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Spatial organization of process domains in headwater drainage basins of a glaciated foothills region with complex longitudinal profiles
Author(s) -
McCleary Richard J.,
Hassan Marwan A.,
Miller Dan,
Moore R. D.
Publication year - 2011
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr009800
Subject(s) - geology , topographic wetness index , colluvium , geomorphology , foothills , drainage basin , digital elevation model , lithology , hydrology (agriculture) , sinuosity , structural basin , fluvial , landform , physical geography , alluvium , cartography , remote sensing , landslide , geography , paleontology , geotechnical engineering
Lithologic transitions and glaciations create complex longitudinal profiles that control contemporary erosion and deposition processes. In areas with these characteristics, traditional morphometric approaches for predicting process domains, such as area‐slope plots, can be augmented by considering other predictors measured from high resolution lidar‐derived digital elevation models (DEMs). Ordinal logistic regression was used to model the distribution of hillslope, swale, colluvial channel, and fluvial channel domains, as identified during field surveys. The study area was a glaciated region of the Rocky Mountain foothills with a complex lithostructural setting. Relationships between domains and a suite of geographic information system–derived descriptors were explored. Predictors included profile anomalies measured at the reach and basin scale using a normalized stream length–gradient (SL/ k ) index. Drainage area was the dominant factor controlling domains. A model with area as the only predictor was 82% accurate. Reach slope relations were not consistent. A model that also included lithology and basin‐scale SL/ k index variation was 87% accurate. Domain transitions had larger area thresholds in basins with resistant conglomerate versus sandstone or shale formations and where SL/ k index was more variable along a profile. In a restricted model of hillslope, swale, and colluvial channel domains, profile curvature measured over 100 m was also related to domain occurrence. A model for regional‐scale mapping applications with six additional predictors was 95% accurate. The results showed that ordinal logistic regression can be used to predict and map process domains in regions with complex physiography using descriptors measured from high ‐resolution DEMs.