Riverine floods pose a considerable risk to many communities. Improving flood
hazard projections has the potential to inform the design and implementation of
flood risk management strategies. Current flood hazard projections are
uncertain, especially due to uncertain model parameters. Calibration methods
use observations to quantify model parameter uncertainty. With limited
computational resources, researchers typically calibrate models using either
relatively few expensive model runs at high spatial resolutions or many cheaper
runs at lower spatial resolutions. This leads to an open question: Is it
possible to effectively combine information from the high and low resolution
model runs? We propose a Bayesian emulation-calibration approach that
assimilates model outputs and observations at multiple resolutions. As a case
study for a riverine community in Pennsylvania, we demonstrate our approach
using the LISFLOOD-FP flood hazard model. The multiresolution approach results
in improved parameter inference over the single resolution approach in multiple
scenarios. Results vary based on the parameter values and the number of
available models runs. Our method is general and can be used to calibrate other
high dimensional computer models to improve projections