TY  - JOUR
AU  - Zhang, Hongjuan
AU  - Hendricks Franssen, Harrie-Jan
AU  - Han, Xujun
AU  - Vrugt, Jasper A.
AU  - Vereecken, Harry
TI  - State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
JO  - Hydrology and earth system sciences
VL  - 21
IS  - 9
SN  - 1607-7938
CY  - Katlenburg-Lindau
PB  - EGU
M1  - FZJ-2017-06837
SP  - 4927 - 4958
PY  - 2017
AB  - 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-monthcalibration (assimilation) period (March–July 2012) of areal-averaged 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 and, 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 agreementwith the PF using MCMC resampling. Over-all, 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000412245400001
DO  - DOI:10.5194/hess-21-4927-2017
UR  - https://juser.fz-juelich.de/record/838133
ER  -