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量（NPP）至关重要，NPP是碳循环的关键部分。由于这些模型需要更多的输入参数，而不能在大面积上轻易测量，因此根据地表植被类型（土地覆盖类型）分配这些参数的子集。由于这种简化掩盖了土地覆盖类型的可变性，它导致inpp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面碳氮比、比叶面积和叶保留时间。拟议的研究将使用跨气候梯度的实地抽样来检查这些参数与俄勒冈州针叶树土地覆盖类别内气候之间的可能联系。缺乏相关性将支持当前的建模策略，而存在相关性将用于构建输入参数的改进映射。使用这些映射建模的NPP将与在针叶树类中使用单个值派生的输出进行比较。这些数值实验，结合各种正在进行的野外NPP测量，将显著提高我们在太平洋西北针叶林国外地区模拟NPP的能力。

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.