Quantifying hydrological modeling errors through a mixture of normal distributions

By B. Schaefli, D. Talamba, André Musy. 2007

121 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/94ffccdf4685864998296813b43efaf5936b244b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Rajeshwar Mehrotra
2. Hong Yang
3. Jing Yang
4. Peter Reichert
5. Jasper A. Vrugt
6. G. Schoups
7. K. C. Abbaspour
8. Junqiang Xia

In Global and Planetary Change

1. Jasper A. Vrugt
2. Rajeshwar Mehrotra
3. András Bárdossy
4. G. Schoups
5. Ashish Sharma
6. Mojtaba Sadegh
7. Scott Anthony Sisson
8. Jing Yang

In Water Science and Technology

1. Jasper A. Vrugt
2. G. Schoups
3. Hong Yang
4. Jing Yang
5. Peter Reichert
6. Rajeshwar Mehrotra
7. K. C. Abbaspour
8. Junqiang Xia

Citations in the corpus

• Uncertainty in streamflow rating curves: methods, controls and consequences (2014, background): “Many studies assume that model errors follow a normal distribution, yet rarely is this verified or empirically defined (Ajami et al., 2007; Schaefli et al., 2007; Huard andMailhot, 2008).”
• A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010, methodology): “, Bates and Campbell, 2001; Schaefli et al., 2007; Yang et al., 2007]. Inference with an additive Laplacian error model amounts to median or L1 regression, which has long been advocated for its robustness against outliers [Koenker and Bassett, 1978]. [48] The modeling and inference approach presente”
• Generic error model for calibration and uncertainty estimation of hydrological models (2008, background): “While one can assume that the errors on successive days are independent for the meteorological variables this assumption does not hold true for the errors due to the inevitable simplifying process descriptions and effective model parameters, qi, [Kuczera et al., 2006; Schaefli et al., 2007].”
• Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments (2010, methodology): “More recently, Schaefli et al. [2007] attempted to address the nonconstant variance problem through the use of a mixture likelihood corresponding to high and low flow states.”
• Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “Representatives of this class are autoregressive error models as used, e.g., by Sorooshian and Dracup (1980), Kuczera (1983), Duan et al. (1988), Bates and Campbell (2001), Yang et al. (2007a), and Schaefli et al. (2007).”