科研论文

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to …

水文模型是利用数学公式对水文循环过程的描述, 是理解自然界水的时空运动和分布规律的重要工具; 它不仅可以利用观测数据来验证理论的可靠性与适宜性, 还能预测水文变量未来趋势, 实现理解自然与预测自然的目的. 随着计算方法的快速发展、观测与试验手段的持续更新, 目前基于物理 …

Understanding the intricate hydrological interactions between lakes and their surrounding watersheds is pivotal for advancing …

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving …

水文模型是高效且经济的科学实验工具，不仅能结合观测数据验证科学理论、指导观测网络布设，而且对社会水资源管理、灾害防治以及经济决策等有不可或缺的重要价值。数值方法水文模型依据达西定律、理查兹方程和圣维南方程等水文物理公式，充分表现水文参数空间异质性，精细化表达水文物理过 …

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for …

Abstract Understanding how the biological invasion is driven by environmental factors will improve model prediction and advance early …

The coronavirus that is currently causing severe respiratory illness worldwide has now been named SARS-CoV-2, and the disease is COVID-19. When the virus first emerged last December, it was generally described in medical journals as the ‘2019 novel coronavirus’.

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.

Your view that China’s re-vegetation of its deserts could exacerbate water shortages risks oversimplifying an incredibly complex eco-restoration problem (Nature 573, 474–475; 2019). Far from just planting trees in arid areas, China’s re-vegetation codes vary for different regions and greening programmes.

Integrated modeling is a critical tool to evaluate the behavior of coupled human–freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human–freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human–freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.

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.

In this study we applied two spatially explicit models to the problem. One is the Penn State Integrated Hydrologic Model (PIHM), a hydrologic model that partitions the water balance in space and time over the urban catchment. The other is the Cellular Automata Land Use Change Model (CALUC), a land use change model, which simulates the evolution of land use classes based on physical measures associated with population change and land use demand factors. We selected two study sites, one modern and one ancient, to highlight the capability of coupling catchment hydrology with land use change models. The goal is to assess the role of hydrologic change in urbanizing watersheds and to evaluate the contemporaneous impacts of climate change. The modern sites are the Conestoga watershed and the Lancaster PA urban center; the historical site is the ancient Maya city of Tikal in Peten region of Guatemala. In each setting, the essential data was developed and the models were used to evaluate how urbanization and land use change gradually altered the entire water balance often in unexpected ways.

We designed and implemented an integrated modeling environment (HIME) for hydrological and land surface modeling purpose in a much extendable, efficient and easy use manner. With such design, a physical process was implemented as a module, or component. A new model can be generated in an intuitive way by linking module icons together and establishing their relationships. Following an introduction to the overall architecture, the designs for module linkage and data transfer between modules are described in details. Using XML based meta-information, modules in either source codes or binary form can be utilized by the environment. As a demonstration, with the help of HIME, we replaced the evaporation module of TOPMODEL with the evapotranspiration module from the Noah land surface model which explicitly accounts for vegetation transpiration. This example showed the effectiveness and efficiency of the modeling environment on model integration.