Introduction
The potential to improve land surface simulations of soil moisture by
assimilating information derived from satellite measurements is well known
. Soil moisture products based on data from a number of
missions have been used, e.g. ASCAT ,
AMSR-E or a
combination of both . Launched
in November 2009, the Soil Moisture and Ocean Salinity (SMOS) spacecraft is
the first mission specifically designed to map soil moisture from space
, with the second one being the similar SMAP mission
launched in 2015 . The passive Microwave Imaging Radiometer
with Aperture Synthesis (MIRAS) instrument aboard SMOS, sensitive to 1.4 GHz
electromagnetic emissions, measures multi-angular top-of-atmosphere
brightness temperatures at horizontal (H) and vertical (V) polarisation.
These brightness temperatures are ingested into a complex retrieval
algorithm, resulting in soil moisture estimates readily usable for
analysis, input for higher-level products or data assimilation. When
assimilating these products, which roughly represent the top 5 cm of
the soil column, into the according model layers , the assimilation impact in deeper layers will
depend on model physics . Alternatively, by making use of one of the key
advantages of the various implementations of the Kalman filter
, deeper and unobserved layers can be updated directly.
For plants, these deeper layers act as the root zone, where soil moisture has
a profound effect on biochemical processes, thus limiting the effect of data
assimilation not only to soil moisture .
Examples for assimilating SMOS soil moisture retrievals are, among others,
given by , showing that the GLEAM evapotranspiration model
can benefit from assimilating these data over Australia, or
, who conclude that the positive assimilation impact on soil
moisture can improve streamflow simulations for the variable infiltration capacity (VIC) model, as shown in
the Murray–Darling Basin. The impact on both streamflow and evaporation is
evaluated by for western Denmark.
assimilate SMOS soil moisture products into the
Distributed Hydrology Soil Vegetation Model (DHSVM), improving water table depth and streamflow simulations, thereby
greatly reducing the uncertainties introduced by the use of uncorrected near-real-time
precipitation forcings. have assimilated
SMOS retrievals together with CO2 measurements to constrain the global
carbon cycle.
Apart from assimilating the retrieved soil moisture products, it is also
possible to directly assimilate the brightness temperatures, which should, in
theory, eliminate a number of problems. For instance, the SMOS Level 2 soil
moisture retrievals represent the optimum fits between simulated brightness
temperatures and the observed satellite signal . The simulated
top-of-atmosphere signal thereby depends on both static and dynamic ancillary
data based on input and output of a specific land surface model, e.g. for
SMOS retrievals the European Centre for Medium-Range Weather Forecasts
HTESSEL land surface model . When using a modified
or different land surface model, it can be beneficial to directly assimilate
the brightness temperatures in order to use consistent auxiliary information
for the land surface model and the radiative transfer model. In the case of
assimilating soil moisture retrievals, the auxiliary data used by the model
are likely to be correlated with the data used by the retrievals. This
inevitably leads to cross-correlated errors between the model and the
retrievals, which may have a negative impact on the assimilation performance
. Some examples of brightness temperature
assimilation studies are given by ,
, and
. Taken as a whole, assimilating L-band brightness
temperatures in practical terms is quite a new concept which still needs
further exploring. The assimilation impact is mostly evaluated by comparing
soil moisture time series to a limited number of in situ measurements. Given
the proven positive impact and the increased availability of longer time
series of satellite observations, hydrological monitoring systems, such as
for droughts or floods, are likely to benefit from these data. However,
little is still known about long-term assimilation impacts, e.g. on
quantiles, which are often used for applications such as drought monitoring.
Within this study, we assimilate SMOS brightness temperatures at H
polarisation over Australia from January 2010 until December 2015 into the
Community Land Model (version 4.5, ) and evaluate the
assimilation impact both in terms of correlation improvements towards in situ
measurements and in terms of long-term induced model biases, i.e. changes in
quantiles, for the state variable soil moisture. We place the findings within
the context of hydrological monitoring systems, which mostly use cumulative distribution functions (CDFs) as a
basis to classify areas of interest. A good overview on the evolution of such
hydrological monitoring systems is given by .
We have selected Australia as a study site as we consider it as an ideal test
domain for the long-term brightness temperature assimilation. It is quite
heterogeneous in terms of climate and largely uninfluenced by human activity
and therefore mostly unaffected by radio frequency interference
. Although large parts are covered by drylands, the land
cover varies along the coastline and includes some densely forested areas in
the Australian Alps as well as pasture and areas of intense agricultural
activity in the south-east and south-west. The lack of large, densely
vegetated areas, which mask out the L-band emissions sensitive to soil
moisture, is beneficial. Furthermore, soil moisture information based on
satellite data is often advertised as being especially useful for monitoring
hydrological extremes such as floods and droughts, both of which Australia is
susceptible to . In addition to
the ones already mentioned, a number of studies have focused on Australia,
covering soil moisture retrieval , assimilation studies
, validation studies and field campaigns for SMOS
, as well as SMAP and soil
moisture downscaling experiments . More recently, SMOS soil moisture
and GRACE water storage data have been jointly assimilated by
. A comparison of SMOS satellite soil moisture
retrievals with products based on other sensors is given by .
The potential of AMSR-E soil moisture retrievals has been shown by
and the joint assimilation of ASCAT and AMSR-E
data has been tested by . Downscaled AMSR-E
soil moisture observations were assimilated within the Murrumbidgee Basin by
.
The Community Land Model (CLM) provides all outputs required for the
brightness temperature forward simulations, which further motivates the
direct assimilation of brightness temperatures. Being part of the fully
coupled Community Earth System Model (CESM), it can be used for future
coupled land–atmosphere studies using a similar setup as for the brightness
temperature assimilation. A full description of the CLM surface data used for
modelling the Australian continent will be given in Sect. .
In order to obtain the brightness temperature forward simulations, the CLM is
coupled to the Community Microwave Emission Model (version 5.1,
) forward operator within the data assimilation
system DasPy . The increments are computed with the
local ensemble transform Kalman filter (LETKF) . The observation bias between forward simulations and observed
brightness temperatures is encountered by assimilating anomalies. Remaining
differences in mean and variance are resolved by quantile mapping the entire
observation anomaly time series towards the open-loop forward simulation
anomalies at each grid point. Details on the implementation of the
assimilation system, the forward simulations and the observation treatment
will be given in Sect. .
The in situ data used for the validation are from the OzNet and CosmOz
measurement networks and were obtained
through the International Soil Moisture Network (ISMN)
. For the quantile analysis, quantiles at
1 % steps are computed at each model grid point, allowing the empirical
estimation of the cumulative distribution functions. To exemplify the effects
of quantile changes, we show a dry event defined at the 10 % quantile
level and to what extent its spatial extent changes when comparing the
open-loop run to the data assimilation results. Part of the experiments is
also to show how the CLM translates assimilation updates restricted to the
upper soil layers into the root zone purely through model physics as compared
to directly updating both the upper soil layers as well as the root zone,
with the findings being set in relation to the quantile analysis. The
results of the experiments will be given in Sect. , followed by
the discussion and conclusion in Sect. .
Assimilation system
The assimilation experiments are performed with the open-source multivariate
data assimilation system DasPy. Mainly coded in Python, its modular design,
in principle, allows the coupling of different models, observation operators
and assimilation algorithms. The version used within this study is coupled to
the Community Land Model and the Community Microwave Emission Model (CMEM;
) observation operator. Furthermore, the system uses
the LETKF implementation by for computing
the actual increments. Several studies have been performed using DasPy,
including the assimilation of synthetic brightness temperatures within the
Babaohe River basin in north-western China and in the
Rur catchment in Germany . The system allows for dual
state parameter estimation, as shown in .
DasPy has been developed with a focus on high-performance computing.
Parallelism is achieved through ParallelPython, OpenMP, the Message Parsing
Interface (MPI) and MPI4Python. Ensemble members can be distributed across
different nodes with the core assimilation system, including the LETKF, being
confined to one node. Some of the operations are implemented in C++ within
the Python environment, using Weave, to further optimise performance. The
LETKF itself is a fully parallel Fortran implementation called through F2PY
(Fortran2Python).
Local ensemble transform Kalman filter
The local ensemble transform Kalman filter is one of the
implementations of the ensemble Kalman square root filter and is
deterministic as opposed to stochastic, thus not introducing random noise
into the observations. The LETKF has the advantage over other non-localised
implementations that the analysis performed for each grid point is limited to
a local domain, which makes it computationally more efficient and less
susceptible to long-range spurious correlations. The original SMOS footprint
is 43 km across and thus covers more than a single model grid cell, which
would justify the assimilation in 3-D. However, mostly for reasons of
simplicity, and also due to the previously performed inverse distance
observation regridding partially accounting for this, we only use
observations directly covering a grid cell.
Mathematically, the LETKF can be described as follows. Model states for each
ensemble member k from a total of K ensemble members are propagated over
time by the model M, starting at a previous time step, n-1, e.g. a
previous analysis step within the data assimilation scheme,
xn-1a. This results in a new background estimate of the state
vector xb consisting of the soil moisture states for all ensemble
members at the current time step n.
xn,kb=Mnxn-1,ka
The background ensemble perturbations Xb at the current time step
can be computed as
Xb=x1b-x‾b|…|xKb-x‾b.
The individual ensemble states xb are mapped into observation space
using a forward operator H, in this case CMEM:
ykb=Hxkb,
and the forward simulation perturbations are defined as
Yb=y1b-y‾b|…|yKb-y‾b.
Within the ensemble space, with R being the observation variance,
the analysis error covariance P̃a is computed
through
P̃a=(K-1)I+(Yb)TR-1Yb-1,
allowing for the computation of w‾a as the mean
weighting vector, with yo being the observations,
w‾a=P̃a(Yb)TR-1yo-y‾b,
resulting in the analysis mean x‾a.
x‾a=x‾b+Xbw‾a
The analysis perturbations are defined as Xa:
Xa=Xb+Wa,
with
Wa=(K-1)P̃a,
where the analysis error covariance Pa is given by
Pa=XbP̃a(Xb)T.
CLM soil layer depths and relative layer thickness with respect to the sum
of the two top layers. The relative thickness was used as a scaling factor
for the soil perturbations, effectively decreasing ensemble spread and error
covariance for lower levels.
Layer depth (m)
0.018
0.045
0.09
0.17
0.290
0.493
0.829
1.383
2.296
3.802
Perturb. scaling
1
1
1
0.60
0.36
0.22
0.13
0.08
0.05
0.03
Ensemble generation
Model uncertainty is simulated by running the model in ensembles with
perturbations applied either to the atmospheric forcings, surface dataset,
model parameters or possible combinations of these. In order to account for
the model uncertainty in this study, CLM is run with 32 ensembles with
spatially uncorrelated perturbations added to some of the ERA-Interim forcing
data, namely air temperature, shortwave radiation and precipitation.
Shortwave radiation is perturbed with multiplicative noise with a standard
deviation of 0.3, whereas for temperature additive noise with a standard
deviation of 2.5 K is applied. Finally, precipitation is perturbed with
multiplicative log-normal noise with a standard deviation of 0.3. The
perturbation factors are the same as used by
and . No spatially correlated noise was added, as the
experiments are carried out in 1-D, only using one observation per grid cell.
To avoid ensemble collapse during dry periods, soil texture is also perturbed
once at model startup. Here, multiplicative noise with a standard deviation
of 10 % for clay and sand for the top two soil layers is applied. For
lower layers, the top-layer multiplicative factor is rescaled by using the
inverse relationship between the thickness of each soil layer and the summed
soil layer thickness of the two top layers (see Table ). This is to ensure that increments in lower soil
layers do not result in very large changes in soil water in absolute terms,
since soil layer thickness greatly increases towards lower layers. With CLM
deriving hydraulic properties based on soil texture, it is to be noted that
as a consequence each ensemble member runs with slightly modified model
physics.
Observation operator
Forward simulations from the model space to the observation space are
performed with CMEM version 5.1.
Model output at each observation time, with the observation time rounded to
the full hour, serves as input in order to simulate brightness temperatures as
measured by the satellite. SMOS ascending and descending orbits have local
overpass times of approximately 06:00 and 18:00 UTC. Forward simulations are thus
computed at 08:00 UTC on the same day and 20:00 UTC on the previous day for
descending and ascending acquisitions, respectively, assuming an average time
shift of -10 h for the entire Australian continent. This greatly
decreases the number of analysis steps, since individual orbits within one
day can be assimilated at once, assuming that a sufficiently correct temporal
alignment between observations and model forward simulations is provided.
With the western parts of Australia deviating by 2 h and the ERA-Interim
forcings being interpolated from 3-hourly to 1-hourly data, we consider this
approach to be acceptable for the purpose of the study.
CMEM requires time-invariant information such as soil layer depth, sand, clay
and water fractions, surface height as well as the vegetation types covering
the grid cell. The CLM plant functional types need to be reclassified to
ECOCLIMAP vegetation classes and the percentage of
the low and high vegetation types is then used by the CMEM. Based on this
reclassification, the LAI information is assigned to the ECOCLIMAP low
vegetation classes accordingly. For the offline forward simulations, CLM was
run with LAI as daily output in order to make use of the model-internal LAI
interpolation, creating a smooth LAI time series based on the monthly surface
dataset. This also ensures that the LAI values used for the CMEM forward
simulation are the same as those used within CLM during assimilation. LAI
values for high vegetation classes are fixed within CMEM and not taken from
the CLM input data. Other dynamic fields used in the forward simulations are
soil moisture and soil temperature and 2 m air temperature. CMEM supports
different types of submodules for specific calculations. Within this study,
the Mironov model has been chosen for the dielectric
constant computation. Vegetation temperature is computed directly by CLM and
used as an input without the need of an approximation, e.g. through air
temperature. Effective temperature is obtained through the Wigneron model
and applied in the dielectric model. For
smooth surface emissivity, soil roughness and vegetation opacity, the
Fresnel, Choudhury and Wigneron
models are used, respectively. Finally, atmospheric
contributions are estimated with the Pellarin methodology
. For all modules, the standard parameters for CMEM 5.1
remained unchanged and the forward observation model was not calibrated.
Although the standard parameters are very unlikely to be perfect for the
different land cover classes, we argue that this approach is not necessarily
worse than the alternative of calibrating the radiative transfer model. By
modifying parameters, such as surface roughness, the bias between forward
simulations and observations can be removed, but in some cases at the expense
of a reduced sensitivity towards soil moisture. Therefore, we remove the bias
between simulations and observations through the computation of forward
simulation and observation anomalies.
Observations and anomaly preparation
Large biases are common between modelled and observed brightness temperatures
due to the many uncertainties involved, such as in the atmospheric forcing,
the land surface representation, the land surface model itself as well as the
radiative transfer model and its parameterisation , with this study being no exception. The assimilation is
expected to correct random errors only, i.e. bias blind, and it is therefore
necessary to remove the bias prior to assimilation
. Calibrating the radiative transfer model to
closely match the observed time series is a possible solution, as shown by
, and ,
with the alternative being the rescaling of the measurements to mimic more
closely the forward simulations , as mentioned above. The
details of preparing the observations prior to assimilation are given here.
SMOS Level 3 daily brightness temperatures at horizontal H polarisation and
42.5 incidence angle provided by Centre Aval de Traitement des Données
(CATDS) are used in the study and processed for the years 2010–2015
(version 310). The data are rigorously filtered by using ancillary data from
the corresponding Level 3 soil moisture products (version 300), excluding
measurements with a probability of radio frequency interference (RFI) greater
than 0.2 and a Data Quality Index (DQX) value greater than 0.07. Measurements
with a number of activated science flags, namely strong topography, snow,
flooding, urban areas, coastal zone and precipitation, are not considered
either. The filtered observation data are regridded from the Equal-Area
Scalable Earth Grid 2 (EASE2) 25 km grid to the 0.25∘ rectangular model
grid by using inverse-distance interpolation.
On the basis of these data, we compute the climatology for each day and at
each grid point for the years 2010–2015 by averaging along a 14-day moving
window across the 6 years, producing separate climatologies for ascending and
descending orbits. Anomalies are then computed by subtracting the original
SMOS time series from the climatologies. Brightness temperature forward
simulations based on an open-loop run with 32 ensembles are performed and the
ensemble mean climatology and forward simulation anomalies are derived in the
same way as for the observations. The SMOS anomalies are then
quantile matched to the ensemble average forward simulation anomalies to
account for the differences in variance. The full approach of anomaly
computation and quantile matching is to account for seasonal differences
between simulations and observations (see, e.g.
) and to remove the bias without more
aggressive CDF-matching techniques at seasonal level being required. The
original brightness temperature simulations over the entire period exhibited
a mean warm bias of 21 K for the ascending orbit and 26 K for the descending
orbit. Anomaly correlations prior to quantile matching are 0.21 and 0.39, and
after quantile matching 0.38 and 0.60 for ascending and descending orbits,
respectively. Based on the scaling factor between the standard deviation of
the original and CDF-matched SMOS anomalies, the observation variance is
recomputed. The unscaled observation variance R=5 K2 was defined,
accounting for a standard instrument error of 3 K and an assumed combined
standard mean error of 4 K for the forward simulations and representativeness
error. The instrument error can be seen as a low estimate and is based on the
assumption that the brightness temperature binning around the 42.5∘
incidence angle results in a slight reduction, when compared to the 4 K
instrument error usually applied for Level 1 data.
During assimilation at each time step, the current forward simulation is
subtracted from the precomputed forward simulation climatology to compute
on-the-fly anomalies. The difference between this simulated anomaly and the
SMOS anomaly is the innovation, which is used within the LETKF algorithm. The
assumption is made that the forward simulation climatology does not
significantly change during the assimilation run. In total, there are 2063 and
2044 observations for the ascending and descending orbits, respectively.
Data assimilation and results
In total, three assimilation experiments are carried out, updating either
top-layer soil moisture or both top-layer and root-zone soil moisture. Only
updating the upper soil moisture allows for testing the ability of the model
to feed the assimilation effects into the root zone through model physics
only. Updating the root zone is carried out with two sets of soil texture
perturbations, which largely influence the modelled background error. The
objective is to validate the assimilation impact by comparing the time series
before and after assimilation to a number of in situ measurements. In
addition, shifts in the soil moisture quantiles with respect to the open-loop
run are analysed to highlight some long-term effects of the data
assimilation. A set of quantiles is computed at each grid cell to allow the
empirical estimation of the cumulative distribution functions, since shifts,
both positive or negative, are possible at different quantile levels. Both
the impact on correlation and the long-term effects are set in relation
to which layers are updated and
to the model background error in the root zone. The experiments and their
results are described in the following and set into the context of their
potential effect on hydrological monitoring systems, as shown for the
exemplary classification of a dry event. We believe this to be relevant,
since L-band data, or data from other sources, are in the long run likely to
be incorporated into more and more operational systems.
The spatial patterns of the open-loop soil moisture simulations at different
depths were compared to the locally optimised AWRA-L land surface model
(http://www.bom.gov.au/water/landscape) to ensure that the CLM simulations
are plausible.
In the first experiment (DA 1), only the upper three CLM soil layers,
corresponding to a depth of 9 cm, are updated. Although the brightness
temperatures are only sensitive to soil moisture in up to 5 cm depth, DA 1
was defined as updating the top three layers, since a number of in situ
measurements are taken from a depth of up to 8 cm. For these in situ sites,
measurements are also available for deeper layers, and we thus define top-layer
soil moisture as the soil moisture updated in DA 1. The upper six model
layers, reaching 50 cm soil depth, are updated in the second experiment (DA 2).
We refer to the lower three of these soil layers as the root zone. These
two experiments enable us to examine to what extent CLM model physics alone
are sufficient to update the root zone through the effects of the
assimilation on the upper layers, as in comparison to directly applying the
increments in this depth. For the experiments DA 1 and DA 2, soil texture
perturbations were incrementally reduced with layer depth, minimising the
impact of potentially large updates in deep layers. Since increments are
computed in relative soil moisture, identical increments affect absolute soil
water very differently, greatly exaggerating the assimilation impact for
deeper layers. Perturbations for the two top layers remain unchanged, thus
not decreasing the ensemble spread for the layers where SMOS is sensitive to
soil moisture. The soil texture of the subsequent layers is perturbed by
decreasing the perturbation factor by the inverse ratio between the
respective layer thickness and the layer thickness of the two top layers (see
ensemble generation under Sect. and Table ). Within a third experiment (DA 0), homogeneous soil
texture perturbations are applied across all layers, highlighting the problem
of large increments in lower layers. As will be shown, the larger ensemble
spread in DA 0 actually further improves the correlation with in situ
measurements but at the expense of introducing strong long-term effects. For
all experiments, the brightness temperature forward simulations are computed
by using the CLM output of all layers. The L-band simulations are thereby
mostly affected by the output of up to 5 cm depth, which corresponds to the
sensitivity of the SMOS sensor.
CLM plant functional types based on
MODIS MCDQ12 land cover classification and ECOCLIMAP climate zones at 500 m
resolution prior to the aggregation to 0.25∘. Some classes are here
aggregated for visualisation purposes, e.g. evergreen temperate and evergreen
tropical forests are both shown as woodland. The boundary of the Murrumbidgee
catchment, which is the site of the OzNet in situ measurements, is
outlined.
![]()
Correlation with in situ observations
For validation, hourly CLM soil moisture output is compared to in situ
measurements obtained from the International Soil Moisture Network (ISMN)
. OzNet in situ measurement probes are located
within the Murrumbidgee catchment in south-east Australia, a limited spatial
domain which does, however, cover a range of different land cover classes
representative of Australia . The Murrumbidgee
catchment was also chosen as a site for a SMOS validation campaign
. Measurements within the OzNet network are taken
with time-domain reflectometry (TDR) probes at shallow levels (5 or 8 cm) and at deeper layers (30, 60
and 90 cm). The in situ measurements that are part of the CosmOz network are
taken by using cosmic-ray neutron probes and are therefore representative of
a larger horizontal footprint than the more traditional measurements. CosmOz
measurement sites are located within the Murrumbidgee catchment as well as at
selected locations close to the Australian coast. In addition to the original
description of the measurement networks, and
, for instance, offer an extensive overview of the CosmOz
and OzNet measurement sites. give more details on the
Murrumbidgee catchment and the locations of the OzNet measurement sites. For
all in situ measurements sites, the weighted average of the corresponding CLM
soil moisture layers is taken, with the layer thickness being used as the
respective weights. Figure shows where the
Murrumbidgee catchment is situated, as well as the land cover data used for
the CLM simulations.
Taking into account only measurements with at least 1 year of data, not
necessarily consecutive, correlations improve from 0.613 for the open-loop
run to 0.640, 0.678 and 0.681 for DA 1, DA 2 and DA 0 for top-layer soil
moisture (number of stations n=17). Root-zone soil moisture improvements
are smaller, with average correlation coefficients of 0.626, 0.644 and 0.648
for DA 1, DA 2 and DA 0 compared to 0.601 for the open-loop run (n=31). For
the upper-level soil moisture, correlation improves for all in situ
measurement stations, whereas for the root-zone soil moisture a single
in situ station shows a deterioration of correlation for DA 1. In the case of
DA 2 and DA 0, the correlation at two stations, albeit at different ones,
slightly deteriorates. On average, upper soil moisture behaviour thus
improves by additionally updating deeper layers, whereas deeper-layer soil
moisture is slightly enhanced through only updating top-level soil moisture,
with the assimilation effects only being applied through model physics. All
in all, updating the top six CLM layers results in the largest improvements,
even more so if the identical soil texture perturbations are applied to all
soil layers within experiment DA 0, thereby increasing the assimilation
impact through an increased ensemble spread and background error. The
individual in situ measurements around the area of the Murrumbidgee catchment
and the respective correlation changes for all three experiments towards the
open-loop run are shown in Fig. . For top-layer soil
moisture, the largest improvements are visible for the sites located in the
centre of the catchment (Yanco site) with clear improvements for DA 2 and DA 0
when compared to DA 1. In the case of the root zone, multiple measurements
at different depths were averaged using the measurement depth as weights and
compared to the corresponding weighted average soil moisture simulations.
Here, improvements are also highest for the Yanco site, except for one
measurement location showing a deterioration of correlation for DA 0. The
area around the Yanco site is flat and semi-arid with mostly low vegetation
and thus more ideal for L-band soil moisture sensitivity. The lesser
improvements for the other in situ sites towards the east therefore could be
explained by the more complex terrain, less homogeneous soil texture and
higher vegetation influencing the L-band signal, as discussed by
.
Change in correlation R for experiments DA 1, DA 2 and DA 0 both
for top-layer soil moisture (a) as well as the root-zone soil
moisture (b) within the Murrumbidgee catchment. In the case of
multiple measurements at the same location, the weighted average of the
measured as well as modelled soil moisture was computed in accordance with the
corresponding CLM layer thickness.
![]()
Taylor diagrams showing assimilation impact on top-layer soil
moisture, defined as 8 cm soil depth (a), and lower-level soil
moisture (b) in terms of correlation coefficient R, standard
deviation and normalised root mean square error (RMSE) for all in situ measurement sites. Measurements
at multiple depths are not aggregated.
![]()
Figure shows the Taylor diagrams for the in situ
validation of experiment DA 2. As opposed to Fig. , all
original measurements are included with no vertical aggregation performed.
The Taylor diagrams reveal a slightly decreased normalised standard deviation
when compared to the open-loop time series. In terms of standardised root mean square error (RMSE), it
is less conclusive, with RMSE being slightly reduced for some stations and
slightly increased for others, but never significantly. These findings
correspond well to experiments DA 0 and DA 1 (not shown).
When comparing the average changes in correlation for the non-vertically
aggregated sites, only for the 10 used CosmOz sites, correlation increases by
about 0.016 for DA 1 (three sites with +0.03 and two very close to zero). For
DA 2, the average correlation increases by 0.02 (two sites showing +0.05 and
one -0.02) and for DA 3 by 0.13 (three sites slightly deteriorating). This
highlights that the assimilation improvements are stronger for the OzNet
sites located in the Murrumbidgee catchment. Partly, this might be attributed
to the fact that the CosmOz measurements are valid for a variable soil depth,
depending on the current soil moisture conditions. For validation, a single
soil depth was used, which is reported to the ISMN network. Also, CosmOz
sites are partly situated along the coast or close to water bodies and within
areas of higher vegetation, making improvements through data assimilation
more challenging, as reported by . When only
considering the CosmOz sites, correlation decreases for DA 3 with respect to
DA 2, which contradicts the findings when taking all measurements into account.
Mean of all increments for experiment DA 0 for top-layer soil
moisture (a, c) and root-zone soil moisture (b, d) for
ascending (a, b) and descending orbits (c, d).
![]()
Altogether, the results demonstrate that the assimilation system is capable
of improving soil moisture simulations across the Australian continent, both
for top-layer soil moisture and the root zone. However, as with most
assimilation studies, validation sites are sparse and do not cover the
manifold combinations of soil texture, land cover, climate, etc., which might
all have an impact on the assimilation performance. The representativeness
error of the in situ measurement equally remains a problem, with the spatial
support of the measurements (in the case of TDR probes, only point
measurements) being smaller than the area covered by satellite. The
assimilation system is therefore not designed to remove the relative bias
between soil moisture simulations and observations, since the exact truth
remains unknown, but to improve the temporal behaviour of the simulations,
which has for the most part been achieved.
Increments standard deviation of all increments for experiment DA 0
for top-layer soil moisture (a, c) and root-zone soil
moisture (b, d) for ascending (a, b) and descending
orbits (c, d). Increments for the root-zone soil moisture are fairly
similar to the top soil layers, due to the homogeneous texture perturbations
applied across all layers.
![]()
Soil moisture increments
Especially over long time periods, the mean increments in a bias-blind
assimilation system can be expected to be very close to zero.
Figures and show the
mean soil moisture increments over the assimilation period (2010–2015) for
the experiments DA 0, DA 1 and DA 2, separately for the ascending and
descending orbits. Both the top three soil layers and the root-zone soil
layers were averaged. Distinctive areas of mean positive increments for the
ascending orbit are visible in the north, south-west and south-east of
Australia, seemingly being linked to the occurrence of vegetation (see
Fig. ). The areas in the south-west and south-east as
well as in the north correspond well to the subtropical,
temperate and tropical climate
zones, respectively, and are subject to higher precipitation than the dry
areas inland, although other areas along the coast have similar
precipitation. The areas in the south-west and south-east correspond to the
wheat-growing areas of Australia. The misrepresentation of these areas
through the CLM surface datasets, such as the use of climatological LAI
instead of actual LAI, might well be the source of such patterns. Also,
satellite-based estimates of leaf area index are not error-free, as has been
shown specifically for the Murrumbidgee catchment
. Irrigation, which is predominantly applied
within the south-east, could be an additional source of error for limited
areas, since the forward simulations will be based on seasonally too-low soil
moisture, causing an incorrect estimation of the brightness temperature
seasonality and the subsequent anomaly computation.
Mean of all increments for experiments DA 1 and DA 2 for top-layer soil
moisture and root-zone soil moisture for ascending (a) and
descending orbits (b). Biases are strongest for the ascending orbit,
and distinctive spatial patterns are visible. Biases are strongly reduced
both for deeper layers and descending orbits.
![]()
Standard deviation of increments for experiments DA 1 and DA 2 for
top-layer soil moisture and root-zone soil moisture for
ascending (a) and descending orbits (b). Increments are
strongest for the ascending orbit and for top-layer soil moisture and even
stronger when restricting assimilation to these layers, as in DA 1.
Increments are very low or zero for the forested areas along the coastline,
either due to the absence of observations or the high LAI values masking any
soil moisture signal within the forward operator.
![]()
Nevertheless, for the ascending overpasses, the positive biases hardly exceed
0.5 % soil moisture and the remaining parts of Australia either show no mean
increment bias or slightly negative values, both in areas covered by mostly
sparse vegetation and the inner drylands. For the descending orbits, the
patterns are still visible but are weaker, both for top layers and the
root zone. The exception is DA 0, where little differences between top-layer
and deep-layer increments are noticeable. Interesting to highlight is the
fact that top-layer deviations from zero are strongest for the assimilation
experiment DA 1, compared to DA 0 and DA 2, which both update top-layer soil
as well as root zone. The reason may be that updating deeper layers results
in a more lasting effect, moving the model closer to subsequent observations
and thus reducing subsequent increments.
Figures and show the
increment standard deviations. Spatial patterns of the assimilation impact
are very distinctive but do not necessarily correspond to the patterns seen
in the mean increments, although they do partly match, as for the south-west
and south-east. The relatively large increments in the western Wheatbelt and
the Murray–Darling Basin, some areas close to the western coast of Queensland
and the eastern coast of the Northern Territory show a standard deviation of
2.5 %. The areas in the north seem to be consistent with the occurrence of
tussock grasses, as shown by the Australian National land cover map
(http://www.ga.gov.au/scientific-topics/earth-obs/). Minimal or zero
increments in all layers, especially along the eastern coast, are due to a
lack of observations, as these were removed due to the active vegetation
science flags or the fact that the high LAI values for high vegetation
prescribed within the forward operator mask all signals from soil moisture.
Concerning the different assimilation experiments, top-layer increments are
largest for DA 1, followed by DA 2 and then DA 0. Being the most dominant
dynamic factor for the ensemble generation, precipitation leads to an
immediate increase in ensemble spread and, as a consequence, to a larger
background error for the very shallow soil layers, thereby also increasing
the observation impact. This impact on the ensemble spread will, however, be
dampened and temporally lagged for deeper layers. With increasing layer
depth, the soil texture perturbations play a more important role in
determining the background error. This is visible for DA 0, where homogeneous
soil texture perturbations were applied across all layers, and increments in
the root zone are not significantly smaller than for the layers above
(Fig. ). In contrast, root-zone increments applied
within experiment DA 2 are far smaller than for the upper layers. For the
validation with in situ measurements, we showed that these larger increments
for DA 0 actually result in a slightly increased correlation over DA 1.
Increment standard deviation for ascending orbits for
June–August (a, b) and January–March (c, d) for
experiment DA 2. For the austral winter, increments are strongest for the
south of Australia, especially the agricultural areas. During austral summer,
the increments are strongest for the northern grasslands.
![]()
Concluding on the behaviour of increment bias and increment standard
deviation, it seems that there is a relationship to root-zone updates. Both
increment bias and increment standard deviation are largest for DA 1, where
the root zone is not updated at all. The top-layer increment standard
deviation decreases for DA 2, whilst also updating the root zone, with a
slight decrease of the increment bias. Compared to DA 2, the increment
standard deviation is larger in the root zone for DA 0 and the top-layer
increment bias decreases substantially.
As for the differences between the ascending and descending orbits, we
conclude that they can be partly explained by referring back to the fact that
soil moisture retrievals are expected to be of a higher quality for the
ascending orbits .
To highlight some of the seasonal effects, Fig.
shows the increment standard deviation exemplary for DA 2 and the ascending
orbit both for the months January to March and June to August. For the
austral winter, increments are largest for the agricultural areas in the
south-west and south-east, now in the growing season, and these seasonal
effects clearly dominate the average of the increment standard deviation
(compare to Fig. ). Similarly, the patterns in the
north, mostly linked to grassland, are visible in the yearly average and the
shown months contribute the most to their existence. Differing seasonal
effects of the assimilation impact were also observed by
and , although the observed patterns are distinctively
different.
Differences in relative soil moisture between open-loop and DA 0
experiments (DA 0 – OL) for the 10 % quantile. The individual panels
correspond to the top nine CLM soil layers.
![]()
Differences in relative soil moisture between open-loop and DA 1
experiments (DA 1 – OL) for 10 % quantile. The individual panels
correspond to the top nine CLM soil layers titled with their depth.
![]()
When comparing the winter patterns to areas where irrigation takes place, as
shown by , irrigated areas within the Murray–Darling
Basin can be identified through an increased increment standard deviation.
Here, the SMOS observations correct soil moisture dynamics which are not
explicitly modelled. ,
and have similarly reported on the potential of
SMOS to observe irrigation.
Differences in relative soil moisture between open-loop and DA 2
experiments (DA 2 – OL) for the 10 % quantile. The individual panels
correspond to the top nine CLM soil layers.
![]()
Soil moisture quantiles
Apart from looking at the increments, we compute a set of quantiles at
1 % intervals for each CLM soil layer and each grid point, both for the
assimilation experiments and the open-loop run. Although in principle the
assimilation system should be designed bias-free with similar positive and
negative increments, the previous section has revealed that small increment
biases do exist, potentially causing long-term effects in the resulting
analysis. Figures , and show
the 10 % quantile changes, thus very dry conditions, in relation to the
open-loop run for the top nine CLM layers. For experiment DA 1
(Fig. ), assimilation has an impact on the topmost soil layer
with the quantile increasing by a maximum of approximately 1 % for large areas and
by up to 4 % for spatially very limited areas. Much smaller changes are
visible for the second and third layers, with some areas also showing a
negative impact by up to 2 %. CLM model physics result in changes being
also visible within the root zone, CLM layers 3–6, and below. One of the
visible patterns is again south-east Australia. For the very deep layers,
some independent patterns emerge which are not visible for the above layers.
Most notably in the Nullarbor Plain, on the south coast of Australia, where
the 10 % quantile increases by up to 2 %. Such patterns are related
to strong singular increments in very dry areas which accumulate in the
deepest layers. Due to the low temporal dynamics in these lower layers, any
added water will have a lasting effect especially on lower quantiles. For
experiment DA 2 (Fig. ), with the root zone also being
updated, larger impacts on the quantiles in deeper layers can be observed.
For the most part, the patterns reflect well the ones identified for DA 1.
Figure shows the impact on the 10 % quantile for
experiment DA 0 with homogeneous soil texture perturbations being used. As
expected, significant effects are visible especially within the root zone,
with quantiles being decreased over wide areas of the Australian inland by up
to 5 %. This is the result of the mean increments being slightly negative
for inland Australia, which has a large effect when allowing large updates.
Also, since absolute soil moisture increases with layer depth due to
increasing layer thickness, removal of water in low layers increases drainage
in the above layers, resulting in these drying out. This is especially
visible for layers 2 and 3, where inland Australia to a far greater extent
shows a lowered 10 % quantile in comparison to DA 1 and DA 2. For the
lowest layer, a clear positive quantile shift is visible in the area of Lake
Eyre. The land cover map in Fig. shows this as the
only area that is classified as bare soil, although it is mostly a salt plain
with water levels of the lake itself being strongly seasonal. A number of
observations were therefore flagged, making the computation of a stable
brightness temperature climatology challenging.
Figures – focus on the changes of the 10 %
quantile. However, the spatial patterns identified do not necessarily reflect
changes at other quantile levels. The complex nature of these shifts
throughout the entire CDFs is shown in Fig. . The
continental average empirical cumulative distribution functions are plotted
for soil layers 1–6 for the open-loop run as well as for DA 1 and DA 2.
Lower quantiles are increased on average through data assimilation, although
at extremely low levels the behaviour tends to reverse again. Here, the
quantiles decrease when compared to the open loop. For the upper quantiles, a
small decrease can also be observed. The point where the decrease turns into
an increase, with the assimilation having an on average neutral impact, is
roughly the 50 % quantile for the top layer. For the subsequent layers, this
point decreases towards the 40 % quantile. Although DA 0 resulted in the
best correlation with the in situ measurements, it was disregarded at this
point since the assimilation impact was too disruptive by strongly drying out
the model across many layers.
For DA 1 and DA 2, the interplay of the quantile changes at the various
levels results in an average decrease of the standard deviation of the soil
moisture analysis, which to a certain extent could be attributed to the
anomaly rescaling. First of all, due to the low sample count, any
quantile-mapping procedure tends to be a challenge around the extremes of the
distributions. Additionally, the exact observation error for each observation
is unknown and although expected to be zero on average, with a small sample
size, the observation error might on average deviate from zero, affecting the
rescaling, since the true limits of the observation anomaly CDFs are unknown.
However, many other reasons will equally play a role and, as it has been
shown, increment bias and standard deviation are linked to certain geographic
areas. The disentanglement of the desired systematic enhancements from
erroneously introduced effects remains a challenge. There is still no perfect
approach for rescaling the observations to match the model or for calibrating
the forward observation model. Looking at long-term CDF changes induced by
assimilation can be part of evaluating these different approaches with the
final application of analysis data to be kept in mind.
Cumulative distribution functions (CDFs) for the upper six CLM soil
layers for experiments DA 1 and DA 2, based on quantiles computed for all
data across the model domain. CDFs for open-loop simulations are shown in
black and assimilation results in red. Both panels show changes in CDF
behaviour for the layers being updated in the respective experiments, i.e.
layers 1–3 for DA 1 and layers 1–6 for DA 2. Soil moisture increases
systematically with soil depth allowing for the easy identification of the
layers within the plot. The dashed vertical line marks the 10 % quantile,
corresponding to Figs. and .
![]()
Dry period in February 2010, showing only the root-zone soil moisture below the 10 %
quantile level for the open loop (a) and experiment
DA 2 (b). The different spatial extent and differences in soil
moisture itself, depending on the dataset used, on three different days are
clearly visible. The figure is centred around the central coast of New South
Wales.
![]()
Finally, as an example, we want to place quantile behaviour within the context
of possible hydrological monitoring systems which directly make use of grid
cell quantiles and empirical CDFs. The correction of short-term behaviour
alone, i.e. hourly or daily, has a minor effect when analysing phenomena that
spread across larger spatial scales and time intervals, although large
increments, e.g. due to corrected precipitation during a storm, can have an
effect on the start and end points of an observed phenomenon, such as a
drought. When classifying such an event defined at a specific quantile level,
there will be a 2-fold impact from the assimilation: the changes in the
quantile of interest as well as the change in soil moisture itself. Here, we
highlight a sample dry event on the east coast to show to what extent its
classification changes through the assimilation impact. Figure
shows root-zone soil moisture at or below the 10 %
quantile level for the open-loop run as well as the data assimilation
experiment DA 2 for soil moisture conditions in early 2010, thus at the
beginning of the assimilation period. Due to the higher 10 % quantile
for DA 2, as seen in Figs. and , the spatial extent
of the cluster for DA 2 is reduced, but the spatial patterns of soil moisture
remain largely the same. At some time periods, not shown here, a higher
degree of noise is noticeable within the assimilation dataset. This is likely
due to the fact that non-spatially correlated noise was applied to the
meteorological forcings, resulting in a heterogeneous background error field
for grid points. We thus conclude that despite having carried out the
assimilation in 1-D, spatially correlated noise is recommended for such
applications. An alternative would be to further increase the ensemble size
but at the expense of higher computational resources. Additionally, when
trying to extract meaningful statistics on the occurrence of events, such as
droughts, it might be particularly important to clean up the dataset in the
case of data assimilation using simple filter algorithms, such as those applied by
. We want to highlight the fact that the shown event is
for demonstration purposes and not linked to any major drought event, which
would require a more in-depth analysis and references to independent data
sources.
Discussion and conclusion
The Community Land Model was set up for the Australian continent and coupled
to the Community Microwave Emission Model. We have substituted the surface
datasets with higher resolution and more recent data. Additionally, we have
replaced the offline forcings with the ERA-Interim reanalysis. The
assimilation over 6 full years, from 2010 to 2015, of SMOS brightness
temperature anomalies with the LETKF improved soil moisture simulations when
compared to in situ measurements on the order of up to 11 % for top soil
moisture. Both the CLM model and the forward observation model were not
calibrated, therefore implying that the assimilation system could be applied
to other areas.
In detail, three data assimilation experiments were carried out. Within the
first experiment, the top three layers were updated, which mostly correspond
to the depth where SMOS is sensitive to changes in soil moisture and top-layer
in situ measurements are available. The correlation with top-layer soil
moisture measurements increased by 5 %; root-zone soil moisture increased by
4 %. Within the second experiment, both top soil moisture and the root zone
were updated, resulting in correlation improvements of 11 and 7 %,
respectively. The CLM is therefore able to translate top-layer updates into
deeper-layer soils. Greater improvements can be achieved by additionally
updating the root zone directly. For these two experiments, soil texture
perturbations were reduced with increasing layer depth. With CLM layer
thickness vastly increasing with depth, homogeneous soil perturbations across
all layers result in large deep-layer updates in terms of absolute soil
moisture. This was demonstrated in a third experiment, where correlation with
in situ measurements was highest compared to the first two experiments,
namely 11 and 8 % for top and root-zone soil moisture, respectively. This
coincides with the findings by , who report
that soil moisture simulations profit more from assimilation with an
exaggerated coupling between top-layer and root-zone soil moisture than vice
versa. Within this context, we interpret the overly large root-zone updates
for the third experiment, based on the large covariance between root-zone and
top-layer ensembles, as an artificially exaggerated coupling.
also state that when compared to other land
surface models, CLM actually shows an overall lower coupling strength. Larger
improvements in root-zone soil moisture simulations therefore might be
possible when using the identical assimilation setup with a different land
surface model.
Mean increments showed distinctive patterns with slight positive biases up to
1 % soil moisture in areas covered by denser vegetation and neutral to
slightly negative impact for areas mostly covered by sparse vegetation. A
possible cause could be the use of climatological LAI data, which is common
practice within current land data assimilation systems. Due to the abundance
of operational available vegetation data, we would like to encourage future
studies to look into possible improvements by using non-climatological LAI,
where cloud cover permits. Climatological LAI might especially pose a problem
for the monitoring of extreme events, such as droughts, since these tend to
result in lower LAI values again influencing the forward simulations.
Further, it might also be useful to add perturbations to the LAI data in
order to better simulate the uncertainties of the forward simulations. It is
known that remotely sensed LAI data are erroneous.
, for example, show for the Murrumbidgee Basin that
MODIS MOD15A2 estimates are too high for lightly vegetated areas and too low
for densely vegetated areas. This coincides well with the contradicting
patterns of this study with the increments showing a slight positive bias
for densely vegetated areas and vice versa.
further describe the quasi-Gaussian distribution of the LAI uncertainties
with a bias of -0.35 and a standard deviation of 0.82.
The dependence of SMOS soil moisture retrievals on landscape features, such
as vegetation, within the Murrumbidgee Basin was also shown by
. The ability of explicitly accounting for these effects
within the forward simulations and to avoid cross correlations with ancillary
data used within the soil moisture retrieval is one of the advantages of the
brightness temperature assimilation when compared to the assimilation of
retrievals. have evaluated AMSR-E soil moisture
over Australia which correlates well with in situ measurements. The seasonal
soil moisture patterns also well reflect the ones observed within this study,
most notably in the northern tropical regions as well as in the east.
argue that although vegetated areas mostly
correspond to higher soil moisture, the retrievals might also be contaminated
by the vegetation signal.
Areas of denser vegetation are also mostly linked to higher precipitation.
Seasonal variations for the increments are clearly linked to the seasonal
precipitation patterns and thus to the vegetation growing season. The
multiplicative precipitation perturbations applied here have a large impact
on the total simulated model uncertainty and the impact of the observations
across different geographic areas. applied an
average multiplicative error of 60 % over Australia, closely matching the
50 % applied in this study, for the BAWAP rain-gauge-based precipitation data
based on the analysis by . Strong spatial variations
exist for these precipitation estimates, largely influenced by the amount of
locally available gauge stations. The ERA-Interim analysis data used within
this study, produced by assimilating a multitude of both satellite as well as
in situ data, equally have errors linked to geographic areas. Estimates on
these are, however, not provided with the product. The updated ERA-5 reanalysis
data, which will include information on ensemble mean and spread, could
therefore be a significant step forward in characterising the background
error induced by the meteorological forcings when using global, non-locally
optimised data. have assimilated AMSR-E soil
moisture data across the Murrumbidgee Basin using global precipitation data
as well as locally optimised forcing data. Since the latter increases the
open-loop accuracy, the positive assimilation effect here is actually
reduced.
Within the broader context of Australian soil moisture analysis, a study
comparing soil moisture output from several models as well as satellite
products to in situ data carried out by showed that
SMOS retrievals are favourable across measurement sites, except for one with
dense tropical vegetation. This is attributed to the likely advantage of
their deeper penetration capability. The same study also shows that
retrievals from ascending orbits perform best. These findings relate to our
study, where soil moisture simulations were mostly improved across all
measurement sites and the unscaled ascending brightness temperature
acquisitions both showed a smaller bias towards the forward simulations and
had a larger effect on the soil moisture analysis. However,
report that their comparison of SMOS retrievals to in situ measurements
within the Murrumbidgee Basin showed that the descending orbits performed
better. also show that similarities are largest within
the group of the satellite retrievals and within the group of the different
model outputs, on average, with larger differences between models and
retrievals. This further motivates us to combine observations and model output
in an optimal way through data assimilation specifically for Australia, as
performed here. compare soil moisture from simple
model output to complex land surface simulations, arguing that the latter
perform better within Australia. The CLM land surface model is a fully
physical-based land surface scheme solving the energy and mass balance and
provides all data required for the forward simulation of the brightness
temperatures, allowing the full use of L-band brightness temperature
observations.
This enables the correct temporal alignment between observations and forward
simulations is especially important to achieve a high accuracy for the
forward simulations. Here, we slightly simplified the forward simulations by
merging all ascending or descending daily overpasses and computing the
forward simulations at one common time. The temporal offset is thereby
maximum 3 h, which is within the temporal resolution of the forcing data.
No artefacts were identified, although we encourage a more precise temporal
alignment within operational systems, as is mostly done. Soil moisture
retrievals are only valid for one specific time instance and inter-daily
variation of soil moisture can be considerable due to precipitation events.
The assimilation of observations into a model thus is advantageous, since it
allows for more correct daily estimates by averaging the model time steps,
which is 30 min here.
CLM uses fixed soil layer depths, which is likely the most beneficial model
structure for comparing the assimilation effects spatially, since the
covariance between observations and state variables does not vary depending on
spatially non-uniform layer depths, as is the case with some other land
surface models. The validity of updating very deep layers with information
derived from surface observations is, however, questionable. Therefore, joint
assimilation schemes also assimilating data from satellites such as GRACE are
preferable, as was done by .
Long-term assimilation effects were analysed by estimating the cumulative
distribution functions for each grid cell prior to and after assimilation. On
average, lower quantiles are shifted towards wetter conditions and higher
quantiles are slightly shifted to drier conditions, resulting in reduced
analysis variability. Spatial patterns in the quantiles do, however, change
significantly at different quantile levels. We have shown these exemplary for
the 10 % quantile. Here, for the experiment using homogeneous soil texture
perturbations across all layers, the root-zone soil moisture showed a strong
reduction compared to the open-loop run at the 10 % quantile level. Patterns
visible in the increment bias were strongly exaggerated, highlighting the
problem of too-large updates within the root zone and the general sensitivity
towards model perturbations.
The uncalibrated forward operator and the therefore necessary rescaling of
the observations might be one possible cause for the reduced analysis
variability as well as the spatial patterns. The observation rescaling is
especially a challenge around the very low or very high values. The number of
samples within these regions of the CDFs is small and the observations are
contaminated with errors, which might not be zero on average. In this case,
the tails of the observation distribution will not represent the true maximum
and minimum values. Furthermore, the standard parameterisation of the forward
operator is certainly not perfect. The spatiotemporal patterns linked to
geographic areas or specific land cover classes, such as areas of higher
vegetation, could likely be reduced by calibrating the forward observation
model towards the observations at a grid cell level. This, however, comes with
its own problems, for instance, a possible reduction of the sensitivity
towards soil moisture or several parameter sets achieving the same result
(i.e. equifinality).
Hydrological monitoring systems, where it is important to identify the
relative occurrence of certain soil moisture levels and to monitor patterns
both over space and time, are more and more likely to incorporate the
assimilation of brightness temperatures with sufficiently long data records
becoming available. have shown that data
assimilation is able to correct modelled soil moisture also at longer time
intervals from subseasonal to seasonal scale and seasonal differences in the
assimilation effect are reported across many studies, as also shown here.
Existing hydrological monitoring systems, such as the US Drought Monitor
, the African Flood and Drought Monitor
, the German Drought Monitor
or the Australian Water Resource Assessment
all use soil moisture quantiles at
grid cell level to characterise different levels of severity and facilitate
the comparison of soil moisture levels between grid cells. We have shown that
the assimilation-induced quantile changes will have an effect on the
spatiotemporal classification of areas above or below a certain quantile
level, although the characteristics of these changes will be highly dependent
on the model and data assimilation system. Hopefully, assimilation will
benefit the monitoring and analysis of future severe events, such as the
Millennium Drought in Australia . Modelling the complex
feedback processes between soil moisture and vegetation is likely best
performed using raw brightness temperatures and therefore the use of
consistent data between the land surface model and the forward simulations.
In this paper, a relatively long time series of SMOS brightness temperatures
has been assimilated into the Community Land Model across the Australian
continent and soil moisture simulations are improved for the very largest
part of in situ measurements, both for top-layer and root-zone soil moisture.
Finally, the Community Land Model is part of the Community Earth System Model
and the here-presented data assimilation system will in future also enable
the analysis of the long-term impact of L-band brightness temperature
assimilation within coupled land–atmosphere experiments.