Geology Reports⌕ Search

USGS · 70271265

Comparison of creek and bay influences on salt marsh sediment budget and deposition patterns

Abstract

The resilience of salt marshes with low organic production depends on their effective capture and retention of mineral sediment from adjacent waters. Little prior work has directly compared mechanisms of sediment import from wave-influenced marsh boundaries against those of tidal creeks. We used simultaneous deployment of net-deposition tiles and oceanographic sensors to identify the timing and magnitude of sediment import/export to, and redistribution within, a marsh in south San Francisco Bay. As the marsh has both an eroding bay-exposed scarp and a prominent tidal creek, we investigated the mechanisms and magnitudes of sediment import from the marsh-bay versus the marsh-creek interface. The strong daily sea breezes of the summer season produced most of the wave-driven erosion of the marsh scarp and controlled suspended sediment concentrations; the winter season had weaker winds punctuated by a few storms. A large seasonal difference in suspended sediment concentrations influenced both flood and ebb sediment fluxes to the marsh and led to much higher rates of import in the summer. Both bay-side and creek-side processes were important to total marsh sediment budget. Bay-side sediment contributions were more variable in time due to the bay-influenced environment, and creek-side contributions were overall larger, reflecting the large proportion of the marsh fed by creek water. Sediment was redistributed throughout the system, with erosion near the bay-edge, accretion near the creek-edge and slow import to the marsh interior. The marsh was net importing sediment in the summer and exporting in the winter from different rates of these processes; on an annual scale, the marsh was net importing despite rapid lateral marsh loss. These findings emphasize that a positive sediment budget does not imply a stable marsh and that both creek- and edge-side dynamics are important for marsh sedimentation and geomorphic trajectories. Further, we expand understandings of non-storm and seasonal controls on marsh sedimentation.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 37.53784183376638° to 37.58939504635727° latitude; -122.1656695070773° to -122.10301033557374° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lukas T. WinklerPrins, Jessica R. Lacy, Mark T. Stacey, Karen M. Thorne, McKenna Leigh Bristow, Scott Jones. 2025. Comparison of creek and bay influences on salt marsh sediment budget and deposition patterns. https://doi.org/10.1002/esp.70137

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

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related USGS reports

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↗

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↗