diplomanden-doktoranden-seminar bonn, now cologne – 8 januar 2007 global one-meter soil moisture...

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

Global one-meter soil moisture fields

from satellellite

Ralf Lindau


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


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 :


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.


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

+


Application: DEKLIM

BALTIMOS within DEKLIM (Deutsches Klimaforschungsprogramm):

Validation of a 10-years climate run of the regional model REMO using SMMR. Example: Oder catchment


Application: AMSR

GEOLAND within GMES (Global Monitoring for Environment and Security):

Derivation of global soil moisture fields from AMSR

Long

term

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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.


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).


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


Mean 1979 – 1987

CPC and SMMR soil

moisture patterns are

in good agreement

CPC

SMMR


Comparison CPC - SMMR

Correlation: 0.904Global means: 272 mm / 206 mm

CPC wetter in Himalayas, Rockies, AndesCPC dryer in India, Amazonia


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


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


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


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


Correlation SM vs TB

r = -0.675 r = -0.810


Outlook

Cheerless.

Deadline is already exceeded.

Shall Dr Lindau continue anyhow

or

should he better complete the Financial Reports for EU-Projects

or

should he better finish the work he is paid for?

diplomanden-doktoranden-seminar bonn, now cologne – 8 januar 2007 global one-meter soil moisture...

Documents

temporal variance

mean variance

soil moisture variance

seminar bonn

spatial variance dominating

largest source of variance

temporal anomaly slide

soil texture0