Characterising forest landscapes using a stationary and a roving tower eddy covariance system: linking point data to landscape-level characterisations of growth and yield

基于涡旋协方差的净生态系统生产力(NEP)测量对正在生长的森林提供了可能的最好的林分产量测量方法。基于森林过渡规则的林分水平森林模型或模拟的单株树木之间的相互作用(林隙模型)提供了不同精度水平的林分生长和木材生产的概括。这些模型中的大多数依赖于历史树数据来进行模型制定-校准，其生长行为已被很好地记录下来。在不断变化的气候条件下，树木的生长行为最有可能发生变化，这使得用森林过渡模型预测未来的森林生长变得不合适。此外，林隙模型的景观应用是令人望而却步的，因为需要大量的计算机资源来校准和运行可能有数万个林分的景观模型，其中许多林分具有不同的属性。由于模型的复杂性和数据要求，现有的基于生态系统的NEP模型的景观应用也是不现实的。由于这些限制，拟议的工作是开发森林G&Y，将现有的10参数NEP模型扩展到景观，将该模型的每月生长预测与当地生物物理条件联系起来。这些条件将通过将温度和叶面积的遥感图像与太阳辐射和土壤水分的景观级模拟相结合来得出。森林生态系统的特征和模型参数的设定将主要基于固定基站和流动塔系统收集的信息。这座37米高的固定塔将提供长期的基础天气和生态数据。这些信息连同在有流动塔的森林地点收集的净初级生产力和天气数据将被用来得出不同森林覆盖率所需的特定森林生长参数，并将净初级生产力的3-4周时间序列扩展到景观一级应用。这项拟议的工作为G&Y模型提供了重大的科学创新。

Eddy covariance-based net ecosystem productivity (NEP) measurements taken over growing forests provide the best possible measure of stand wood production. Stand-level forest models based on forest transition rules or modelled interactions between individual trees (gap models) provide generalisations of stand growth and wood production at various levels of accuracy. Most of these models rely on historical tree data for model formulation-calibration, for which growth behavior is well documented. In a changing climate regime, tree growth behavior is most likely to change, rendering future forest growth projections with forest transition models unsuitable. Also, landscape application of gap models is prohibitive because of the huge amount of computer resources needed to calibrate and run the model for a landscape of potentially tens of thousands stands, many with different attributes. Landscape application of existing ecosystem-based NEP models is also not practical, because of the models' complexity and data requirements. Because of these limitations, the proposed work is to develop a forest G&Y by extending an existing 10-parameter NEP model to the landscape by linking the model's monthly growth projections to local biophysical conditions. These conditions are to be derived by combining remote sensing-based images of temperature and leaf area with landscape-level simulations of solar radiation and soil water content. Forest ecosystem characterisation and model parameter setting are to be based mostly on information collected by a stationary base station and roving tower system. The 37-m stationary tower will provide long term base weather and ecological data. This information along with NEP and weather data collected at forest sites with a roving tower will be used to derive the required forest-specific growth parameters for different forest cover, and extend 3-4 week-long time series of NEP for application at the landscape level. The proposed work provides a major scientific innovation in G&Y modelling.