Post on 19-Dec-2015
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Global one-meter soil moisture fields
from satellellite
Ralf Lindau
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
ANOVA of Soil Moisture measurements
Variance in mm2 Number of bins
Error of the total
mean
Seeming external variance
Error of external means
Internal variance
True external variance
Relative external variance
Annual Cycle 36 2 388 51 10343 338 3.16%
Interstation 48 2 9133 10 1558 9123 85.40%
Interannual 8 2 39 12 10654 26 0.25%
Total variance External variance Internal variance
= Variance between + Mean variance
the means of the within the subsamples subsamples
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Local longtime means
single cumulative
Climatolog. rain 58.6 58.6
Soil texture 0.5 69.0
Vegetation 37.7 72.8
Terrain slope 2.8 73.0
73% of the soil moisture variance is explained by four parameters :
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Temporal Anomalies
In a second step 10 Ghz measurements are used to retrieve the remaining temporal part of the variance.
A correlation of 0.609 is
attained.
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Two-step Retrieval
Climatological mean
derived from:
• Longterm precipitation• Soil texture• Vegetation density• Terrain slope
Temporal anomalies from:
• Brightness temperatures at 10 GHz• Anomalies of rain and air temperature
+
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Application: DEKLIM
BALTIMOS within DEKLIM (Deutsches Klimaforschungsprogramm):
Validation of a 10-years climate run of the regional model REMO using SMMR. Example: Oder catchment
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Application: AMSR
GEOLAND within GMES (Global Monitoring for Environment and Security):
Derivation of global soil moisture fields from AMSR
Long
term
mea
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empo
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nom
aly
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Reviewer 1Why is spatial variance dominating?
First, it seemed a bit surprising to me that not the annual cycle but the spatial variability is the largest source of variance. It is not fully clear to me that this is caused by the fact that also precipitation variability across locations is larger than the annual cycle, or whether this is (partly) an artefact of definitions of wilting point/field capacity, which are locally strongly varying. The latter source of variability is often filtered out while analysing land surface model results and/or compare these to observations (like in the Global Soil Wetness Project, GSWP), by comparing a scaled soil water content. It would be good to have a bit more insight in the origin of this dominating spatial variation.
I doubt that SMMR is really useful to explain the temporal variance.
Second, the (relative) contributions of various datasets is well demonstrated in the temporal mean external variability in Table 2, but a similar demonstration is missing for the temporally varying components. A similar table is actually needed to support the claim in the conclusion that SMMR data are a useful addition in this analysis.
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Reviewer 2I am not convinced (J. Fischer) that SMMR is useful.
Concerning section 6: I'm not convinced, that the usefulness of passive microwave data is demonstrated.
It is not shown, that the observations are better reproduced, if microwave data are considered. This can be done by excluding these data from the analysis, replotting figure 6 and 7 and comparing them with the old plots.
Comparison with existing datasets is indispensable!
A comparison with already existing soil moisture datasets is indispensable. Both, model based (reanalysis) and remote sensing data should be discussed (e.g. look at Dirmeyer et al., 2004, Journal of Hydrometeorology,5,1011-1033).
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Global Soil Moisture Data
NCEP-CPC: Model product (Climate Prediction Center)
Soil water column (mm)
Constant soil depth of 160 cm
Constant porosity 0.475
1948 - present
ERS: Satellite product from ERS-1 and ERS-2 (European Remote Sens.)
Active microwave (5.3 GHz) scatterometer
Soil Water Index (between wilting point and field capacity)
1992 - 2000
SMMR: Satellite plus ancillary data (rain, soil, vegetation, ...)
Passive microwave (10.7 GHz) SMMR
Soil water column (mm)
1979 - 1987
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Mean 1979 – 1987
CPC and SMMR soil
moisture patterns are
in good agreement
CPC
SMMR
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Comparison CPC - SMMR
Correlation: 0.904Global means: 272 mm / 206 mm
CPC wetter in Himalayas, Rockies, AndesCPC dryer in India, Amazonia
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Soil Water Index from ERS
Soil moisture given in SWI instead of water coulumn.
Wettest regions in cold climate.
Suspected difficulties due to permanent snow and vegetation density
Comparison to CPC shows low correlation. r = 0.435
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Temporal comparison
CPC – SMMR comparison for 1979 – 1987 for a grid box near Berlin
- CPC wetter due to deeper soil layer.
- CPC has higher variability
- But: Correlation is not bad.r = 0.652
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Map of Correlations
Correlations of 0.6 prevail over Europe.
High up to 0.8 around Adriatic, Baltic States, South Sweden, ...
Low (0.3) over the forests of Carpathians and Tatra
Problems with sea ice at coast of Bothnian and Finnish Bay
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Internal Pixel VarianceBelarussian soil moisture data
21 stations, 10 daily during about 10 years
Average spatially and compare the reduced variance to the total variance
40% are left for 400-km-Pixels59% are left for SMMR-Pixels
The left external variance is equal to the maximum correlation between point measurements and area averages
Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007
Correlation SM vs TB
r = -0.675 r = -0.810