Appropriate Models In Decision Support Systems For River Basin Management

In recent years, new ideas and techniques appear very quickly, like sustainability, adaptive management, Geographic Information System, Remote Sensing and participations of new stakeholders, which contribute a lot to the development of decision support systems in river basin management. However, the role of models still needs to be emphasized, especially for model-based decision support systems. This paper aims to find appropriate models for decision support systems. An appropriate system is defined as ‘the system can produce final outputs which enable the decision makers to distinguish different river engineering measures according to the current problem’. An appropriateness framework is proposed mainly based on uncertainty and sensitivity analysis. A flood risk model is used, as a part of the Dutch River Meuse DSS to investigate whether the appropriate framework works. The results showed that the proposed approach is applicable and helpful to find appropriate models

Reversible self-Kerr-nonlinearity in an N-type atomic system through a switching field

We investigate the self-Kerr nonlinearity of a four-level N-type atomic system in 87Rb and observe its reversible property with the unidirectional increase of the switching field. For the laser arrangement that the probe field interacts with the middle two states, the slope and the sign of the self-Kerr nonlinearity around the atomic resonance can not only be changed from negative to positive, but also can be changed to negative again with the unidirectional increasing of the switching field. Numerical simulation agrees very well with the experimental results and dressed state analysis is presented to explain the experimental results

A two-step approach to investigate the effect of rating curve uncertainty in the Elbe decision support system

For river basin management, the reliability of the rating curves mainly depends on the accuracy and time period of the observed discharge and water level data. In the Elbe decision support system (DSS), the rating curves are combined with the HEC-6 model to investigate the effects of river engineering measures on the Elbe River system. In such situations, the uncertainty originating from the HEC-6 model is of significant importance for the reliability of the rating curves and the corresponding DSS results. This paper proposes a two-step approach to analyze the uncertainty in the rating curves and propagate it into the Elbe DSS: analytic method and Latin Hypercube simulation. Via this approach the uncertainty and sensitivity of model outputs to input parameters are successfully investigated. The results show that the proposed approach is very efficient in investigating the effect of uncertainty and can play an important role in improving decision-making under uncertaint