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 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.

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 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 Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

2021 ◽  
Vol 13 (15) ◽  
pp. 2899
Author(s):  
Ghada Y.H. El Serafy ◽  
Blake A. Schaeffer ◽  
Merrie-Beth Neely ◽  
Anna Spinosa ◽  
Daniel Odermatt ◽  
...  
Keyword(s):  
Water Quality ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Management Policy ◽  
Quality Data ◽  
Water Quality Data ◽  
Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

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.

scholarly journals Pioneering water quality data on the Lake Victoria watershed: effects on human health

2015 ◽  
Vol 13 (3) ◽  
pp. 920-930 ◽  
Author(s):  
Tamie J. Jovanelly ◽  
Julie Johnson-Pynn ◽  
James Okot-Okumu ◽  
Richard Nyenje ◽  
Emily Namaganda
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Lake Victoria ◽  
Fecal Coliforms ◽  
World Health ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Forest Reserves ◽  
Elevated Exposure

Four forest reserves within 50 km of Kampala in Uganda act as a critical buffer to the Lake Victoria watershed and habitat for local populations. Over a 9-month period we capture a pioneering water quality data set that illustrates ecosystem health through the implementation of a water quality index (WQI). The WQI was calculated using field and laboratory data that reflect measured physical and chemical parameters (pH, dissolved oxygen, biological oxygen on demand, nitrates, phosphates, fecal coliform, and temperature turbidity). Overall, the WQI for the four forest reserves reflect poor to medium water quality. Results compared with US Environmental Protection Agency and World Health Organization drinking water standards indicate varying levels of contamination at most sites and all designated drinking water sources, with signatures of elevated nitrates, phosphates, and/or fecal coliforms. As critical health problems are known to arise with elevated exposure to contaminants in drinking water, this data set can be used to communicate necessary improvements within the watershed.

Coproducing a water quality dashboard: Data communication for decision support in the Brantas River basin, Indonesia

2021 ◽  
Author(s):  
Christa Nooy ◽  
Schuyler Houser ◽  
Reza Pramana ◽  
Astria Nugrahany ◽  
Daru Rini ◽  
...  
Keyword(s):  
Water Quality ◽  
Decision Support ◽  
Citizen Science ◽  
River Basin ◽  
Decision Makers ◽  
Quality Data ◽  
User Needs ◽  
Policy Decisions ◽  
Water Quality Data ◽  
Chemical Water

<p>Interconnected processes of IWRM demand involvement of many stakeholders negotiating a variety of competing interests and goals in agenda-setting, formulation, implementation, and evaluation. These processes – and the decision taken therein – naturally involve a wide variety of data inputs. But in many contexts, available data are partial or analytically insufficient; utilization is low due to inattention to user needs; key data are not readily available; or generated evidence is scientifically rigorous but poorly matched with the most relevant policy questions. These conditions nudge policy systems towards “knowledge creep,” “decision accretion,” and “policy layering.”</p><p>The participatory turn in water governance presents an additional set of opportunities and demands. Committees, consultative groups, coordinating bodies, and citizen science programs engage a broad array of actors in knowledge co-production and consumption for water resource decisions. Expansion of the knowledge and decision network introduces valuable new data but also new considerations regarding the use of data, practicalities of data aggregation, and how data should be combined and disseminated to meet various user needs and minimize “information overload.”</p><p>This research examines how standard chemical water quality data, participatory citizen science outputs, and other qualitative data are currently used in policy decisions regarding water quality management in the Brantas River Basin in Indonesia, where decisions are undertaken in highly consultative settings. Initial findings via interviews with key users suggest that there is space to extend the use of scientific data and citizen science outputs for decision support and public information. Chemical water quality data is considered legitimate yet partial, not easily interpreted by decision-makers in tabular form, and insufficient to inform some policy decisions, including those related to solid waste and industrial pollution. Citizen science outputs, on the other hand, are recognized to serve important educational purposes but are not actively used to inform policy. Moreover, water quality conditions are not immediately apparent to decision-makers and citizens with respect to seasonal fluctuations and variations across the upper and lower reaches.</p><p>This exploratory study also tests a co-productive approach to constructing, testing, and revising a digital Water Quality dashboard to improve the uptake and interpretability of data, identify data gaps, and offer decision-makers and other stakeholders a usable overview of conditions. The iterative process involves systematic and participative appraisal of decision support needs and constraints; collation of disparate hydrologic data sets to test integration and visualization alternatives and identify sampling gaps; inclusion of citizen science and textual data; and testing of visualization and dissemination alternatives for various uses. Citizen-science data will include water quality and biomonitoring data, micro-plastics analysis, and geo-tagged data on sources of pollution. Data dissemination alternatives are to be iteratively evaluated and revised based on criteria of policy and educational relevance, interpretability, and feasibility of data maintenance.</p>

A New Assessment Model for Coastal Water Quality Based on Nonlinear Mapping Theory

2013 ◽  
Vol 726-731 ◽  
pp. 1073-1077
Author(s):  
Ren Qiang Lu
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Water Quality Management ◽  
Mapping Method ◽  
Assessment Model ◽  
Quality Data ◽  
Nonlinear Mapping ◽  
Water Quality Data ◽  
Coastal Water Quality ◽  
Mapping Theory

A new assessment model for coastal water quality was proposed based on the nonlinear mapping theory. Taking the water quality monitoring data of Tianjins coastal marine as an example, firstly, the high-dimension water quality data were mapped to the two-dimension plane by nonlinear mapping method. Secondly, the water quality assessment model was established according to the position relationship of mapping points. Then, the water quality was assessed based on the model. Through application we could found the method proposed in this paper was simple and practicable. It is science and effectiveness of applying the nonlinear mapping method to assessment the water quality. It could be used to supply the decision support for the coastal water quality management.

Water Quality Data Availability for Model Calibration Using EPA's BASINS

2000 ◽  
Vol 2000 (6) ◽  
pp. 2179-2201
Author(s):  
J. Beebe ◽  
D. Barton ◽  
R. Whittemore ◽  
J. Palumbo
Keyword(s):  
Water Quality ◽  
Model Calibration ◽  
Data Availability ◽  
Quality Data ◽  
Water Quality Data

scholarly journals On the value of water quality data and informative flow states in karst modelling

2017 ◽  
Vol 21 (12) ◽  
pp. 5971-5985 ◽  
Author(s):  
Andreas Hartmann ◽  
Juan Antonio Barberá ◽  
Bartolomé Andreo
Keyword(s):  
Water Quality ◽  
Water Resource ◽  
Quality Data ◽  
Model Parameters ◽  
Flow State ◽  
Saturated Flow ◽  
Water Quality Data ◽  
Model Parameter ◽  
Time Period ◽  
Flow States

Abstract. If properly applied, karst hydrological models are a valuable tool for karst water resource management. If they are able to reproduce the relevant flow and storage processes of a karst system, they can be used for prediction of water resource availability when climate or land use are expected to change. A common challenge to apply karst simulation models is the limited availability of observations to identify their model parameters. In this study, we quantify the value of information when water quality data (NO3− and SO42−) is used in addition to discharge observations to estimate the parameters of a process-based karst simulation model at a test site in southern Spain. We use a three-step procedure to (1) confine an initial sample of 500 000 model parameter sets by discharge and water quality observations, (2) identify alterations of model parameter distributions through the confinement, and (3) quantify the strength of the confinement for the model parameters. We repeat this procedure for flow states, for which the system discharge is controlled by the unsaturated zone, the saturated zone, and the entire time period including times when the spring is influenced by a nearby river. Our results indicate that NO3− provides the most information to identify the model parameters controlling soil and epikarst dynamics during the unsaturated flow state. During the saturated flow state, SO42− and discharge observations provide the best information to identify the model parameters related to groundwater processes. We found reduced parameter identifiability when the entire time period is used as the river influence disturbs parameter estimation. We finally show that most reliable simulations are obtained when a combination of discharge and water quality date is used for the combined unsaturated and saturated flow states.

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