INTRODUCTION - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 7

In developing countries, data collection programmes are most of the time data driven rather than needs
or objective driven. Regrettably, many countries including developed countries, entrust data
programmes to agencies that have data-collection as their primary mandate, with the result that water
quality data programmes exhibit a high degree of inertia and for which there are few identified users of
the data. The consequence has been the realization that these mainly chemistry-focused programmes
are expensive, they focus on data production rather than on data use, and collect more data than is
necessary. They often do not reflect the types of data that managers need, and can often be replaced by
cheaper and more effective methods. The outcome in Canada and the United States, as an example,
has been a substantial shrinkage of conventional water quality data programmes and an expansion of
alternative approaches. Nowadays these are expensive, and the often ineffective chemistry-focused
approach is the one now being adopted by most developing countries and is being recommended by
international and multilateral organizations (Ongley, 2000). This is one of the main reasons that most
of the collected water quality data in developing countries is not used efficiently in real analysis and
management plans. Water quality analysis is mainly done through trends and the development of
simple indicators, but detailed water quality modelling is not yet effectively taken into consideration in
the real implementation of existing DSS or management plans.
For developing appropriate water quality management tools there should be an effective objectively-
oriented monitoring system that serves the real needs of the management sector, rather than just
collecting huge amounts of data that might not be needed in managing real water quality problems.
Also, integrating or complementing the monitoring systems or data collection programmes with other
effective types of data is an important step in developing new approaches for managing water quality.
Such types of data may include digital maps and remote sensing information based on in-situ
measurements and satellite images. This type of integrated approaches is considered a step forward in
developing reliable DSSs for surface water quality management.
1.6. THE NEED FOR INTEGRATED MANAGEMENT TOOLS
In view of the physical complexity of the watersheds and the need to deal with large and various
amounts of data in integrated and interacting watersheds and water systems, there is a requirement for
reliable and powerful analysis tools. To study water quality problems associated with such systems,
access to information for managing such complicated problems is an essential and fundamental need.
The use of Geographic Information Systems in combination with remote sensing is considered the
most suitable method that enables realistic, direct and reliable access to information. It is also very
important to understand the different surface water problems. This understanding indeed becomes an
important part of the solution. Modelling is the primary tool to support this task (Radwan, 2002). In
particular, proper management of a complicated network of canals and drains requires different levels
of modelling pollutants and their transport, especially the non-point sources. An extensive knowledge
is required of the different categories and the collection of various amounts of data including: land use,
soil properties, land slope, agricultural activities, socioeconomic background, in addition to the
different types of data related to the network itself, and the existing connection and relation between
the drainage network water and other water bodies.
The main challenge in building effective information systems for integrated water quality management
is the integration of dynamic models with the capabilities of GIS and Remote Sensing. The GIS can
provide a common framework of reference for the various tools and models addressing a range of
problems in river basin management as a whole. In a multi-tool framework, it can also provide a
common interface to the various functions of an information and decision support system for
integrated water quality management. This interface has to translate the available data and model
functionality into information that can directly support the decision making processes (Fedra, 1996).
Remote sensing is considered a very strong data acquisition tool, which has great possibilities
regarding the determination of some water quality parameters and detailed information on the lakes. In
addition, to data acquisition, remote sensing is considered a powerful tool in estimating some water