Beyond the 100-Year Flood: Probabilistic Flood Hazard Assessment for King and Pierce Counties under Future Climate Scenarios
Abstract. Coastal areas, such as the Salish Sea, are becoming increasingly vulnerable to compound flooding due to the interaction between storm surge, tides, and river outflow. This hazard is anticipated to increase under sealevel rise and climate change. This research offers a high-resolution flood hazard mapping for King and Pierce Counties of Washington State (United States of America) using the SFINCS (Super-Fast INundation of CoastS) model to facilitate a Continuous Flood Response Modeling (CFRM) framework wherein decades of dynamic coastal and fluvial processes are simulated. By applying a cell-by-cell extreme value analysis, we predict flood areas for return periods of 1–100 years and compute the Expected Annual Flooded Area (EAFA) as a probability-weighted indicator of flood exposure. Model validation against National Oceanic and Atmospheric Administration (NOAA) and United States Geological Survey (USGS) gauge data demonstrates skill (RMSE: 14–17 cm for coastal water levels; unbiased RMSE: 49–116 cm for river water levels), and comparison with FEMA Special Flood Hazard Areas shows high spatial agreement of flooding (hit rates: 0.75–0.83). The timing statistics of the flooding reveal that the December 28, 2022, event was responsible for most historically observed flooding across the area. Climate simulations for today show EAFA ranges from 56 to 200 hectares in King County and from 250 to 644 hectares in Pierce County. Future projections show that sea level rise is the main contributor to increasing flood extent, whereas climate change drivers such as storm pattern change have little additional effect. We also identified a threshold around 100–150 cm of sea level rise at which the flood-exposed area increases substantially. Additionally, simplified deterministic flood maps can underestimate flood hazard by up to 0.5 m if not all relevant drivers are included. These results support the use of probabilistic, event-independent flood metrics such as EAFA to inform more rational and spatially responsive flood risk management.
This cell by cell extreme value analysis is presented as novel, but it has been carried out by other, similar studies as well. Therefore, the authors need to clarify what the novelty of their manuscript is, in light of what has been done before. It might be that the novelty is the specific model train used, or the location of application, or a combination of these. Either way, please clarify the novelty.
For example of other applications of the cell-by-cell EVA:
Deb, M., Sun, N., Yang, Z., Wang, T., Judi, D., Cooper, M. G., & Wigmosta, M. S. (2025). Extreme flood return levels in a US mid-Atlantic estuary using 40-year fluvial-coastal model simulations. Scientific Data, 12(1), 1459.
Son, S., Xu, C., Davlasheridze, M., Ross, A. D., & Bricker, J. D. (2025). Effectiveness of the Ike Dike in mitigating coastal flood risk under multiple climate and sea level rise projections. Risk Analysis.
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C., & Ward, P. J. (2016). A global reanalysis of storm surges and extreme sea levels. Nature communications, 7(1), 11969.
The introduction is somewhat long. Much space is spent describing the transition from event-based to probabilistic hazard analysis, but this is already quite common knowledge in the community, so this section can be shortened.
Fig 1. The label "bathymetry" is confusing, because this cover topography over land. Perhaps just call it "elevation". Also, the lable of Weirs should be explained more in the caption, as these are not apparent on the map.
Line 216. You mention roughness values but don't specify which roughness parameterization. Darcy roughness length? Chezy? Hazen Williams? I assume this is Manning, but you need to specify this.
Line 230. There is a variation of assumed fraction of wave height for wave setup, typically from 5% to 20%. You choose 20%, which is OK, but should be tested via a sensitivity analysis. For example of the 5% assumption see
Feng X, Yin B, Yang D, William P (2011) The effect of wave-induced radiation stress on storm surge during Typhoon Saomai (2006). Acta Oceanol Sin 30(3):20–26. https://doi.org/10.1007/s13131-011-0115-6
Yamanaka Y, Shibata R, Tajima Y, Okami N (2020) Inundation Characteristics in Arida City Due to Overtopping Waves Induced by 2018 Typhoon Jebi. APAC 2019. Springer Singapore, Singapore., pp 199–206Return to ref 2020 in article
Line 330. Why did you randomize tidal phase instead of applying the actual phase during the reanalysis simulation? Also, what reanalysis dataset did you use?
In your CMIP-driven simulations, how did you determine the upstream river discharges? Did you couple these with a hydrological model for each river watershed?
How does the PGW approach deal with shifts in frequency of events due to climate change, if it is based on multiplying the historical time series by a factor?
Section 3.6 model skill. What are the data being compared here? Historical data vs. present climate simulated data?
Fig 5 is confusing. It needs a larger-scale locator map, and also needs more visible borders between each subplot.
Fig 6 also needs a locator map and better boundaries. Also, what is the source for the extent of each reported flood event? How were these areas determined?
Since you used 100 years of synthetic analysis instead of statistical distributions, do you have PDFs or CDFs of the resulting 100 years of water level or depth data for some of the cells you analyzed? It would be informative to compare these to what would come from fitting standard statistical distributions, to help determine which would be a better predictor in the practice, moving forward.
And please clarify. IS SFINCS used in a wave-phase-resolving way, so as to quantify wave runup and/or overtopping? Or is it only still water level that is being assessed?