By the USGS Pacific Coastal and Marine Science Center
Sediment bulk density is a physical property of the sediment bed that tells scientists how compacted the particles are. It’s a key parameter for calculating sediment budgets, modeling sediment transport, and predicting bed erosion, and can be used to calculate carbon density when measured with carbon content to quantify sequestration rates.
These analyses are used in beneficial sediment reuse and marsh restoration projects in places like San Francisco Bay, where marshes buffer shorelines from storms but are in danger of drowning due to sea-level rise if sediment accumulation can’t keep up.
Accurately measuring sediment bulk density (referred to as ρdry) is difficult and time-consuming. The units of ρdry have not been consistently reported in the literature, leading to confusion, particularly in the calculation of sediment budgets that typically require integrating mass-based and volumetric components. To accurately calculate ρdry in a system as complex and dynamic as the San Francisco Bay and Sacramento–San Joaquin Delta, USGS scientists decided to create a site-specific model, described in a newly published study.
“Models showing relationships between ρdry and sediment composition have been developed for multiple regions and differ between systems—but none of them use sediment from San Francisco Bay,” said USGS Oceanographer Samantha McGill, lead author of the study. “Our model analyzes dozens of samples collected over the past decade throughout San Francisco Bay and the Sacramento–San Joaquin Delta to show that percent fines—the percentage of fine-grained particles in a sediment sample—predict bulk density with high accuracy.”
Outputs from this new bulk density model were compared to four models from the literature, which were developed using sediments from other regions. Results suggest that the local model provides more accurate bulk density predictions than those developed using sediments from a different region.
This local model could help improve both sediment transport models and sediment budgets by providing more accurate bulk density estimates. In addition, the data presented in this study increase the spatial resolution of measured bulk density for the San Francisco estuary for use in sediment-transport models and could potentially support planning of beneficial reuse projects.
RESEARCH ARTICLE: Predicting Sediment Bulk Density for San Francisco Estuary
By McGill, Samantha C.; Lacy, Jessica R.
Abstract: Sediment bulk density (ρ-dry) and particle size are two important parameters for predicting sediment bed erosion. ρ-dry, however, is difficult to measure accurately. The units of ρdry have not been consistently reported in the literature, leading to confusion, particularly in the calculation of sediment budgets that typically require integrating mass-based and volumetric components. Relationships between ρdry and sediment composition have been developed for multiple regions and differ between systems. Developing a system-specific predictive model for ρdry can help fill data gaps and improve sediment budgets, model accuracy, and estimates of quantities of sediment needed for restoration. In this study, we investigate whether ρdry in San Francisco Estuary can be predicted from organic carbon content or percent of fines, which are more easily or frequently measured than ρdry. We compiled sediment properties from samples collected over the past decade throughout the intertidal and subtidal regions of San Francisco Bay and the Sacramento–San Joaquin Delta to examine this relationship. Sample composition ranged from 2.18 to 99.97% fines (particles < 0.0625 mm), ρ-dry ranged from 0.22 to 1.60 g cm-3, and organic carbon ranged from 0.06 to 7.98%. Regression analysis indicates that the percent of fines explains 93% of the variation of ρ-dry (p-value < 0.05, N = 81). The coefficient of determination decreased by ~1% when organic carbon was incorporated in the regression analysis. Comparison of this predictive ρ-dry model to four published models based on samples from other regions supports previous findings that the relationship between ρdry and grain size may vary by system. We also examined additional factors that may affect sediment erodibility, such as hydrographic and oceanographic conditions. Classification of sample sites as intertidal vs. subtidal or wavy vs. non-wavy each significantly explained the residuals from the ρdry model, and both intertidal and wavy conditions were associated with higher ρ-dry values.
