Water quality management in the Crocodile River catchment, Eastern Transvaal, South Africa

1995 ◽  
Vol 32 (5-6) ◽  
pp. 201-208
Author(s):  
P. J. Ashton ◽  
F. C. van Zyl ◽  
R. G. Heath
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Quality Data ◽  
Catchment Management ◽  
Land Uses ◽  
Water Quality Data ◽  
River Catchment ◽  
Management Actions

The Crocodile River catchment lies in an area which currently has one of the highest rates of sustained economic growth in South Africa and supports a diverse array of land uses. Water quality management is vital to resource management strategies for the catchment. A Geographic Information System (GIS) was used to display specific catchment characteristics and land uses, supplemented with integrative overlays depicting land-use impacts on surface water resources and the consequences of management actions on downstream water quality. The water quality requirements of each water user group were integrated to optimise the selection of rational management solutions for particular water quality problems. Time-series water quality data and cause-effect relationships were used to evaluate different water supply scenarios. The GIS facilitated the collation, processing and interpretation of the enormous quantity of spatially orientated information required for integrated catchment management.

scholarly journals River Dissolved Oxygen Prediction Based on Random Forest and LSTM

2021 ◽  
Vol 37 (5) ◽  
pp. 901-910
Author(s):  
Juan Huan ◽  
Bo Chen ◽  
Xian Gen Xu ◽  
Hui Li ◽  
Ming Bao Li ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Water Quality Management ◽  
Quality Data ◽  
Mean Square ◽  
Water Quality Data ◽  
Better Than

HighlightsRandom Forest (RF) and LSTM were developed for river DO prediction.PH is the most important feature affecting DO prediction.The model base on RF is better than the model not on RF, and the dimensionality of the input data is reduced by RF.RF-LSTM model is outperformed SVR, RF-SVR, BP, RF-BP, LSTM, RNN models in DO prediction.Abstract. In order to improve the prediction accuracy of dissolved oxygen in rivers, a dissolved oxygen prediction model based on Random Forest (RF) and Long Short Term Memory networks (LSTM) is proposed. First, the Random Forest performs feature selection, which reduces the input dimension of the data and eliminates the influence of irrelevant variables on the prediction of dissolved oxygen. Then build the LSTM river dissolved oxygen prediction model to fit the relationship between water quality data and dissolved oxygen, and finally use real water quality data in the river for verification. The experimental results show that the mean square error (MSE), absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of the RF-LSTM model are 0.658, 0.528, 13.502, 0.811, 0.744, respectively, which are better than other models. The RF-LSTM model has good predictive performance and can provide a reference for river water quality management. Keywords: Dissolved oxygen prediction, LSTM, Random forest, Time series, Water quality management.

The role of citizen science as a tool of public information in water quality management in the Brantas catchment, Indonesia

2021 ◽  
Author(s):  
Reza Pramana ◽  
Schuyler Houser ◽  
Daru Rini ◽  
Maurits Ertsen
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Citizen Science ◽  
Water Resource ◽  
Water Resource Management ◽  
Water Quality Management ◽  
Data Availability ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set

<p>Water quality in the rivers and tributaries of the Brantas catchment (about 12.000 km<sup>2</sup>) is deteriorating due to various reasons, including rapid economic development, insufficient domestic water treatment and waste management, and industrial pollution. Various parameters measured by agencies involved in water resource development and management and environmental management consistently demonstrate exceedance of the local water quality standards. Between the different agencies, water quality data are available – intermittently from 2009 until 2019 at 104 locations, but generally on a monthly basis. Still, opportunities to improve data availability are apparent, both to increase the amount and representability of the data sets. The opportunity to expand available data via citizen science is simultaneously an opportunity to provide education on water stewardship and empower citizens to participate in water quality management. We plan to involve people from eight communities living close to the river and researchers from two local universities in a citizen-science campaign. The community members would sample weekly at 10 locations, from upstream to downstream of the catchment. We will use probes and test strips to measure the temperature, electrical conductivity, pH, nitrate, phosphate, ammonia, iron, and dissolved oxygen. The results will potentially be combined with the data from government agencies to construct an integrated water quality data set to improve decision making and the quality of community engagement in water resource management.</p>

scholarly journals Modelling Water Quality Improvements in a South Korean Inter-Basin Water Transfer System

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3173
Author(s):  
Hye Won Lee ◽  
Bo-Min Yeom ◽  
Jung Hyun Choi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Constructed Wetlands ◽  
Management Practices ◽  
Water Quality Management ◽  
Quality Data ◽  
Watershed Model ◽  
Selective Withdrawal ◽  
Lake Water Quality ◽  
Water Quality Data

In this study, we investigated the feasibility of using constructed wetlands for non-point source pollution reduction. The effect of constructed wetlands in reducing suspended solids (SS) was analyzed using an integrated modeling system of watershed model (HSPF), reservoir model (CE-QUAL-W2), and stream model (EFDC) to investigate the behavior and accumulation of the pollution sources based on 2017 water quality data. The constructed wetlands significantly reduced the SS concentration by approximately 30%, and the other in-lake management practices (e.g., artificial floating islands and sedimentation basins) contributed an additional decrease of approximately 7%. Selective withdrawal decreased in the average SS concentration in the influents by ~10%; however, the effluents passing through the constructed wetlands showed only a slight difference of 1.9% in the average SS concentration. In order to meet the water quality standards, it was necessary to combine the constructed wetlands, in-lake water quality management, and selective withdrawal practices. Hence, it was determined that the model proposed herein is useful for estimating the quantitative effects of water quality management practices such as constructed wetlands, which provided practical guidelines for the application of further water quality management policies.

scholarly journals Catchment process affecting drinking water quality, including the significance of rainfall events, using factor analysis and event mean concentrations

2010 ◽  
Vol 8 (4) ◽  
pp. 751-763 ◽  
Author(s):  
Kathy Cinque ◽  
Niranjali Jayasuriya
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Drinking Water ◽  
Quality Parameters ◽  
Quality Data ◽  
Catchment Management ◽  
Water Quality Data ◽  
Management Actions ◽  
Erosion Factor ◽  
Water Catchment

To ensure the protection of drinking water an understanding of the catchment processes which can affect water quality is important as it enables targeted catchment management actions to be implemented. In this study factor analysis (FA) and comparing event mean concentrations (EMCs) with baseline values were techniques used to asses the relationships between water quality parameters and linking those parameters to processes within an agricultural drinking water catchment. FA found that 55% of the variance in the water quality data could be explained by the first factor, which was dominated by parameters usually associated with erosion. Inclusion of pathogenic indicators in an additional FA showed that Enterococcus and Clostridium perfringens (C. perfringens) were also related to the erosion factor. Analysis of the EMCs found that most parameters were significantly higher during periods of rainfall runoff. This study shows that the most dominant processes in an agricultural catchment are surface runoff and erosion. It also shows that it is these processes which mobilise pathogenic indicators and are therefore most likely to influence the transport of pathogens. Catchment management efforts need to focus on reducing the effect of these processes on water quality.

Water Quality Management – A Total Catchment Management Approach

1989 ◽  
Vol 21 (2) ◽  
pp. 281-288 ◽  
Author(s):  
W. J. Hawkins ◽  
D. A. Geering
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Management Approach ◽  
Catchment Management ◽  
Know How ◽  
Wild And Scenic Rivers ◽  
Resource Managers ◽  
Resource Protection ◽  
Development Resource

Water quality standards set in the past have not helped resource managers in the decisions that they face in seeking sustainable development. Resource managers are looking for meaningful information on water quality so as to evaluate the resource, set priorities for action, and to monitor progress. Resource managers need to know how water quality affects, and is affected by, catchment uses and activities. Examples of three wild and scenic rivers, the Nymboida, Murrumbidgee, and Hawkesbury/Nepean River systems, demonstrate how a ‘Total Catchment Management' approach to resource use and resource protection has advantages for water quality management.

Marine water quality management in South Africa

1995 ◽  
Vol 32 (2) ◽  
pp. 281-288
Author(s):  
Susan Taljaard ◽  
Willem A. M. Botes
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Management Plan ◽  
Specific Reference ◽  
Information Requirements ◽  
Water Quality Guidelines ◽  
Quality Guidelines ◽  
Beneficial Uses

In South Africa the ultimate goal in water quality management is to keep the water resources suitable for all “beneficial uses”. Beneficial uses provides a basis for the derivation of water quality guidelines, which, for South Africa, are defined in Water quality guidelines for the South African coastal zone (DWAF, 1991). The CSIR has developed a practical approach to marine water quality management, taking into account international trends and local experience, which can be applied to any coastal development with potential influence on water quality. The management plan is divided into three logical components, i.e. • site-specific statutory requirements and environmental objectives; • system design with specific reference to influences on water quality; and • monitoring programmes. Within this management approach water quality issues are addressed in a holistic manner, through focused procedures and clear identification of information requirements. This paper describes the procedures and information requirements within each component of the water quality management plan, with specific reference to marine disposal systems. Ideally, the management plan should be implemented from the feasibility and conceptual design phase of a development and the timing of the different procedures within the development process are therefore also highlighted. However, the logical lay-out of procedures allows for easy initiation (even to existing disposal system) at any stage of development.

Systems approach to effective water quality management in the Republic of South Africa, existing situation and expected future developments

1998 ◽  
Vol 38 (11) ◽  
pp. 77-85
Author(s):  
P. Marjanovic ◽  
M. Miloradov ◽  
F. van Zyl
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Quality Management ◽  
Water Policy ◽  
Systems Approach ◽  
Water Quality Management ◽  
Water Quality Monitoring ◽  
Point Sources ◽  
Sources Of Pollution ◽  
Management Functions

The new National water policy will change the way water quality is managed in South Africa. The paper considers the water policy and the repercussions it will have for water quality management in South Africa and proposes a system that can be used to come up with optimum solutions for water quality management. The proposed solution integrates policy and institutional arrangements with the Cadastral system for point and non point sources of pollution and optimisation tools to ensure optimal management of water quality at any given time. The water quality management functions catered for by the proposed system are: resource allocation for pollution discharge, water quality protection, water quality monitoring, planning, development and operation.

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.

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