School of GeoSciences and NERC Centre for Terrestrial Carbon Dynamics, University of Edinburgh, Edinburgh EH9 3JN, United Kingdom.
Ecol Appl. 2011 Jul;21(5):1506-22. doi: 10.1890/09-1183.1.
We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small ( 10% of the total net flux), while parameterization uncertainty was larger, 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was > 100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly compensated for each other. The time scales on which precipitation errors occurred in the simulations were shorter than the temporal scales over which drought developed in the model, so drought events were reasonably simulated. The approach outlined here provides a means to assess the uncertainty and bias introduced by meteorological drivers in regional-scale ecological forecasting.
我们分析了参数不确定性和驱动因素不确定性对净碳通量估计值置信区间的相对大小和贡献。模型参数可能难以或不切实际地进行测量，而驱动因素领域很少是完整的，由于传感器故障和观测网络稀疏，数据存在空白。参数通常通过某种优化方法得出，而驱动因素可能是从可用数据源插值得到的。在本研究中，我们使用了来自俄勒冈州梅特罗利乌斯的年轻辐射松林的数据，以及一个简单的耦合碳和水通量的每日模型（DALEC）。使用集合卡尔曼滤波器和净碳交换的涡度协方差测量值生成了一组可接受的参数化方案。为该地点生成了一组符合从 13 个当地气象站获得的观测数据固有的时空自相关的气象驱动变量的集合模拟。模拟气象数据通过模型传播，以得出由于空间广泛的建模研究中典型的驱动因素不确定性而导致的 CO2 通量的不确定性。此外，模型不确定性在温度和降水之间进行了划分。在距离研究地点 25 公里以内至少有一个气象站的情况下，驱动因素不确定性相对较小（总净通量的 10%），而参数化不确定性较大，占总净通量的 50%。驱动因素不确定性的最大来源是温度（总通量的 8%）。参数和驱动因素不确定性的综合影响占总净通量的 57%。然而，当最近的气象站距离研究地点>100 公里时，由气象驱动因素引起的净生态系统交换（NEE）预测的不确定性增加了 88%。与温度估计相比，降水估计是 NEE 估计中更大的偏差源，尽管偏差相互部分补偿。模拟中降水误差发生的时间尺度短于模型中干旱发展的时间尺度，因此干旱事件得到了合理的模拟。这里概述的方法提供了一种评估气象驱动因素在区域生态预测中引入的不确定性和偏差的方法。
