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Volume 22, issue 9 | Copyright
Hydrol. Earth Syst. Sci., 22, 4633-4648, 2018
https://doi.org/10.5194/hess-22-4633-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Sep 2018

Research article | 06 Sep 2018

A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations

Alessio Pugliese1, Simone Persiano1, Stefano Bagli2, Paolo Mazzoli2, Juraj Parajka3, Berit Arheimer4, René Capell4, Alberto Montanari1, Günter Blöschl3, and Attilio Castellarin1 Alessio Pugliese et al.
  • 1Department DICAM, University of Bologna, Bologna, Italy
  • 2GECOsistema srl, Cesena, Italy
  • 3Institute for Hydraulic and Water Resources Engineering, TU Wien, Vienna, Austria
  • 4Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden

Abstract. Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to  ≈ 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000km2).

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This research work focuses on the development of an innovative method for enhancing the predictive capability of macro-scale rainfall–runoff models by means of a geostatistical apporach. In our method, one can get enhanced streamflow simulations without any further model calibration. Indeed, this method is neither computational nor data-intensive and is implemented only using observed streamflow data and a GIS vector layer with catchment boundaries. Assessments are performed in the Tyrol region.
This research work focuses on the development of an innovative method for enhancing the...
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