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Subseasonal hydrometeorological ensemble predictions in small-and medium-size mountainous catchments: Benefits of the NWP approach
Type of publication
Peer-reviewed
Publikationsform
Original article (peer-reviewed)
Author
Monhart Samuel, Zappa Massimiliano, Spirig Christoph, Schär Christoph, Bogner Konrad,
Project
HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues
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Original article (peer-reviewed)
Journal
Hydrology and Earth System Sciences Discussions
Page(s)
1 - 32
Title of proceedings
Hydrology and Earth System Sciences Discussions
DOI
10.5194/hess-2018-458
Open Access
URL
http://doi.org/10.5194/hess-2018-458
Type of Open Access
Publisher (Gold Open Access)
Abstract
Abstract. Traditional Ensemble Streamflow Prediction systems (ESP) are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Prior of using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to account to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modeling steps remains open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions with areas between 80 and 1700 km 2 . Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP based approach can provide superior prediction than the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow related processes for subseasonal streamflow predictions in mountainous regions.
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