Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

By Hamid Moradkhani, K. Hsu, Hoshin V. Gupta, Soroosh Sorooshian. 2005

746 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/acedeb162d0a1e7af8767127470b48b0425b57a1

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In Environmental Engineering

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Citations in the corpus

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “…al., 1991; van Straten and Keesman, 1991; Vrugt et al., 2001), multiple criteria (Gupta et al., 1998; Yapo et al., 1998; Boyle et al., 2000, Madsen, 2000; Madsen, 2003; Vrugt et al., 2003b) and sequential data assimilation methods (Madsen et al., 2003; Vrugt et al., 2005; Moradkhani et al., 2005).”
Obstacles to High-Dimensional Particle Filtering (2008, methodology): “Some support for this claim comes from the results of Nakano et al. (2007), who apply the particle filter with a variety of ensemble sizes to the fully nonlinear, 40-variable model of Lorenz (1996).”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “Recently, stochastic data assimilation methods have been developed and applied to parameter estimation problems [e.g., Thiemann et al., 2001; Moradkhani et al., 2005a, 2005b].”
Reconciling theory with observations: elements of a diagnostic approach to model evaluation (2008, methodology): “The literature includes applications of the Kalman Filter (Kalman, 1960), Ensemble Kalman Filter (Evensen, 1994), and various Bayesian approaches such as particle filtering (Gordon et al., 1993; Moradkhani et al., 2005), Bayesian recursive estimation (Thiemann et al.”
Ensemble‐based assimilation of discharge into rainfall‐runoff models: A comparison of approaches to mapping observational information to state space (2009, methodology): “Examples of the use of the PF to update the states of hydrologic models using discharge data are given by Moradkhani et al. [2005] and Weerts and El Serafy [2006].”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…et al., 2002; Wagener et al., 2003; Beven, 2006; Vrugt and Robinson, 2007), emerging approaches include state-space ﬁltering (Vrugt et al., 2005; Moradkhani et al., 2005a, b; Slater and Clark, 2006; Reichle, 2008; Salamon and Feyen, 2009; DeChant and Moradkhani, 2012; Vrugt et al., 2013),…”
Physically based modeling in catchment hydrology at 50: Survey and outlook (2015, methodology): “…states but also for uncertainty estimation, model performance diagnostics, parameter estimation, and sensor failure analysis [Van Geer et al., 1991; Moradkhani et al., 2005; Goegebeur and Pauwels, 2007; Liu and Gupta, 2007; Hendricks Franssen and Kinzelbach, 2008; Sun et al., 2009; Trudel et al.,…”
Towards a comprehensive assessment of model structural adequacy (2012, neutral): “…equation structure is commonly represented via stochastic perturbations to the model state variables and/or parameters [Vrugt et al., 2005a, 2006; Moradkhani et al., 2005a, 2005b; Beven and Freer, 2001] or by using stochastic time varying parameters [Kuczera et al., 2006; Reichert and…”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, methodology): “The general concept of the model is based on the probability distribution of soil moisture modeling and has been used for the demonstration of the state and parameter estimation by the EnKF [e.g., Vrugt et al., 2005; Moradkhani et al., 2005a, 2005b ].”
Data assimilation: making sense of Earth Observation (2014, methodology): “…of new sensors (Andreadis et al., 2007; Durand et al., 2008); (iii) need to combine state and input (forcing) information with parameter updates (Moradkhani et al., 2005b; Liu et al., 2011; Vrugt et al., 2012); (iv) need to improve representation of observational and model errors, and specify…”
Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models (2006, methodology): “So far they have received little to no recognition for the application to hydrological models [Moradkhani et al., 2005a].”
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging (2007, methodology): “Most recently, techniques based on the ensemble Kalman filter (EnKF) [Evensen, 1994] have been shown to have the power and flexibility required for data assimilation using conceptual watershed models [Vrugt et al., 2005, 2006a; Moradkhani et al., 2005a, 2005b].”
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “…et al., 2003, 2006a, b; Kuczera et al., 2006), simultaneous optimization and data assimilation (Vrugt et al., 2005), sequential data assimilation (Moradkhani et al., 2005), integrated Bayesian uncertainty estimation (Ajami et al., 2007), and use of time-dependent parameters for exploring model…”