Doctoral Dissertation Research in Geography and Regional Science

地理学与区域科学博士论文研究

• 批准号: 9304604
• 负责人: T. Nelson Caine
• 金额: $ 1万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-06-15 至 1995-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9304604&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Geography; Regional

In order to better understand complex hydrologic processes and to test hypotheses regarding hydrologic responses to changing physical or climatic regimes, many hydrologists have organized their research around development and testing of physically based, distributed-parameter models. These models are founded on a number of assumptions, one of the most important of which is that spatially distributed data on crucial variables and parameters is available. In high alpine systems, the storage of water in snowpacks and the release of that water through snowmelts is a major component of hydrological systems. Alpine snowpacks are spatially heterogeneous, however, with their distribution and rates of accumulation and melting a function of the influences of rugged topography on precipitation, the redistribution of snow by winds, and surface radiative energy fluxes. This doctoral dissertation project will develop and test a dynamic snowpack initialization model, which will provide improved data on the spatial and temporal patterns of snowpacks and meltwaters for use in hydrologic models of alpine regions. The model will be tested using data for three watersheds in the Front Range of the Rocky Mountains in Colorado. Data scanned from aerial photographs will be used in conjunction with data acquired within snowpacks and in downstream portions of watersheds to provide more reliable hydrological information about the water content of snow and the spatial and temporal patterns of melting. Analyses of these data will permit refinement of models related to meltwater inputs from snowpacks into hydrologic models for the watersheds. This project will provide valuable new information from a number of different perspectives. Through exploration of the potential for using new photogrammetric techniques to infer snowpack characteristics from aerial photographs and development of a dynamic snowpack initialization model, the project will advance research methodologies. The project also will make substantive contributions as it enhances understandings of the complex factors that affect the geographic and temporal distributions of snow and meltwaters in alpine areas. As a doctoral dissertation improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

为了更好地理解复杂的水文过程并测试有关水文对不断变化的物理或气候状况的响应的假设，许多水文学家围绕基于物理的分布式参数模型的开发和测试组织了他们的研究。 这些模型建立在许多假设的基础上，其中最重要的假设之一是关键变量和参数的空间分布数据是可用的。 在高山系统中，积雪中的水储存以及通过融雪释放水是水文系统的主要组成部分。 然而，高山积雪在空间上是不均匀的，其分布、积雪和融化速度是崎岖地形对降水、风雪重新分布和表面辐射能通量影响的函数。 该博士论文项目将开发和测试动态积雪初始化模型，该模型将提供有关积雪和融水空间和时间模式的改进数据，用于高山地区的水文模型。 该模型将使用科罗拉多州落基山脉前山脉三个流域的数据进行测试。 从航空照片扫描的数据将与积雪内和流域下游部分获取的数据结合使用，以提供有关雪水含量以及融化的空间和时间模式的更可靠的水文信息。 对这些数据的分析将允许将积雪融水输入相关的模型细化为流域的水文模型。 该项目将从多个不同角度提供有价值的新信息。 通过探索使用新摄影测量技术从航空照片推断积雪特征的潜力以及开发动态积雪初始化模型，该项目将推进研究方法。 该项目还将做出实质性贡献，因为它增强了对影响高山地区积雪和融水地理和时间分布的复杂因素的理解。 作为博士论文改进奖，该奖项还将为有前途的学生建立强大的独立研究生涯提供支持。