scholarly journals Evaluating lake stratification and temporal trends by using near-continuous water-quality data from automated profiling systems for water years 2005-09, Lake Mead, Arizona and Nevada

2012 ◽  
Author(s):  
Ronald J. Veley ◽  
Michael J. Moran
Keyword(s):  
Water Quality ◽  
Temporal Trends ◽  
Quality Data ◽  
Lake Mead ◽  
Water Quality Data ◽  
Lake Stratification

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

Long-term variations of water quality in a reservoir in China

2012 ◽  
Vol 65 (8) ◽  
pp. 1454-1460 ◽  
Author(s):  
Y. Y. Chen ◽  
C. Zhang ◽  
X. P. Gao ◽  
L. Y. Wang
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Yuqiao Reservoir

To study the spatial and temporal trends of water quality in the Yuqiao Reservoir (Ji County, Tianjin) in China, water quality data for ten physical and chemical parameters from three monitoring stations (S1, S2 and S3) was collected from 1989 to 2007 and from an other three stations (S4, S5 and S6) during the period of 1999–2007. A one-way ANOVA was employed to evaluate the spatial variation of water quality for each station. The results showed that there were statistically significant spatial differences for most water quality parameters except temperature and dissolved oxygen in the entire reservoir, and the concentrations of most parameters were higher in the uppermost part of the reservoir. The temporal trend study was conducted using the Seasonal–Kendall's test. The results revealed improving trends of water quality from 1989 to 2007, including a reduction of total phosphorous, temperature and biochemical oxygen demand and an increase of dissolved oxygen. High N:P ratios, ranging from 52.61 to 78.75, indicated that the reservoir was a phosphorous-limited environment. This study suggests long-term spatial and temporal variations of water quality in the Yuqiao Reservoir, which could be informative for water quality managers and scientists.

Water-quality data collected at Lake Anne, Reston, Virginia, 1997-1999

2000 ◽  
Author(s):  
Kathryn M. Conko ◽  
Margaret M. Kennedy ◽  
Karen C. Rice
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Water Quality Data
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