Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70029600

Using cosmogenic nuclides to contrast rates of erosion and sediment yield in a semi-arid, arroyo-dominated landscape, Rio Puerco Basin, New Mexico

P.R. Bierman·J.M. Reuter·M. Pavich·A. C. Gellis·M.W. Caffee·J. Larsen
View original source ↗Export metadata ↓

Abstract

Analysis of in-situ-produced 10Be and 26Al in 52 fluvial sediment samples shows that millennial-scale rates of erosion vary widely (7 to 366 m Ma-1) through the lithologically and topographically complex Rio Puerco Basin of northern New Mexico. Using isotopic analysis of both headwater and downstream samples, we determined that the semi-arid, Rio Puerco Basin is eroding, on average, about 100 m Ma-1. This rapid rate of erosion is consistent with estimates made using other techniques and is likely to result from a combination of easily eroded lithologies, sparse vegetation, and monsoon-dominated rainfall. Data from 331 stream water samples collected by the US Geological Survey between 1960 and 1995 are consistent with basin-wide, average chemical denudation rates of only about 1??4 m Ma-1; thus, the erosion rates we calculate may be considered rates of sediment generation because physical weathering accounts for almost 99 per cent of mass loss. The isotopic data reveal that sediment is generally well mixed downstream with the area-weighted average sediment generation rate for 16 headwater samples (234 ton km-2 a-1 for basin area 170 to 1169 km2) matching well that estimated from a single sample collected far downstream (238 ton km-2 a-1, basin area = 14 225 km2). A series of 15 samples, collected from an arroyo wall and representing deposition through the late Holocene, indicates that 10Be concentration in sediment delivered by the fluvial system has not changed appreciably over the last 1200 years despite at least two cycles of arroyo cutting and filling. Other samples (n = 21) were collected along the drainage network. Rio Puerco erosion rates scale directly with a variety of metrics describing vegetation, precipitation, and rock erodibility. Using the headwater basins for calibration, the erosion rates for both the downstream samples and also the data set as a whole, are best modelled by considering a combination of relief and vegetation metrics, both of which co-vary with precipitation and erodibility as inferred from lithology. On average, contemporary sediment yields, determined by monitoring suspended-sediment discharge, exceed cosmogenically determined millennial-scale erosion rates by nearly a factor of two. This discrepancy, between short-term rates of sediment yield and long-term rates of erosion, suggests that more sediment is currently being exported from the basin than is being produced. Because the failure of incised channel walls and the head cutting of arroyo complexes appear to be the main sources of channel sediment today, this incongruence between rates of sediment supply and sediment yield is likely to be transitory, reflecting the current states of the arroyo cycle and perhaps the influence of current or past land-use patterns. Copyright ?? 2005 John Wiley & Sons, Ltd.

Explore related subjects

Earth Surface Processes and LandformsArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P.R. Bierman, J.M. Reuter, M. Pavich, A. C. Gellis, M.W. Caffee, J. Larsen. 2005-08-30. Using cosmogenic nuclides to contrast rates of erosion and sediment yield in a semi-arid, arroyo-dominated landscape, Rio Puerco Basin, New Mexico. https://doi.org/10.1002/esp.1255

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

KEEP EXPLORING

Related USGS reports

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↗
USGS2026

Numerical modeling of Late Pleistocene to Holocene earthquake-induced progressive rock slope damage in the paraglacial Serpentine valley, Prince William Sound, Alaska

Landslides in deglaciating fjords pose a potential tsunami threat to nearby communities; however, processes contributing to long-term progressive rock damage and landslide conditioning remain poorly constrained in paraglacial settings. Here, we analyse the role of earthquake-induced rock mass damage over late Pleistocene to Holocene time scales, as a conditioning factor for modern landslides, using distinct element numerical modelling to assess spatial and temporal patterns of fracture propagation influenced by varying glacier thickness. Conceptualised numerical models were parameterised by in situ rock mass, glacial and topographic conditions in Serpentine valley, located in Prince William Sound, Alaska, where several large landslides are actively developing along the western valley wall. Results show that although rigid glacier buttressing reduces co-seismic rock mass damage, it does not suppress it completely, and damage occurs both above and below the glacier surface elevation. We demonstrate that topography, and especially steep slopes with topographic convexity, as well as preexisting damage of joint networks and faults inherited from tectonic and exhumation induced stresses, exert primary control on the location of new co-seismic damage. Simulations representing a simplified deglaciation sequence over the past 25 ka, with a series of 10 evenly spaced earthquakes, generated rock mass damage patterns that qualitatively match in situ landslide structural and kinematic observations at one instability in Serpentine valley. Our conceptual study helps clarify the role of repeated seismicity over glacial timescales as a long-term conditioning process for paraglacial rock slope failure, highlighting spatial and temporal patterns of progressive damage accumulation, with outcomes relevant for modern landslide hazard assessment.

Alaska↗