舒乐乐 水文学博士，现加州大学戴维斯分校博士后研究员，2017年毕业与美国 宾夕法尼亚州立大学 土木工程系水资源工程专业，副修学位计算科学。分别与兰州大学和中科院寒旱所获得学士和硕士学位。专注于数值方法的分布式水文模型、水文大数据的机器学习、气候/人类活动对水循环的影响和集成模型耦合研究。
水资源工程博士, 计算科学副修学位(Computational Science), 2017
兰州大学， 学士, 2005
Talks, Poster, Workshop, …
This paper introduces the design of SHUD, from the conceptual and mathematical description of hydrological processes in a watershed to computational structures. To demonstrate and validate the model performance, we employ three hydrological experiments: the V-Catchment experiment, Vauclin's experiment, and a study of the Cache Creek Watershed in northern California, USA.
Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well-developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual-empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi-disciplinary tool that captures diverse processes that operate at multiple scales, including land-management decision-making, hydrologic-solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross-disciplinary dialogue to move CNHS lake-catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision-making and policy.