Abstract
Abstract. In this study, we used meteorological ensemble forecasts as input to hydrological models to quantify the uncertainty in forecasted streamflow, with a particular focus on the effect of temperature forecast calibration on the streamflow ensemble forecast skill. In catchments with seasonal snow cover, snowmelt is an important flood-generating process. Hence, high-quality air temperature data are important to accurately forecast streamflows. The sensitivity of streamflow ensemble forecasts to the calibration of temperature ensemble forecasts was investigated using ensemble forecasts of temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) covering a period of nearly 3 years, from 1 March 2013 to 31 December 2015. To improve the skill and reduce biases of the temperature ensembles, the Norwegian Meteorological Institute (MET Norway) provided parameters for ensemble calibration, derived using a standard quantile mapping method where HIRLAM, a high-resolution regional weather prediction model, was used as reference. A lumped HBV (Hydrologiska Byråns Vattenbalansavdelning) model, distributed on 10 elevation zones, was used to estimate the streamflow. The results show that temperature ensemble calibration affected both temperature and streamflow forecast skill, but differently depending on season and region. We found a close to 1:1 relationship between temperature and streamflow skill change for the spring season, whereas for autumn and winter large temperature skill improvements were not reflected in the streamflow forecasts to the same degree. This can be explained by streamflow being less affected by subzero temperature improvements, which accounted for the biggest temperature biases and corrections during autumn and winter. The skill differs between regions. In particular, there is a cold bias in the forecasted temperature during autumn and winter along the coast, enabling a large improvement by calibration. The forecast skill was partly related to elevation differences and catchment area. Overall, it is evident that temperature forecasts are important for streamflow forecasts in climates with seasonal snow cover.
Highlights
Floods can severely damage infrastructure, buildings, and farmland, and can have high economic impacts on society (Dobrovicová et al, 2015)
To reduce the amount of presented results, the remaining part of this paper focuses on CRPSS for a lead time of 5 days
Box plots of validation scores for all catchments and lead times in Fig. 3 show that, on average, both raw Tens and calibrated the calibrated (Tcal) temperature ensembles were more skillful with a higher CRPSS, for shorter as compared to longer lead times, and that Tcal was more skillful than Tens
Summary
Floods can severely damage infrastructure, buildings, and farmland, and can have high economic impacts on society (Dobrovicová et al, 2015). The Norwegian flood-forecasting system, operated by the Norwegian Water Resources and Energy Directorate (NVE), uses deterministic forecasts of air temperature and precipitation as forcing for hydrological models in 145 catchments across the country. Hegdahl et al.: Streamflow forecast sensitivity to air temperature forecast calibration (day 3 to 9). The Hydrologiska Byråns Vattenbalansavdelning model (HBV) (Bergström, 1976; Sælthun, 1996; Beldring, 2008) is used as the hydrological forecasting model, which combined with statistical uncertainty models (Langsrud et al, 1998, 1999) provides probabilistic streamflow forecasts. The uncertainty model accounts for the strong autocorrelation in forecast errors and estimates an uncertainty band around the deterministic temperature, precipitation, and streamflow forecasts
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