Using mesoscale climate simulations to reduce input data errors in energy balance snow hydrology models

利用中尺度气候模拟减少能量平衡雪水文模型中的输入数据误差

• 批准号: 0838166
• 负责人: Jessica Lundquist
• 金额: $ 30.64万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0838166&HistoricalAwards=false
• 关键词: Using; mesoscale; climate; simulations; reduce

Project AbstractThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Mountain snowmelt is an essential resource to 40% of the world?s population but is threatened by warming associated with climate change. Accurate model predictions of the climate sensitivity of individual watersheds are needed for watershed impacts analysis, local and regional planning processes, and the development of water policy. Currently the quality of meteorological driving data sets in mountain environments is a major obstacle to characterizing the sensitivity of particular watersheds and simulating climate related impacts using hydrologic models. In particular, accurately characterizing where precipitation falls as rain vs. snow on an event basis, and when and how fast snow melts is essential to the assessment of climate sensitivity at small to medium (10-1000 km2) spatial scales. Errors in precipitation, temperature and radiative data sets in mountain environments are related to the scarcity of high elevation measurements, poor measurement quality due to adverse conditions, relatively few measured variables, and interpolation errors in complex terrain. Such highly scale-dependent errors are propagated through hydrologic simulation models in a complex manner. Because the density of in situ data will always be less than ideal for most watersheds, we propose to develop and evaluate new methods for combining physical process-based information from meso-scale climate models with low elevation meteorological stations and high resolution topographic information to remap available meteorological driving data more accurately across complex terrain. The performance of meso-scale climate models in this application is currently poorly understood, and we will use the intensively-monitored Tuolumne and North Fork American River watersheds in the Sierra Nevada, California to develop, evaluate, and refine these new techniques at basin scales ranging from 10 to 800 km2. While specific research experiments will focus on two mountain watersheds in the western United States, the research is designed to develop and evaluate tools that will be broadly applicable in mountain environments across the western U.S. and potentially at the global scale. The following research questions will be addressed:1) What forcing data errors contribute most to hydrologic errors in the simulation of snow accumulation and melt processes at various spatial scales?2) How can simulations from high-resolution meteorological models best be combined with topography and limited low-elevation station data on multiple scales to produce distributed forcing data for hydrologic simulations in complex terrain?3) What are the strengths and limitations of meso-scale climate models when used for long-term hydrologic predictions?Throughout, the study will focus on impacts on water resources, and results will be shared with resource managers. We will develop a course on combined hydrologic data collection and modeling for UW graduate students and will employ both undergraduates and graduates in data collection and analysis. We will work with National Park Service interpretive rangers to communicate climate-hydrology connections to the general public.

项目摘要该奖项由 2009 年美国复苏和再投资法案（公法 111-5）资助。 山地融雪是世界 40% 人口的重要资源，但受到气候变化引起的变暖的威胁。 流域影响分析、地方和区域规划过程以及水政策的制定需要对各个流域的气候敏感性进行准确的模型预测。 目前，山区环境中气象驱动数据集的质量是表征特定流域敏感性和使用水文模型模拟气候相关影响的主要障碍。 特别是，准确地描述事件中降水在何处以雨与雪的形式发生，以及雪融化的时间和速度对于评估中小（10-1000 km2）空间尺度的气候敏感性至关重要。 山地环境下降水、温度和辐射数据集的误差与高海拔测量的缺乏、恶劣条件下测量质量差、测量变量相对较少以及复杂地形的插值误差有关。 这种高度依赖于尺度的误差以复杂的方式通过水文模拟模型传播。 由于大多数流域的实地数据密度始终低于理想值，因此我们建议开发和评估新方法，将中尺度气候模型中基于物理过程的信息与低海拔气象站和高分辨率地形信息相结合，以重新绘制地图在复杂地形上更准确地提供气象驾驶数据。 目前对该应用中中尺度气候模型的性能知之甚少，我们将利用加利福尼亚州内华达山脉的图奥勒米和北叉美洲河流域进行集中监测，在流域尺度上开发、评估和完善这些新技术面积从10平方公里到800平方公里不等。 虽然具体的研究实验将集中在美国西部的两个山区分水岭，但该研究旨在开发和评估可广泛适用于美国西部山区环境并可能在全球范围内使用的工具。 将解决以下研究问题：1）在不同空间尺度的积雪和融化过程模拟中，什么强迫数据误差对水文误差影响最大？2）高分辨率气象模型的模拟如何最好地与地形和地形相结合？有限的多尺度低海拔站数据来生成复杂地形水文模拟的分布式强迫数据？3）中尺度气候模型用于长期水文预测时的优点和局限性是什么？将重点关注对水资源的影响，结果将与资源管理者分享。 我们将为华盛顿大学研究生开发一门关于水文数据收集和建模相结合的课程，并将雇用本科生和研究生进行数据收集和分析。 我们将与国家公园管理局解说护林员合作，向公众传达气候与水文学的联系。