Severe and sudden floods have always led to irreparable damages, including the deaths of thousands and significant economic losses. Rapid flood zoning is one of the most important and fundamental aspects of crisis management and reducing flood-related damages. To mitigate risks, damages, and take timely action, it is essential to quickly identify areas prone to flooding and inundation and take necessary measures. In this study, a new user-friendly module of STE software was developed to solve shallow water equations more rapidly. By introducing and examining new grid discretization and subgrid variability methods, the process of flood zoning and simulation has been accelerated. To evaluate the proposed methods, flood modeling was conducted for the flooding event caused by Hurricane Harvey in 2017 in the vicinity of Hempstead city, Texas, USA, along a 22-kilometer stretch of the Brazos River. The comparison of results obtained from accurate and long-term flood modeling using shallow water equations with the rapid methods presented in the study demonstrates that subgrid variability methods can significantly reduce the modeling time for flood simulation from 1090 minutes to 17 to 36 minutes (a 97% reduction) while maintaining modeling accuracy. This is achieved with a flood zone accuracy of 94% and a root mean square error (RMSE) of 0.14 for flow depth modeling.