Preprocessing intense precipitation forecasts to improve flood predictability for small and quick responding catchments

Small catchments have a quick flood response subject to intense precipitation. Previous studies show a lack of predictability for rain induced floods in these catchments. The aims of this study are to (i) apply processing techniques that focus on improving high to extreme precipitation forecasts, and (ii) evaluate how and if the spatial distribution of precipitation within a catchment affects the flood forecast, and the ultimate effect on flood predictability. Even though preprocessing precipitation has shown to improve flood prognosis, high (intense) precipitation values are still difficult to forecast correctly. In this study, we use precipitation forecasts from the regional weather forecasting model AROME-MetCoOp (MEPS, a 30-member lagged ensemble with a grid resolution of 2.5 km) and the global European Center of Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF HRes and ENS, grid resolution of ~8km and for the ensemble ~16 km). MEPS serves as the reference forecast and is used in the operational flood forecasting system in Norway. For the ECMWF HRes and ENS we will apply techniques focusing on the high precipitation values. We will use Bayesian Model Averaging and apply a sampling approach that ensures that the tail of the posterior distribution is represented. We will also use a quantile regression method that employs an extreme value distribution in the tail. To assess the streamflow forecasts from all ensemble forecasts, a gridded HBV model run at a 3 hourly temporal resolution is used. 

The performance of flood forecasts for the different preprocessing approaches for the precipitation ensemble forecasts will be evaluated. For intense precipitation events the spatial distribution of precipitation within a catchment will be evaluated with an emphasis on the ultimate effect on estimating flood peaks for small and quick responding catchments.