Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity
By Guillaume Évin, Mark A. Thyer, Dmitri Kavetski, David McInerney, George A. Kuczera. 2014
156 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/80698a7c7856867cc7c73c3e6cefa09d9685371c
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
- Dmitri Kavetski
- George A. Kuczera
- Mark A. Thyer
- Jasper A. Vrugt
- David McInerney
- Julien Lerat
- M. Clark
- Keith Beven
In Water Science and Technology
- Dmitri Kavetski
- George A. Kuczera
- Mark A. Thyer
- Jasper A. Vrugt
- David McInerney
- Julien Lerat
- M. Clark
- Keith Beven
Citations in the corpus
- Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors (2017, methodology): “Fat-tailed (leptokurtic) residual errors are often reported in hydrological modeling [e.g., Schoups and Vrugt, 2010; Evin et al., 2014].”