Dissertation Research: Relating Biogeochemical Model Parameters to Climatic Gradients

生物地球化学模型对于我们理解全球净初级生产力（NPP）至关重要，这是碳循环的关键部分。由于这种模式需要更多的输入参数比可以很容易地测量大面积，一个子集的thoseparameters分配根据地表植被类型（土地覆盖类型）。由于这种简化掩盖了土地覆盖类型的变化，它导致了NPP输出的潜在误差。现有的博士项目利用概率方法来检查潜在的错误，重点是BiomeBGC过程模型。感兴趣的三个参数是叶片碳氮比、比叶面积和叶片停留时间。这项研究将使用跨气候梯度的实地采样来研究这些参数与俄勒冈州针叶林土地覆盖类气候之间的可能联系。缺乏相关性将支持当前的建模策略，而相关性的存在将用于构建输入参数的改进地图。使用这些地图模拟的NPP将与使用针叶树类中的单个值得出的输出进行比较。这些数值试验，结合各种正在进行的NPP测量，将显着提高我们的能力，模拟在太平洋西北部针叶林广泛地区的NPP。

Biogeochemical models are critical for our understanding of global net primaryproduction (NPP), a key piece of the carbon cycle. Because such models require moreinput parameters than can be readily measured over large areas, a subset of thoseparameters are assigned according land surface vegetative type (landcover type). Becausethis simplification masks variability within landcover types, it leads to potential error inNPP output. The existing PhD project utilizes a probabilistic approach to examine thepotential error, focusing on the BiomeBGC process model. The three parameters ofinterest are foliar carbon:nitrogen ratio, specific leaf area, and leaf retention time. Theproposed study will use field sampling across a climatic gradient to examine possiblelinks between these parameters and climate within the conifer landcover class in Oregon.Lack of correlation would support current modeling strategies, while existence ofcorrelation would be used to construct improved maps of input parameters. NPP modeledusing these maps will be compared against output derived using single values within theconifer class. These numerical experiments, in combination with a variety of on-goingfield NPP measurements, will significantly improve our ability to simulate NPP over abroad area of coniferous forest in the Pacific Northwest.