A Bayesian network model for integrative river rehabilitation planning and management

As rehabilitation of previously channelized rivers becomes more common worldwide, flexible integrative modeling tools are needed to help predict the morphological, hydraulic, economic, and ecological consequences of the rehabilitation activities. Such predictions can provide the basis for planning and long-term management efforts that attempt to balance the diverse interests of river system stakeholders. We have previously reported on a variety of modeling methods and decision support concepts that can assist with various aspects of the river rehabilitation process. Here, we bring all of these tools together into a probability network model that links management actions, through morphological and hydraulic changes, to the ultimate ecological and economic consequences. Although our model uses a causal graph representation common to Bayesian networks, we do not limit ourselves to discrete-valued nodes or conditional Gaussian distributions as required by most Bayesian network implementations. This precludes us from carrying out easy probabilistic inference but gives us the advantages of functional and distributional flexibility and enhanced predictive accuracy, which we believe to be more important in most environmental management applications. We exemplify model application to a large, recently completed rehabilitation project in Switzerland.