INTEGRATING TOOLS FOR SURFACE WATER QUALITY MANAGEMENT - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 48

Traditionally, monitoring programmes collect data either from chemical and biological analysis of
water samples or from field equipment. However, depending on available laboratory facilities,
instruments transport and human resources, for example, all monitoring programmes are restricted in
some way and may collect data primarily by direct sampling. A number of information gaps often
have to be filled, therefore, before a rational decision about monitoring system design can be taken
with respect to a specific water quality problem. Although they are less accurate, indirect techniques
for obtaining the necessary information exist for a variety of water quality-related factors. It is
possible, for example, to obtain reasonable estimates of pollution quantities from various sources from
knowledge of the activities causing the pollution. In some catchments where the monitoring does not
cove large areas, indirect estimation of pollutants of loads based on representative measurements of
similar catchments could be possible.
Another frequent problem associated with traditional monitoring programmes is the lack of coupling
between measured concentrations and water flow or discharge measurements, thereby rendering
quantification of pollution transport difficult. Estimation techniques also exist for these situations,
where hydrometric networks are not established or functioning, or where instruments are not available
for measuring flow, such as in wastewater discharges. The actual design of a fully operational and
adequate national monitoring system must, from the beginning, take account of the requirements of the
additional management tools which are being considered for use. The complexity and size of the area
to be monitored, the number of pollutants monitored, and the frequency of monitoring, have to be
balanced against the resources available for monitoring. To a large extent the data that become
available determine the level of complexity of the management tools that can be supported by the
monitoring system.
Monitoring programmes should have clear objectives and ultimate goals for data acquisition. There
should be a long term vision for monitoring in addition to traditional assessment of water quality
problems and calculation of statistical trends in water quality. Since there are some limitations in
monitoring programmes related to time and space variations, if the objectives of monitoring are linked
to the management tools that are used such as mathematical models, the programs could be adjusted to
serve the use of these tools. Also this would clarify where could be the gaps in data acquisition
techniques and could introduce the use of complementary tools to enhance the management process
for example the use of remote sensing as a data acquisition tool.
The integration in monitoring programmes is needed to establish management plans on a watershed
scale, for example the monitoring of catchment water quality should not be separated from lakes
monitoring programmes. A common observation of lakes water quality programmes is that they tend
to be inefficient, the data are of uncertain reality if it exist at all in some locations, programme
objectives are poorly linked to management needs for data, the analytical technology is often old and
inefficient, focus is on water chemistry even though water is known to be a poor monitoring medium
for many toxic chemicals, and data bases are incapable of mobilizing for management purposes
(Ongley, 1993).
3.2. GIS APPLICATIONS IN WATER QUALITY : DATA HANDLING , PROCESSING AND
MODELLING
To avoid the "data rich but information poor" syndrome, data analysis, information generation and
reporting should be given the same attention as the generation of the data themselves. Water pollution
control requires access to statistical, graphical and modelling tools for analysis and interpretation of
data. Data used for water pollution control, such as water quality, hydrology, climate, pollution load,
land use and fertiliser application, are often measured in different units and at different temporal and
spatial scales. In addition, the data sources are often very diverse (Demayo and Steel, 1996). Most
published definitions of “geographic information systems” refer to both data and operations, as in “a
system for input, storage, manipulation, analysis and output of geographical referenced information”.
In turn, geographical referenced information can defined fairly robustly as information linked to