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Water 2016, 8(4), 115; doi:10.3390/w8040115

Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland
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Academic Editor: Paolo Reggiani
Received: 16 December 2015 / Revised: 8 March 2016 / Accepted: 16 March 2016 / Published: 24 March 2016
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
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Abstract

Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of the phrase “post-processing” in meteorology and hydrology, in this paper, it is regarded as a method to correct model outputs (predictions) based on meteorological (1) observed input data, (2) deterministic forecasts (single time series) and (3) ensemble forecasts (multiple time series) and to derive predictive uncertainties. So far, the majority of the research has been related to floods, how to remove bias and improve the forecast accuracy and how to minimize dispersion errors. Given that global changes are driving climatic forces, there is an urgent need to improve the quality of low-flow predictions, as well, even in regions that are normally less prone to drought. For several catchments in Switzerland, different post-processing methods were tested with respect to low stream flow and flooding conditions. The complexity of the applied procedures ranged from simple AR processes to more complex methodologies combining wavelet transformations and Quantile Regression Neural Networks (QRNN) and included the derivation of predictive uncertainties. Furthermore, various verification methods were tested in order to quantify the possible improvements that could be gained by applying these post-processing procedures based on different stream flow conditions. Preliminary results indicate that there is no single best method, but with an increase of complexity, a significant improvement of the quality of the predictions can be achieved. View Full-Text
Keywords: error correction; forecasts; floods; droughts; wavelets; neural nets; quantile regression; predictive uncertainty error correction; forecasts; floods; droughts; wavelets; neural nets; quantile regression; predictive uncertainty
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bogner, K.; Liechti, K.; Zappa, M. Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods. Water 2016, 8, 115.

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