Identification of Combined Hydrological Models and Numerical Weather Predictions for Enhanced Flood Forecasting in a Semiurban Watershed

Abstract

Flood forecasting in urban and semiurban catchments is often limited by the capability of the combined hydrological models and forecast inputs to predict floods accurately. The objective of this research is to develop an approach (1) to identify the best model forecast from multiple integrations of various hydrological models and numerical weather predictions (NWP), and (2) to find the best forecast combination method for an improved short-range flood forecasting. Seven selected hydrological models were coupled, each with two high-resolution NWP forecasts to provide several alternatives of deterministic hydrological forecasts at a catchment outlet. As such, the different model-input combinations were used to generate 14 hydrological forecasts. Hydrological forecast verification was then carried out over a one-year hindcast period. A comparison between six forecast combination methods, including a benchmark Bayesian model averaging (BMA) method, was also performed for the multiple available short-term streamflow forecasts. Results indicate that the coupling of the Sacramento soil moisture accounting (SACSMA) model with both High-Resolution Deterministic Precipitation System and High-Resolution Rapid Refresh inputs outperformed other model-input integrations. Maximum forecast errors in all model-input integration outputs occurred at forecast lead times of 12–14 h, corresponding to the time of concentration of the catchment. Providing constraints on the estimation of model weights was found to be a significant factor for obtaining an improved combined streamflow forecast. In general, the regression-based forecast combination method of the constrained ordinary least squares (CLS) has emerged as a possible alternative to the widely used BMA method for hydrology application.