Adaptive risk-informed decision making for flood management and water resource planning under climate change uncertainty

Uncertainty on future climate scenarios leads to increased uncertainty in the planning of flood protection, water resources and related measures. Since decisions on water infrastructure are long-term decisions (e.g. structural flood protection measures are designed for a service life time of 100 years, and land use planning has even a longer time horizon), these uncertainties must be addressed in the decision making process. Based on existing climate projection scenarios and hydrological models, the aim of this project is

  1. a better understanding of the uncertainty associated with extreme events under climate change and
  2. development of methods for identification of optimal watershed management strategies under these uncertainties.

Because uncertainties on future climate and vulnerability will decrease as more information becomes available, adaptive decision strategies can be beneficial. Such adaptive strategies include the staggered construction of infrastructure, flexible temporary and operational measures, but also the increased monitoring of climate and watershed behavior. Using the "value of information" concept, such adaptive strategies can be evaluated against more classical fixed measures. In contrast to previous projects, this comparison is performed fully quantitatively, based on rigorous mathematical modeling of the uncertainties and Bayesian decision analysis for assessing the optimality of risk mitigation strategies. The project focuses primarily on the flood risk management. Planning of water resources and hydropower will also be considered, as they are tightly interrelated with each other, but on a less detailed level. While the project is a basic research project, it is expected that the insights gained in this project will influence the future planning of flood mitigation measures in practice. Close collaboration with practice (agencies and private consultancies) shall ensure this.


Investor: Deutsche Forschungsgemeinschaft (DFG)
Project start: April 2014
Project end: April 2017
Project management: Olga Špačková
Project assistence: Maria Kaiser, Beatrice Dittes
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