Abstract
Abstract. Flood prediction systems rely on good quality precipitation input data and forecasts to drive hydrological models. Most precipitation data comes from daily stations with a good spatial coverage. However, some flood events occur on sub-daily time scales and flood prediction systems could benefit from using models calibrated on the same time scale. This study compares precipitation data aggregated from hourly stations (HP) and data disaggregated from daily stations (DP) with 6-hourly forecasts from ECMWF over the time period 1 October 2006–31 December 2009. The HP and DP data sets were then used to calibrate two hydrological models, LISFLOOD-RR and HBV, and the latter was used in a flood case study. The HP scored better than the DP when evaluated against the forecast for lead times up to 4 days. However, this was not translated in the same way to the hydrological modelling, where the models gave similar scores for simulated runoff with the two datasets. The flood forecasting study showed that both datasets gave similar hit rates whereas the HP data set gave much smaller false alarm rates (FAR). This indicates that using sub-daily precipitation in the calibration and initiation of hydrological models can improve flood forecasting.
Highlights
Numerical Weather Prediction models (NWP) produce operational forecasts that can drive hydrological models to produce flood forecasts
The precipitation calculated from HP showed higher skill than the data calculated from the DP, even though the spatial resolution is better represented by DP data (Fig. 3)
The resolution of the European Centre for Medium Range Weather forecasting (ECMWF) forecast is much coarser than the network of daily stations, so the potential advantage of a better spatial resolution in the DP data did not yield a higher score than the HD (Fig. 3a)
Summary
Numerical Weather Prediction models (NWP) produce operational forecasts that can drive hydrological models to produce flood forecasts. Observed precipitation is often collected with a daily or 12 h resolution, typically at 09:00 or 21:00 UTC, and this creates a discrepancy when the models are run operationally with 6-hourly forecast data, which are issued at time intervals starting at 12:00 UTC (Fig. 2). In this study we take the opposite approach and evaluate the quality of the input precipitation using the forecast from the European Centre for Medium Range Weather forecasting (ECMWF) as benchmark. This second part of the paper studies whether the use of hourly data in the calibration and spin-up of two hydrological models improves their ability to forecast floods. One of the models is evaluated as a forecasting tool using HP and DP data sets for calibration and initialisation
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