- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2190-5495
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Sensitivity analysis of rainfall–runoff parameters models to estimate flows ; volume:11 ; number:2 ; day:18 ; month:1 ; year:2021 ; pages:1-13 ; date:2.2021
Applied water science ; 11, Heft 2 (18.1.2021), 1-13, 2.2021
- Creator
-
Yogi, Fernando
Correa, Carina Julia Pensa
Arruda, Emerson Martins
Tonello, Kelly Cristina
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s13201-020-01348-3
- URN
-
urn:nbn:de:101:1-2021041706214910385585
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:48 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Yogi, Fernando
- Correa, Carina Julia Pensa
- Arruda, Emerson Martins
- Tonello, Kelly Cristina
- SpringerLink (Online service)
Other Objects (12)
The PDM rainfall-runoff model
Why our rainfall-runoff models keep underestimating the peak flows?
Rainfall-runoff modeling in arid areas
Why do our rainfall–runoff models keep underestimating the peak flows?
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns
CLASSIC: a semi-distributed rainfall-runoff modelling system
Towards a comprehensive physically-based rainfall-runoff model
Deep learning rainfall–runoff predictions of extreme events
Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia
Comparative evaluation of conceptual and physical rainfall–runoff models
The PDM rainfall-runoff model
Why our rainfall-runoff models keep underestimating the peak flows?
Rainfall-runoff modeling in arid areas
Why do our rainfall–runoff models keep underestimating the peak flows?
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns
CLASSIC: a semi-distributed rainfall-runoff modelling system
Towards a comprehensive physically-based rainfall-runoff model
Deep learning rainfall–runoff predictions of extreme events
Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia
Comparative evaluation of conceptual and physical rainfall–runoff models
The PDM rainfall-runoff model
Why our rainfall-runoff models keep underestimating the peak flows?
Rainfall-runoff modeling in arid areas
Why do our rainfall–runoff models keep underestimating the peak flows?
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns
CLASSIC: a semi-distributed rainfall-runoff modelling system
Towards a comprehensive physically-based rainfall-runoff model
Deep learning rainfall–runoff predictions of extreme events
Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia