Fourth Study Conference on BALTEX Scala Cinema Gudhjem

Multichannel Microwave Radiometer flown on Nimbus-7).
A linear regression yields:
SM1 = -1.32 t' + 21.52 rr'' - 1.341 tb10h + 5.5 (2)
where t' denotes the anomaly against the long-time local air
temperature averaged over the last 3 month. rr'' is the
anomaly against the long-term local annual cycle of rainfall
averaged over the past two months, and tb10h the
horizontally polarized brightness temperature from satellite.
A correlation of 0.609 is attained which might seem to be
relatively weak. But we have to keep in mind that the
remaining part of the variance (about 15% of the total
variance) is considered here which includes nearly the
complete error variance of the soil moisture observations.
Hence, it is more difficult to explain a larger part of it,
(which is on the other hand small compared to the total
variance).
Thus, soil moisture can be derived with a correlation of
nearly 0.8 by combining eq.(1) and eq.(2). It is obvious that
about three quarters of the spatial variance is explained in
this way, as it is treated explicitly in equation and figure 1.
For the temporal variance the circumstances are more
complicated since observation errors, annual cycle and interannual
variability are lumped together. To asses the
performance of the algorithm we computed the mean
monthly soil moisture for the entire region and compared the
results to the measurements. The mean annual cycle is
reproduced with an excellent correlation of 0.979. An
analogous procedure provides for the inter-annual variability
a value of 0.525.
4. Verification
In order to assess the algorithm's quality, it has to be applied
to independent data. Spread over Illinois, 19 soil moisture
stations operate since several decades. We extracted
measurements from the period 1979 to 1999 and computed
the long-time mean soil moisture in the uppermost meter at
each station. Such temporal averages are alternatively
derived with our proposed retrieval algorithm by using
globally available information of four climatological
parameters as given in eq.(1). An excellent agreement
between algorithm and measurement is found. The measured
total mean of 330 mm is reproduced with a deviation of only
4 mm. The accomplished quality control is quite an acid test
since the algorithm is transferred to another continent and
into a climate region with soil moistures much higher than
those prevailing in the former Soviet Union where the
algorithm has been derived.
However, at first glance the correlation between
measurements and retrieval appears with 0.235 rather low.
The explanation becomes obvious when we compare the
error variance of the algorithm with the variability that is
covered by the Illinois measurements. The error variance of
the algorithm appears as unexplained variance in figure 1
and amounts to about 51 mm 2 which is even larger than the
variability comprised in the Illinois dataset. Thus,
considered on continental scale all 19 stations in Illinois are
located in immediate mutual proximity, and represent
effectively only a single site. Hence, the detected low
correlation is a foregone conclusion.
5. Application
In the following, the presented algorithm is used to validate
the soil moisture of REMO (REgional MOdel). A ten-year
climate run provided by the MPI for Meteorology in
Hamburg is examined in two aspects according to the twostep
retrieval algorithm. The first is concerned with long-
- 13 -
Figure 3. Long-time mean soil moisture from
REMO (right) and from the retrieval (left).
term averages, the second is addressed to the temporal
variance. As an exemplary region the Oder catchment is
analysed. The modelled long-time mean soil moisture
averaged over the entire model area is almost identical to
the corresponding total mean retrieved by the algorithm.
However, strong regional differences occur especially near
the border between Sweden and Finland where the
prescribed model soil type cause extremely high soil
moistures. If true at all, such extreme values are not
resolvable by the algorithm, because the used datasets
have a coarser resolution than the model. For the Oder
catchment the temporal evolution of soil moisture is
computed as it is modelled by REMO and as it is retrieved
by our algorithm. The model shows in general a more
pronounced annual cycle and inter-annual variations found
in the model are not reflected in the retrieval. However,
the total averages are again in good agreement showing
that no regional bias is found for this particular catchment.
6. Conclusions
The major part of soil moisture variance originates from
spatial differences between long-time means at each
location. Our algorithm is capable to reproduce this
variance to a large extend by using easily available data,
i.e. precipitation, vegetation density, soil texture and
terrain slope. In a second step the temporal variance is
explained so that the retrieved annual cycle is found to be
in good agreement with the measurements. Independent
soil moisture measurements from Illinois confirm the
quality of the presented retrieval algorithm.
Using the retrieval as validation for modelled soil moisture
from REMO, large differences occur in both, the regional
long-term mean soil moisture and the temporal variance
within a region. However, total averages are in good
agreement.
References
Vinnikov, K. Ya. & I. B. Yeserkepova, Soil moisture:
empirical data and model results. J. Climate, 4, pp. 66-79,
1991.
Willmott, C. J. & S. M. Robeson, Climatologically Aided
Interpolation (CAI) of Terrestrial Air Temperature.
International Journal of Climatology, 15, pp. 221-229,
1995.