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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-108
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-108
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 09 Jul 2018

Model description paper | 09 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

The probabilistic hydrological model MARCS (MARkov Chain System): the theoretical basis for the core version 0.2

Elena Shevnina1 and Andrey Silaev2 Elena Shevnina and Andrey Silaev
  • 1Finnish Meteorological Institute, Helsinki, FI-00560, Finland
  • 2National Research University Higher School of Economics, Nizhny Novgorod, 603155, Russia

Abstract. A question of environmental risks of social and economic infrastructure has become apparent recently due to an increase in the number of extreme weather events. Extreme runoff events include floods and droughts. In water engineering extreme runoff is described in terms of probability, and uses methods of frequency analysis to evaluate an exceedance probability curve (EPC) of runoff. It is assumed that historical observations of runoff are representative for the future; however trends in observed time series doubt this assumption. The paper describes an Advance Frequency Analysis (AFA) approach to be applied to predict future extreme runoff. The approach combines traditional methods of hydrological modelling and frequency analysis, and results in a probabilistic hydrological model markov Chain System (marcs). The MARCS model simulates statistical estimators of a multi-year runoff to perform future runoff projections in probabilistic form. Projected statistics of meteorological variables available in climate scenarios force the MARCS model. This study introduces a new model core (version 0.2), and provides an user guide as well as an example of the model set up for a single case study. In this case study, the model simulates projected exceedance probability curves of annual runoff under three climate scenarios. The scope of applicability and limitations of the model are discussed.

Elena Shevnina and Andrey Silaev
Interactive discussion
Status: open (extended)
Status: open (extended)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Elena Shevnina and Andrey Silaev
Data sets

The probabilistic hydrological model MARCS (MARkov Chain System): the core code (Version 1.0) E. Shevnina and A. Krasikov https://doi.org/10.5281/zenodo.1220096

Model code and software

The probabilistic hydrological model MARCS (MARkov Chain System): the core code (Version 1.0) E. Shevnina and A. Krasikov https://doi.org/10.5281/zenodo.1220096

Elena Shevnina and Andrey Silaev
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Latest update: 13 Dec 2018
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Short summary
The paper provides a theory and assumptions behind an Advance of Frequency Analysis (AFA) approach in a long term hydrological forecasting. In this paper, a new core of the probabilistic hydrological model Markov Chain System (MARCS) was introduced together with the code and example of a long term runoff forecasting on a catchment scale. The low computational cost of a hydrological forecast (in form of probability density function) is among features of the MARCS model.
The paper provides a theory and assumptions behind an Advance of Frequency Analysis (AFA)...
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