Modular hydrologic ensemble prediction system

模块化水文集合预报系统

• 批准号: 249582-2011
• 负责人: Coulibaly, Paulin
• 金额: $ 1.53万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570651
• 关键词: Modular; hydrologic; ensemble; prediction; system

Continued changes in land cover such as urbanization, industrial development, mining, etc drastically affect the surface runoff in Canadian river basins. In addition, it is now well established that those environmental changes coupled with climate change are resulting in unprecedented changes in surface water (river flows, lakes, and reservoirs) at local and regional scales. Consequently, traditional hydrologic (e.g. streamflow) forecasting tools are not able to account for such dynamic changes in the watershed. Therefore, novel adaptive hydrologic prediction systems that can account for the impacts of human-climate-water interactions are critically needed to provide accurate and reliable hydrologic forecasts which is the essential information for water resources management. Accurate and reliable hydrologic forecasts are essential to domestic and industrial water supply planning, flood warning, drought mitigation, inland navigation, and hydroelectric power generation. The proposed research program will resort to recent advances in land surface monitoring technology (satellite, ground-based radar and monitoring networks), along with advances in meteorology, and emerging information processing technologies (namely 'sequential data assimilation', and 'Bayesian forecasting techniques') for the development of novel adaptive hydrologic prediction tool. This challenging task will be achieved by first investigating robust sequential data assimilation methods suitable for hydrologic forecasting at the watershed scale. Then, the optimal sequential data assimilation method identified will be coupled with a Bayesian forecasting technique and a physical watershed model to develop an adaptive hydrologic prediction system. A flexible prediction system that can automatically assimilate various sources of information will be a robust tool for taking full advantage of modern land observation technology to account for dynamic changes in the watershed, and to provide more accurate and reliable hydrologic forecasts. This will significantly contribute to better water resources planning and management in Canada.

土地覆被的持续变化，如城市化、工业发展、采矿等，极大地影响了加拿大河流流域的地表径流。此外，现在已经确定的是，这些环境变化加上气候变化正在导致地方和区域范围内地表水（河流、湖泊和水库）发生前所未有的变化。因此，传统的水文（如径流）预测工具是无法解释这种动态变化的分水岭。因此，新的自适应水文预测系统，可以考虑到人类-气候-水的相互作用的影响，迫切需要提供准确和可靠的水文预报，这是水资源管理的基本信息。准确可靠的水文预报对于家庭和工业供水规划、洪水预警、干旱缓解、内陆航行和水力发电至关重要。 拟议的研究计划将诉诸最新进展的陆地表面监测技术（卫星，地基雷达和监测网络），沿着在气象学的进步，和新兴的信息处理技术（即“顺序数据同化”和“贝叶斯预测技术”）的发展新的自适应水文预测工具。这一具有挑战性的任务将通过首先研究适用于流域尺度水文预报的稳健的序列数据同化方法来实现。然后，确定的最佳顺序数据同化方法将与贝叶斯预测技术和物理流域模型相结合，以开发自适应水文预报系统。一个能够自动吸收各种信息来源的灵活预测系统将是充分利用现代土地观测技术的有力工具，以说明流域的动态变化，并提供更准确和可靠的水文预报。这将大大有助于改善加拿大的水资源规划和管理。