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@ARTICLE{Han:155400,
author = {Han, Xujun and Franssen, Harrie-Jan Hendricks and Montzka,
Carsten and Vereecken, Harry},
title = {{S}oil moisture and soil properties estimation in the
{C}ommunity {L}and {M}odel with synthetic brightness
temperature observations},
journal = {Water resources research},
volume = {50},
number = {7},
issn = {0043-1397},
address = {Washington, DC},
publisher = {AGU},
reportid = {FZJ-2014-04567},
pages = {6081 - 6105},
year = {2014},
abstract = {The Community Land Model (CLM) includes a large variety of
parameterizations, also for flow in the unsaturated zone and
soil properties. Soil properties introduce uncertainties
into land surface model predictions. In this paper, soil
moisture and soil properties are updated for the coupled CLM
and Community Microwave Emission Model (CMEM) by the Local
Ensemble Transform Kalman Filter (LETKF) and the state
augmentation method. Soil properties are estimated through
the update of soil textural properties and soil organic
matter density. These variables are used in CLM for
predicting the soil moisture retention characteristic and
the unsaturated hydraulic conductivity, and the soil texture
is used in CMEM to calculate the soil dielectric constant.
The following scenarios were evaluated for the joint state
and parameter estimation with help of synthetic L-band
brightness temperature data assimilation: (i) the impact of
joint state and parameter estimation; (ii) updating of soil
properties in CLM alone, CMEM alone or both CLM and CMEM;
(iii) updating of soil properties without soil moisture
update; (iv) the observation localization of LETKF. The
results show that the characterization of soil properties
through the update of textural properties and soil organic
matter density can strongly improve with assimilation of
brightness temperature data. The optimized soil properties
also improve the characterization of soil moisture, soil
temperature, actual evapotranspiration, sensible heat flux,
and soil heat flux. The best results are obtained if the
soil properties are updated only. The coupled CLM and CMEM
model is helpful for the parameter estimation. If soil
properties are biased, assimilation of soil moisture data
with only state updates increases the root mean square error
for evapotranspiration, sensible heat flux, and soil heat
flux.},
cin = {IBG-3},
ddc = {550},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {246 - Modelling and Monitoring Terrestrial Systems: Methods
and Technologies (POF2-246) / 255 - Terrestrial Systems:
From Observation to Prediction (POF3-255)},
pid = {G:(DE-HGF)POF2-246 / G:(DE-HGF)POF3-255},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000342632000041},
doi = {10.1002/2013WR014586},
url = {https://juser.fz-juelich.de/record/155400},
}