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Research about Cheney Reservoir

Source-linked reports with geographic coverage including Cheney Reservoir.

6 recordsLinked to original sources
USGS2017

Twenty years of water-quality studies in the Cheney Reservoir Watershed, Kansas, 1996-2016

Since 1996, the U.S. Geological Survey (USGS), in cooperation with the City of Wichita, has done studies in the Cheney Reservoir watershed to understand environmental effects on water-quality conditions. Early studies (1996–2001) determined subwatershed sources of contaminants, nutrient and sediment loading to Cheney Reservoir, changes in reservoir sediment quality over time, and watershed sources of phosphorus. Later studies (2001–present) focused on nutrient and sediment concentrations and mass transport from the watershed; the presence of cyanobacteria, cyanotoxins, and taste-and-odor compounds in the reservoir; and development of regression models for real-time computations of water-quality constituents of interest that may affect drinking-water treatment. This fact sheet summarizes key results from studies done by the USGS during 1996–2016 in the Cheney Reservoir watershed and Cheney Reservoir.

Kansas↗
USGS2017

Occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir, Kansas, 2001-16

Cheney Reservoir, located in south-central Kansas, is one of the primary drinking-water supplies for the city of Wichita and an important recreational resource. Since 1990, cyanobacterial blooms have been present occasionally in Cheney Reservoir, resulting in increased treatment costs and decreased recreational use. Cyanobacteria, the cyanotoxin microcystin, and the taste-and-odor compounds geosmin and 2-methylisoborneol have been measured in Cheney Reservoir by the U.S. Geological Survey, in cooperation with the city of Wichita, for about 16 years. The purpose of this report is to describe the occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir during May 2001 through June 2016 and to update previously published logistic regression models that used continuous water-quality data to estimate the probability of microcystin and geosmin occurrence above relevant thresholds. Cyanobacteria, microcystin, and geosmin were detected in about 84, 52, and 31 percent of samples collected in Cheney Reservoir during May 2001 through June 2016, respectively. 2-methylisoborneol was less common, detected in only 3 percent of samples. Microcystin and geosmin concentrations exceeded advisory values of concern more frequently than cyanobacterial abundance; therefore, cyanobacteria are not a good indicator of the presence of these taste-and-odor compounds in Cheney Reservoir. Broad seasonal patterns in cyanobacteria and microcystin were evident, though abundance and concentration varied by orders of magnitude across years. Cyanobacterial abundances generally peaked in late summer or early fall (August through October), and smaller peaks were observed in winter (January through February). In a typical year, microcystin was first detected in June or July, increased to its seasonal maxima in the summer (July through September), and then decreased. Seasonal patterns in geosmin were less consistent than cyanobacteria and microcystin, but geosmin typically had a small peak during winter (January through March) during most years and a large peak during summer (July through September) during some years. Though the relation between cyanobacterial abundance and microcystin and geosmin concentrations was positive, overall correlations were weak, likely because production is strain-specific and cyanobacterial strain composition may vary substantially over time. Microcystin often was present without taste-and-odor compounds. By comparison, where taste-and-odor compounds were present, microcystin frequently was detected. Taste-and-odor compounds, therefore, may be used as indicators that microcystin may be present; however, microcystin was present without taste-and-odor compounds, so taste or odor alone does not provide sufficient warning to ensure human-health protection. Logistic regression models that estimate the probability of microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter and geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter were developed. Models were developed using the complete dataset (January 2003 through June 2016 for microcystin [14-year dataset]; May 2001 through June 2016 for geosmin [16-year dataset]) and an abbreviated 4-year dataset (January 2013 through June 2016 for microcystin and geosmin). Performance of the newly developed models was compared with previously published models that were developed using data collected during May 2001 through December 2009. A seasonal component and chlorophyll fluorescence (a surrogate for algal biomass) were the explanatory variables for microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in all models. All models were relatively robust, though the previously published and 14-year models performed better over time; however, as a tool to estimate microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in a real-time notification system near the Cheney Dam, the 4-year model is most representative of recent (2013 through 2016) conditions. All models for geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter had different explanatory variables and model forms. The previously published and 16-year models were not robust over time, likely because of changing environmental conditions and seasonal patterns in geosmin occurrence. By comparison, the abbreviated 4-year model may be a useful tool to estimate geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter in a real-time notification system near the Cheney Dam. The better performance of the abbreviated 4-year geosmin model during 2013 through 2016 relative to the previously published and 16-year models demonstrates the need for continuous reevaluation of models estimating the probability of occurrence.

Kansas↗
USGS2017

Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Kansas↗
USGS2016

Elucidation of taste- and odor-producing bacteria and toxigenic cyanobacteria in a Midwestern drinking water supply reservoir by shotgun metagenomics analysis

While commonplace in clinical settings, DNA-based assays for identification or enumeration of drinking water pathogens and other biological contaminants remain widely unadopted by the monitoring community. In this study, shotgun metagenomics was used to identify taste-and-odor producers and toxin-producing cyanobacteria over a 2-year period in a drinking water reservoir. The sequencing data implicated several cyanobacteria, including Anabaena spp ., Microcystis spp ., and an unresolved member of the order Oscillatoriales as the likely principal producers of geosmin, microcystin, and 2-methylisoborneol (MIB), respectively. To further demonstrate this, quantitative PCR (qPCR) assays targeting geosmin-producing Anabaena and microcystin-producing Microcystis were utilized, and these data were fitted using generalized linear models and compared with routine monitoring data, including microscopic cell counts, sonde-based physicochemical analyses, and assays of all inorganic and organic nitrogen and phosphorus forms and fractions. The qPCR assays explained the greatest variation in observed geosmin (adjusted R 2 = 0.71) and microcystin (adjusted R 2 = 0.84) concentrations over the study period, highlighting their potential for routine monitoring applications. The origin of the monoterpene cyclase required for MIB biosynthesis was putatively linked to a periphytic cyanobacterial mat attached to the concrete drinking water inflow structure. We conclude that shotgun metagenomics can be used to identify microbial agents involved in water quality deterioration and to guide PCR assay selection or design for routine monitoring purposes. Finally, we offer estimates of microbial diversity and metagenomic coverage of our data sets for reference to others wishing to apply shotgun metagenomics to other lacustrine systems.

Kansas↗
USGS2013

Model documentation for relations between continuous real-time and discrete water-quality constituents in Cheney Reservoir near Cheney, Kansas, 2001--2009

Cheney Reservoir, located in south-central Kansas, is one of the primary water supplies for the city of Wichita, Kansas. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station in Cheney Reservoir since 2001; continuously measured physicochemical properties include specific conductance, pH, water temperature, dissolved oxygen, turbidity, fluorescence (wavelength range 650 to 700 nanometers; estimate of total chlorophyll), and reservoir elevation. Discrete water-quality samples were collected during 2001 through 2009 and analyzed for sediment, nutrients, taste-and-odor compounds, cyanotoxins, phytoplankton community composition, actinomycetes bacteria, and other water-quality measures. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physicochemical properties to compute concentrations of constituents that are not easily measured in real time. The water-quality information in this report is important to the city of Wichita because it allows quantification and characterization of potential constituents of concern in Cheney Reservoir. This report updates linear regression models published in 2006 that were based on data collected during 2001 through 2003. The update uses discrete and continuous data collected during May 2001 through December 2009. Updated models to compute dissolved solids, sodium, chloride, and suspended solids were similar to previously published models. However, several other updated models changed substantially from previously published models. In addition to updating relations that were previously developed, models also were developed for four new constituents, including magnesium, dissolved phosphorus, actinomycetes bacteria, and the cyanotoxin microcystin. In addition, a conversion factor of 0.74 was established to convert the Yellow Springs Instruments (YSI) model 6026 turbidity sensor measurements to the newer YSI model 6136 sensor at the Cheney Reservoir site. Because a high percentage of geosmin and microcystin data were below analytical detection thresholds (censored data), multiple logistic regression was used to develop models that best explained the probability of geosmin and microcystin concentrations exceeding relevant thresholds. The geosmin and microcystin models are particularly important because geosmin is a taste-and-odor compound and microcystin is a cyanotoxin.

Kansas↗