Doctoral Dissertation Research: Characterizing Spatial Patterns of River Morphology and Hydraulics: Remote Mapping and Geostatistical Modeling of a Dynamic Fluvial System

博士论文研究：表征河流形态和水力学的空间模式：动态河流系统的远程测绘和地统计建模

• 批准号: 0602569
• 负责人: Thomas Dunne
• 金额: --
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2008-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0602569&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Characterizing; Spatial

Non-linear interactions among river channels and the water and sediment they convey result in a broad spectrum of process and form across a range of spatial and temporal scales. Attempts to distill this complexity have long been stymied by the non-uniform conditions found in natural rivers and by the dual role of channel morphology as both a consequence of and a control on flow and sediment transport processes. Similarly, traditional approaches emphasizing the prediction of cross-sectional averages have not addressed two-dimensional spatial variations of depth and velocity within and among reaches, despite their fundamental role in the relationship between channel morphology and sediment transport. Efforts to characterize and model these important interactions have been hindered by the difficulty of measuring channel and floodplain topography with sufficient precision and economy. The goals of this doctoral dissertation research are to: develop remote sensing and geostatistical methods for characterizing spatial patterns of river morphology and hydraulics; apply these techniques to a pristine watershed in Yellowstone National Park; and relate the observed patterns of topography and flow to sediment transport and channel change. The technical innovations resulting from this study include a flexible, physics-based approach to remote sensing of rivers and a specialized geostatistical framework for obtaining improved representations of channel form and quantitative descriptions of reach-scale spatial structure. The field component of the project involves detailed topographic surveys and spatially distributed measurements of flow depth and velocity and bed material grain size in several stream reaches at a range of discharges. To provide context for these intensive field sites, a time series of remotely sensed data spanning the period from 1949 to 2004 will be used to map variations in water depth, document changes in channel planform and valley floor land cover, and estimate long-term rates of sediment transfer. This project will introduce a new, distinctly spatial perspective that will allow geomorphic theory to move beyond one-dimensional prediction of mean depths and velocities for individual cross-sections to parametric modeling of the variability and spatial pattern of these hydraulic quantities. The techniques developed will also facilitate examination of the relationship between channel morphology and sediment transport across an expanded range of scales. These tools will be particularly useful for river restoration, where effective monitoring and adaptive management are critical to project success but are often compromised by the difficulty of collecting even basic field data and by the lack of objective criteria for assessing progress toward such nebulous goals as "increased geomorphic complexity.'"

河道与它们输送的水和沉积物之间的非线性相互作用导致了跨越一系列空间和时间尺度的广泛过程和形式。长期以来，由于天然河流中发现的不均匀条件以及河道形态的双重作用（既是水流和沉积物输送过程的结果，又是对水流和沉积物输送过程的控制）的双重作用，提取这种复杂性的尝试一直受到阻碍。同样，强调横截面平均值预测的传统方法并未解决河段内和河段间深度和速度的二维空间变化，尽管它们在河道形态和泥沙输运之间的关系中发挥着重要作用。由于难以以足够的精度和经济性测量河道和洪泛区地形，因此阻碍了对这些重要相互作用进行表征和建模的努力。本博士论文研究的目标是：开发遥感和地统计方法来表征河流形态和水力学的空间模式；将这些技术应用于黄石国家公园的原始流域；并将观察到的地形和水流模式与沉积物输送和河道变化联系起来。这项研究产生的技术创新包括灵活的、基于物理的河流遥感方法和专门的地质统计框架，用于获得改进的河道形态表示和河段尺度空间结构的定量描述。该项目的现场部分包括详细的地形调查以及在一定流量范围内的几条河流中的水流深度和速度以及河床物质颗粒尺寸的空间分布测量。为了为这些密集的现场地点提供背景信息，将使用 1949 年至 2004 年期间的遥感数据时间序列来绘制水深变化图，记录河道平面和谷底土地覆盖的变化，并估计沉积物转移的长期速率。该项目将引入一种新的、独特的空间视角，使地貌理论超越单个横截面平均深度和速度的一维预测，发展到这些水力量的变化和空间模式的参数化建模。所开发的技术还将有助于在更大范围内检查河道形态与沉积物输送之间的关系。这些工具对于河流恢复特别有用，其中有效的监测和适应性管理对于项目的成功至关重要，但往往因收集基本现场数据的困难以及缺乏评估实现“地貌复杂性增加”等模糊目标的进展情况的客观标准而受到损害。