Uncertainty Quantification in Future Projections of Flood-Producing Precipitation Events

The changing frequency, spatial distribution and severity of flooding are some of the most societally relevant impacts of climate change. Decision makers traditionally use static flood hazard areas that fail to account for the physics of flood-generating processes as well as the expected consequences of climate change. Further, despite its crucial importance for decision-making, uncertainty quantification (UQ) is largely absent or incomplete, leading to improper risk assessment. As part of the Office of Naval Research, Dept. of Defense, Multidisciplinary University Research Initiative (MURI), our team is investigating historical and future flooding at two different DoD installations: the U.S. Army Garrison Detroit Arsenal, and Fort Cavazos (formerly Hood) near Killeen, TX. Our team includes scientists in the Physical, Human and Decision Sciences. Our goal is to quantify current / future flooding challenges, understand and anticipate human behavior, and improve decision making with explicit UQ. Here we will describe the atmospheric sciences component, which blends weather forecasting and climate projections.

In this work, we incorporate UQ by generating and analyzing a large ensemble of potential future extreme precipitation fields while explicitly accounting for the physical mechanisms that generate flood-producing precipitation events. The proposed method combines: 1) detailed analysis of historical flood-generating meteorological conditions 2) analysis of CMIP6 GCM extreme precipitation intensity and frequency in historical and future simulations 3) large ensembles of WRF (Weather Research and Forecasting Model) simulations at the convection permitting (CP) kilometer scale and 4) pseudo-global warming CP simulations to evaluate the changes in these flood-producing storms in a future climate. This allows us to incorporate uncertainty associated with future greenhouse gas forcing and choice of GCM model as done in climate projections. In addition, the WRF-CP ensemble design incorporates different physics options, initial/lateral-boundary conditions, and stochastic processes like those used in operational weather forecasting. The result is a large ensemble of precipitation fields at the km-scale that can be used as input for the tRIBS (TIN-based Real-Time Integrated Basin Simulator) hydrological and hydraulic model.