26 research outputs found
Streamflow forecast sensitivity to air temperature forecast calibration for 139 Norwegian catchments
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.</p