U.S.-New Zealand Cooperative Research: Spatially Distributed Hydrologic Models and Spatial Patterns

美国-新西兰合作研究：空间分布水文模型和空间模式

• 批准号: 9724720
• 负责人: David Tarboton
• 金额: $ 1.39万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-11-01 至 1998-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9724720&HistoricalAwards=false
• 关键词: U.S.; New; Zealand; Cooperative; Research

9724720 TARBOTON Spatially distributed hydrologic models are being used increasingly in environmental management and design. They have the advantage that they can capture to a large degree the spatial variability in hydrologic processes and can give spatially distributed predictions. However spatially distributed models suffer from problems associated with the large number of variables involved. When calibrated against integrated quantities, such as basin outflow, they do not generally represent internal processes correctly. The work proposed has the objective to develop and formalize methods for using spatially distributed data and spatial pattern comparison in the validation and testing of spatially distributed models. This work will be a collaboration between David Tarboton at Utah State University and Charles Pearson and Ross Woods at NIWA, New Zealand. David Tarboton has distributed hydrologic modeling experience in the semi-arid western U.S., while Woods and Pearson have experience in hydrologic modeling focusing on scale issues in New Zealand and Australia. Data from the Reynolds Creek experimental watershed in Idaho, USA as well as the Buller and Grey Rivers on the South Island of New Zealand will be used. The research will develop methods that extend model calibration and validation from reliance on integrated measures only to the use of spatially distributed data. The benefits will be greater reliability of model results and forecasts and better understanding of spatially distributed hydrological processes due to better representation of the internal watershed dynamics and variability.

小行星9724720 空间分布水文模型越来越多地用于环境管理和设计。 它们的优点是可以在很大程度上捕捉水文过程的空间变异性， 分布式 预测。 然而 空间分布模型遭受与涉及的大量变量相关联的问题。当根据综合量（如流域流出量）进行校准时，它们通常不能正确地表示内部过程。 拟议工作的目标是制定和正式确定在空间分布模型的验证和测试中使用空间分布数据和空间模式比较的方法。 这项工作将是犹他州州立大学的大卫塔博顿和新西兰NIWA的查尔斯皮尔逊和罗斯伍兹之间的合作。大卫·塔博顿 美国西部半干旱地区的分布式水文模拟经验， 而伍兹和皮尔森 在新西兰和澳大利亚有水文模型方面的经验，重点是规模问题。将使用来自美国爱达荷州雷诺兹溪实验流域以及新西兰南岛布勒河和格雷河的数据。 该研究将开发方法，将模型校准和验证从仅依赖综合措施扩展到使用空间分布数据。 其好处是，模型结果和预测的可靠性将得到提高，而且由于更好地反映了流域内部的动态和变化，因此可以更好地了解空间分布的水文过程。