Participatory Bayesian Network modelling to assess climate change risks and adaptation regarding water supply: integrating multi-model ensemble hazard information and local expert knowledge
- 1Goethe University Frankfurt, Institute of Physical Geography, Hydrology, Frankfurt am Main, Germany (firstname.lastname@example.org)
- 2Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
Local climate change risk assessments and climate resilient adaptation are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. However, it is challenging to take into account the complex information of climate change projections and uncertainties in participatory risk assessments with decision-makers. We propose to use Bayesian Networks (BNs) for this task. Bayesian Networks are a cutting-edge integrated modelling approach for combining qualitative and quantitative knowledge in uncertain and complex domains, such as climate change impacts on water. To quantify potential future hazards of climate change on water, it is state-of-the-art to rely on multi-model ensembles to integrate the uncertainties of both climate and impact modelling. At the same time, local expert knowledge needs to be integrated in local climate change risk assessments. We show how to integrate freely-available output of multiple global hydrological models into BNs, in order to probabilistically assess risks for water supply. To this end, a roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use in a participatory manner was co-developed with water experts from Spain and the Maghreb. Multi-model information on hydrological variables was computed by three global hydrological models driven by the output of four global climate models for four greenhouse gas emissions scenarios. The output of projected relative changes of hydrological hazards was pre-processed using MATLAB, taking into account local information on water availability and use, to set up the BN. Results show that the method is useful for probabilistically computing climate change impacts on water stress and to assess potential adaptation measures in a participative process with stakeholders and decision-makers. Local water experts positively evaluated the BN application for local climate change risk assessments. While requiring certain training, the presented approach is suitable for application in the many local risk assessments necessary to deliver efficient and successful climate resilient adaptation.
How to cite: Kneier, F., Woltersdorf, L., and Döll, P.: Participatory Bayesian Network modelling to assess climate change risks and adaptation regarding water supply: integrating multi-model ensemble hazard information and local expert knowledge, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8861, https://doi.org/10.5194/egusphere-egu23-8861, 2023.