EAGER: The implications of interacting land use legacies and drought cycles for lake district carbon cycling

当模型等框架指导使用哪些数据以及如何联合收割机组合它们时，过去的数据可以为未来提供有用的信息。预测全球变化的后果特别复杂，但也是一个紧迫的问题。该项目将从整个威斯康星州的湖泊收集的长期数据与新的建模框架相结合，以预测湖泊沉积物中储存的碳和其他营养物质的数量，从而预测湖泊对温室气体排放和未来气候的贡献程度。结果将改善现有的气候模型的预测，纳入湖泊的作用，这是占主导地位的景观特征。该研究团队是垂直整合（教师，博士后研究员，研究生，本科生）允许机会提供和接受各级科学过程中的指导。项目成果将纳入圣母大学的三门课程，并将通过编制课程模块，吸引印第安纳州南本德一所急需中学的理科教师参与，该项目侧重于湖泊生态系统及其两项气候调节服务，即碳埋藏和温室气体排放，认识到区域贡献是当地埋藏和排放过程的新特性。其目标是开发模型，作为利用区域气象和土地利用长期数据的工具，以预测区域湖泊碳埋藏和温室气体排放。模型数据同化将被用来解决两个问题：十年干旱周期如何改变湖泊生态地球化学，以及农业集约化及其遗产如何改变湖泊对干旱的反应？ 现有的、空间上明确的、由水驱动的湖泊地球化学模型将增加子模型，以确定水文和土地利用之间相互作用的紧急影响，这些影响将长期物质负荷推到湖泊，包括依赖于温度的湖泊地球化学速率，解释湖泊沉积物和水柱之间的物质交换，并运行该模型的蒙特卡罗模拟，以传播湖泊形态测量学和地球化学速率的不确定性。这些建模工作之后，将同化一个30年的数据库，量化水文，水化学和初级生产在多个周期的干旱和跨空间梯度，包括森林和农业土地利用。在用模型数据同化的结果测试了具体问题后，研究人员将联合收割机结合回顾性和实验性建模来预测未来20年的湖泊碳地球化学。这项研究将为湖泊对区域元素循环的贡献提供新的见解，并将更广泛地探索时间变异性和遗产如何相互作用以改变生态系统过程。

Data from the past can usefully inform the future when frameworks such as models direct which data to use and how to combine them. Predicting the consequences of global change is particularly complex, yet of pressing concern. This project combines long-term data collected from lakes throughout Wisconsin with a new modeling framework to predict the amount of carbon and other nutrients stored in lake sediments, and thus the extent to which lakes contribute to greenhouse gas emissions and future climate. Results will improve predictions of existing climate models by incorporating the role of lakes, which are dominant landscape features. The research team is integrated vertically (faculty, postdoctoral researcher, graduate, undergraduate students) allowing opportunities to provide and receive mentoring in the scientific process at all levels. Project results will be incorporated into three courses at the University of Notre Dame and will engage science teachers from a high need intermediate school in South Bend, Indiana, through the development of curricular modules.The project focuses on lake ecosystems and two of their climate regulating services, carbon burial and greenhouse gas emission, recognizing regional contributions as emergent properties of burial and emission processes at local sites. Its goal is to develop models as tools to leverage long-term data on regional meteorology and land use in order to predict regional lake carbon burial and greenhouse gas emissions. Model-data assimilation will be used to address two questions: how do decadal drought cycles modify lake biogeochemistry, and how does agricultural intensification and its legacies alter lake responses to drought? An existing, spatially-explicit, landscape-driven biogeochemistry model will be augmented with sub-models to identify emergent effects of interactions between hydrology and land use that drive long-term material loads to lakes, include temperature-dependent biogeochemical rates, account for material exchange between lake sediments and the water column, and run Monte Carlo simulations of the model to propagate uncertainties in lake morphometry and biogeochemical rates. These modeling efforts will be followed by assimilation of a thirty-year database quantifying hydrology, water chemistry, and primary production across multiple cycles of drought and across spatial gradients including forest and agricultural land use. After testing specific questions with the results from model-data assimilation, the researchers will combine retrospective and experimental modeling to predict lake carbon biogeochemistry over the next 20 years. The research will contribute new insights into lake contributions to regional elemental cycling and will more generally explore how temporal variability and legacies interact to alter ecosystem processes.