Catchment-scale modelling of carbon fluxes in the Great Lakes basin

这项提议的目的是为五大湖流域开发一个集水尺度的碳模型，该模型可以用来预测从加拿大陆地环境的溶解有机碳(DOC)通量进入五大湖。这一点很重要，因为DOC的分解可能导致大量温室气体的产生。*我们将重点关注几个将被选为主要土地利用类别的大流域；那些流入伊利湖和/或安大略省的将主要是农业，而那些流入休伦湖和/或苏必利尔湖的将主要是森林。我们的目的是将我们的结果推断到五大湖盆地的其他部分，以便我们可以估计总的碳通量。我们计划在这项研究中使用两种截然不同的建模方法。首先，我们将使用一种简单的方法，基于与土地利用和流域的溶解有机碳(DOC)通量相关的经验关系，以及将水化学和碳在湖泊沉积物中的永久储存和退化产生温室气体之间的分配联系起来。后一种途径导致加拿大的温室气体排放。*这种方法将允许我们估计从加拿大一侧进入五大湖的有机碳通量。但是，这是一个稳态模型，不能用来直接预测外部因素变化的影响。例如，气候对DOC通量的影响。第二种方法将利用基于过程的动态模拟。我们将从现有的两个模型开始，一个是在氮素研究中广泛使用的营养模型(INCA)，另一个(MAGIC)是一个在土壤和水酸化研究中广泛使用的流域尺度生物地球化学模型。这些模型中的每一个都可以调整以包括碳。前者由土地利用和水文数据驱动，而后者利用水文数据、土壤和水化学以及关于人为压力源的信息，尤其是酸沉积。它们都可以而且已经通过对水文的改变以及通过与气候有关的对矿化率等过程的影响与气候变化联系在一起。

The purpose of this proposal is to develop a catchment-scale carbon model for the Great Lakes basin that can be used to predict dissolved organic carbon (DOC) fluxes from the Canadian portion of the terrestrial environment into the Great Lakes.  This is important because the breakdown of DOC can lead to the production of significant quantities of greenhouse gases.  We will focus on several large watersheds that will be selected to represent dominant land use categories; those draining into Lakes Erie and/or Ontario will be predominately agricultural while those draining into Huron and/or Superior will be primarily forested.  Our intent is to extrapolate our results to other parts of the Great Lakes basin so that we can estimate total carbon flux. We plan to employ two very different modelling approaches in this study.  Firstly, we will use a simple approach based on empirical relationships relating land use and dissolved organic carbon (DOC) flux from catchments, and relating water chemistry and partitioning of carbon between permanent storage in lake sediments and degradation to produce greenhouse gases.  The latter pathway contributes to Canada's greenhouse gas emissions.  This approach will allow us to estimate the organic carbon flux into the Great Lakes from the Canadian side.  However, this is a steady-state model, and it cannot be used to make direct predictions about the impacts of changes in external factors, e.g. climate, on DOC flux.  The second approach will utilize process-based dynamic modelling.  We will start with two existing models, one a nutrient model (INCA) that has been widely used in the study of nitrogen, and the other (MAGIC) a catchment-scale biogeochemistry model that has been widely used in the study of soil and water acidification.  Each of these models can be adapted to include a carbon.  The former model is driven by land use and hydrologic data, while the latter utilizes hydrologic data, soil and water chemistry and information about anthropogenic stressors, particularly acid deposition.  Each can and has been linked to climate change through both alterations to hydrology and through climate-related effects on processes such as mineralization rates.