TY  - JOUR
A1  - Kneier, Fabian
A1  - Woltersdorf, Laura
A1  - Peiris, Thedini Asali
A1  - Döll, Petra
T1  - Participatory Bayesian Network modeling of climate change risks and adaptation regarding water supply: Integration of multi-model ensemble hazard estimates and local expert knowledge
T2  - Environmental modelling & software
N2  - Local climate change risk assessments (LCCRAs) are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. We propose to use Bayesian Networks (BNs) for this task and show how to integrate freely-available output of multiple global hydrological models (GHMs) into BNs, in order to probabilistically assess risks for water supply. Projected relative changes in hydrological variables computed by three GHMs driven by the output of four global climate models were processed using MATLAB, taking into account local information on water availability and use. A roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use was co-developed with experts from the Maghreb (Tunisia, Algeria, Morocco) who positively evaluated the BN application for LCCRAs. We conclude that the presented approach is suitable for application in the many LCCRAs necessary for successful adaptation to climate change world-wide.
KW  - Bayesian network
KW  - Climate change
KW  - Risk assessment
KW  - Multi-model ensemble
KW  - Uncertainty
KW  - Participatory process
KW  - Roadmap
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/79043
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-790436
SN  - 1364-8152
VL  - 168
IS  - 105764
PB  - Elsevier
CY  - Amsterdam
ER  -