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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-379
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency
Diana Lucatero1, Henrik Madsen2, Jens C. Refsgaard3, Jacob Kidmose3, and Karsten H. Jensen1 1Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
2DHI, Hørsholm, Denmark
3Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

Citation: Lucatero, D., Madsen, H., Refsgaard, J. C., Kidmose, J., and Jensen, K. H.: Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-379, in review, 2017.
Diana Lucatero et al.
Diana Lucatero et al.
Diana Lucatero et al.

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Short summary
The skill of an experimental streamflow forecast system in the Ahlergaard catchment, Denmark is analyzed. Inputs to generate the forecasts are taken from the ECMWF System 4 seasonal forecasting system and an ensemble of observations (ESP). Reduction of biases is made by processing the meteorological and/or streamflow forecasts. In general, the reduction of biases is not enough to ensure a higher level of accuracy than the ESP, indicating a modest added value of a seasonal meteorological system.
The skill of an experimental streamflow forecast system in the Ahlergaard catchment, Denmark is...
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