In Integrated Water Resource Management (IWRM), water-related planners and decision makers make use of a range of tools, techniques and models tailored for the integration of all stakeholders into any water-related decision process. In water management, researchers and practitioners tend to agree that each case use best a particular type of tool or different model - it is simply up to the planner to select the best approach. In this sense, the Global Water Partnership, one of the largest forums crated around the IWRM concept, created a set of policies and approaches they recommend to practitioners interested in the implementation of IWRM. Their recommendations include legal, financial and institutional actions and reforms that need to be done at the regional and national levels to provide the overarching framework within which IWRM can be successfully implemented. In addition, it includes references to a set of Management Instruments, which are the proposed techniques to control water supply and demand. For these techniques, many models have been designed to facilitate integration between various aspects of catchment hydrology, including surface water, groundwater, vegetation, ecology, and even agricultural economics. Examples include NELUP [O'Callaghan, 1995], MIKE SHE [Refsgaard, 1995], and TOPOG [Vertessy, 1994]. Such types of model are excellent for water resource assessments and impact on the environment, but in most cases they do not link directly to the wider social, cultural and economic aspects of water management. Which is why researchers have proposed decision support systems (DSSs), as complementary tools to models. A DSS is a means of collecting data from many sources to inform a decision. Information can include experimental or survey data, output from models or, where data is scarce, and expert knowledge. Authors in [Cai, 2001] identify a number of the more widely used types of DSS and list some of the associated commercial packages; the types include influence diagrams, decision trees, mathematical models, multi-criteria analysis and spreadsheets.

Such DSS tools and models were proposed in various studies about water monitoring/management [De Zwart, 1995], and as mentioned before, are usually specifically tailored for one particular problem, to sustain the case being presented in each work. For example, diffuse of pollution from nutrients, namely nitrogen and phosphorus was presented in a vast study in [Munafo, 2005]. As the article specifies, the number of chemicals released into surface water bodies is extremely large; their dynamics are complex and it is difficult to measure the global impact. The European inventory of existing chemical substance (EINECS) identified more than 100,000 chemicals, but there is not satisfactory knowledge of their routes of entry into surface waters yet. Furthermore, EINECS is likely to have underestimated the number of pollutants, for it does not take into account all by-products deriving from physical, chemical and biological degradation. The management of non-point pollution of rivers and its prevention are priority factors in water monitoring and restoration programmes.

The scientific community proposed many models for depicting the dynamics of pollutants coming from diffuse sources. In fact, most of them can be grouped into two broad categories: statistical models and physically based models. A major drawback of statistical or physically based models for non-point pollution is the large amount of data required both as input and for calibration and validation of the model. Other possible problems are long computing time, complexity related to the development of appropriate models, and the highly skilled operators required for using them. More recently, PNPI was proposed as s a GIS-based, watershed-scale tool designed using multi-criteria technique to pollutant dynamics and water quality [Munafo, 2005]. The method for calculating PNPI follows an approach quite similar to the environmental impact assessment. The pressure exerted on water bodies by diffuse pollution coming from land units is expressed as a function of three indicators: land use, run-off and distance from the river network. They are calculated from land use data, geological maps and a digital elevation model (DEM). The weights given to different land uses and to the three indicators were set according to experts’ evaluations and allow calculation of the value of the PNPI for each node of a grid representing the watershed; the higher the PNPI of the cell, the greater the potential impact on the river network.