Decision Making under Uncertainty in a Decision Support System for the Red River


Kort, Inge A.T. de and Booij, Martijn J. (2004) Decision Making under Uncertainty in a Decision Support System for the Red River. In: Claudia Pahl-Wostl & S. Schmidt & A.E. Rizzoli & A.J. Jakeman (Eds.), Complexity and Integrated Resources Management. Trans. Second Biennial Meeting of the International Environmental Modelling and Software Society (Vol. II), 14-17 June 2004, Osnabrück, Germany. , pp. 556-561.

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Abstract:Decision support systems (DSSs) are increasingly being used in water management for the evaluation
of impacts of policy measures under different scenarios. The exact impacts generally are unknown and
surrounded with considerable uncertainties. These uncertainties stem from natural randomness, uncertainty in
data, models and parameters, and uncertainty about measures and scenarios. It may therefore be difficult to make
a selection of measures relevant for a particular water management problem. In order to support policy makers to
make a strategic selection between different measures in a DSS while taking uncertainty into account, a
methodology for the ranking of measures has been developed. The methodology has been applied to a pilot DSS
for flood control in the Red River basin in Vietnam and China. The decision variable is the total flood damage
and possible flood reducing measures are dike heightening, reforestation and the construction of a retention basin.
For illustrative purposes, only parameter uncertainty is taken into account. The methodology consists of a Monte
Carlo uncertainty analysis employing Latin Hypercube Sampling and a ranking procedure based on the
significance of the difference between output distributions for different measures. The significance is determined
with the Student test for Gaussian distributions and with the non-parametric Wilcoxon test for non-Gaussian
distributions. The results show Gaussian distributions for the flood damage in all situations. The mean flood
damage in the base situation is about 2.2 billion US$ for the year 1996 with a standard deviation due to parameter
uncertainty of about 1 billion US$. Selected applications of the measures reforestation, dike heightening and the
construction of a retention basin reduce the flood damage with about 5, 55 and 300 million US$ respectively. The construction of a retention basin significantly reduces flood damage in the Red River basin, while dike heightening and reforestation reduce flood damage, but not significantly. [math mode missing closing $]
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