INTEGRATING TOOLS FOR SURFACE WATER QUALITY MANAGEMENT - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 74

climate (Parker 1993) and distributed pollution sources (Shepherd et al. 1999) are poorly defined and
themselves cannot be modelled with much precision. Clearly, identification of suitable water quality
policy must take account of the uncertainties associated with both the validity of the models and the
driving forces. However, as increased model complexity hinders the formal evaluation of uncertainty,
due to the large number of uncertain model components to be simultaneously analysed, there is a
danger that our ability to evaluate uncertainty will decrease. To allow intelligent use of complex
simulation models, and to allow informed interpretation and application of model predictions, it is
essential that a new generation of tools is developed and disseminated. These should be directed at
evaluation of model uncertainty, as well as its minimisation, with respect to the modelling tasks. For
results to be justified and interpreted properly, methods used for uncertainty analysis must be
theoretically or intuitively well-founded and transparent to the modeller. For methods to be practical
for day-to-day use, they should be relatively easy and fast to implement.
Ascough et al., (2008), presented in their study on future research challenges for incorporation of
uncertainty in environmental and ecological decision-making, some of the important issues that need
to be addressed in relation to the incorporation of uncertainty in environmental decision-making
processes and these include: (1) the development of methods for quantifying the uncertainty
associated with human input; (2) the development of appropriate risk-based performance criteria that
are understood and accepted by a range of disciplines; (3) improvement of fuzzy environmental
decision-making through the development of hybrid approaches (e.g., fuzzy-rule-based models
combined with probabilistic data-driven techniques); (4) development of methods for explicitly
conveying uncertainties in environmental decision-making through the use of Bayesian probability
theory; (5) incorporating adaptive management practices into the environmental decision-making
process, including model divergence correction; (6) the development of approaches and strategies for
increasing the computational efficiency of integrated models, optimization methods, and methods for
estimating risk-based performance measures; and (7) the development of integrated frameworks for
comprehensively addressing uncertainty as part of the environmental decision-making process.
As model complexity increases in order to better represent environmental and socio-environmental
systems, there is a connected need to identify potential sources of uncertainty and to quantify their
impact so that appropriate management options can be identified with confidence Ascough et al .,
(2008). Many studies have focused on the identification and quantification of certain aspects of
uncertainty, such as the development of risk-based performance measures (e.g., Hashimoto et al.,
1982), and the incorporation of uncertainty into environmental models (e.g., Burges and Lettenmaier,
1975; Chadderton et al., 1982; Eheart and Ng, 2004), optimization methods (e.g., Cieniawski et al.,
1995; Vasquez et al., 2000; Ciu and Kuczera, 2005), multicriteria methods (e.g., Rios Insua, 1990;
Barron and Schmidt, 1988; Hyde et al., 2004), multi-period multicriteria model uncertainty analysis
(e.g., Choi and Beven, 2007), decision-support tools (e.g., Pallottino et al., 2005; Reichert and Borsuk,
2005), and adaptive management systems (e.g., Prato, 2005). Only a few research studies have taken
an integrated approach that identifies and incorporates all sources of uncertainty into the decision-
making process (e.g., Maguire and Boiney, 1994; Reckhow, 1994; Labiosa et al., 2005), and several
regional co-operative research efforts are underway to address this issue.
Several research traditions provide concepts, logic and modeling tools with the intent of facilitating
better decisions about the environment (Jaeger et al., 2001). Important factors that have an impact on
whether and how environmental and ecological problems are addressed are shown in Fig. (3-7) .
Firstly, environmental problems need to be identified and brought to the attention of managers and
decision-makers in the Problem Structuring phase. This can be done through the reporting of routine
data, modeling efforts, or input from local stakeholders and/or lobby groups. Once a particular
problem is on the agenda, a decision to take action has to be made. This decision will depend on
factors such as the perceived importance and magnitude of the problem, as well as financial and
possibly political considerations. Following a decision to act, the selection of appropriate assessment
criteria and a list of alternative solutions have to be generated.