Articles | Volume 22, issue 9
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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (10 Feb 2018) by Fuqiang Tian
AR by Alessio Pugliese on behalf of the Authors (04 May 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (21 May 2018) by Fuqiang Tian
RR by William Farmer (22 May 2018)
RR by Anonymous Referee #3 (15 Jun 2018)
ED: Publish subject to minor revisions (review by editor) (28 Jun 2018) by Fuqiang Tian
AR by Alessio Pugliese on behalf of the Authors (03 Jul 2018)  Author's response    Manuscript
ED: Publish as is (13 Jul 2018) by Fuqiang Tian
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.