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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Overcoming Equifinality: Leveraging Long Time Series for Stream Metabolism Estimation
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Overcoming Equifinality: Leveraging Long Time Series for Stream Metabolism Estimation

机译：克服的平等性：利用长时间序列进行流代谢估计

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The foundational ecosystem processes of gross primary production (GPP) and ecosystem respiration (ER) cannot be measured directly but can be modeled in aquatic ecosystems from subdaily patterns of oxygen (O_2) concentrations. Because rivers and streams constantly exchange O_2 with the atmosphere, models must either use empirical estimates of the gas exchange rate coefficient (K_(600)) or solve for all three parameters (GPP, ER, and K_(600)) simultaneously. Empirical measurements of K_(600) require substantial field work and can still be inaccurate. Three-parameter models have suffered from equifinality, where good fits to O_2 data are achieved by many different parameter values, some unrealistic.We developed a new three-parameter, multiday model that ensures similar values for K600 among days with similar physical conditions (e.g., discharge). Our new model overcomes the equifinality problem by (1) flexibly relating K_(600) to discharge while permitting moderate daily deviations and (2) avoiding the oft-violated assumption that residuals in O_2 predictions are uncorrelated. We implemented this hierarchical state-space model and several competitor models in an open-source R package, streamMetabolizer. We then tested the models against both simulated and field data. Our new model reduces error by as much as 70% in daily estimates of K600, GPP, and ER. Further, accuracy benefits of multiday data sets require as few as 3 days of data. This approach facilitates more accurate metabolism estimates for more streams and days, enabling researchers to better quantify carbon fluxes, compare streams by their metabolic regimes, and investigate controls on aquatic activity.
机译：总初级生产力（GPP）和生态系统呼吸（ER）的基础生态系统过程不能被直接测量，但可以在水生生态系统从（O_2）的浓度的氧subdaily图案进行建模。因为河流和溪流不断与大气交换O_2，模型必须使用气体交换率系数的经验估计（K_（600）），或解决所有三个参数（GPP，ER，和K_（600））同时进行。 K_（600）的经验测量需要大量的野外工作，仍然可以是不准确的。三参数模型已经从等定局，其中最佳的匹配，以O_2数据是由许多不同的参数值实现深受其害，一些unrealistic.We开发，以确保类似的物理条件中日为K600相似值的新三参数，多日模型（例如， 释放）。我们的新模型克服了灵活与K_（600），以排出，同时允许每天适度偏差和（2）避免了经常违反假设在O_2预测残差不相关的问题等定局（1）。我们实现了这个层次状态空间模型和几个竞争对手的车型在开放式源R包，streamMetabolizer。然后，我们测试的对阵双方模拟和现场数据的模型。我们的新车型多达70％的K600，GPP和ER的日常估计减少了错误。此外，多天的数据集上的精度利益需要尽可能少的数据3天。这种方法有利于更多的溪流和天，使研究人员能够更好地量化碳通量更准确的代谢估计，它们的代谢机制比较流，并探讨水上活动的控制。

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