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

本提案的目的是为五大湖流域开发一个流域尺度的碳模型，该模型可用于预测从陆地环境的加拿大部分进入五大湖的溶解有机碳（DOC）通量。这一点很重要，因为DOC的分解会导致大量温室气体的产生。我们将重点关注几个大流域，这些流域将被选中代表主要的土地利用类别；流入伊利湖和/或安大略湖的主要是农业，而流入休伦湖和/或苏必利尔湖的主要是森林。我们的目的是将我们的结果外推到五大湖盆地的其他部分，以便我们可以估计总碳通量。我们计划在本研究中采用两种截然不同的建模方法。首先，我们将采用一种简单的方法，基于土地利用与集水区溶解有机碳（DOC）通量之间的经验关系，以及湖泊沉积物永久储存与退化产生温室气体之间的水化学和碳分配关系。后一种途径增加了加拿大的温室气体排放。这种方法将使我们能够估计从加拿大一侧进入五大湖的有机碳通量。然而，这是一个稳态模式，不能用于直接预测外部因子（如气候）变化对DOC通量的影响。第二种方法将利用基于过程的动态建模。我们将从两个现有的模型开始，一个是营养模型（INCA），它已广泛用于氮的研究，另一个（MAGIC）是一个集水区尺度的生物地球化学模型，它已广泛用于土壤和水的酸化研究。这些模型中的每一个都可以调整为包含一个碳。前一种模式由土地利用和水文数据驱动，而后一种模式利用水文数据、土壤和水化学以及有关人为压力源的信息，特别是酸沉积。通过对水文的改变和对矿化率等过程的气候相关影响，每一种都可以并且已经与气候变化联系起来。

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.