Diagnostic modeling of trace metal partitioning in south San Francisco Bay

T. W. Wood; A. M. Baptista; J.S. Kuwabara; A.R. Flegal

doi:10.4319/lo.1995.40.2.0345

Home/Archive/USGS

USGS · 70174331

Diagnostic modeling of trace metal partitioning in south San Francisco Bay

T. W. Wood·A. M. Baptista·J.S. Kuwabara·A.R. Flegal

View original source ↗Export metadata ↓

Abstract

The two-dimensional numerical model ELAmet was used to investigate the effect of adsorption kinetics on the apparent distribution coefficients of Cu, Cd, and Zn in south San Francisco Bay, California. The numerical experiments were designed to determine whether adsorption kinetics can control the basin-scale variability of the observed partitioning and to define the conditions under which adsorption kinetics could account for strong interannual variability in partitioning. The numerical results indicate that aqueous speciation will control basin-scale spatial variations in the apparent distribution coefficient, K d a , if the system is close to equilibrium. However, basin-scale spatial variations in K d a are determined by the location of the sources of metal and the suspended solids concentration of the receiving water if the system is far from equilibrium. The overall spatial variability in K d a also increases as the system moves away from equilibrium.

Explore related subjects

Limnology and Oceanography Article

Source-reported bounding extent: 37.41598184194613° to 37.81195385919268° latitude; -122.42340087890624° to -121.89331054687499° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

T. W. Wood, A. M. Baptista, J.S. Kuwabara, A.R. Flegal. 2003-12-22. Diagnostic modeling of trace metal partitioning in south San Francisco Bay. https://doi.org/10.4319/lo.1995.40.2.0345

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

KEEP EXPLORING

Related USGS reports

USGS2026

Increasing artificial light at night enhances salmonid predator foraging efficiency in an urbanized lake

Artificial light at night (ALAN) poses a threat to ecosystems globally. It includes both direct and indirect light, or skyglow, which occurs when ALAN scatters in the atmosphere, extending beyond its original source. We analyzed ALAN trends in Lake Washington, WA, from 2014 to 2023 using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light measurements, evaluated the relationship between in situ and satellite measurements, and modeled predator search volumes for a juvenile salmon predator, cutthroat trout ( Oncorhynchus clarki ), as a function of ambient light and turbidity conditions. Open water regions experienced significant increases in ALAN, while nearshore areas primarily showed no or negative trends, revealing the role of skyglow in shaping open water light environments. Using a visual foraging model for cutthroat trout, we found that juvenile salmon at the shallow southern pelagic site experienced light 28 times brighter, resulting in a 168% greater predation vulnerability than those at the northern site. In the Ship Canal, a narrow corridor for outmigrating salmon, predator search volumes were 249% higher than at the southern site. These contrasts in predation vulnerability demonstrate how local conditions influence predator–prey dynamics and provide critical insight for targeting mitigation of both nearshore and distant light sources.

Washington↗

USGS2026

Temperate lake heatwaves accelerate non-linearly as timing synchronizes across a diverse landscape

Temperate lakes are experiencing intensifying heatwaves, or prolonged periods of anomalously warm water, with potentially severe ecological consequences. Previous studies have predominantly used linear trend analyses to understand heatwave dynamics, potentially masking temporal acceleration patterns and shifts in variance. Here, we analyzed long-term, nonlinear trends in heatwave frequency, intensity, and timing across 4391 New York lakes using simulated daily surface water temperatures from an Entity-Aware Long Short-Term Memory deep learning model (validated root mean square error = 1.82°C; 1980–2020). We applied generalized additive mixed models to quantify complex temporal patterns while accounting for lake-specific effects and environmental characteristics (lake surface area, elevation, and latitude). All heatwave metrics demonstrated nonlinear and dynamic temporal trends. Heatwave frequency showed early increases in the number of events (0.05–1.32 events during the 1980s) followed by accelerated growth after 2000; other metrics exhibited more complex decadal patterns. Temporal variance increased significantly for both heatwave frequency ( τ = 0.73, p < 0.001) and intensity ( τ = 0.33, p = 0.003). Lake characteristics drove some heatwave dynamics; smaller lakes experienced higher intensities and complex elevation-latitude interactions varying by heatwave metric. Spatial patterns revealed increasing heterogeneity for heatwave intensity and frequency while heatwave timing homogenized to earlier dates across the landscape. Our findings reveal nonlinear heatwave dynamics with increasing variability, which may reduce predictability and intensify ecological vulnerabilities beyond current projections.

New York↗

USGS2025

Unique thermal mixing patterns in Lake Ontario revealed by novel year-round observations of thermal stratification

Year-round records of thermal stratification in the Great Lakes are rare, and there are few observations of thermal stratification during winter. In this paper, we analyze temperature data from 13 temperature logger chains and from over 130 benthic acoustic receivers that were deployed across Lake Ontario for 2 yr. The timing and duration of the fall overturn correlate with the local average water depth, and shallow sites (< 50 m depth) overturn up to a month before deep sites (> 100 m depths). Likewise, in spring, the shallow sites warm faster. Lake Ontario has partial ice cover, so wind-driven mixing stirs the water column throughout winter, and inverse thermal stratification is largely absent. The depth-averaged winter water temperatures vary between 0°C and 4°C, with the coldest temperatures (near 0.1°C) found in the shallow Kingston basin and warmest temperatures (near 4°C) at sites near the 244 m deep Rochester Basin. Lake Ontario appears to be a warm monomictic lake, rather than having a dimictic mixing pattern as previously described—there is no sustained ice cover or inverse stratification that inhibits vertical mixing in winter. Winter is a poorly understood season for many aquatic processes, including fish bioenergetics, fish distribution, biochemical processes, invertebrate distribution, and production. Moreover, the lack of knowledge of winter has hampered the use of correct initial conditions for running large lake hydrodynamic models.

Lake Ontario↗