Search USGSSearch

USGS · 70202301

Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions

Abstract

Quantifying sediment flux within rivers is a challenge for many disciplines due, mainly, to difficulties inherent to traditional sediment sampling methods. These methods are operationally complex, high cost, and high risk. Additionally, the resulting data provide a low spatial and temporal resolution estimate of the total sediment flux, which has impeded advances in the understanding of the hydro‐geomorphic characteristics of rivers. Acoustic technologies have been recognized as a leading tool for increasing the resolution of sediment data by relating their echo intensity level measurements to suspended sediment. Further effort is required to robustly test and develop these techniques across a wide range of conditions found in natural river systems. This article aims to evaluate the application of acoustic inversion techniques using commercially available, down‐looking acoustic Doppler current profilers (ADCPs) in quantifying suspended sediment in a large sand bed river with varying bi‐modal particle size distributions, wash load and suspended‐sand ratios, and water stages. To achieve this objective, suspended sediment was physically sampled along the Paraná River, Argentina, under various hydro‐sedimentological regimes. Two ADCPs emitting different sound frequencies were used to simultaneously profile echo intensity level within the water column. Using the sonar equation, calibrations were determined between suspended‐sand concentrations and acoustic backscatter to solve the inverse problem. The study also analyzed the roles played by each term of the sonar equation, such as ADCP frequency, power supply, instrument constants, and particle size distributions typically found in sand bed rivers, on sediment attenuation and backscatter. Calibrations were successfully developed between corrected backscatter and suspended‐sand concentrations for all sites and ADCP frequencies, resulting in mean suspended‐sand concentration estimates within about 40% of the mean sampled concentrations. Noise values, calculated using the sonar equation and sediment sample characteristics, were fairly constant across evaluations, suggesting that they could be applied to other sand bed rivers.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: -32.11514862261243° to -31.421631960419596° latitude; -60.934295654296875° to -60.29296874999999° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ricardo N. Szupiany, Cecilia Lopez Weibel, Massimo Guerrero, Francisco Latosinski, Molly S. Wood, Lucas Dominguez Ruben, Kevin Oberg. 2019-01-30. Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions. https://doi.org/10.1002/esp.4572

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

KEEP EXPLORING

Related USGS reports

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

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

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