When management decisions are made about water resources systems, decision-makers do so in the belief, or hope, that the predicted changes will be realised as a result of implementing remedial actions.  These predictions are often qualitative and based on the knowledge and experience of the decision maker, or in part based on quantitative information provided by mathematical or computer-based models (Loucks and van Beek, 2005)[1].

A model is a simplification of the real world and reflects the knowledge of how natural and man-made water resource systems interact.  Because it is a simplification, it can help decision makers identify which catchment processes needs to be controlled to meet the management objectives, i.e. where should management efforts be focussed.  It also helps to identify the spatial and temporal scale of catchment processes and the scale at which interventions should be implemented.  Lastly, it helps decision makers to identify and priorities interventions to manage problems in the catchment by simulating the possible outcomes beforehand. 

Ongely and Booty (1999)[2] made a strong case for utilising local knowledge to support decision making in situations where there is a low to moderate level of expertise to run conventional models, or where there are too little data to run conventional models. Mathematical modelling to support IWRM was the usual method of choice in data-rich developed countries and it required substantial investment in reliable data, scientific capacity and a sophisticated management culture.  These were generally not found in developing countries.  They found that in developing countries modelling was expensive, posed numerous technical problems, required a high degree of input by foreign experts, and rarely left residual capacity in the developing country. They argued for the development of decision support systems which combines simpler or scoping level models with local expertise to address complex environmental decisions. In their paper they distinguished between conventional mathematical modelling and knowledge-based approaches.  

In this section both conventional modelling approaches and knowledge-based approaches are described, and how the two approaches can be integrated in developing countries.  The section also describes how the impacts of climate change on water resources systems can be accommodated in water resources planning and management.  The section concludes with risk ranking, i.e. considering the likelihood, significance, and consequence of risks occurring, as identified through the situation assessment process (refer to Chapter 5, Section 5.4, Step 2).


[1] Loucks, DP and van Beek, E. 2005. Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications.  UNESCO Publishing. Paris.

[2] Ongley, ED and Booty, WG. 1999. Pollution remediation planning in developing countries: Conventional modelling versus Knowledge-based prediction.  Water International, 24(1): 31-38.