Current and future water issues in the Oldman River Basin of Alberta, Canada

2006 ◽  
Vol 53 (10) ◽  
pp. 327-334 ◽  
Author(s):  
J. Byrne ◽  
S. Kienzle ◽  
D. Johnson ◽  
G. Duke ◽  
V. Gannon ◽  
...  
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Best Management Practices ◽  
Management Practices ◽  
Animal Health ◽  
Water Supplies ◽  
Rural And Urban ◽  
Increased Risk ◽  
Oldman River Basin ◽  
Oldman River

Long-term trends in alpine and prairie snow pack accumulation and melt are affecting streamflow within the Oldman River Basin in southern Alberta, Canada. Unchecked rural and urban development also has contributed to changes in water quality, including enhanced microbial populations and increased water-borne pathogen occurrence. In this study we look at changing environment within the Oldman River Basin and its impact on water quality and quantity. The cumulative effects include a decline in net water supplies, and declining quality resulting in increased risk of disease. Our data indicates that decreases in the rate of flow of water can result in sedimentation of bacterial contaminants within the water column. Water for ecosystems, urban consumption, recreation and distribution through irrigation is often drawn from water-holding facilities such as dams and weirs, and concern must be expressed over the potential for contaminate build-up and disproportionate potential of these structures to pose a risk to human and animal health. With disruption of natural flow rates for water resulting from environmental change such as global warming and/or human intervention, increased attention needs to be paid to use of best management practices to protect source water supplies.

Water quality monitoring: the basis for watershed management in the Oldman River Basin, Canada

2006 ◽  
Vol 53 (10) ◽  
pp. 153-161 ◽  
Author(s):  
C.W. Koning ◽  
K.A. Saffran ◽  
J.L. Little ◽  
L. Fent
Keyword(s):  
Water Quality ◽  
Point Source ◽  
River Basin ◽  
Watershed Management ◽  
Health Concern ◽  
Quality Data ◽  
Oil And Gas Development ◽  
Water Quality Data ◽  
Oldman River Basin ◽  
Oldman River

The Oldman River flows 440 km from its headwaters in south-western Alberta, through mountains, foothills and plains into the South Saskatchewan River. Peak flows occur in May and June. Three major reservoirs, together with more than a dozen other structures, supply water to nine irrigation districts and other water users in the Oldman basin. Human activity in the basin includes forestry, recreation, oil and gas development, and agriculture, including a large number of confined livestock feeding operations. Based on the perception of basin residents that water quality was declining and of human health concern, the Oldman River Basin Water Quality Initiative was formed in 1997 to address the concerns. There was limited factual information, and at the time there was a desire for finger pointing. Results (1998–2002) show that mainstem water quality remains good whereas tributary water quality is more of a challenge. Key variables of concern are nutrients, bacteria and pesticides. Point source discharges are better understood and better regulated, whereas non-point source runoff requires more attention. Recent data on Cryptosporidium and Giardia species are providing benefit for focusing watershed management activities. The water quality data collected is providing a foundation to implement community-supported urban and rural better management practices to improve water quality.

Modeling Water Quality and the Effects of Agricultural Best Management Practices in the Iowa River Basin

1985 ◽  
Vol 17 (6-7) ◽  
pp. 1141-1153 ◽  
Author(s):  
B. R. Bicknell ◽  
Anthony S. Donigian ◽  
T. A. Barnwell
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Best Management Practices ◽  
Management Practices ◽  
Agricultural Runoff ◽  
Large River ◽  
Water Quality Modeling ◽  
Hydrological Simulation ◽  
Best Management ◽  
Creek Watershed

This paper describes a demonstration application of comprehensive hydrology and water quality modeling on a large river basin to evaluate the effects of agricultural nonpoint pollution and proposed best management practices (BMP). The model application combines detailed simulation of agricultural runoff and soil processes, including calculation of surface and subsurface pollutant transport to receiving water, with subsequent simulation of instream transport and transformation. The result is a comprehensive simulation of river basin water quality. The investigation of the Iowa River Basin described in this paper was part of a large study which included application and evaluation of the Hydrological Simulation Program - FORTRAN (HSPF) to both the data-intensive Four Mile Creek watershed and the Iowa River above Coralville Reservoir. In this study, the methodology developed on Four Mile Creek was extrapolated to the Iowa River Basin to demonstrate its applicability and functionality on a large river basin. Many model parameter values from Four Mile Creek were applied directly to the study area without adjustment while other parameters were modified based on available information and calibration. This study allowed the exploration of problems associated with modeling hydrology, sediment, and chemical fate and transport in a large river basin with varying meteorologic conditions, soils, and agricultural practices.

scholarly journals Comparative Water Environment Simulation Study of Two Typical Models with BMPs in a Karst Basin

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Jing Zhang ◽  
Peiqi Zhang ◽  
Yongyu Song
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Best Management Practices ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Pollution Load ◽  
Hspf Model ◽  
Lijiang River

Carbonate rocks are widely distributed in southwest China, forming a unique karst landscape. The Lijiang River Basin provides a typical example of an area with concentrated karst. Research on the laws of hydrology and water quality migration in the Lijiang River Basin is important for the management of the water resources of Guilin City and similar areas. In this study, we combined three meteorological data with the soil and water assessment tool (SWAT) model and the hydrological simulation program-Fortran (HSPF) model to simulate the hydrological and water quality processes in the Lijiang River Basin separately. We chose the Nash–Sutcliffe efficiency (NSE) coefficient, coefficient of determination (R2), root mean square error-observations standard deviation ratio (RSR), and mean absolute error (MAE) as the metrics used to evaluate the models. The results, combined with the time-series process lines, indicated that the SWAT model provides a more accurate performance than the HSPF model in streamflow, ammonia nitrogen (NH3-N), and dissolved oxygen (DO) simulations. In addition, we divided the karst and non-karst areas, and we analyzed the differences between them in water balance, sediment transport, and pollution load. We further identified the key source areas of pollution load in the Lijiang River Basin, evaluated the pollution reduction effect of best management practices (BMPs) on surface source pollution, and proposed some pollution control countermeasures. Each scenario, especially returning farmland to forest and creating vegetation buffer zones, reduces the NH3-N and DO pollution load.

A Survey of Nursery Water Quality Best Management Practices

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 501e-502
Author(s):  
Cody J. White ◽  
Michael A. Schnelle ◽  
Gerrit W. Cuperus
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Management Practices ◽  
Cultural Practices ◽  
Plant Materials ◽  
Best Management ◽  
Employee Safety ◽  
Current Water ◽  
Management Approaches ◽  
Environmentally Sound

A survey was designed to assess high-risk areas with respect to environmental contamination, specifically how it relates to water quality. Oklahoma growers of all economic levels, retail and/or wholesale, were queried at their place of business for their current state of implementing best management practices (BMPs) and other strategic actions that could potentially affect current and future water quality standards. Specific areas such as the physical environment of the nursery, primary pesticides and fertilizers used, Integrated Pest Management (IPM) practices, and employee safety training were covered as well as other aspects germane to preserving and protecting current water quality and related environmental issues. More than 75 nurseries were surveyed and given the opportunity to participate in future training at Oklahoma State Univ. Results indicated that nurseries have not fully implemented many BMPs, but have adopted fundamental IPM approaches. The stage is set for the implementation of the next phase of expansion and refinement into ecologically based programs such as propagation and sale of low pesticide input plant materials, improved cultural practices, and the integration of environmentally sound management approaches. As an example, many growers are in the process of phasing out calendar-based pesticide application programs in favor of aesthetic and/or economic threshold-driven pesticide spray programs.

Pesticide Contamination of Groundwater in Virginia: BMP Impact Assessment

1993 ◽  
Vol 28 (3-5) ◽  
pp. 379-387 ◽  
Author(s):  
S. Mostaghimi ◽  
P. W. McClellan ◽  
R. A. Cooke
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Best Management Practices ◽  
Water Samples ◽  
Management Practices ◽  
Water Quality Monitoring ◽  
Pesticide Contamination ◽  
Cropping Patterns ◽  
Creek Watershed ◽  
Surface And Groundwater

The Nomini Creek Watershed/Water Quality monitoring project was initiated in 1985, as part of the Chesapeake Bay Agreement of 1983, to quantify the impacts of agricultural best management practices (BMPs) on improving water quality. The watershed monitoring system was designed to provide a comprehensive assessment of the quality of surface and groundwater as influenced by changes in land use, agronomic, and cultural practices in the watershed over the duration of the project. The primary chemical characteristics monitored include both soluble and sediment-bound nutrients and pesticides in surface and groundwater. Water samples from 8 monitoring wells located in agricultural areas in the watershed were analyzed for 22 pesticides. A total of 20 pesticides have been detected in water samples collected. Atrazine is the most frequently detected pesticide. Detected concentrations of atrazine ranged from 0.03 - 25.56 ppb and occurred in about 26 percent of the samples. Other pesticides were detected at frequencies ranging from 1.6 to 14.2 percent of all samples collected and concentrations between 0.01 and 41.89 ppb. The observed concentrations and spatial distributions of pesticide contamination of groundwater are compared to land use and cropping patterns. Results indicate that BMPs are quite effective in reducing pesticide concentrations in groundwater.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

Long-term Water Quality Trends after Implementing Best Management Practices in South Florida

2009 ◽  
Vol 38 (4) ◽  
pp. 1683-1693 ◽  
Author(s):  
Samira H. Daroub ◽  
Timothy A. Lang ◽  
Orlando A. Diaz ◽  
Sabine Grunwald
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Management Practices ◽  
South Florida ◽  
Best Management ◽  
Water Quality Trends

scholarly journals Using Video Messages to Promote Residential Adoption of Fertilizer Best Management Practices

EDIS ◽  
2018 ◽  
Vol 2018 (5) ◽  
Author(s):  
Amanda D. Ali ◽  
Laura A. Sanagorski Warner ◽  
Peyton Beattie ◽  
Alexa J. Lamm ◽  
Joy N. Rumble
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Diffusion Of Innovations ◽  
Management Practices ◽  
Relative Advantage ◽  
Water Quantity ◽  
Extension Programs ◽  
Best Management ◽  
Homeowner Associations ◽  
Quality Water

Residents are inclined to over-irrigate and over-fertilize their lawns to uphold landscape appearances influenced by homeowner associations and neighborhood aesthetics (Nielson & Smith (2005). While these practices affect water quantity and quality, water quality is most impacted by fertilizer runoff (Nielson & Smith, 2005; Toor et al., 2017). Supporting water programs and engagement in fertilizer best management practices (BMPs) can have positive impacts on water quality. The Diffusion of Innovations (DOI) theory can be used to explain how a population accepts and adopts fertilizer best management practices (BMPs) over time (Rogers, 2003). Adoption can be understood through a population's perception of relative advantage, compatibility, complexity, observability, and trialability of fertilizer BMPs. The information presented here is an exploration of how extension can use video messages to influence residents' perception of these factors which influence adoption. The videos positively influence residents' perceptions of fertilizer BMPs, and recommendations are offered for applying this research to extension programs. 

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