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

当模型等框架指示使用哪些数据以及如何组合它们时，来自过去的数据可以有用地告知未来。预测全球变化的后果尤其复杂，但也令人担忧。该项目结合了从威斯康星州各地湖泊收集的长期数据和一个新的建模框架，以预测湖泊沉积物中储存的碳和其他营养素的数量，从而预测湖泊对温室气体排放和未来气候的贡献程度。结果将通过纳入湖泊的作用来改进现有气候模型的预测，湖泊是主要的景观特征。研究团队是垂直整合的(教师、博士后研究员、研究生、本科生)，从而有机会在所有级别的科学过程中提供和接受指导。项目成果将被纳入圣母大学的三门课程，并将通过课程模块的开发，吸引印第安纳州南本德一所高需求中学的科学教师。该项目重点研究湖泊生态系统及其两项气候调节服务--碳埋和温室气体排放，承认区域贡献是当地遗址掩埋和排放过程的紧急特性。其目标是开发模型作为工具，利用区域气象和土地利用的长期数据，预测区域湖泊碳埋藏和温室气体排放。模式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的生物地球化学，以及农业集约化及其遗留问题如何改变湖泊对干旱的反应？现有的、空间上明确的、由景观驱动的生物地球化学模型将加入子模型，以确定水文和土地利用之间相互作用的紧急影响，这些相互作用推动长期的物质负荷进入湖泊，包括依赖温度的生物地球化学速率，解释湖泊沉积物和水柱之间的物质交换，并对该模型进行蒙特卡罗模拟，以传播湖泊形态测量和生物地球化学速率的不确定性。在这些建模工作之后，将同化一个30年的数据库，该数据库量化了多个干旱周期和包括森林和农业土地利用在内的空间梯度的水文学、水化学和初级生产力。在用模型数据同化的结果测试特定问题后，研究人员将结合回顾和实验建模来预测未来20年的湖泊碳生物地球化学。这项研究将有助于对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来改变生态系统过程。

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.