Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-42
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
23 Feb 2016
Review status
A revision of this discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Joint State and Parameter Estimation of Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter
Hongjuan Zhang1,2, Harrie-Jan Hendricks Franssen1,2, Xujun Han1,2, Jasper Vrugt3,4, and Harry Vereecken1,2 1Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
2Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Forschungszentrum Jülich, Jülich, Germany
3Department of Civil and Environmental Engineering, University of California, Irvine, USA
4Department of Earth Systems Science, University of California, Irvine, USA
Abstract. Land surface models (LSMs) contain a suite of different parameters and state variables to resolve the water and energy balance at the soil-atmosphere interface. Many of the parameters of these models cannot be measured directly in the field, and require calibration against flux and soil moisture data. In this paper, we use the Variable Infiltration Capacity Hydrologic Model (VIC) and the Community Land Model (CLM) to simulate temporal variations in soil moisture content at 5, 20 and 50 cm depth in the Rollesbroich experimental watershed in Germany. Four different data assimilation (DA) methods are used to jointly estimate the spatially distributed water content values, and hydraulic and/or thermal properties of the resolved soil domain. This includes the Ensemble Kalman Filter (EnKF) using state augmentation or dual estimation, the Residual Resampling Particle Filter (RRPF) and Markov chain Monte Carlo Particle Filter (MCMCPF). These four DA methods are tuned and calibrated for a five month data period, and subsequently evaluated for another five month period. Our results show that all the different DA methods improve the fit of the VIC and CLM model to the observed water content data, particularly if the maximum baseflow velocity (VIC), soil hydraulic (VIC) properties and/or soil texture (CLM) are jointly estimated along with the model states. In the evaluation period, the augmentation and dual estimation method performed slightly better than RRPF and MCMCPF. The differences in simulated soil moisture values between the CLM and VIC model were larger than variations among the data assimilation algorithms. The best performance for the Rollesbroich site was observed for the CLM model. The strong underestimation of the soil moisture values of the third VIC-layer are likely explained by an inadequate parameterization of groundwater drainage.

Citation: Zhang, H., Hendricks Franssen, H.-J., Han, X., Vrugt, J., and Vereecken, H.: Joint State and Parameter Estimation of Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-42, in review, 2016.
Hongjuan Zhang et al.
Hongjuan Zhang et al.
Hongjuan Zhang et al.

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Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We found that all DA methods can improve prediction of states, and that differences between DA methods were limited but the differences between LSMs were much larger.
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most...
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