1. The 2015 Workshop at MOISST: The Dawn of the Soil Moisture Information Age?
Stillwater 2nd June 2015
New information content from soil moisture data: rainfall estimation and flood modelling
Luca Brocca, Christian Massari, Luca Ciabatta, Tommaso Moramarco
Research Institute for Geo-Hydrological Protection (IRPI-CNR), Perugia, Italy
…and many others: W. Wagner, W. Dorigo, S. Hahn, S. Hasenauer, C. Albergel, P. De Rosnay, S. Puca, S. Gabellani, R. Parinussa, R. De Jeu, P. Matgen, D. Penna, J. Martinez-Fernandez, V. Levizzani, … 1

2. INTRODUCTION Runoff generation Drought monitoring Erosion modelling
Brocca et al. (2010, HESS+WRR, 2012 TGRS, …)
Casentino basin central Italy
30% increase of soil moisture produces a 8-fold increase of peak discharge!
Drought monitoring
Rahmani et al. (2015, JAG, in press)
Erosion modelling
Todisco et al. (2015, HESSD)
Landslide prediction
Brocca et al. (2012, RS)
Numerical Weather Prediction
Capecchi & Brocca (2014, METZET)
Plant production
Bolten et al. (2010, JSTARS)
Water quality modelling
Han et al. (2012, HYP)

3. INTRODUCTION
Soil moisture is needed by all GEO Social Benefit Areas and was ranked the second top priority parameter (behind precipitation) in a year 2010 GEO report on "Critical Earth Observation Priorities".

4. RAINFALL ESTIMATION FROM SOIL MOISTURE OBSERVATIONS: SM2RAIN
With this term we refer to the relative wetness or dryness of a watershed. In Mediterranean regions they are high variable and can have significant effect on the flow response in these systems during wet weather

5. What is SM2RAIN? SM2RAIN RAINFALL SOIL MOISTURE
The soil moisture variations are strongly related to the amount of rainfall falling into the soil. Therefore, we can use soil moisture observations for estimating rainfall by considering the “soil as a natural raingauge”.
SM2RAIN

6. SM2RAIN algorithm + during rainfall Soil water balance equation
relative saturation
surface runoff
precipitation
drainage
Soil water balance equation
soil water capacity
= soil depth X porosity
evapotranspiration
Inverting for p(t):
Assuming: +
during rainfall 6

7. SM2RAIN: in situ observations In situ soil moisture observations
calibration
validation R R
fRMSE
fRMSE 7

8. SM2RAIN: in situ observations
Actual Evapotranspiration 4%
Drainage 30%
Surface runoff 2%
Soil moisture variations 64%
Percentage contribution to the total simulated rainfall of the different components of the water balance 8

9. SM2RAIN: in situ observations
Potential Evapotranspiration
Actual Evapotranspiration
Potential Evapotranspiration during rainfall
Actual Evapotranspiration during rainfall
RAINFALL
On average, during rainfall the ratio of the sum of actual evapotranspiration and rainfall is less than 1% 9

10. GLOBAL SCALE APPLICATIONS WITH SATELLITE SOIL MOISTURE PRODUCTS
With this term we refer to the relative wetness or dryness of a watershed. In Mediterranean regions they are high variable and can have significant effect on the flow response in these systems during wet weather

11. SM2RAIN CRASH TEST
1) Global land surface simulation with the latest ECMWF land surface model (period ) driven by ERA-Interim atmospheric reanalysis
2) Extraction of modelled soil moisture data for the first three soil layers (0-7, 0-28, cm)
3) Application of SM2RAIN to modelled soil moisture data for each soil layer (0-7, 0-28, cm)
4) Comparison of SM2RAIN-derived rainfall with true rainfall data (from ERA-Interim) used to drive the land surface simulations

12. CRASH TEST: 1 layer vs 3 layers
1st soil layer (0-7 cm)
Root-zone (0-100 cm)
Proxy of the investigation depth of satellite sensors
Added-value of root-zone information, with 2 soil layers (0-28 cm) results are similar (median R=0.790).

13. CRASH TEST: timeseries
Southern USA
Siberia
Congo
Central Italy
Central Australia

14. Global scale: real satellite observations
Correlation map between 5-day rainfall from GPCC and the rainfall product obtained from the application of SM2RAIN algorithm to ASCAT, AMSR-E and SMOS data plus TMPA 3B42RT (VALIDATION period ) 14

15. Global monthly rainfall from ASCAT15

16. Application to QUIKSCAT, AMSR2
Improved algorithm
Distributed calibration
Application of filtering techniques (e.g. wavelet)
Application to QUIKSCAT (Ku-band scatterometer),
Application to AMSR2 (C+X-band radiometer) 16

17. ASCAT+QUIKSCAT vs TMPA-RT
ERA-Interim as benchmark
5-day cumulated
The correlation is 25% higher than TMPA real time rainfall product
0.508  17

18. MERGING MULTIPLE SATELLITE SOIL MOISTURE PRODUCTS
With this term we refer to the relative wetness or dryness of a watershed. In Mediterranean regions they are high variable and can have significant effect on the flow response in these systems during wet weather

19. Estimation of 5-day rainfall for 2-year data
SMOS + ASCAT
0.58
0.59
0.53
EXTENDED PERIOD:
Mean values
ASCAT
SMOS
Estimation of 5-day rainfall for 2-year data
ASCAT+ SMOS
R increases from 0.65 (0.63) to 0.78
RMSE decreases from 28.0 (30.0) to 22.8 mm (28%)

20. Merging ASCAT and AMSR2 Time step: 1-day Lon,Lat: -5,40
AMSR2+ASCAT
Lon,Lat: -5,40
Period: may 2013-dec 2014
ASCAT: Metop A+B
AMSR2: Asc+Desc, X-band
Benchmark: ERA-Interim
R=0.76
Time step: 1-day

21. Merging Metop-A and B Metop-A Blu: MetopA+MetopB Red: MetopA
Rainfall events detected only by considering both sensors Metop A and B
Blu: MetopA+MetopB
Red: MetopA
Black: rainfall
Metop-B

22. Merging Metop-A and B Time step: 1-day Metop-A Metop-B
Metop A+B improves by 14% wrt single sensors
Ciabatta et al. (2015) – JHM
Median R= 0.44
T=0 (no SWI application)
Metop A+B improves by 37% wrt Ciabatta et al. JHM (calibration period)
Data Period:
2-may-2013  31-dec-2014
Metop-A+B
Time step: 1-day

23. INTEGRATION OF SM2RAIN WITH STATE-OF-THE-ART RAINFALL PRODUCTS
With this term we refer to the relative wetness or dryness of a watershed. In Mediterranean regions they are high variable and can have significant effect on the flow response in these systems during wet weather

24. SMOS+TMPA in Australia
Period
AWAP rainfall as benchmark
Calibration with 3B42
5-day cumulated
3B42RT median R=0.714
SM2RSMOS median R=0.733
SM2RSMOS +3B42RT median R=0.757

25. ASCAT+H-SAF in Italy
Time step: 1-day

26. SM2RAIN FOR IMPROVING FLOOD MODELLING
With this term we refer to the relative wetness or dryness of a watershed. In Mediterranean regions they are high variable and can have significant effect on the flow response in these systems during wet weather

27. VALESCURE CATCHMENT (France)
IN SITU OBSERVATIONS
We obtained an increase in performance from NS=0.49 to NS=0.83 when SM2RAIN-derived rainfall is used for correcting rainfall observations during the flood event
BLACK: simulated Q with raingauge+SM2RAIN data
RED: simulated Q with raingauge data only
GREEN: observed discharge (Q)
BLUE: observed rainfall (Pobs)

28. SATELLITE OBSERVATIONS Submitted for publication
ITALY: 4 catchments
SATELLITE OBSERVATIONS
Submitted for publication
Runoff simulation by using as input
1. Observed rainfall
2. SM2RAINASCAT
3. SM2RAINASCAT + Observed Rainfall 28

29. Conclusions
Compelling evidence that it is possible to estimate rainfall from satellite soil moisture data
The soil moisture derived products are found to estimate accurately the accumulated rainfall addressing the difficulties of estimating light rainfall from state-of-the-art products
Merging multiple satellite soil moisture products allows to obtain good performance also at daily time scale
The integration of the SM-derived products with state-of-the- art products provides a significant improvement, very useful for hydrological applications
With this term we refer to the relative wetness or dryness of a watershed. In Mediterranean regions they are high variable and can have significant effect on the flow response in these systems during wet weather
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