The “data-rich but information-poor” syndrome in water quality monitoring

Abstract

/ Water quality monitoring conducted routinely over time at fixed sites has been a part of most water quality management efforts for many years. It has been assumed that such monitoring plays a major role in management. However, the lack of routine data analysis, and reporting of information derived from such analysis, points up the fact that the exact nature of the role of routine, fixed-station monitoring is poorly defined.There is a need to very clearly define this role in the design of such systems if routine monitoring is to efficiently and effectively meet the information expectations placed on it. Design of routine monitoring systems will therefore have to consider not only the where, what, and when of sampling, but also why. A framework for including the "why" of monitoring in the design process is proposed and experience with using the framework in New Zealand is discussed.Most professionals involved in the design or operation of water quality monitoring programs recognize the symptoms: file cabinets bursting with data sheets or boxes and boxes of floppy disks full of numbers; large monthly bills for sampling and laboratory analyses; observations coming in daily, weekly, or monthly and piling on top of other observations; managers, government agencies, legislative bodies, and John Q. Public constantly asking nagging questions like, "Is the water quality in our area actually getting better or worse?"; and, finally, no way to answer them. The disease is, of course, the "data-rich but information-poor" syndrome, which appears to permeate throughout the water quality management field. Many cures have been proposed by practitioners and researchers (including the authors) alike. In spite of these suggestions, water quality data often keep piling up without being utilized to provide needed information. This is indeed a precarious position for monitoring programs to be in during these days of budget slashing.The authors, frustrated by the failure of technological innovation to extract meaningful information from water quality monitoring programs, have spent the past year reviewing their own previous work and that of others in an attempt to identify the weak links in monitoring system design and to suggest ways of strengthening them. It is their hope that this fresh look at the lack-of-information problem will help to improve both well-established monitoring programs, such as those found in the United States, and new efforts, such as those anticipated for New Zealand.

Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management in Irrigated Watersheds

“…In the past, many monitoring programmes have been characterised by the "data rich, information poor syndrome" (DRIP syndrome; Ward et al, 1986). There should be more attention on the analysis and further use of collected data so that the end product of monitoring is information.…”

“…Increasingly, their potential contribution to broad water management systems and ecological management systems has been recognised. To begin to address the problem, the concept of a "water quality information system" was proposed by Ward (1979) and Ward (1986). Examples of these efforts include a "5 step framework for designing water quality information systems (Ward et al, 1990) and the development of "Data Analysis Protocols" (Atkins, 1993).…”

“…As a consequence, in many countries, data gathering programmes are considered expendable, and are being reduced or even eliminated because there is no clear view of the information product and of the cost-efficiency of monitoring (Ward et al, 1986;Ongley, 1995;Ward, 1995a). In recent years there has been an increasing consensus of opinion that information is meant for action, decision-making and use.…”

Assessment of Informativeness of Groundwater Monitoring in Developing Regions (Gaza Strip Case Study)

“…As soon as adequate data banks are built up, the region will face the same problems as those currently confronting developed regions, the major one being the lack of agreement between the objectives and available data (e.g., Ward et al, 1986).…”

Long-term Monitoring Plan for the Shoal Underground Nuclear Test

“…Major institutional data-gathering efforts, without clearly stated goals and objectives, lead to passive groundwater quality monitoring programs that are "data-rich but information-poor" (Ward et al, 1986;Lee and Jones, 1983a, b). Therefore, defining quantifiable objectives is a first step in the design of cost-effective monitoring programs (Mar et al, 1986;Bernstein and Zalinski, 1983).…”