Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70035104

Rapid incision of the Colorado River in Glen Canyon - insights from channel profiles, local incision rates, and modeling of lithologic controls

K. L. Cook·K.X. Whipple·A.M. Heimsath·Thomas C. Hanks
View original source ↗Export metadata ↓

Abstract

The Colorado River system in southern Utah and northern Arizona is continuing to adjust to the baselevel fall responsible for the carving of the Grand Canyon. Estimates of bedrock incision rates in this area vary widely, hinting at the transient state of the Colorado and its tributaries. In conjunction with these data, we use longitudinal profiles of the Colorado and tributaries between Marble Canyon and Cataract Canyon to investigate the incision history of the Colorado in this region. We find that almost all of the tributaries in this region steepen as they enter the Colorado River. The consistent presence of oversteepened reaches with similar elevation drops in the lower section of these channels, and their coincidence within a corridor of high local relief along the Colorado, suggest that the tributaries are steepening in response to an episode of increased incision rate on the mainstem. This analysis makes testable predictions about spatial variations in incision rates; these predictions are consistent with existing rate estimates and can be used to guide further studies. We also present cosmogenic nuclide data from the Henry Mountains of southern Utah. We measured in situ 10 Be concentrations on four gravel-covered strath surfaces elevated from 1 m to 110 m above Trachyte Creek. The surfaces yield exposure ages that range from approximately 2??5 ka to 267 ka and suggest incision rates that vary between 350 and 600 m/my. These incision rates are similar to other rates determined within the high-relief corridor. Available data thus support the interpretation that tributaries of the Colorado River upstream of the Grand Canyon are responding to a recent pulse of rapid incision on the Colorado. Numerical modeling of detachment-limited bedrock incision suggests that this incision pulse is likely related to the upstream-dipping lithologic boundary at the northern edge of the Kaibab upwarp. ?? 2009 John Wiley & Sons, Ltd.

Explore related subjects

Earth Surface Processes and LandformsArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K. L. Cook, K.X. Whipple, A.M. Heimsath, Thomas C. Hanks. 2009-03-23. Rapid incision of the Colorado River in Glen Canyon - insights from channel profiles, local incision rates, and modeling of lithologic controls. https://doi.org/10.1002/esp.1790

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Remote detection of streambank erosion and channel incision using high-resolution, repeat lidar-derived metrics in Raleigh, North Carolina

This study evaluated the use of lidar data to detect streambank erosion and channel incision throughout Raleigh, North Carolina. The study included five components. (1) Generation of lidar-derived rasters, including a digital elevation model of difference that represented channel erosion and positive openness rasters that represented channel incision. (2) Completion of site-based field assessments throughout the study area. (3) Spatial analysis of raster datasets at three spatial scales: erosion patches, channel segments, and subbasins. (4) Comparison of field assessment results with lidar-derived metrics. (5) Pairing of landscape attributes with lidar-derived metrics to evaluate potential causative factors associated with variations in channel incision. Field assessment results indicated 74% of surveyed sites had Bank Erosion Hazard Index ratings ranging from ‘high’ to ‘extreme’ and 77% had ‘high’ or ‘severe’ degrees of channel incision. Select field measures showed agreement with lidar-derived metrics. Lidar-derived erosion patches, ranging in size from 20 to 948 m 2 , were detected in 24.3% of channel segments, and patch density increased with Strahler stream order. Channel segments, ranging from 50 to 150 m, had inconsistent spatial patterns of erosion and incision. Erosion in channel segments increased with Strahler stream order and 36% of channel segments became more incised during the study period. Lidar-derived metrics across subbasins revealed variability of erosion and incision throughout the study area. The assessment of landscape drivers could not explain variability at the channel-segment scale, but subbasins with fewer forests tended to have greater erosion and incision. However, unexplained variability remained, demonstrating the complexity of stream stability in urban watersheds. As lidar datasets become more widespread, pairing remote data with field assessments may be a viable approach to characterize and understand stream geomorphology in urban settings.

North Carolina↗
USGS2026

Deep learning models for mapping surficial geology in selected physiographic regions of New York

Surficial geologic mapping is required for decisions including infrastructure and water-resource development and management. Deep learning, a type of machine learning that uses training data to self-learn and perform tasks, is explored as a tool to help increase efficiency of the labour- and time-intensive mapping process. Deep learning models were trained, and their potential to aid in mapping surficial geology was explored for two physiographic regions in New York: the high-relief Allegheny Plateau and the low-relief Erie-Ontario Lowlands. Key to the development of deep learning models is the availability of highly detailed surficial geologic maps in each of these regions from which the models can learn. Through exploring different groupings of surficial deposits and their corresponding spatial data, deep learning models were iteratively developed to reproduce published mapping for greater than 79% of the training areas, and with similar accuracy in test areas within the same physiographic region. These models were trained using only two inputs: high-resolution lidar data and previously published surficial geologic maps. This straightforward approach is intended to make the methods and models created easily reproducible using widely accessible datasets. This research describes the strengths and limitations of lidar-derived surficial geologic models and how broadly grouping surficial types, characteristics of different physiographic regions, and testing models in physiographic regions outside of their training areas affect model creation and performance. In addition, the models created could be used as a tool for mapping surficial geology in similar physiographic regions, in addition to establishing a framework for creating similar models elsewhere.

New York↗
USGS2026

Regional models for postfire debris-flow likelihood and rainfall thresholds across the western United States

The U.S. Geological Survey (USGS) uses an empirical model developed with logistic regression (the ‘M1’ model) to rapidly assess debris-flow likelihood and to identify quantitative rainfall thresholds for debris flows after wildfire in the western United States. The M1 model was calibrated to a debris-flow inventory from southern California (United States) and has been applied throughout the western United States. Limited spatial coverage in the calibration dataset has motivated evaluation of M1 model accuracy outside the calibration region (e.g., the Sierra Nevada or the eastern Cascade Range, United States). Previous test cases showed that M1 overpredicts debris-flow likelihood and underpredicts rainfall thresholds for some locations (e.g., Arizona, northern California, Colorado, New Mexico, United States). We sought to improve the regional applicability of a debris-flow likelihood model by expanding the debris-flow inventory used for calibration, testing multiple potential models and generating an updated model framework. The updated inventory includes 3788 observations from 67 burned areas paired with short duration rainfall ratios. The updated model framework consists of a modified model structure and sets of coefficients calibrated separately to the entire updated inventory and to subsets of the inventory that intersect three Environmental Protection Agency (EPA) Level 2 ecoregions (Mediterranean California, Upper Gila Mountains and Western Cordillera). Comparisons of predictions from the updated models with observed rainfall and debris-flow activity show that the updated models outperform the M1 model by ~15%–60% and improve the uniformity of predictive performance across the western United States. The updated models also reduce false positive rates relative to M1 and generate rainfall thresholds that are better aligned with relative differences in regional climatology and debris-flow activity.

Arizona, California, Colorado, Idaho, Montana, Nev↗