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Discussion papers
https://doi.org/10.5194/hess-2018-373
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-373
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 31 Jul 2018

Research article | 31 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Streamflow forecast sensitivity to air temperature forecast calibration for 139 Norwegian catchments

Trine J. Hegdahl1,2, Kolbjørn Engeland1,2, Ingelin Steinsland3, and Lena M. Tallaksen2 Trine J. Hegdahl et al.
  • 1Norwegian Water Resources and Energy Directorate, Hydrological Modelling, 0301 Oslo, Norway
  • 2University of Oslo, Department of Geosciences, 0316 Oslo, Norway
  • 3Norwegian University of Science and Technology, Department of Mathematical Sciences, 7034 Trondheim, Norway

Abstract. The Norwegian flood forecasting system is based on a flood forecasting model running on catchments located all across Norway. The system relies on deterministic meteorological forecasts and uses an auto-regressive post-processing algorithm to achieve probabilistic streamflow forecasts and thus a measure of uncertainty. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. In catchments with seasonal snow cover, snowmelt is an important flood generating process. Hence, high quality air temperature data are important for accurate forecasting of streamflow. In this study, the sensitivity of hydrological ensemble forecasts to the calibration of temperature ensemble forecasts was investigated. Ensemble forecasts of temperature from ECMWF covering a period of nearly three years, from 01.03.2013 to 31.12.2015, were used. To improve skill and reduce bias of the temperature ensembles, the Norwegian Meteorological Institute provided parameters for ensemble calibration. The calibration parameters are derived using a standard quantile mapping method. Estimated observed daily temperature and precipitation were obtained from the SeNorge-dataset, which is station data interpolated to a 1×1km2 grid covering all of Norway. The operational flood-forecasting model, a lumped HBV model distributed on 10 elevation zones, was used to calculate streamflow.

The results show that temperature ensemble calibration influenced 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 influenced by sub-zero temperature improvements, which accounted for the biggest temperature biases and corrections during autumn and winter. The skill differs between regions, which could partly be related to elevation differences and catchment area. It is evident, however, that temperature forecasts are important for streamflow forecasts in climates with seasonal snow cover. This indicates that further studies are needed, specifically addressing catchment specific calibration methods, for improved air temperature forecasts.

Trine J. Hegdahl et al.
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Short summary
Flood forecasting relies on high quality meteorological data. This study shows how improved temperature forecasts improve streamflow forecasts in most cases, with the degree of improvement depending on season and region. To improve temperature forecasts further, catchment specific methods should be developed to account for these seasonal and regional differences. In short, for climates with a seasonal snow cover, higher quality temperature forecasts clearly improve flood forecasts.
Flood forecasting relies on high quality meteorological data. This study shows how improved...
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