Leveraging Probabilistic High Resolution Model Guidance to Improve Flash Flood Forecasting Across Southern Utah

The Southern Utah backcountry continues to draw an increasing number of outdoor recreationalists, for example annual visitation to Zion National Park has nearly doubled in the last 10 years from 2.8 million to about 5 million. This results in increased exposure to flash flood risk in remote areas where there is little to no means for receiving Flash Flood Warnings. To address this problem, the National Weather Service Forecast Office in Salt Lake City has worked closely with land management agencies, including the National Park Service, to develop a Flash Flood Potential Rating which extends out to 2 days. This product is designed to give advance notice to those with plans to recreate in the backcountry on days with a greater potential for flash flooding. Providing this risk information at longer lead times than Flash Flood Warnings gives visitors and our partners time to better prepare or alter plans ahead of time. Partners at our National Parks and recreation areas increasingly rely on this forecast to inform and educate visitors of this risk, which includes prominent signage featuring this forecast at the entrance to popular hikes and printable forecasts at visitor centers. Given the important role of this forecast, it is vital to ensure we are providing the most reliable and scientifically robust information possible.

Historically this Flash Flood Potential Product has focused on environments favorable for flash flooding, including precipitable water and storm motion. Additionally, recent local research has focused on a locally developed Random Forest machine learning algorithm utilizing forecast thermodynamic and kinematic parameters from the 3km NAM to identify favorable days for flash flooding across southern Utah. This algorithm has shown some skill in identifying flash flood days across Southern Utah, however its utility in determining specific threat areas is limited based on the fact it relies on environmental parameters from a single deterministic model which does not allow it to focus on small scale basins, nor are there enough cases per small basin to properly train a Random Forest Model.

This study approaches the problem from a different probabilistic angle by leveraging high resolution output from Convective Allowing Models, in an attempt to better identify specific threat areas on a given day. QPF neighborhood probability of exceedance values from both the HREF and SREF are being archived for the 2023 Monsoon Season, and will be analyzed to determine whether these forecasts are skillful in identifying potential flash flood days across small flash flood prone basins across southern Utah. Using flash flood guidance for small scale basins across southern Utah, probability of exceedance values for these basins will be used to generate a probability for flash flooding on a given day using the HRRR, SREF, a combination of HRRR and SREF for the 2023 monsoon season. These results will be used to determine whether utilizing these probability of exceedance forecasts exhibits a skillful improvement in differentiating the flood potential in small basins over the random forest algorithm, and the official Flash Flood Potential forecast issued by the NWS Salt Lake City. The end goal is to develop a robust methodology utilizing high resolution model output to forecast flash flood potential within flash flood prone small scale basins across southern Utah.