An Investigation of Hydrologic Scale: Natural Variability Modeling, and Data Collection

水文规模的研究：自然变异建模和数据收集

• 批准号: 9218602
• 负责人: Dennis McLaughlin
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-03-01 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9218602&HistoricalAwards=false
• 关键词: Investigation; Hydrologic; Scale; Natural; Variability

Problem scale has a significant effect on the way that hydrologic phenomena are observed and explained. Recent hydrologic research has clearly demonstrated that laboratory results may not adequately reflect field scale behavior, that models developed at one scale may not be valid at other scales, and that small scale variability may have large scale consequences. This research project is designed to help hydrologists obtain a clearer understanding of scale effects and their relevance to practical hydrologic investigations. It examines the complex relationship between natural variability and the models and data collection programs that from the basis for scientific hydrology. The first phase of the project reviews stochastic methods for describing natural variability over a range of time and space scales. The second phase examines new techniques for deriving scale-dependent estimates of hydrologic quantities. The final phase of research considers how field data can be used to improve the accuracy of these estimates. The stochastic approach adopted in the project provides a way to quantify important scale-dependent tradeoffs between model resolution, sampling effort, and estimation accuracy. This approach is illustrated with applications to groundwater flow and transport.

问题量表对观察和解释的水文现象的方式具有重大影响。 最近的水文研究清楚地表明，实验室结果可能无法充分反映现场尺度的行为，该模型在一个尺度上开发的模型可能在其他尺度上无效，并且小规模的可变性可能会带来较大的尺度后果。 该研究项目旨在帮助水文学家对规模效应及其与实际水文研究的相关性有更清晰的了解。 它研究了自然变异性与模型和数据收集程序之间的复杂关系，这些模型和数据收集程序从科学水文的基础上。 该项目的第一阶段回顾了随机方法，用于描述在时间和空间范围内的自然变异性。 第二阶段研究了用于得出水文量规模依赖性估计的新技术。 研究的最后阶段考虑了如何使用现场数据来提高这些估计的准确性。 项目中采用的随机方法提供了一种方法，可以在模型分辨率，采样工作和估计准确性之间量化重要的规模依赖性权衡。 用在地下水流和运输的应用中说明了这种方法。