Flood Forecasting for a Small Watershed: Case Study of Cypress Creek, Texas

An accurate prediction of flood timing and magnitude will improve the preparedness of the community, reducing potential damage and life loss. Quantitative Precipitation Forecast (QPF) is one of the primary inputs for flood forecasting framework, which uses a rainfall-runoff model to quantify streamflow, and then determine flood inundation by the hydraulics model. The result of the previous studies illustrates QPF datasets work well for medium to the large watershed. However, QPF datasets have limitations such as the forecasting bias and relatively coarse resolution compared to the small watershed area. This paper will explore the QPF datasets for flood forecasting in a relatively small watershed area and quantify the optimum forecasting time. This study assesses the quality of the Global Forecast System (GFS) and North America Mesoscale Model (NAM), therefore, compare the two QPFs with 4km NEXRAD stage IV. All QPF datasets will be implemented in hydrological simulation for short to medium-range forecast and be applied in the small watershed of Cypress Creek, Texas. The result from the studies, as mentioned above, will then be compared with stream gauge in the scenario of Hurricane Harvey event.