Statistical modeling of ecosystem respiration using eddy covariance data: Maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models
Statistical modeling of ecosystem respiration using eddy covariance data: Maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models
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DOI：
10.1016/j.agrformet.2005.05.008
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发表时间：
2005-08-31
影响因子：
6.2
通讯作者：
Hollinger, DY
中科院分区：
文献类型：
--
作者：
Richardson, AD;Hollinger, DY
无论目标是填补通量记录中的空白，还是从涡流协方差数据中提取生理参数，研究人员经常有兴趣将生态系统生理学的简单模型拟合到测量数据中。目前，最佳使用模型或理想优化标准尚无共识。我们证明，鉴于我们对霍兰德（美国缅因州缅因州）Ameriflux位点的随机不确定性分布的估计值，使用普通最小二乘（OLS）优化拟合生态系统呼吸模型是不正确的。结果表明，通量不确定性遵循双指数（拉普拉斯）分布，并且不确定性（Sigma（Delta））的标准偏差遵循强烈的季节性模式，随着温度的指数功能而增加。这些特征都违反了OLS的假设。我们建议要获得模型参数的最大似然估计，拟合应基于最小化绝对偏差的加权总和：测量的Sigma垂直条 - 建模垂直杆/Sigma（Delta）。我们详细研究了这种拟合范式对三种简单但常用的生态系统呼吸模型的参数估计和模型预测的影响。指数Lloyd＆Taylor模型始终为测量数据提供最佳拟合。与OLS相比，使用绝对偏差标准可将估计的年呼吸总量减少约10％（70-145 g C M（-2）Y（-1））；这在大小上是可比的，但在符号上与使用一系列合理的IT进行过滤夜间数据的效果相反。阈值。加权方案还会影响年度呼吸之和：指定或（6）作为空气温度的函数始终导致最小的总数。但是，在大多数情况下，无论使用哪种模型，年总和在不确定性估计中都具有可比性（在不确定性估计中）。蒙特卡洛模拟表明，年度呼吸之和的95％置信区间约为2040 g c m（-2）y（-1），但取决于模型，优化标准，最重要的是加权方案有所不同。 （c）2005 Elsevier B.V.保留所有权利。
Whether the goal is to fill gaps in the flux record, or to extract physiological parameters from eddy covariance data, researchers are frequently interested in fitting simple models of ecosystem physiology to measured data. Presently, there is no consensus on the best models to use, or the ideal optimization criteria. We demonstrate that, given our estimates of the distribution of the stochastic uncertainty in nighttime flux measurements at the Howland (Maine, USA) AmeriFlux site, it is incorrect to fit ecosystem respiration models using ordinary least squares (OLS) optimization. Results indicate that the flux uncertainty follows a double-exponential (Laplace) distribution, and the standard deviation of the uncertainty (sigma(delta)) follows a strong seasonal pattern, increasing as an exponential function of temperature. These characteristics both violate OLS assumptions. We propose that to obtain maximum likelihood estimates of model parameters, fitting should be based on minimizing the weighted sum of the absolute deviations: Sigma vertical bar measured - modeled vertical bar/sigma(delta). We examine in detail the effects of this fitting paradigm on the parameter estimates and model predictions for three simple but commonly used models of ecosystem respiration. The exponential Lloyd & Taylor model consistently provides the best fit to the measured data. Using the absolute deviation criterion reduces the estimated annual sum of respiration by about 10% (70-145 g C m(-2) y(-1)) compared to OLS; this is comparable in magnitude but opposite in sign to the effect of filtering nighttime data using a range of plausible it. thresholds. The weighting scheme also influences the annual sum of respiration: specifying or(6) as a function of air temperature consistently results in the smallest totals. However, annual sums are, in most cases, comparable (within uncertainty estimates) regardless of the model used. Monte Carlo simulations indicate that a 95% confidence interval for the annual sum of respiration is about 2040 g C m(-2) y(-1), but varies somewhat depending on model, optimization criterion, and, most importantly, weighting scheme. (c) 2005 Elsevier B.V. All rights reserved.