Geology topics

Rebecca B. Carvin

Publications and source records attributed to Rebecca B. Carvin.

6 recordsLinked to original sources

USGS2026

Assessing the influence of conservation implementation on water quality during surface runoff events at edge-of-field monitoring sites located in the Laurentian Great Lakes basin

The Laurentian Great Lakes are a vital freshwater resource in the United States, and nonpoint source (NPS) nutrient pollution, specifically phosphorus (P) and nitrogen (N), from agricultural land use continues to negatively impact water quality throughout the Great Lakes basin. One focus of the Great Lakes Restoration Initiative (GLRI), a mechanism to coordinate conservation efforts in the Great Lakes that began in 2010, is reducing NPS nutrient pollution through the implementation of conservation practices in priority watersheds (Genesee River, Fox River, Maumee River, and Saginaw River). As part of GLRI efforts, the objective of the study presented here was to evaluate the effects of conservation implementation, specifically increasing vegetative cover on fields and in primary flowpaths through perennial or cover crop planting and grassed waterways, on surface-runoff water quality at 12 agricultural fields (six paired and six unpaired) located in priority watersheds. We determined the percentage difference in mean event response variables between the periods before and after conservation implementation at individual sites, describing patterns across sites to synthesize lessons learned from these GLRI evaluations. Generally, we found that mean event flow-weighted concentration (FWC) and yield (kilograms per hectare) decreased for suspended sediment (SS) and nitrate (NO 3 – -N) across many sites. Mean event FWC and yield for total P (TP) showed mixed results across sites, while mean event FWC and yield for orthophosphate generally increased across sites. These results indicate that perennial or cover crop planting and grassed waterways effectively reduce SS and NO 3 – -N losses in surface runoff from agricultural fields, but mitigating TP and dissolved P losses remains a challenge.

Laurentian Great Lakes basin↗

USGS2019

Seasonal variation in sediment and phosphorus yields in four Wisconsin agricultural watersheds

Agricultural water quality projects in two distinct topographic regions in Wisconsin collected 5 to 10 yr of continuous stream discharge, suspended sediment (SS), total P (TP), and total dissolved P (TDP) in four watersheds (2100–5000 ha) from 2006 to 2016. Previous agricultural nonpoint SS and TP reduction efforts in two of these watersheds documented cold versus warm season differences in water quality response. The goal of this study was to identify seasonal partitioning of SS, TP, and TDP in storm event loads to inform stream water quality protection efforts. We used National Weather Service Coop Observer frost depth reports to identify dates when watershed soils were frozen. By comparing daily mean event discharge for dates relative to frost, we identified a 32‐d post‐frost high‐discharge “thaw” period. Combined, the frozen and thaw periods contributed about half of the annual SS and TP runoff event loads, ranging from 47 to 63% for SS and from 45 to 51% for TP. The proportion of runoff event TDP during this time was even higher, 62 to 79%, with the majority during thaw. Watershed average volumetric runoff coefficients (event flow/precipitation and snowmelt) were two to four times higher during the freeze and the thaw compared with the rest of the year. To reduce total stream loads in regions with similar climates to Wisconsin, this study indicates that using management practices that curb sediment and P delivery to streams in the winter and early spring may be as important as those designed for nonfrozen conditions.

Wisconsin↗

USGS2018

Testing a two-scale focused conservation strategy for reducing phosphorus and sediment loads from agricultural watersheds

This study tested a focused strategy for reducing phosphorus (P) and sediment loads in agricultural streams. The strategy involved selecting small watersheds identified as likely to respond relatively quickly, and then focusing conservation practices on high-contributing fields within those watersheds. Two 5,000 ha (12,360 ac) watersheds in the Driftless Area of south central Wisconsin, previously ranked in the top 6% of similarly sized Wisconsin watersheds for expected responsiveness to conservation efforts to reduce high P and sediment loads, were chosen for the study. The stream outlets from both watersheds were monitored from October of 2006 through September of 2016 for streamflow and concentrations of sediment, total P, and, beginning in October of 2009, total dissolved P. Fields and pastures having the highest potential P delivery to the streams in each watershed were identified using the Wisconsin P Index (Good et al. 2012). After three years of baseline monitoring (2006 to 2009), farmers implemented both field- and farm-based conservation practices in one watershed (treatment) as a means to reduce sediment and P inputs to the stream from the highest contributing areas, whereas there were no out-of-the-ordinary conservation efforts in the second watershed (control). Implementation occurred primarily in 2011 and 2012. In the four years following implementation of conservation practices (2013 through 2016), there was a statistically significant reduction in storm-event suspended sediment loads in the treatment watershed compared to the control watershed when the ground was not frozen ( p = 0.047). While there was an apparent reduction in year-round suspended sediment event loads, it was not statistically significant at the 95% confidence level ( p = 0.15). Total P loads were significantly reduced for runoff events ( p < 0.01) with a median reduction of 50%. Total P and total dissolved P concentrations for low-flow conditions were also significantly reduced ( p < 0.01) compared to the control watershed. This study demonstrated that a strategy that first identifies watersheds likely to respond to conservation efforts and then focuses implementation on relatively high-contributing fields within those watersheds can be successful in reducing stream P concentrations and loads.

Wisconsin↗

USGS2016

Predicting recreational water quality advisories: A comparison of statistical methods

Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

Environmental Modelling and Software↗

USGS2015

Human and bovine viruses and bacteria at three Great Lakes beaches: Environmental variable associations and health risk

Waterborne pathogens were measured at three beaches in Lake Michigan, environmental factors for predicting pathogen concentrations were identified, and the risk of swimmer infection and illness was estimated. Waterborne pathogens were detected in 96% of samples collected at three Lake Michigan beaches in summer, 2010. Samples were quantified for 22 pathogens in four microbial categories (human viruses, bovine viruses, protozoa, and pathogenic bacteria). All beaches had detections of human and bovine viruses and pathogenic bacteria indicating influence of multiple contamination sources at these beaches. Occurrence ranged from 40 to 87% for human viruses, 65–87% for pathogenic bacteria, and 13–35% for bovine viruses. Enterovirus, adenovirus A, Salmonella spp. , Campylobacter jejuni , bovine polyomavirus, and bovine rotavirus A were present most frequently. Variables selected in multiple regression models used to explore environmental factors that influence pathogens included wave direction, cloud cover, currents, and water temperature. Quantitative Microbial Risk Assessment was done for C. jejuni , Salmonella spp. , and enteroviruses to estimate risk of infection and illness. Median infection risks for one-time swimming events were approximately 3 × 10 –5 , 7 × 10 –9 , and 3 × 10 –7 for C. jejuni , Salmonella spp., and enteroviruses, respectively. Results highlight the importance of investigating multiple pathogens within multiple categories to avoid underestimating the prevalence and risk of waterborne pathogens.

Wisconsin↗

USGS2013

Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.) . Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.

Great Lakes↗