Process-based hydrological modelling: The potential of a bottom-up approach for runoff predictions in ungauged catchments-Reference-Cited by-同舟云学术

Process-based hydrological modelling: The potential of a bottom-up approach for runoff predictions in ungauged catchments

Published:2017-06-28 Issue:16 Volume:31 Page:2902-2920
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrol. Process.

Author:

Antonetti Manuel¹²^ORCID,Scherrer Simon³,Kienzler Peter Matthias³,Margreth Michael⁴,Zappa Massimiliano¹

Affiliation:

1. Swiss Federal Research Institute WSL; Zürcherstrasse 111 8903 Birmensdorf Switzerland

2. Department of Geography; University of Zurich; Winterthurerstrasse 190 8057 Zurich Switzerland

3. Scherrer AG; Schönmattstrasse 8 4153 Reinach Switzerland

4. SoilCom GmbH; Probusweg 22 8057 Zurich Switzerland

Funder

Swiss Federal Office for the Environment (FOEN)

Publisher

Wiley

Subject

Water Science and Technology

Reference94 articles.

1. Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments;Addor;Water Resources Research,2014

2. Mapping dominant runoff processes: An evaluation of different approaches using similarity measures and synthetic runoff simulations;Antonetti;Hydrology and Earth System Sciences,2016

3. Überprüfung eines prozessnahen Abflussbildungsmoduls auf der Hangskale und in klein- und mesoskaligen Gebieten;Antonetti;In Forum Für Hydrologie Und Wasserbewirtschaftung,2016

4. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation;Bahremand;Hydrology and Earth System Science,2016

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