Evaluation of a Synthetic Rainfall Model, P-CLIPER, for Use in Coastal Flood Modeling Abstract
With the projected increase in both tropical cyclone (TC) intensity and proportion of the global population living near the coast, adequate preparation to protect against TC flooding is in the economic interest of coastal cities worldwide. Numerical models that describe TC properties, e.g., storm surge and wind fields, are currently employed to simulate the component of flooding that results from seawater inundation of areas along the coast (i.e., saltwater flooding). However, without the inclusion of freshwater flooding, contributed by inland surface flow and direct precipitation, a total water level (TWL) system for TC flooding lacks a complete picture of the actual coastal flood levels. Working toward a true TWL system, this research investigates the efficacy of the simple and efficient parametric TC rainfall model P-CLIPER (PDF Precipitation-Climatology and Persistence) to provide historically representative TC rainfall to a TWL system. This research demonstrates the success of this novel use of P-CLIPER through calibration and validation to the Tar–Pamlico River and Neuse River coastal watershed in North Carolina. In particular, the comparison of hydrographs at observation stations shows that hydrologic model output forced with P-CLIPER matches that forced with radar-observed precipitation for both timing and peaks, with the proper parameter choices for P-CLIPER. Similarly with proper parameter selection, P-CLIPER captures the peak rate and spatial pattern of observed rainfall for Hurricane Isabel. Due to the model’s simplicity, this work also reveals that P-CLIPER can be used as a parametric rainfall model in ensemble simulations, which could lead toward improved floodplain mapping, emergency management decisions, and stormwater infrastructure planning.
Geoghegan, K. M., Fitzpatrick, P. J., Kolar, R. L., & Dresback, K. M. (2018). Evaluation of a Synthetic Rainfall Model, P-CLIPER, for Use in Coastal Flood Modeling. Natural Hazards. 92(1), 1-28. DOI:10.1007/s11069-018-3220-4.