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Hillslope to fluvial process domain transitions in headwater catchments.

机译:源头集水区的坡面到河流过程域过渡。

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摘要

The landscape is partitioned into hillslopes and unchanneled valleys (hollows), and colluvial (hillslope controlled) and alluvial (self-formed) channels. The key issue for any study of headwater catchments is the rational distinction between these elements. Accurate identification of process domain transitions from hillslopes to hollows, hollows to colluvial channels and colluvial to alluvial channels, are not obvious either in the field or from topographic data derived from remotely sensed data such as laser derived (LIDAR) digital elevation models. The research in this dissertation investigates the spatial arrangement of these landforms and how hillslope and fluvial process domains interact in two pairs of headwater catchments in southwest and central Montana, using LIDAR data. This dissertation uses digital terrain analysis of LIDAR-derived topography and field studies to investigate methods of detection, modeling, and prediction of process transitions from the hillslope to fluvial domains and within the fluvial domain, from colluvial to alluvial channel reaches. Inflections in the scaling relationships between landscape parameters such as flowpath length, unit stream power (a metric of the energy expended by the channel in doing work), and drainage area were used to detect transitions in flow regimes characteristic of hillslope, unchanneled valleys, and channeled landforms. Using the scale-invariant properties of fluvial systems as a threshold condition, magnitude-frequency distributions of curvature and the derivative of aspect were also used to detect hillslope, fluvial, and transitional process domains. Finally, within the classification of channeled landforms, the transition from colluvial to alluvial channels was detected using the presence/absence of repeating patterns in the power spectra of fluvial energy and channel form parameters. LIDAR-derived scaling relations and magnitude-frequency distributions successfully detected and predicted locations of mapped channel heads and hollows and spatial regions of process transitions. Subreaches of arguably alluvial channel conditions were also identified in power spectra. However, extrinsic forcing limits ability to detect a clear transition from colluvial to fully alluvial conditions. Headwater catchments present a mosaic of process domains, in large determined by local structure and lithology. However, process domain transitions appear detectable and statistically, though not deterministically, predictable, irrespective of setting.
机译:景观分为山坡和未开挖的山谷(凹陷),以及冲积(冲坡控制)和冲积(自形成)渠道。任何研究源头流域的关键问题是这些要素之间的合理区分。从野外到凹陷,从凹陷到冲积通道以及从冲积到冲积通道的过程域转换的准确识别在现场还是从遥感数据衍生的地形数据(例如激光衍生(LIDAR)数字高程模型)中都是不明显的。本文利用LIDAR数据,研究了西南部和中部蒙大拿州两对源头集水区中这些地形的空间分布以及坡度和河流过程域之间的相互作用。本文利用基于激光雷达的地形的数字地形分析和现场研究,研究了从山坡到河流域以及河流域内从冲积河道到冲积河道的过程转换的检测,建模和预测方法。景观参数之间的比例关系变化,例如流径长度,单位流功率(工作中通道消耗的能量的度量)和排水面积,用于检测坡度,非通道性山谷和河道等流态的过渡。渠道地貌。使用河流系统的尺度不变性质作为阈值条件,还使用了曲率的幅度-频率分布和方面的导数来检测坡度,河流和过渡过程域。最后,在沟渠地貌的分类中,利用河流能量和沟渠形态参数的功率谱中重复模式的存在/不存在,检测了从冲积河道向冲积河道的过渡。 LIDAR得出的比例关系和幅度-频率分布成功地检测到并预测了映射的通道顶部和凹陷的位置以及过程过渡的空间区域。在功率谱中还确定了可以说是冲积河道条件的子范围。但是,外部强迫限制了检测从冲积状态到完全冲积条件的明显转变的能力。源头集水区呈现出过程域的马赛克,很大程度上取决于局部结构和岩性。但是,无论设置如何,过程域转换似乎都是可检测且统计上的,尽管不是确定性的,可预测的。

著录项

  • 作者

    Williams, Karen Mary.;

  • 作者单位

    Montana State University.;

  • 授予单位 Montana State University.;
  • 学科 Hydrology.;Remote Sensing.;Geomorphology.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 233 p.
  • 总页数 233
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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