DOI: 10.5194/hess-21-4927-2017
Scopus记录号: 2-s2.0-85030546280
论文题名: State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
作者: Zhang H ; , Franssen H ; -J ; H ; , Han X ; , Vrugt J ; A ; , Vereecken H
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 9 起始页码: 4927
结束页码: 4958
语种: 英语
Scopus关键词: Bandpass filters
; Calibration
; Carbon
; Carbon dioxide
; Digital storage
; Groundwater
; Interface states
; Kalman filters
; Moisture
; Passive filters
; Soil moisture
; Soil surveys
; Soils
; State estimation
; Surface measurement
; Vegetation
; Community land models
; Data assimilation methods
; Ensemble Kalman Filter
; Soil moisture measurement
; Spatiotemporal patterns
; State and parameter estimations
; Variable infiltration capacity models
; Water content measurements
; Parameter estimation
; baseflow
; calibration
; data assimilation
; energy balance
; land surface
; model validation
; parameter estimation
; performance assessment
; soil moisture
; vadose zone
; water content
; water storage
; water table
; Eifel
; Germany
; Rhineland-Palatinate
英文摘要: 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 areal-averaged SPADE soil moisture measurements at 5, 20, and 50ĝ€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 50ĝ€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. © 2017Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79037
Appears in Collections: 气候变化事实与影响
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作者单位: Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany; Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Forschungszentrum Jülic, Jülich, Germany; Department of Civil and Environmental Engineering, University of California Irvine, Irvine, United States; Department of Earth Systems Science, University of California Irvine, Irvine, United States
Recommended Citation:
Zhang H,, Franssen H,-J,et al. State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter[J]. Hydrology and Earth System Sciences,2017-01-01,21(9)