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- Title
Clustering River Profiles to Classify Geomorphic Domains.
- Authors
Clubb, Fiona J.; Bookhagen, Bodo; Rheinwalt, Aljoscha
- Abstract
The structure and organization of river networks has been used for decades to investigate the influence of climate and tectonics on landscapes. The majority of these studies either analyze rivers in profile view by extracting channel steepness or calculate planform metrics such as drainage density. However, these techniques rely on the assumption of homogeneity: that intrinsic and external factors are spatially or temporally invariant over the measured profile. This assumption is violated for the majority of Earth's landscapes, where variations in uplift rate, rock strength, climate, and geomorphic process are almost ubiquitous. We propose a method for classifying river profiles to identify landscape regions with similar characteristics by adapting hierarchical clustering algorithms developed for time series data. We first test our clustering on two landscape evolution scenarios and find that we can successfully cluster regions with different erodibility and detect the transient response to sudden base level fall. We then test our method in two real landscapes: first in Bitterroot National Forest, Idaho, where we demonstrate that our method can detect transient incision waves and the topographic signature of fluvial and debris flow process regimes; and second, on Santa Cruz Island, California, where our technique identifies spatial patterns in lithology not detectable through normalized channel steepness analysis. By calculating channel steepness separately for each cluster, our method allows the extraction of more reliable steepness metrics than if calculated for the landscape as a whole. These examples demonstrate the method's ability to disentangle fluvial morphology in complex lithological and tectonic settings. Key Points: Hierarchical clustering of longitudinal river profiles allows identification of landscape similarityAnalyzing spatial patterns of similar river profiles allows linking to a common set of lithological, climatic, or tectonic driversClustering detects landscape heterogeneity that is not identified by normalized channel steepness analysis
- Subjects
BITTERROOT National Forest (Mont. &; Idaho); SANTA Cruz Island (Calif.); GEOMORPHOLOGY; HIERARCHICAL clustering (Cluster analysis); PLATE tectonics
- Publication
Journal of Geophysical Research. Earth Surface, 2019, Vol 124, Issue 6, p1417
- ISSN
2169-9003
- Publication type
Article
- DOI
10.1029/2019JF005025