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

Author(s):  
Christa Nooy ◽  
Schuyler Houser ◽  
Reza Pramana ◽  
Astria Nugrahany ◽  
Daru Rini ◽  
...  

<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>

1991 ◽  
Author(s):  
Patrick Edelmann ◽  
Julie Altamore Scaplo ◽  
Don Anthony Colalancia ◽  
Brian B. Elson

1989 ◽  
Vol 21 (12) ◽  
pp. 1821-1824
Author(s):  
M. Suzuki ◽  
K. Chihara ◽  
M. Okada ◽  
H. Kawashima ◽  
S. Hoshino

A computer program based on expert system software was developed and proposed as a prototype model for water management to control eutrophication problems in receiving water bodies (Suzuki etal., 1988). The system has several expert functions: 1. data input and estimation of pollution load generated and discharged in the river watershed; 2. estimation of pollution load run-off entering rivers; 3. estimation of water quality of receiving water bodies, such as lakes; and 4. assisting man-machine dialog operation. The program can be used with MS-DOS BASIC and assembler in a 16 bit personal computer. Five spread sheets are utilized in calculation and summation of the pollutant load, using multi-windows. Partial differential equations for an ecological model for simulation of self-purification in shallow rivers and simulation of seasonal variations of water quality in a lake were converted to computer programs and included in the expert system. The simulated results of water quality are shown on the monitor graphically. In this study, the expert system thus developed was used to estimate the present state of one typical polluted river basin. The river was the Katsura, which flows into Lake Sagami, a lake dammed for water supply. Data which had been actually measured were compared with the simulated water quality data, and good agreement was found. This type of expert system is expected to be useful for water management of a closed water body.


2006 ◽  
Vol 53 (10) ◽  
pp. 153-161 ◽  
Author(s):  
C.W. Koning ◽  
K.A. Saffran ◽  
J.L. Little ◽  
L. Fent

The Oldman River flows 440 km from its headwaters in south-western Alberta, through mountains, foothills and plains into the South Saskatchewan River. Peak flows occur in May and June. Three major reservoirs, together with more than a dozen other structures, supply water to nine irrigation districts and other water users in the Oldman basin. Human activity in the basin includes forestry, recreation, oil and gas development, and agriculture, including a large number of confined livestock feeding operations. Based on the perception of basin residents that water quality was declining and of human health concern, the Oldman River Basin Water Quality Initiative was formed in 1997 to address the concerns. There was limited factual information, and at the time there was a desire for finger pointing. Results (1998–2002) show that mainstem water quality remains good whereas tributary water quality is more of a challenge. Key variables of concern are nutrients, bacteria and pesticides. Point source discharges are better understood and better regulated, whereas non-point source runoff requires more attention. Recent data on Cryptosporidium and Giardia species are providing benefit for focusing watershed management activities. The water quality data collected is providing a foundation to implement community-supported urban and rural better management practices to improve water quality.


2012 ◽  
Vol 599 ◽  
pp. 237-240 ◽  
Author(s):  
Faridah Othman ◽  
Mohamed Elamin Alaa Eldin

The Klang river basin is located within the state of Selangor and Kuala Lumpur, Malaysia. The Klang River drains an area of 1,288 km2 from the steep mountain rain forests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, covering a distance of 120 km. It originates from the northern part of Selangor, drains the Klang Valley, and finally discharges itself into the Straits of Malacca. The pollution discharges for various locations along the river basin was obtained from the Water Quality and GIS group. The pollutants can come from point sources (PS) such as industrial wastewater, municipal sewers, wet market, sand mining and landfill. Pollutants can also come from non-point sources (NPS) such as agricultural or urban runoff, and commercial activity such as forestry, and construction due to rainfall event. Mathematical–computational modeling of river water quality is possible but requires an extensive validation. Besides it requires previous knowledge of hydraulics and hydrodynamics. To overcome these difficulties, a water quality index (WQI) was developed. The water quality index (WQI) is a mathematical instrument used to transform large quantities of water quality data into a single number. The purpose of this research is to classify the upstream and downstream of the Klang main river based on WQI value.


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