WATER QUALITY MANAGEMENT IN CONNECTED CATCHMENT- SHALLOW LAKE SYSTEMS - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 41

Data is always the key issue for the design and implementation of management information systems.
The first design criterion in any water quality program is to determine the management issues for
which water quality data are required . The technical aspects of data collection will flow from this
decision, especially as there are now very cost-effective alternatives to conventional monitoring
practice. Establishing of data objectives in Mexico, for example, resulted in a radical shift in national
monitoring practice which produced the savings in the funds dedicated to monitoring networks. Also,
these new methods will permit a much higher level of regulatory compliance. Most importantly, data
programs are now seen to have value insofar as they will provide a service for someone other than the
monitoring agency itself.
Most developing countries are “data-poor” environments as well as being challenged by economic
restrictions. This, together with lack of sufficient technical and institutional capacity and often a poor
scientific knowledge base, suggests that the conventional “western” approach to water quality
monitoring and management is not well suited to many if not most developing countries. It is,
therefore, timely to promote a new water quality paradigm that is more suitable, affordable, and
sustainable in developing countries. For more advanced developing countries or where there are
issues such as contamination from point and non-point sources, the conventional and expensive
chemical approach to monitoring can be effectively replaced by new diagnostic tools such as
diagnostic chemistry and biological assessment. While these never completely replace bench
chemistry, the trend is to use these inexpensive diagnostic tools to determine whether or not the
pollutant load meet certain predetermined levels of risk before any expensive chemistry is performed.
Another area of technical innovation that has considerable merit in developing countries is the
application of new decision-support (DSS) capabilities drawn from the field of information
technology (IT). These techniques are particularly useful in data-poor environments that are
typical of developing countries. There is a large knowledge-base ( domain knowledge ) in the
scientific community on most types of water quality management issues which, when
supplemented by local knowledge , can greatly facilitate decisions on water quality management.
The objective of a well-designed decision support system (DSS) is to put domain knowledge into
the hands of local practitioners in such a way that the user is guided through a complex task to a
conclusion for which the results can be expressed in degrees of confidence. Although decision-
support technology is now well known, there has been little effort by the international community
to systematically develop these technologies and related data and knowledge bases so that these
can be applied to typical water management issues in data-poor or knowledge-poor environments.
Existing Management Issues and Difficulties
Since the intended use of water dictates the water quality requirements, delineation of water allocated
for specific uses is mandatory. The traditionally accepted beneficial uses of water reflect the multi-
interest utilization of water resources. These uses include domestic water supply, industrial, water
supply, agricultural water supply, fisheries, urban development, hydropower generation, transportation
(navigation), recreational waters, sanitation, assimilation of wastes and other activities. Clearly these
uses span a wide spectrum of water quality requirements. In developing countries, a key management
problem in this context is the lack of dedicated monitoring objective for each of these different uses of
water.
Monitoring networks and data gathering and dissemination systems are not designed based on clear
information needs and priorities but rather on available tools and capacities. This has led to
'monitoring for monitoring's sake' and 'data rich but information poor' situations. Most developing
countries have initiated data collection and water quality monitoring. Many different institutes are
involved in water quality monitoring and carry out their assigned mandate for their own objectives,
independent of other related activities. For example, in Egypt, the Ministry of Health monitors water