Participatory Bayesian modelling for sustainable and efficient river restoration projects: Feedback from the case study of the Gave de Pau River, Hautes‐Pyrénées, France. (29th September 2020) - Yassine, Rabab; Pérès, François; Frysou, Olivier; Roux, Hélène; Cassan, Ludovic
Abstract: Through the diversity of criteria and stakes, the uncertain nature of the entailed phenomena and the multi‐scale aspects to be taken into account, a river restoration project can be considered as a complex problem. Integrative approaches and modelling tools are thus needed to help river managers make predictions on the evolution of hydromorphological, socio‐economic, safety and ecological issues. Such approach can provide valuable information for handling long‐term management plans that consider the interaction and the balance of stakeholders interests and river system functioning. In this paper, we present a probabilistic participatory modelling (PM) method that assesses the effects of given restoration actions, knowing the hydromorphological modifications that they may induce on the safety, ecological and socio‐economic aspects with the help of local stakeholders through several workshops. To support this strategy, we used Bayesian networks (BNs) as modelling tools as their causal graphs can combine multidimensional knowledge and data from diverse natures. We introduce the causal graphs elaborated with the help of the stakeholders and convert it into BNs that can assist restoration decisions by considering the available decision and utility functions to provide guidance to decision‐makers. This was applied to the "Lac des Gaves" reach in the Hautes‐Pyrénées, France, a reach that has gone through severe sediment extractions for over 50 years. Each network represents possible restoration decisions linked to one of the observed issues. The paper demonstrates how BNs used as a decision support system (DSS) can help to assess the influence of given management strategies on the river system with the consideration of stakeholders' knowledge and integration in all the modelling process.