Integration of evolutionary based assimilation into Kalman-type methods for streamflow simulations in ungauged watersheds

Dumedah, Gift; Coulibaly, Paulin

doi:10.1016/j.jhydrol.2012.10.033

Cited by 13 publications

(3 citation statements)

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“…Typical time-dependent methods that are frequently used in hydrological DA include the linear KF (Kalman 1960) and its variants, such as the Extended KF (EKF) (Puente and Bras 1987), the Ensemble KF (EnKF) (Evensen 1994b), the Unscented KF (UKF) (Wan and Van Der Merwe 2000), the PF (Pham 2001, Moradkhani et al 2005a, Weerts and El Serafy 2006, and the H-infinity filter (Moradkhani et al 2005a, Wang and Cai 2008, Lü et al 2010), among others. There are also some alternative approaches to solving specific issues in hydrological DA, most notably in the application of a genetic algorithm (GA) in the estimate of pixel-based soil hydraulic parameters for hydroclimatic modelling (Ines and Mohanty 2008) and evolutionary-based assimilation in streamflow simulations in ungauged watersheds (Dumedah and Coulibaly 2012). The spatial variation of the relationship between the background field and observation field of the variable in question is of less concern in time-dependent methods.…”

Section: Time-dependent Methodsmentioning

confidence: 99%

“…Although it increases the computational cost, the EnKF is one of the most widely used hydrological DA methods. Despite some comparison case studies (Reichle et al 2002b, El Serafy and Mynett 2004, Dumedah and Coulibaly 2012, there is no critical review that specifically focuses on the KF. The objective of this review is to fill this gap by assessing the latest developments and analysing the challenges of Kalman-type hydrological DA, especially the EKF and EnKF.…”

Section: Introductionmentioning

confidence: 99%

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Review of the Kalman-type hydrological data assimilation

Sun

Seidou

Nistor

et al. 2016

Hydrological Sciences Journal

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There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalmantype DA but also provide an important reference to other hydrological DA types.

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Section: Time-dependent Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of the Kalman-type hydrological data assimilation

Sun

Seidou

Nistor

et al. 2016

Hydrological Sciences Journal

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“…The EDA is a fairly new DA methodology that employs an advanced analytical evolutionary strategy to merge and account for uncertainties in model prediction and observation data. Evolutionary algorithms have been applied in several hydrological studies including [Dumedah et al, 2012;Ines and Mohanty, 2009;Chemin and Honda, 2006], and the EDA has been applied in assimilating soil moisture [Dumedah and Coulibaly, 2012a;Dumedah et al, 2011], and streamflow in gauged and ungauged catchments [Dumedah, 2012;Dumedah and Coulibaly, 2012b].…”

Section: Introductionmentioning

confidence: 99%

Joint model state-parameter retrieval through the evolutionary data assimilation approach

Dumedah

Walker

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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Data assimilation (DA) methods have been widely used to improve model state estimation by merging model outputs with observations. Traditionally, studies have focused on updating model state variables but recent studies have augmented model parameters alongside model state variables to improve the estimation procedure. The updated model ensemble members represent a compromised estimation between prediction and observation. The compromise, which is usually in objective space subject to agreement between observation and model predictions, is important. However, few studies have actually employed DA procedures to investigate the updated members in decision space, through examination of the temporal changes of model states and parameters. Usually, the model states and parameters evolve/change: (i) subject to changes in observation, (ii) to account for the varied uncertainties in different land surface conditions, and (iii) due to their intricate connection with hydrologic conditions which evolve across assimilation time periods. Moreover, the update procedure in most DA methods is controlled predominantly by matchings between observation and model predictions with limited impact from decision space through model state variables and parameters. As a result, DA procedures are needed to tightly link the compromise in objective space to decision space, with the capability to examine the temporal changes of model states and parameters.

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Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

Dumedah

Coulibaly

2013

Advances in Water Resources

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Integration of evolutionary based assimilation into Kalman-type methods for streamflow simulations in ungauged watersheds

Cited by 13 publications

References 50 publications

Review of the Kalman-type hydrological data assimilation

Review of the Kalman-type hydrological data assimilation

Joint model state-parameter retrieval through the evolutionary data assimilation approach

Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

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