Articles | Volume 22, issue 9
https://doi.org/10.5194/hess-22-4633-2018
https://doi.org/10.5194/hess-22-4633-2018
Research article
 | 
06 Sep 2018
Research article |  | 06 Sep 2018

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

Alessio Pugliese, Simone Persiano, Stefano Bagli, Paolo Mazzoli, Juraj Parajka, Berit Arheimer, René Capell, Alberto Montanari, Günter Blöschl, and Attilio Castellarin

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Latest update: 21 Jun 2024
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Short summary
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.