Research projects

IntelSamp: Intelligent parameter sampling

Among different uncertainty sources, the parameter uncertainty is most often considered in environmental modeling. This parameter uncertainty is usually estimated by means of resampling from the possible parameter space and the resulting uncertainty estimates are communicated in terms of ensembles.

This project aims at developing an innovative method to select a representative sample of ensemble members and model parameters to be used within a complex model chain (i.e., with numerous input or future scenarios). In this way, computational and time requirements of estimating uncertainty ensembles should be optimized.

Related publications:

Sikorska-Senoner, A.E., Schaefli, B., and Seibert, J. (2020) Downsizing parameter ensembles for simulation of rare floods, Nat. Hazards Earth Syst. Sci., https://doi.org/10.5194/nhess-2020-79, accepted.

Sikorska-Senoner, A.E., Schaefli, B., and Seibert, J. (2020) Navigating through extreme flood simulations with intelligently chosen parameter sets, EGU General Assembly 2020, Online, 4–8 May 2020, https://doi.org/10.5194/egusphere-egu2020-10348.