Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model-data fusion framework
Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model-data fusion framework
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DOI：
10.5194/bg-12-1299-2015
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发表时间：
2015-01-01
期刊：
影响因子：
4.9
通讯作者：
Williams, M.
中科院分区：
文献类型：
--
作者：
Bloom, A. A.;Williams, M.
全球陆地碳模型所代表的许多关键过程在很大程度上仍然不受限制。例如，除了在少数得到深入研究的地点外，全球对植物碳库的分配模式和停留时间知之甚少。由于数据稀缺，碳模型往往是不确定的，因此可以产生类似的净通量，但参数和内部动态非常不同。为了解决这些问题，我们提出了一系列关于模型参数和初始条件的生态和动态约束（EDCs），作为在缺乏本地数据的情况下约束生态系统变量相互依赖性的一种手段。EDCs由一系列条件组成，包括：(a)碳库周转和分配比率，(b)稳态接近度，以及(c)模型碳库的生长和衰减。我们在模型-数据融合框架中使用一个简单的生态系统碳模型来确定这些约束在缺乏数据的环境中的附加价值。仅基于叶面积指数（LAI）时间序列和土壤碳数据，我们估算了(a) 40个综合实验点和(b) 3个AmeriFlux塔点的净生态系统交换（NEE）。对于合成实验，我们表明，EDCs导致模型参数的总体相对误差降低34%，在3年NEE 90%置信范围内降低65%。在AmeriFlux站点的应用中，所有新能源经济性估算都独立于新能源经济性测量。与这些观察结果相比，与标准3年NEE中位偏差（-1.17至-0.84 kgCm(2)）相比，EDCs导致3年累积NEE中位偏差（-0.26至+0.08 kg Cm-2）减少69-93%。鉴于这些发现，我们建议在未来陆地碳循环的模型-数据融合分析中使用EDCs。
Many of the key processes represented in global terrestrial carbon models remain largely unconstrained. For instance, plant allocation patterns and residence times of carbon pools are poorly known globally, except perhaps at a few intensively studied sites. As a consequence of data scarcity, carbon models tend to be underdetermined, and so can produce similar net fluxes with very different parameters and internal dynamics. To address these problems, we propose a series of ecological and dynamic constraints (EDCs) on model parameters and initial conditions, as a means to constrain ecosystem variable inter-dependencies in the absence of local data. The EDCs consist of a range of conditions on (a) carbon pool turnover and allocation ratios, (b) steady-state proximity, and (c) growth and decay of model carbon pools. We use a simple ecosystem carbon model in a model-data fusion framework to determine the added value of these constraints in a data-poor context. Based only on leaf area index (LAI) time series and soil carbon data, we estimate net ecosystem exchange (NEE) for (a) 40 synthetic experiments and (b) three AmeriFlux tower sites. For the synthetic experiments, we show that EDCs lead to an overall 34% relative error reduction in model parameters, and a 65% reduction in the 3 yr NEE 90% confidence range. In the application at AmeriFlux sites all NEE estimates were made independently of NEE measurements. Compared to these observations, EDCs resulted in a 69-93% reduction in 3 yr cumulative NEE median biases (-0.26 to +0.08 kg Cm-2), in comparison to standard 3 yr median NEE biases (-1.17 to -0.84 kgCm(2)). In light of these findings, we advocate the use of EDCs in future model-data fusion analyses of the terrestrial carbon cycle.