EAR-PF: Climate sensitivity in mountain lakes of the Sierra Nevada, CA: controls on thermal regimes and carbon cycling

Adrianne Smitts博士已获得NSF EAR博士后奖学金，在加州大学戴维斯分校开展研究和教育活动。主要的研究目标是研究高海拔湖泊的物理特征如何在确定湖泊温度和碳化学时调节融雪的影响。这项研究将促进对高海拔流域气候、地貌和湖温之间的相互作用以及山地湖泊碳循环的景观控制的理解。这项研究将通过开发预测高海拔湖泊温度和识别湖泊对气候变化的敏感性的模型，为山区水资源管理提供帮助。史密茨博士将直接让本科生参与拟议的研究，培训他们科学方法和分析能力。此外，史密斯博士将通过与加州大学戴维斯分校的艺术/科学融合计划合作，开发将艺术和科学结合在一起的教育和推广材料，以便与广泛的受众交流这项研究的结果。这项研究的目标是(I)通过调查当地和区域驱动因素如何相互作用来影响湖泊热量收支和升温速率，建立高海拔湖泊温度的预测经验模型，以及(Ii)量化景观格局和对山地湖泊生态系统新陈代谢和碳排放的控制。将结合现场高频温度数据、遥感产品、热量平衡算法和时间序列分析来实现第一个目标。斯密茨博士将改进现有的量化湖泊热量收支的算法，将融雪的平流作用包括在内，从而更好地反映山地湖泊的水文状况。为了实现第二个目标，史密斯博士将对相关环境梯度的碳生物地球化学进行多湖调查，并将使用氧气质量平衡方法来量化生态系统代谢。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Dr. Adrianne Smits has been granted an NSF EAR Postdoctoral Fellowship to carry out research and educational activities at the University of California, Davis. The main research goal is to examine how physical characteristics of high elevation lakes mediate the influence of snowmelt in determining lake temperature and carbon chemistry. This study will advance understanding of interactions between climate, geomorphology, and lake temperature in high elevation watersheds, as well as landscape controls on carbon cycling in mountain lakes. This study will benefit water resource management in mountain regions by developing models that predict high elevation lake temperature and identify lake sensitivity to climate change. Dr. Smits will directly involve undergraduate students in the proposed research, training them in scientific methods and analysis. In addition, Dr. Smits will develop education and outreach materials that integrate art and science via collaboration with the Art/Science Fusion Program at UC Davis, in order to communicate the results of this research with broad audiences. The objectives of this research are (i) to develop predictive empirical models of high elevation lake temperature by investigating how local and regional drivers interact to affect lake heat budgets and warming rates and (ii) to quantify landscape patterns and controls on ecosystem metabolism and carbon emissions in mountain lakes. A combination of in-situ high frequency temperature data, remote sensing products, heat budget algorithms, and time series analyses will be used to achieve the first objective. Dr. Smits will improve existing algorithms for quantifying lake heat budgets by including the advectic role of snowmelt, therefore better reflecting the hydrology of mountain lakes. To achieve the second objective, Dr. Smits will conduct a multi-lake survey of carbon biogeochemistry across relevant environment gradients and will use an oxygen mass balance approach to quantify ecosystem metabolism.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.