Low-Level Groundwater Atrazine in High Atrazine Usage Nebraska Counties: Likely Effects of Excessive Groundwater Abstraction.

Recent studies observed a correlation between estrogen-related cancers and groundwater atrazine in eastern Nebraska counties. However, the mechanisms of human exposure to atrazine are unclear because low groundwater atrazine concentration was observed in counties with high cancer incidence despite having the highest atrazine usage. We studied groundwater atrazine fate in high atrazine usage Nebraska counties. Data were collected from Quality Assessed Agrichemical Contaminant Nebraska Groundwater, Parameter-Elevation Regressions on Independent Slopes Model (PRISM), and water use databases. Descriptive statistics and cluster analysis were performed. Domestic wells (59%) were the predominant well type. Groundwater atrazine was affected by well depth. Clusters consisting of wells with low atrazine were characterized by excessive groundwater abstraction, reduced precipitation, high population, discharge areas, and metropolitan counties. Hence, low groundwater atrazine may be due to excessive groundwater abstraction accompanied by atrazine. Human exposure to atrazine in abstracted groundwater may be higher than the estimated amount in groundwater.

Escherichia coli concentrations in waters of a reservoir system impacted by cattle and migratory waterfowl.

Recent pathogenic Escherichia coli contamination of fresh vegetables that originated from irrigation water has increased awareness and importance of identifying sources of E. coli entering agroecosystems. However, inadequate methods for accurately predicting E. coli occurrence and sources in waterways continue to limit the identification of appropriate and effective prevention and treatment practices. Therefore, the primary objectives of this study were to: (1) Determine the concentration of E. coli during storm events in a hydrologic controlled stream situated in a livestock research operation that is located in the Central Flyway for avian migration in the United States. Great Plains; and (2) Identify trends between E. coli concentrations, grazing rotations, and avian migration patterns. The study sampled five rainfall events (three summer and two fall) to measure E. coli concentrations throughout storm events. A combination of cattle density and waterfowl migration patterns were found to significantly impact E. coli concentrations in the stream. Cattle density had a significant impact during the summer season (p < .0001), while waterfowl density had a significant impact on E. coli concentrations during the fall (p = .0422). The downstream reservoir had exceedance probabilities above the Environmental Protection Agency freshwater criteria > 85% of the growing season following rainfall events. Based on these findings, implementation of best management practices for reducing E. coli concentrations during the growing season and testing of irrigation water prior to application are recommended.

Influence of watershed characteristics on streambed hydraulic conductivity across multiple stream orders.

Streambeds are critical hydrological interfaces: their physical properties regulate the rate, timing, and location of fluxes between aquifers and streams. Streambed vertical hydraulic conductivity (Kv) is a key parameter in watershed models, so understanding its spatial variability and uncertainty is essential to accurately predicting how stresses and environmental signals propagate through the hydrologic system. Most distributed modeling studies use generalized Kv estimates from column experiments or grain-size distribution, but Kv may include a wide range of orders of magnitude for a given particle size group. Thus, precisely predicting Kv spatially has remained conceptual, experimental, and/or poorly constrained. This usually leads to increased uncertainty in modeling results. There is a need to shift focus from scaling up pore-scale column experiments to watershed dimensions by proposing a new kind of approach that can apply to a whole watershed while incorporating spatial variability of complex hydrological processes. Here we present a new approach, Multi-Stemmed Nested Funnel (MSNF), to develop pedo-transfer functions (PTFs) capable of simulating the effects of complex sediment routing on Kv variability across multiple stream orders in Frenchman Creek watershed, USA. We find that using the product of Kv and drainage area as a response variable reduces the fuzziness in selecting the "best" PTF. We propose that the PTF can be used in predicting the ranges of Kv values across multiple stream orders.

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern.

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM) clustering were also used to develop models for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although the majority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the other models. The ANFIS models have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season.