scholarly journals Water-Quality Characteristics for Sites in the Tongue, Powder, Cheyenne, and Belle Fourche River Drainage Basins, Wyoming and Montana, Water Years 2001-05, with Temporal Patterns of Selected Long-Term Water-Quality Data

2007 ◽  
Author(s):  
Melanie L. Clark ◽  
Jon P. Mason
Keyword(s):  
Water Quality ◽  
Temporal Patterns ◽  
Quality Characteristics ◽  
Quality Data ◽  
Drainage Basins ◽  
Water Quality Data ◽  
River Drainage

scholarly journals Identification of long-term trends and seasonality in high-frequency water quality data from the Yangtze River basin, China

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0188889 ◽  
Author(s):  
Weili Duan ◽  
Bin He ◽  
Yaning Chen ◽  
Shan Zou ◽  
Yi Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Quality Data ◽  
The Yangtze River ◽  
Water Quality Data ◽  
The Yangtze River Basin ◽  
Long Term Trends

scholarly journals Long-Term Change of the Secchi Disk Depth in Lake Maninjau, Indonesia Shown by Landsat TM and ETM+ Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2875
Author(s):  
Fajar Setiawan ◽  
Bunkei Matsushita ◽  
Rossi Hamzah ◽  
Dalin Jiang ◽  
Takehiko Fukushima
Keyword(s):  
Water Quality ◽  
Lake Management ◽  
Landsat Tm ◽  
Quality Data ◽  
Atmospheric Effects ◽  
Secchi Disk ◽  
Secchi Disk Depth ◽  
Water Quality Data ◽  
Using Data

Most of the lakes in Indonesia are facing environmental problems such as eutrophication, sedimentation, and depletion of dissolved oxygen. The water quality data for supporting lake management in Indonesia are very limited due to financial constraints. To address this issue, satellite data are often used to retrieve water quality data. Here, we developed an empirical model for estimating the Secchi disk depth (SD) from Landsat TM/ETM+ data by using data collected from nine Indonesian lakes/reservoirs (SD values 0.5–18.6 m). We made two efforts to improve the robustness of the developed model. First, we carried out an image preprocessing series of steps (i.e., removing contaminated water pixels, filtering images, and mitigating atmospheric effects) before the Landsat data were used. Second, we selected two band ratios (blue/green and red/green) as SD predictors; these differ from previous studies’ recommendation. The validation results demonstrated that the developed model can retrieve SD values with an R2 of 0.60 and the root mean square error of 1.01 m in Lake Maninjau, Indonesia (SD values ranged from 0.5 to 5.8 m, n = 74). We then applied the developed model to 230 scenes of preprocessed Landsat TM/ETM+ images to generate a long-term SD database for Lake Maninjau during 1987–2018. The visual comparison of the in situ-measured and satellite estimated SD values, as well as several events (e.g., algal bloom, water gate open, and fish culture), showed that the Landsat-based SD estimations well captured the change tendency of water transparency in Lake Maninjau, and these estimations will thus provide useful data for lake managers and policy-makers.

scholarly journals Anthropogenic and catchment characteristic signatures in the water quality of Swiss rivers: a quantitative assessment

2018 ◽  
Author(s):  
Martina Botter ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Water Quality ◽  
Anthropogenic Impacts ◽  
Stream Water ◽  
Anthropogenic Activities ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Water Quality Data ◽  
Natural And Anthropogenic Factors

Abstract. The hydrological and biogeochemical response of rivers carries information about solute sources, pathways, and transformations in the catchment. We investigate long-term water quality data of eleven Swiss catchments with the objective to discern the influence of catchment characteristics and anthropogenic activities on delivery of solutes in stream water. Magnitude, trends and seasonality of water quality samplings of different solutes are evaluated and compared across catchments. Subsequently, the empirical dependence between concentration and discharge is used to classify different solute behaviors. Although the influence of catchment geology, morphology and size is sometime visible on in-stream solute concentrations, anthropogenic impacts are much more evident. Solute variability is generally smaller than discharge variability. The majority of solutes shows dilution with increasing discharge, especially geogenic species, while sediment-related solutes (e.g. Total Phosphorous and Organic Carbon species) show higher concentrations with increasing discharge. Both natural and anthropogenic factors impact the biogeochemical response of streams and, while the majority of solutes show identifiable behaviors in individual catchments, only a minority of behaviors can be generalized across catchments that exhibit different natural, climatic and anthropogenic features.

scholarly journals Long-Term Water Quality Patterns of a Flow Regulated Tropical Lowland River

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 37 ◽  
Author(s):  
Lizaan de Necker ◽  
Tinyiko Neswiswi ◽  
Richard Greenfield ◽  
Johan van Vuren ◽  
Luc Brendonck ◽  
...  
Keyword(s):  
Water Quality ◽  
Base Flow ◽  
Quality Data ◽  
Severe Drought ◽  
Anthropogenic Pressure ◽  
Nutrient Inputs ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Assessment Index

Floodplain ecosystems in Africa are under threat due to direct anthropogenic pressure and climate change. The lower Phongolo River and associated floodplain is South Africa’s largest inland floodplain ecosystem and has been regulated by the Pongolapoort Dam since the 1970s. The last controlled flood release from the dam occurred in December 2014, after which a severe drought occurred and only a base flow was released. The central aims of this study were to determine the historic and present water quality state of the middle and lower Phongolo River and assess the possible effects of the most recent drought may have had. Historic water quality data (1970s to present) were obtained from monitoring stations within the Phongolo River catchment to assess the long-term water quality patterns. Using multivariate statistical analyses as well as the Physicochemical Driver Assessment Index (PAI), a water quality index developed for South African riverine ecosystems, various in situ and chemical water variables were analysed. Key findings included that the water quality of the middle and lower Phongolo River has degraded since the 1970s, due to increased salinity and nutrient inputs from surrounding irrigation schemes. The Pongolapoort Dam appears to be trapping nutrient-rich sediments leading to nutrient-depleted water entering the lower Phongolo River. The nutrient levels increase again as the river flows through the downstream floodplain through input from nutrient rich soils and fertilizers. The drought did not have any significant effect on water quality as the PAI remained similar to pre-drought conditions.

Water-quality data from two agricultural drainage basins in northwestern Indiana and northeastern Illinois: III. biweekly data, 2000-2002

2005 ◽  
Author(s):  
Ronald C. Antweiler ◽  
Richard L. Smith ◽  
Mary A. Voytek ◽  
John Karl Bohlke ◽  
David H. Dupre
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Agricultural Drainage ◽  
Drainage Basins ◽  
Water Quality Data

scholarly journals Anthropogenic and catchment characteristic signatures in the water quality of Swiss rivers: a quantitative assessment

2019 ◽  
Vol 23 (4) ◽  
pp. 1885-1904 ◽  
Author(s):  
Martina Botter ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Water Quality ◽  
Anthropogenic Impacts ◽  
Stream Water ◽  
Temporal Variations ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Water Quality Data ◽  
Catchment Characteristics

Abstract. The hydrological and biogeochemical response of rivers carries information about solute sources, pathways, and transformations in the catchment. We investigate long-term water quality data of 11 Swiss catchments with the objective to discern the influence of major catchment characteristics and anthropic activities on delivery of solutes in stream water. Magnitude, trends, and seasonality of water quality samplings of different solutes are evaluated and compared across catchments. Subsequently, the empirical dependence between concentration and discharge is used to classify the solute behaviors. While the anthropogenic impacts are clearly detectable in the concentration of certain solutes (i.e., Na+, Cl−, NO3, DRP), the influence of single catchment characteristics such as geology (e.g., on Ca2+ and H4SiO4), topography (e.g., on DOC, TOC, and TP), and size (e.g., on DOC and TOC) is only sometimes visible, which is also because of the limited sample size and the spatial heterogeneity within catchments. Solute variability in time is generally smaller than discharge variability and the most significant trends in time are due to temporal variations of anthropogenic rather than natural forcing. The majority of solutes show dilution with increasing discharge, especially geogenic species, while sediment-bonded solutes (e.g., total phosphorous and organic carbon species) show higher concentrations with increasing discharge. Both natural and anthropogenic factors affect the biogeochemical response of streams, and, while the majority of solutes show identifiable behaviors in individual catchments, only a minority of behaviors can be generalized across the 11 catchments that exhibit different natural, climatic, and anthropogenic features.

scholarly journals Water Quality Assessment of 14 Reservoirs in Geum River Basin Using Multivariate Statistical Analysis

2021 ◽  
Vol 43 (3) ◽  
pp. 171-186
Author(s):  
Jin Ho Kim ◽  
Jin Chul Joo ◽  
Chae Min Ahn ◽  
Dae Ho Hwang
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Components ◽  
Organic Pollution ◽  
Quality Characteristics ◽  
Quality Data ◽  
Water Quality Data ◽  
River Watershed ◽  
Internal Production

Objectives : 14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality.Methods : Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering.Results and Discussion : From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants.Conclusions : Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis.

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