Validation of a Model Appropriateness Framework Using the Elbe Decision Support System

Validation of a Model Appropriateness Framework Using the Elbe Decision Support System

Yue-Ping Xu, Martijn J. Booij
DOI: 10.4018/978-1-61520-881-4.ch010
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This paper describes validation of an appropriateness framework, which has been developed in a former study, to determine appropriate models under uncertainty in a decision support system for river basin management. Models are regarded as ‘appropriate’ if they produce final outputs within adequate uncertainty bands that enable decision-makers to distinguish or rank different river engineering measures. The appropriateness framework has been designed as a tool to stimulate the use of models in decision-making under uncertainty and to strengthen the communication between modelers and decision-makers. Through the application to a different river with different objectives in this validation study from the river used in the development stage, this paper investigates whether the appropriateness framework works in a different situation than it was designed for. Recommendations from the development stage are taken into account in this validation case study as well. The final results from the study showed a successful validation of the appropriateness framework and suggested further possibilities for the application in decision support systems for river basin management.
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In river basin management, often a range of models exist which can be used to describe underlying physical, socio-economic and ecological processes of interest. The availability of complex models as management tools is growing as the power of computers increases. Models are normally selected based on the conditions that they ‘best’ fit a particular set of data (Forster, 2000; Wasserman, 2000; Boorman, 2007). However, this condition may not be the only concern when people choose a model especially for management purpose.

Although, until now, there has been no single standard definition of model complexity, Brooks and Tobias (1996) defined model complexity as a measure of the number of constituent parts and relationship in the model. Busemeyer and Wang (2000) argued that complex models often have an excessively large number of parameters. Recently, there are many arguments about the use of complex and simple models (Jakeman & Hornberger, 1993; Nihoul, 1994). In general, modelers often have the intention to develop more complex models than models actually used in river basin management. These complex models help getting a better understanding of the system to be modeled or answering more complex problems, and can provide a good reference background to simpler models used in management. Decision-makers, however, focus more on practical applications of models to solve their management problem and prefer simpler models (Vreugdenhil, 2006). Parker et al. (1995) argued that complex and sophisticated models can be easily misused. The more variables in the model, the more difficult it becomes to use as a practical management tool. Moreover, for complex models, their outputs often have no measure of confidence associated with them. Principles for using a model in a planning study or strategic management are somewhat different from those for model development. According to Vreugdenhil (2002), a model for planning purposes often needs to provide only integrated, not very detailed information.

Several studies of reservoir, hydrologic, flood routing and water quality models have demonstrated that simpler model formulations are often more accurate than more complex formulations (Loague & Freeze, 1985; Palmer & Cohan, 1986; Jakeman & Hornberger, 1993). Robinson and Freebairn (2001) also made an interesting observation: very common among the conclusions of papers at MODSIM (International Congress on Modelling and Simulation) are expressions on the need for future improvements of the models in order to make them realistic. It is much less common to find conclusions suggesting that a problem has been solved or that models can be simpler for management purposes. In the case of decision support systems (DSS), many data, knowledge and models are put together whereas some developers of DSS prefer to use models as complex as possible, which often makes the system hard to understand, use, maintain and, moreover, may cause considerable uncertainty. For decision support systems, it is argued that often the complexity of models largely exceeds the actual requirements.

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