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

作者：Clifford Astill castill@nsf.gov at注日期：3/22/98 7:53 PM优先级：Normal TO: acoles at nsf18主题：摘要：CMS-9733925， Kaye Brubaker， U. Maryland -------------------------------信息内容-------------------------------摘要：CMS-9733925， Kaye Brubaker， U. Maryland在季节性积雪严重的地区，一些最具破坏性的洪水是由突然的融雪或雨雪事件引起的。在另一个水文极端情况下，低于平均水平的冬季积雪可能会导致暖季严重缺水。在美国，在精密观测和模型的帮助下，业务水文学在这些灾难性事件的短期和长期预报方面取得了巨大进展。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，以产生概率预报。这个CAREER项目的目的是开发工具来确定物理变异性和测量不确定性的数学表达式，适用于各种地形气候情况和不同的测量分辨率。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表征融化季积雪/积雪消耗的模式；*将这些时空格局与地形、土地覆盖和气候联系起来；*研究这些模式的尺度特性及其对观测数据分辨率的依赖；*测试这些发现在短期和扩展的流量预测模型中的使用和价值，包括确定性和概率性。预计这项研究将产生：*用于更新流量预报模型的雪水当量估算的不确定性的基于物理的数学表达式。*多传感器积雪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，开展新的水文预测、设计和预报实践研讨会。*将研究成果纳入现有研究生水文学课程和现有本科生概率与统计课程的适度数量的新模块中。新模块将结合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。预期水资源工程师的这一教育目标将导致使用尖端观测和分析技术的培训，包括遥感地球系统数据、地理信息系统和空间数据操作；对水文变量的自然变异性、测量的不确定性、这种变异性和不确定性的数学表达式以及对资源预测和预报的影响有扎实的了解；接触相关学科的技术和观点。+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+= + 诚如Clifford j·阿斯蒂尔所说,Ph值:703-306-1362传真:703-306-0291 castill@nsf.gov Nat威尔逊国际科学Fdn 4201大街。， Rm545阿灵顿，弗吉尼亚州22230 www.eng.nsf.gov/cms/castill.htm

Author: Clifford Astill castill@nsf.gov 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:703-306-0291 castill@nsf.gov Nat'l Science Fdn 4201 Wilson Blvd., Rm545 Arlington, VA 22230 www.eng.nsf.gov/cms/castill.htm