Identification of time‐variant river bed properties with the ensemble Kalman filter

Kurtz, Wolfgang; Franssen, Harrie‐Jan Hendricks; Vereecken, Harry

doi:10.1029/2011wr011743

Cited by 31 publications

(37 citation statements)

References 57 publications

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“…Liu et al, 2012;Abaza et al, 2014). It has also been successfully used to estimate model parameters (Moradkhani et al, 2005;Kurtz et al, 2012;Montzka et al, 2013;Panzeri et al, 2013;Vrugt et al, 2013;Xie and Zhang, 2013;Shi et al, 2014;Xie et al, 2014). For example, Vrugt et al (2013) proposed two Particle-DREAM (DiffeRential Evolution Adaptive Metropolis) methods, i.e., Particle-DREAM for time-variant and time-invariant parameters, to track the evolving target distribution of HyMOD parameters, while both results were approximately similar and statistically coherent since only 3 years of data were used.…”

Section: Introductionmentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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Section: Introductionmentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Therefore, this methodology should also be well suited for the characterization of highly variable river bed properties. In Kurtz et al (2012) EnKF has been applied specifically to river-aquifer systems in order to identify the temporal change of river bed conductivities. In this study we concentrate on the question whether the estimation of a few effective values for river bed hydraulic conductivity can reproduce spatially and temporally strongly variable river-aquifer exchange fluxes with the use of data assimilation.…”

Section: Introductionmentioning

confidence: 99%

Is high-resolution inverse characterization of heterogeneous river bed hydraulic conductivities needed and possible?

Kurtz¹,

Franssen²,

Brunner

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. River-aquifer exchange fluxes influence local and regional water balances and affect groundwater and river water quality and quantity. Unfortunately, river-aquifer exchange fluxes tend to be strongly spatially variable, and it is an open research question to which degree river bed heterogeneity has to be represented in a model in order to achieve reliable estimates of river-aquifer exchange fluxes. This research question is addressed in this paper with the help of synthetic simulation experiments, which mimic the Limmat aquifer in Zurich (Switzerland), where river-aquifer exchange fluxes and groundwater management activities play an important role. The solution of the unsaturatedsaturated subsurface hydrological flow problem including river-aquifer interaction is calculated for ten different synthetic realities where the strongly heterogeneous river bed hydraulic conductivities (L) are perfectly known. Hydraulic head data (100 in the default scenario) are sampled from the synthetic realities. In subsequent data assimilation experiments, where L is unknown now, the hydraulic head data are used as conditioning information, with the help of the ensemble Kalman filter (EnKF). For each of the ten synthetic realities, four different ensembles of L are tested in the experiments with EnKF; one ensemble estimates high-resolution L fields with different L values for each element, and the other three ensembles estimate effective L values for 5, 3 or 2 zones. The calibration of higher-resolution L fields (i.e. fully heterogeneous or 5 zones) gives better results than the calibration of L for only 3 or 2 zones in terms of reproduction of states, stream-aquifer exchange fluxes and parameters. Effective L for a limited number of zones cannot always reproduce the true states and fluxes well and results in biased estimates of net exchange fluxes between aquifer and stream.Also in case only 10 head data are used for conditioning, the high-resolution characterization of L fields with EnKF is still feasible. For less heterogeneous river bed hydraulic conductivities, a high-resolution characterization of L is less important. When uncertainties in the hydraulic parameters of the aquifer are also regarded in the assimilation, the errors in state and flux predictions increase, but the ensemble with a high spatial resolution for L still outperforms the ensembles with effective L values. We conclude that for strongly heterogeneous river beds the commonly applied simplified representation of the streambed, with spatially homogeneous parameters or constant parameters for a few zones, might yield significant biases in the characterization of the water balance. For strongly heterogeneous river beds, we suggest adopting a stochastic field approach to model the spatially heterogeneous river beds geostatistically. The paper illustrates that EnKF is able to calibrate such heterogeneous streambeds on the basis of hydraulic head measurements, outperforming zonation approaches.

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“…However, it does not mean there is no filter inbreeding. In fact, the inflation method of covariance is commonly used to improve the prediction (Kurtz et al 2012). To demonstrate the factor's influence, the graphs of RMSE with and without inflation in experiment 1 and experiment 2 are displayed in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, it has also been proposed to set d as a temporal and spatial variable (Anderson 2007(Anderson , 2009 or only a temporal variable (Kurtz et al 2012) via a Bayesian update. In this paper, the inflation factor is simply treated as a constant.…”

Section: Discussionmentioning

confidence: 99%

Experimental and modeling study on Cr(VI) transfer from soil into surface runoff

Tan

Tong

Liu

et al. 2015

Stoch Environ Res Risk Assess

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With the development of modern agriculture, large amount of fertilizer and pesticide outflow from farming land causes great waste and serious pollution to surface water and groundwater, and threatens ecological environment and even human life. In this paper, laboratory experiments are conducted to simulate adsorbed Cr(VI) transfer from soil into runoff. A two-layer in-mixing analytical model is applied to analyze laboratory experimental results. A data assimilation (DA) method via the ensemble Kalman filter (EnKF) is used to update parameters and improve modeling results. In comparison with the experimental data, DA updated modeling results are much better than those without the updating. To make predictions better, the inflation method with a constant inflation factor via DA method was used to compensate the fast decrease of ensemble spread partially related to filter inbreeding. Based on the used rainfall and relevant physical principles, the updated value of the incomplete mixing coefficient c is about 14.0 times of the value of the incomplete mixing coefficient a in experiment 1 and about 7.4 times in experiment 2, while the difference between the flow rate of runoff and infiltration is not so large even after reaching stable infiltration condition. The results indicate the loss of Cr(VI) in soil solute is mainly due to infiltration, rather than surface runoff. With the increase of mixing layer depth, soil adsorption capacity will increase and the loss of soil solute will decrease. The study results provide information for reducing and even preventing the agricultural nonpoint source pollution.

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Identification of time‐variant river bed properties with the ensemble Kalman filter

Cited by 31 publications

References 57 publications

Identification of hydrological model parameter variation using ensemble Kalman filter

Identification of hydrological model parameter variation using ensemble Kalman filter

Is high-resolution inverse characterization of heterogeneous river bed hydraulic conductivities needed and possible?

Experimental and modeling study on Cr(VI) transfer from soil into surface runoff

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