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Discussion papers | Copyright
https://doi.org/10.5194/nhess-2018-118
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 May 2018

Research article | 02 May 2018

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

Ensemble flood forecasting considering dominant runoff processes: I. Setup and application to nested basins (Emme, Switzerland)

Manuel Antonetti1,2, Christoph Horat1,3, Ioannis V. Sideris4, and Massimiliano Zappa1 Manuel Antonetti et al.
  • 1Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
  • 2University of Zurich, Department of Geography, Zurich, Switzerland
  • 3ETH, Institute for Atmospheric and Climate Science, Zurich, Switzerland
  • 4MeteoSwiss, Swiss Federal Office of Meteorology and Climatology, Locarno, Switzerland

Abstract. Flash floods (FFs) evolve rapidly during and after heavy precipitation events and represent a risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available.

To address this gap, a FF forecasting chain is presented based on: (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip), (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E), (iii) operationally available soil moisture estimations from the PREVAH hydrological model, and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types (RTs), which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is then parametrised a priori based on the results of sprinkling experiments.

This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-size FF prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of RTs, which allowed sensitivity of the forecast performance on the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including a conventional hydrological model relying on calibration.

Results showed a low sensitivity of the predictive power on the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of RTs with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill as a prediction system including a conventional hydrological model. In the small nested subcatchments, the process-based forecasting chains outperformed the conventional system, however, no forecasting chain showed satisfying skill.

The outcomes of this study show that operational FF predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

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Short summary
To predict timing and magnitude peak runoff meteorological and calibrated hydrological models are commonly coupled. A flash floods forecasting chain is presented based on a process-based runoff generation module with no need for calibration. This chain has been evaluated using data for the Emme catchment (Switzerland). The outcomes of this study show that operational FF predictions in ungauged basins can benefit from the use of information on runoff processes. This paper has a companion paper.
To predict timing and magnitude peak runoff meteorological and calibrated hydrological models...
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