Dual state-parameter estimation of hydrological models using ensemble Kalman filter

By Hamid Moradkhani, Soroosh Sorooshian, Hoshin V. Gupta, Paul R. Houser. 2005

927 citations recorded by the source corpus.

Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. 2004 Elsevier Ltd. All rights reserved.

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

View Paper: https://www.semanticscholar.org/paper/0d83b37eedfdba56daae51e196bfb905379b403b

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Citations in the corpus

Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (2005, methodology): “In previous work we have explored the same problem using a dual EnKF approach [ Moradkhani et al., 2005 ], which tracks only the first two moments of the distributions; the important difference here is that particle filters use a full Bayesian updating (correction) scheme.”
Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation / Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée (2009, methodology): “These include: the GLUE methodology (Beven & Binley, 1992), the ensemble Kalman filter (Evensen, 1994; Moradkhani et al., 2005a), the BaRE algorithm (Thiemann et al., 2001), the SCEM-UA method (Vrugt et al., 2003), the DYNIA method (Wagener et al., 2003), the ensemble method (Butts et al., 2004;…”
Inverse methods in hydrogeology: Evolution and recent trends (2014, neutral): “…disciplines such as oceanography, meteorology and hydrology (e.g., Bertino et al.,488 2003; Chen and Zhang, 2006; Houtekamer and Mitchell, 2001; Moradkhani et al., 2005; Nowak, 2009; Wen489 and Chen, 2006).490 Notice that the EnKF is neither an optimization method nor a sampling one, it is a…”
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].”
State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency (2007, background): “Therefore, in several hydrological studies, the potential of parameter estimation through filtering was investigated [Hebson and Wood, 1985; Boulet et al., 2002; Moradkhani et al., 2005; Vrugt et al., 2005], often in combination with state estimation.”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, methodology): “This approach has been used in state space approaches, such as the Ensemble Kalman Filter (EnKf) [e.g.,Moradkhani et al., 2005].”
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].”
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…”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, methodology): “The unconstrained EnKF has been applied for simultaneous state and parameter estimation [e.g., Vrugt et al., 2005; Moradkhani et al., 2005a, 2005b], and the unconstrained EnKF with naive treatment on hard constraints will be used as a baseline for the comparison with respect to the CEnKF methods.”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, background): “Numerous studies have focused on parameter estimation [Hogue et al., 2000; Ajami et al., 2004; Carpenter and Georgakakos, 2004; Vrugt et al., 2005] and on developing calibration algorithms [Duan et al., 1992; Ajami et al., 2004; Moradkhani et al., 2005] for rainfall-runoff models.”
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation (2008, background): “…1992; Freer et al., 1996; Gupta et al., 1998;Vrugt et al., 2003], recent emerging approaches include state space filtering [Vrugt et al., 2005; Moradkhani et al., 2005a, 2005b; Slater and Clark, 2006; Vrugt et al., 2006a], multimodel averaging [Butts et al., 2004; Georgakakos et al., 2004;…”