Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors

By Benjamin Renard, Dmitri Kavetski, George A. Kuczera, Mark A. Thyer, S. Franks. 2010

825 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/56915b3831fc259492e8be559bcbe5446fd59ac4

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

In Water Science and Technology

Citations in the corpus

GLUE: 20 years on (2014, methodology): “This means that an (objective) likelihood function based on aleatory assumptions will overcondition the parameter inference (Beven et al., 2008; Beven and Smith, 2013) or inference about sources of uncertainty (e.g. Vrugt et al., 2008; Renard et al., 2010).”
Uncertainty in streamflow rating curves: methods, controls and consequences (2014, background): “Several recent papers have highlighted the need for, and critical role of, independent/ empirical data for hydrological model improvement (Silberstein, 2006; Fenicia et al., 2008; Krueger et al., 2010; Renard et al., 2010; Schertzer et al., 2010; McMillan et al., 2012).”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “Hydrological modeling is affected by four main sources of uncertainty related to input data, validation data, model structure, and model parameters (e.g., Renard et al., 2010).”
Pursuing the method of multiple working hypotheses for hydrological modeling (2011, methodology): “This is a manifestation of ‘‘nonidentifiability,’’ a key statistical inference limitation encountered in many scientific fields, especially in data-scarce contexts [e.g., Tarantola, 2005; Renard et al., 2010].”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, methodology): “…are also considered to be correct, one can make inferences about the joint distribution of the inputs and model parameters [Kavetski et al., 2006; Renard et al., 2010] or one can adjust the model states to compensate for input, initial conditions, and model structure errors via data assimilation…”
Hydrological uncertainty processor based on a copula function (2018, methodology): “These models accept hydrological input, meteorological input, etc., and utilize conceptualized model parameters; these complex factors inevitably cause uncertainties in the hydrological forecasts (Freer et al. 1996, Montanari 2007, Montanari and Grossi 2008, Renard et al. 2010, Chen et al. 2014a).”
Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study (2018, background): “Differences in the quality and trustworthiness of the historical discharge observations (e.g. due to sampling, measurement and interpretation errors), may potentially result in artificial biases in the validation results (Renard et al 2010).”
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010, methodology): “[49] Our approach also deviates from other studies that have focused on separating the various error contributions that make up the model residuals, including measurement, model input, and model structural errors [e.g., Kuczera et al., 2006; Reichert and Mieleitner, 2009; Renard et al., 2010].”
A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model (2014, background): “…further compounded by overparameterization problems as today's models are getting increasingly complex in a trend to include more and more sub-physics, but the calibration of these models is still done with rather limited data (Jakeman and Hornberger, 1993; Renard et al., 2010; Clark et al., 2011).”