CAREER: Space-Time Patterns in Snowmelt: Research and Education for Hydrologic Forecasting

职业：融雪的时空模式：水文预报的研究和教育

• 批准号: 9733925
• 负责人: Kaye Brubaker
• 金额: $ 20万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-15 至 2003-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9733925&HistoricalAwards=false
• 关键词: CAREER; Space; Time; Patterns; Snowmelt

Author: Clifford Astill [e-mail] at NOTE Date: 3/22/98 7:53 PM Priority: Normal TO: acoles at nsf18 Subject: Abstract: CMS-9733925, Kaye Brubaker, U. Maryland ------------------------------- Message Contents ----------------- -------------- Abstract: CMS-9733925, Kaye Brubaker, U. Maryland In regions with significant seasonal snowpacks, some of the most devastating floods are caused by sudden snowmelt or rain-on-snow events. At the other hydrologic extreme, a below-average winter snowpack is likely to translate into severe warm-season water shortages. In the United States, operational hydrology has made great strides in short-term and extended forecasting of these disastrous events, aided by sophisticated observations and models. Modern hydrologic techniques can incorporate information on uncertainty in the snow pack, precipitation and other weather factors to produce probabilistic forecasts. The aim of this CAREER project is to develop tools to determine mathematical expressions for physical variability and measurement uncertainty, for a variety of topographic climatic situations, and for different resolutions of measurement. The research objectives are: * Characterize patterns in snowpack/snow-cover depletion during the melt season using remote-sensing products at different spatial resolutions, and a Geographic Information System; * Relate these spatio-temporal patterns to topography, land cover, and climate; * Investigate the scaling properties of these patterns and their dependence on the resolution of the observed data; and * Test the use and value of these findings in short-term and extended streamflow forecasting models, both deterministic and probabilistic. It is expected that this research will result in: * Physically-based mathematical expressions for uncertainty in snow-water equivalent estimates used to update streamflow forecast models. * Model validation data sets of spatially-distributed time-series of multi-se nsor snow-cover and weather observations, and weather forecasts for testing and developing forecast models. The educational objectives are: * Develop a new hands-on seminar in hydrologic prediction, design and forecasting, incorporating the research results. * Incorporate the research findings into a modest number of new modules for an existing graduate hydrology class and an existing undergraduate probability and statistics class. The new modules will incorporate innovative classroom technology and pedagogy. * Perform objective and subjective assessments of the value of these innovations in aiding students' learning. It is expected that this educational objective for water-resource engineers will result in training in the use of sophisticated observational and analytical technology, including remotely-sensed Earth systems data, and Geographic Information Systems and spatial data manipulation; a solid understanding of natural variability in hydrologic variables, measurement uncertainty, mathematical expressions for that variability and uncertainty, and the implications for resource prediction and forecasting; and exposure to techniques and perspectives from related disciplines. +=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+= + Clifford J. Astill Ph:703-306-1362 Fax:[phone] [e-mail] Nat'l Science Fdn 4201 Wilson Blvd., Rm545 Arlington, VA 22230 www.eng.nsf.gov/cms/castill.htm

作者：Clifford Astill www.example.com注意日期： 1998年3月22日7：53 PM优先级：正常TO：nsf 18的acoles主题：摘要：CMS-9733925，Kaye Brubaker，U。马里兰州-消息内容-摘要：CMS-9733925，Kaye Brubaker，U。Maryland 在有大量季节性积雪的地区，一些最具破坏性的洪水是由突然的融雪或雪雨事件造成的。在另一个水文极端，低于平均水平的冬季积雪可能会转化为严重的暖季缺水。在美国，业务水文学在这些灾害性事件的短期和长期预报方面取得了很大进展，这得益于先进的观测和模型。现代水文技术可以结合积雪、降水和其他天气因素的不确定性信息，作出概率预报。 这个CAREER项目的目的是开发工具，以确定物理变异性和测量不确定性的数学表达式，为各种地形气候情况下，并为不同的测量分辨率。 研究目标是：* 利用不同空间分辨率的遥感产品和地理信息系统，描述融化季节积雪/积雪减少的模式;* 将这些时空模式与地形、土地覆盖和气候联系起来;* 调查这些模式的比例特性及其对观测数据分辨率的依赖性;* 测试这些研究结果在短期和扩展的径流预测模型中的使用和价值，包括确定性和概率性。 预计这项研究将导致：* 物理为基础的数学表达式的不确定性，在雪水当量估计用于更新径流预测模型。 * 多传感器雪盖和天气观测的空间分布时间序列的模型验证数据集，以及用于测试和开发预测模型的天气预报。 教育目标是：* 结合研究成果，举办一个新的水文预测、设计和预报实践研讨会。 * 将研究成果纳入现有的研究生水文类和现有的本科生概率统计类的新模块数量适中。新的模块将纳入创新的课堂技术和教学法。 * 对这些创新在帮助学生学习方面的价值进行客观和主观的评估。 预计这一针对水资源工程师的教育目标将导致在使用尖端观测和分析技术方面的培训，包括遥感地球系统数据、地理信息系统和空间数据处理;对水文变量的自然变异性、测量不确定性、变异性和不确定性的数学表达式有扎实的理解，以及对资源预测和预报的影响;以及接触相关学科的技术和观点。 +=+=+==+==+= 4201 Wilson Blvd.，RM545 弗吉尼亚州阿灵顿，邮编22230