considered when setting DIN criteria for periphyton outcomes in the study region. The principles we used in developing the models may have broad relevance to the management of periphyton in other regions.
防止过度的周丛生物（以叶绿素 a 定量）是河流管理的主要目标之一。为了在现场尺度上实现周丛生物叶绿素 a 目标，需要有防御性的入流营养标准，这需要强大的预测模型。这些模型一直难以捉摸，因为峰值叶绿素 a 不仅取决于营养物质，还取决于多个因素。一个关键的预测因素可能是累积期，它取决于河流流量的可变性以及开始去除周丛生物生物量的流量大小（有效流量，EF）。在这项研究中，我们使用了来自新西兰 Manawatū-Whanganui 地区 44 个砾石床河流站点的七年数据集，通过回归方法探讨了累积期、营养物质和其他变量在解释峰值叶绿素 a 方面的相对重要性。我们还评估了组合多年数据的效果。以前的实证研究使用通用流量指标（3 × 中值流量）来定义累积期（Da3）。我们计算了特定于站点的 EF，其范围从 2 × 到 15 × 中值流量。基于 EF 的累积期（DaEF）在模型中的表现优于 Da3。然而，在研究区域，叶绿素 a 的更多变化由电导率（EC）和溶解无机氮（DIN）解释，而不是由 DaEF 解释。从多年数据集得出的最佳模型包括 EC、DIN 和 DaEF 作为预测因子，解释了高达 82%的峰值叶绿素 a 的方差。来自年度数据的模型较弱，强度和预测因子的变化较大。这些模型表明，在研究区域为周丛生物结果设置 DIN 标准时，应考虑 EC 和 DaEF。我们在开发模型中使用的原则可能对其他地区周丛生物管理具有广泛的相关性。
