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 并测试有关水文响应变化的假设， 物理或气候状况，许多水文学家组织了 他们的研究围绕着物理上的开发和测试， 基于分布参数模型。 这些模型建立在 其中最重要的一个假设是 关键变量的空间分布数据， 参数可用。 在高山系统中， 积雪中的水以及这些水通过 融雪是水文系统的主要组成部分。 高山 然而，积雪在空间上是异质的， 分布和积累和融化的速度， 地形起伏对降水的影响， 风和表面辐射能对雪的重新分布 剂. 这个博士论文项目将开发和测试 一个动态积雪初始化模型，它将提供 关于积雪的空间和时间模式的改进数据 和冰川融水用于高山地区的水文模型。 该模型将使用三个流域的数据进行测试， 位于科罗拉多的落基山脉前山脉。 扫描数据 将结合航空照片中的 在积雪和下游部分获得 提供更可靠的水文信息 关于雪的含水量以及 融化的模式。 对这些数据的分析将允许 与积雪融水输入相关的模型的改进 流域的水文模型。 该项目将提供有价值的新信息， 不同的视角。 通过探索 使用新摄影测量技术来推断的潜力 从航空照片和发展中获得的积雪特征 动态积雪初始化模型，该项目将 先进的研究方法。 该项目还将使 实质性贡献，因为它增进了对 影响地理和时间的复杂因素 高山地区雪和融水的分布。 作为 博士论文改进奖，这个奖也将 提供支持，使有前途的学生建立一个 独立研究能力强。