[1710] - State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

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Citation
Zhang, H., Hendricks-Franssen, H., Han, X., Vrugt, J. A., Vereecken, H., 2017. State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter. Hydrol. Earth Syst. Sci., 21, 4927 - 4958. DOI: 10.5194/hess-21-4927-2017.
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Identification
Title(s):Main Title: State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
Description(s):Abstract: Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March–July 2012) of arealaveraged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the “calibrated” LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50 cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.
Identifier(s):DOI: 10.5194/hess-21-4927-2017
Responsible Party
Creator(s):Author: Hongjuan Zhang
Author: Harrie-Jan Hendricks-Franssen
Author: Xujun Han
Author: Jasper A. Vrugt
Author: Harry Vereecken
Publisher:European Geosciences Union
Topic
TR32 Topic:Soil
Related Sub-project(s):C6
Subject(s):CRC/TR32 Keywords: LSM, Data Assimilation, Soil Moisture
File Details
File Name:zhangetal_2017a.pdf
Data Type:Text
File Size:15202 kB (14.846 MB)
Date(s):Date Accepted: 2017-07-19
Available: 2017-09-29
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:Kall
Measurement Location:Rollesbroich
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2017
Type:Article
Article Type:Journal
Source:Hydrol. Earth Syst. Sci.
Volume:21
Number Of Pages:32
Page Range:4927 - 4958
Metadata Details
Metadata Creator:Tanja Kramm
Metadata Created:2019-01-31
Metadata Last Updated:2019-01-31
Subproject:C6
Funding Phase:3
Metadata Language:English
Metadata Version:V43
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