Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework

By Yuqiong Liu, Hoshin V. Gupta. 2007

802 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/25c82f4c8647bca725cc37e7c8e7293af725d2bc

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “In this study, we are concerned with the modeling uncertainty and predictive uncertainty (Liu and Gupta, 2007).”
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data (2009, background): “117 In generating analyses and predictions of C dynamics with confidence intervals, resulting 118 error can be attributed to a combination of factors (Liu and Gupta 2007).”
A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, background): “In the literature of surface water hydrology, other approaches consider all these uncertainties in a unified framework (see overviews and discussions in [15,59,91], amongst others).”
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? (2009, methodology): “…is admirably suited for systematically addressing and quantifying the various error sources within a single cohesive, integrated, and hierarchical manner (Kuczera and Parent 1998; Bates and Campbell 2001; Engeland and Gottschalk 2002; Vrugt et al. 2003; Marshall et al. 2004; Liu and Gupta 2007).”
A hybrid support vector regression framework for streamflow forecast (2019, background): “However, in any streamflow forecast model, there are three types of uncertainty caused by a number of factors: input uncertainty, model structure uncertainty and parameter uncertainty (Liu and Gupta, 2007).”
Evaluation of a nonparametric post‐processor for bias correction and uncertainty estimation of hydrologic predictions (2013, background): “INTRODUCTION Ensemble prediction systems (EPS) are increasingly used to quantify and propagate uncertainties through atmospheric and hydrologic models (Liu and Gupta, 2007; Cloke and Pappenberger, 2009;Matott et al., 2009).”
Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm (2011, methodology): “…reservoir modeling and petroleum reservoir modeling, the estimation of spatially distributed parameters and the quantification of the associated uncertainties are important topics for investigation [Zimmerman et al., 1998; Carrera et al., 2005; Liu and Gupta, 2007; HendricksFranssen et al., 2009].”
Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations (2015, methodology): “…appropriate UP method that fits for a specific problem should be selected based on the characteristics of the uncertainty sources, the required level of uncertainty quantification, accuracy and confidence level, as well as computational cost or efficiency (Liu and Gupta, 2007; Lee and Chen, 2009).”