Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations
By Florian Pappenberger, Keith Beven, Matthew S. Horritt, S. Blazková. 2005
530 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/76e5bb19f6d6735fd17d93222428b97ae9dfb2e5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- Jasper A. Vrugt
- Keith Beven
- A. Binley
- Bruce A. Robinson
- Bryan Tolson
- Christine Annette Shoemaker
- D. Rosbjerg
- Roberta-Serena Blasone
In Water Science and Technology
- Jasper A. Vrugt
- A. Binley
- Bryan Tolson
- Keith Beven
- Bruce A. Robinson
- Christine Annette Shoemaker
- Hoshin V. Gupta
- D. Rosbjerg
Citations in the corpus
- Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “To compensate for this drawback, the LHS method typically requires many thousands of model simulations to result in a statistically sufficient number of behavioral parameter sets to draw inferences from (Pappenberger et al., 2005; Montanari, 2005).”
- Influence of uncertain boundary conditions and model structure on flood inundation predictions. (2006, methodology): “[42–46]).”
- GLUE: 20 years on (2014, methodology): “There had also been earlier forms of this approach within GLUE based on fuzzy measures (for example, in Blazkova and Beven, 2004; Freer et al., 2004; Page et al., 2003, 2004, 2007; Pappenberger et al., 2005, 2007).”
- Efficient prediction uncertainty approximation in the calibration of environmental simulation models (2008, background): “For example, Pappenberger et al. [2005] assess the convergence of the cumulative distribution functions (CDFs) of various model predictions by comparing the CDFs constructed with an increasing number of behavioral samples.”
- A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer (2011, background): “There have been plenty of discussions on equifinality of model parameters in physically based hydrological modeling (Schulz et al., 1999; Beven and Freer, 2001; Pappenberger et al., 2005; Todni, 2007; Bardossy, 2007; Bardossy and Singh, 2008).”