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.

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.

A GIS-Based Spatial-Multivariate Statistical Analysis of Water Quality Data in the Porsuk River, Turkey

2009 ◽  
Vol 44 (3) ◽  
pp. 279-293 ◽  
Author(s):  
Ozan Arslan
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Multivariate Statistical Analysis ◽  
Water Quality Assessment ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Porsuk River

Abstract The study offers a GIS-based multivariate statistical analysis strategy to assess river water quality. Multivariate statistical methods and Geographic Information System (GIS) technology have effectively been used for water quality management. Recognizing the fact that the use of standard statistical methods can be restrictive due to the complexity of water quality datasets, geospatial statistical methods have been recommended for the water quality assessment. The objective of the study was to explore the potential capabilities of GIS-based joint multivariate statistical analysis for water quality assessment of Porsuk River in Turkey. A well-known multivariate statistical technique, principal component analysis (PCA), is incorporated into a geographic database for interpretation of water quality data. To characterize spatial variability of water quality data, spatial PCA was performed on the basis of spatial autocorrelation. Application of the joint spatio-multivariate statistical analysis for interpretation of the water quality database offered a better understanding of the hydrochemistry in the study region.

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 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 Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zakaullah ◽  
Naeem Ejaz
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
River Water Quality ◽  
Quality Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Statistical ◽  
Water Quality Data

Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales

2012 ◽  
Vol 434 ◽  
pp. 186-200 ◽  
Author(s):  
Sarah J. Halliday ◽  
Andrew J. Wade ◽  
Richard A. Skeffington ◽  
Colin Neal ◽  
Brian Reynolds ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Short Term ◽  
Water Quality Data ◽  
Long Term Trends
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