Updating real-time flood forecasts via the dynamic system response curve method

The accuracy of flood forecasts generated using spatially lumped hydrological models can be severely affected by errors in the estimates of areal mean rainfall. The quality of the latter depends both on the size and type of errors in point-based rainfall measurements, and on the density and spatial arrangement of rain gauges in the basin. Here we use error feedback correction, based on the dynamic system response curve (DSRC) method, to compute updated estimates of the rainfall inputs. The method is evaluated via synthetic and real-data cases, showing that the method works as theoretically expected. The ability of the method to improve the accuracy of real-time flood forecasts is then demonstrated using 20 basins of different sizes and having different rain gauge densities. We find that the degree of forecast improvement is more significant for larger basins and for basins with lower rain gauge density. The method is relatively simple to apply and can improve the accuracy and stability of real-time model predictions without increasing either model complexity and/or the number of model parameters. Key Points: Presents a method for improving streamflow forecasts by correcting rainfall estimates Testing shows improvement over direct real-time error-feedback correction of streamflow The method can be applied without any modifications to the hydrological model