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，并测试有关对身体或气候变化的水文反应的假设，许多水文学家已经组织了他们的研究，围绕着基于物理的，分布式参数模型的开发和测试。 这些模型建立在许多假设上，其中最重要的是，有关关键变量和参数的空间分布数据。 在高山系统中，积雪中的水的储存和通过融雪的水释放是水文系统的主要组成部分。 但是，高山的积雪在空间上是异质的，但是它们的分布和积累速率和融化了崎topogrice对降水量的影响，风的重新分布以及表面辐射能量的功能。 该博士学位论文项目将开发和测试动态的积雪初始化模型，该模型将提供有关积雪和熔融的空间和时间模式的改进数据，用于用于高山区域的水文模型。 该模型将使用科罗拉多州落基山脉正面的三个流域的数据进行测试。 从航拍照片中扫描的数据将与在积雪中和分水岭下游部分中获取的数据一起使用，以提供有关雪的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对与分水岭的水文模型相关的模型进行改进。 该项目将从多种不同的角度提供有价值的新信息。 通过探索使用新型摄影技术从航拍照片中推断出积雪特征的潜力和动态积雪初始化模型的开发，该项目将推进研究方法。 该项目还将做出实质性的贡献，因为它增强了对影响高山地区雪和熔融的地理和时间分布的复杂因素的理解。 作为博士学位论文改进奖，该奖项还将提供支持，以使有前途的学生能够建立强大的独立研究职业。