1. Assimilation of H-SAF Soil moisture products
H-SAF and HEPEX workshops
on coupled hydrology
Assimilation of H-SAF Soil moisture products
for hydrological modelling in Mediterranean
catchments
Christian Massari, Luca Brocca, Angelica Tarpanelli,
Luca Ciabatta, Tommaso Moramarco Research Institute for Geo-Hydrological Protection (IRPI),
National Research Council (CNR),
Perugia, Italy

2. Numerical Weather Forecasting Climate Prediction
Soil moisture in the hydrological cycle …
Soil moisture plays a critical role in the hydrological cycle since it determines the partitioning of precipitation into runoff, evaporation and groundwater recharge) but also in atmosphere studies
Numerical Weather Forecasting
Climate Prediction
Shallow Landslide Forecasting
Agriculture and Plant Production
Flood modelling and forecasting
Assimilation of H-SAF SM products in Mediterranean catchments 2

3. Potential of soil moisture in flood modelling
1. Soil moisture used the determination of the wetness conditions of the catchment before a flood event (antecedent wetness conditions)
Brocca et al., 2009 (JHE),
Beck et al (AWR)
Tramblay et al. 2012
Cousteau et al. 2012
1st part of the presentation
2. Soil moisture used for rainfall correction and estimation:
Crow et al (WRR)
Brocca et al. 2013, 2014 (JGR)
Pellarin et al. (2013)
2nd part of the presentation
In particular … SOIL MOISTURE is a key variable in rainfall runoff transformation!!
And in rainfall estimation
3. Soil moisture used the improvement of the modelling of the catchment hydrological response (data assimilation):
Brocca et al., 2009 (JHE), 2010 (HESS)
Matgen et al., 2012 (HYP)
Chen et al (AWR)-2014(JoM)
Massari et al (a,b) HESS, AWR
3rd part of the presentation
Assimilation of H-SAF SM products in Mediterranean catchments

4. H-SAF soil moisture products
Most of the climate models projections for the Mediterranean basins have showed that the region is very sensitive to climate change.
(IPCC 2012)
In particular … SOIL MOISTURE is a key variable in rainfall runoff transformation!!
And in rainfall estimation
Assimilation of H-SAF SM products in Mediterranean catchments

5. Antecedent wetness conditions
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
Assimilation of H-SAF SM products in Mediterranean catchments

6. The importance of the antecedent wetness conditions…
Qp = 870 m3/s
Rc = 0.34
Qp = 670 m3/s
Rc = 0.17
For a given catchment the response to rainfall can be very different …
35 mm
Ponte Nuovo
TIBER BASIN
85 mm
Assimilation of H-SAF SM products in Mediterranean catchments, Massari et al.

7. Rd  P Antecedent wetness conditions
… having an estimate of the wetness of the catchment before a flood event is crucial for understanding how severe will be our flood
SOIL CONSERVATION SERVICE METHOD (SCS-CN)
soil moisture
before the
flood event Rd
In the study of the antecedent wetness condition there is the question if some variables can be used to estimate S 
API5
The concept is that …
1) A common indicator used in literature for having an estimate of the antecedent wetness condition can be obtained by inverting the relationship of the Scs
2) Sobs is an indicator of the antecedent wetness conditions at catchment scale and is an indirect measure
3) In the study of the antecedent wetness condition there is the question if some variables that can be directly measured in the catchment can be used to estimate Sobs or if there is a physical relation between Sobs and such quantity
4) A number of studies used Two common variables used are… 5) P
Brocca et al (JoH) , 2009 (JHE)
Assimilation of H-SAF SM products in Mediterranean catchments

8. S vs Θ relation: ERS SCAT
Italy Tiber River
ERS SCATTEROMETER SOIL MOISTURE DATA
Tevere - PN
Assino
Niccone
Many other studies agree that there is a linear inverse relationship between soil moisture and the antecedent wetness conditions
Beck et al., 2010 (JSTARS) (Australia) Tramblay et al., 2010 (JoH), 2011 (NHESS) (France)
Tramblay et al., 2012 (HESS) (Morocco)
11 catchments km2
Cerfone
Genna
Caina
Brocca et al., 2009 (JoH); 2009 (JHE)
Timia
Topino
Nestore
Assimilation of H-SAF SM products in Mediterranean catchments

9. Exploiting the soil moisture importance …
Why not embed directly the relation between the parameter S and the soil moisture into the hydrological model?
Assimilation of H-SAF SM products in Mediterranean catchments

10. “Observed” Soil Moisture
Soil moisture estimate inside an event based model…
“Observed” Soil Moisture
S-θ relationship
Rainfall excess calculation
Routing IUH
SCS-CN
Advantages:
1) No need of continuous rainfall and evapotranspiration datasets. Good in poorly gauged areas.
2) Parsimony and simplicity.
Good for operational purposes.
Rainfall P
Observed rainfall
“Simplified Continuous Rainfall Runoff” model
(SCRRM, Massari et al. 2014, HESS)
Assimilation of H-SAF SM products in Mediterranean catchments

11. Initial conditions from MISDc model (Brocca et al. 2011)
Application of the model in Attica (Greece)...
Area 109 km2
Initial conditions from NS
MISDc model (Brocca et al. 2011)
0.79
ERA-Land
0.80
ASCAT
0.73
AMSR-e
MODEL PERFORMANCES
Early Warning System for Flood and Fire forecasting
Mean Nash-Sutcliffe Index
Lumped version of the model
NS=1 perfect model
NS<0 bad performance
16 flood events
Event 24-Feb-2011
Era-Land
AMSR-e
ASCAT
The model has provided performance comparable with those obtained by a classical continuous model
MISDc
MISDc (Brocca et al. 2011)
Assimilation of H-SAF SM products in Mediterranean catchments

12. Initial conditions from
Application of the model in Attica (Greece)...
Area 109 km2
RESULTS IN VALIDATION
MODEL PERFORMANCES
Early Warning System for Flood and Fire forecasting
Initial conditions from NS
MISDc model
(Brocca et al. 2011)
0.55
ERA-Land
0.56
ASCAT
0.48
AMSR-e
0.47
In situ
0.43
Mean Nash-Sutcliffe Index
16 flood events
NS=1 perfect model
NS<0 bad performance
Lumped version of the model
The model has provided performance comparable with those obtained by a classical continuous model
MISDc
Assimilation of H-SAF SM products in Mediterranean catchments

13. Application of the model to 35 Italian catchments
pixels inside the catchments were averaged for obtaining a single time series
To obtain the root zone soil moisture we used the exponential filter for H07
The model was applied to 35 Italian catchments
areas ranging from 800 to 7400 km2
Period
593 flood events
We use H07 and H14
Wagner et al., 1999 (RSE)
and a depth parameter z which account for the information given by the layers of H14
Assimilation of H-SAF SM products in Mediterranean catchments

14. H14 H07 Application of the model to 35 Italian catchments: results
Results in terms of Nash-Sutcliffe efficiency index
Good
Poor
Mean NS equal to 0.58
Mean NS equal to 0.61
H14
H07
Massari et al (Hydrology)
(Under review)
MISDc model used as benchmark NS=0.66
Assimilation of H-SAF SM products in Mediterranean catchments

15. Application of the model to 35 Italian catchments
Po River at Carigliano Area=3570 km2
MISDc
NS =0.84
H07
NS =0.79
H14
NS=0.81
Sebbene il bacino sia molto strumentato il contenuto d’acqua consente di fornire una buona stima della portata questo sottolinea come esso possa essere usato con successo per la previsione delle piene
Assimilation of H-SAF SM products in Mediterranean catchments

16. Exploiting soil moisture derived rainfall in flood modelling: SM2RAIN
Another important topic which we try to respond is that if we can use soil moisture for estimating rainfall and integrating rainfall with observed for improving flood prediction
that has to do with soil moisture is if can have information of rainfall from soil moisture observation and then use this information for flood modelling
Assimilation of H-SAF SM products in Mediterranean catchments

17. BOTTOM-UP PERSPECTIVE
Rainfall estimation: Top down vs bottom up perspective
radar
raingauge
Remote sensing of rainfall
Remote sensing of soil moisture
Is it raining?
TOP-DOWN PERSPECTIVE
The concept of the method is that instead of looking at the sky to see how much rainfall we look at the ground so we used the soil as natural raingage
BOTTOM-UP PERSPECTIVE
CAN WE USE SOIL MOISTURE DATA TO INFER THE AMOUNT OF WATER FALLING INTO THE SOIL???
Assimilation of H-SAF SM products in Mediterranean catchments 17

18. SM2RAIN concept 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”.
Assimilation of H-SAF SM products in Mediterranean catchments

19. SM2RAIN algorithm + Inverting for p(t): Assuming:
Brocca et al 2013, GRL
relative saturation
surface runoff
precipitation
drainage
Z<
Surface runoff: r(t)
Evapotranspiration: e(t)
Drainage: g(t)
Precipitation: p(t)
Soil water balance equation
Soil water capacity
soil water capacity
= soil depth X porosity
evapotranspiration
Inverting for p(t):
Assuming: +
during rainfall
Assimilation of H-SAF SM products in Mediterranean catchments

20. SM2RAIN Performance: satellite soil moisture data (H07)
Snow-Freeze
Mountain
Desert
Forest
GPCC global precipitation climatology center
PCC Global Precipitation Climatology Centre monthly precipitation dataset from 1901-present is calculated from global station data.
Correlation map between 5-day rainfall from GPCC and the rainfall product obtained from the application of SM2RAIN algorithm to ASCAT data (VALIDATION period )
Assimilation of H-SAF SM products in Mediterranean catchments

21. SM2RAIN Performance: in situ data
12 sites, 5 countries
At least one year of rainfall and soil moisture data at hourly time scale. Most of the sites are located in Italy
Cerbara Colorso Ingegneria
Torano
Bagnoli
Cordevole Ressi
Bibeschbach
Valescure
Remedhus
Yanco
Salento #
Site
Country
Data Period
Depth (cm)
Climate 1
Bagnoli
Southern Italy
25-35
Arid 2
Torano 3
Salento
15-25 4
Cerbara
Central Italy
2011
5-15
Semiarid 5
Colorso 6
Ingegneria 7
Cordevole
Northern Italy
2009
0-30
Humid 8
Ressi 9
Bibeschbach
Luxembourg
4-7 10
Remedhus
Spain 11
Valescure
France 12
Yanco
Australia
GPCC global precipitation climatology center
PCC Global Precipitation Climatology Centre monthly precipitation dataset from 1901-present is calculated from global station data.
Mean R = 0.88
Assimilation of H-SAF SM products in Mediterranean catchments

22. Can we use the SM2RAIN algorithm for improving flood modelling?
Exploiting the SM2RAIN derived rainfall…
Can we use the SM2RAIN algorithm for improving flood modelling?
Assimilation of H-SAF SM products in Mediterranean catchments

23. “Observed” Soil Moisture
SCRRM model …
“Observed” Soil Moisture
S-θ relationship
Rainfall excess calculation
Routing IUH
SCS-CN
The model uses external source of soil moisture only for initialization
Rainfall P
Observed rainfall
Assimilation of H-SAF SM products in Mediterranean catchments

24. “Observed” Soil Moisture
We considered a modified configuration of the previous model …
Initialization
“Observed” Soil Moisture
S-θ relationship
Rainfall excess calculation
Routing IUH
SCS-CN
Rainfall estimation
SM2RAIN rainfall
Integrated rainfall
via Nudging
Rainfall P
Observed rainfall
Soil moisture in this case is used twice, first for initialize the model and then indipendely to correct rainfall
K=0  Pint=Pobs
K=1  Pint=PSM2RAIN
Assimilation of H-SAF SM products in Mediterranean catchments

25. France - Valescure 30 minutes interval recorded data of Rainfall
Catchment of Valescure - France
Area: 3.83 km2
Elevations:
from 244 m to 815m ASL
Mean slope: 56 %
France - Valescure
(Tramblay et al. 2010)
30 minutes interval recorded data of
Rainfall
Temperature
Discharge
Soil moisture (30 cm depth from a representative location)
Assimilation of H-SAF SM products in Mediterranean catchments

26. we obtained an increase in performance from NS=0.49 to NS=0.83
Results …
we obtained an increase in performance from NS=0.49 to NS=0.83
when soil moisture is used also for correcting rainfall during the flood event
Improving
Assimilation of H-SAF SM products in Mediterranean catchments

27. A similar approach with satellite data
Runoff simulation by using as input
1. Observed rainfall
2. SM2RAINASCAT
3. SM2RAINASCAT +
Obs. Rainfall
Tiber River Ponte Felcino central Italy
Ciabatta et al. (2014 in prep.)
MISDc rainfall-runoff model
Assimilation of H-SAF SM products in Mediterranean catchments 27

28. Data assimilation of soil moisture for improving flood predictions
Assimilation of H-SAF SM products in Mediterranean catchments

29. Satellite soil moisture …
Data assimilation of soil moisture - is there a real benefit?
In rainfall-runoff modelling …
In the last decades many studies performed data assimilation experiments and tested different techniques and approaches for soil moisture assimilation within rainfall-runoff modelling …
In situ soil moisture …
Satellite soil moisture …
Loumagne et al., 2001 (HSJ)
Houser et al. (1998) (WRR)
Aubert et al., 2003 (JoH)
Pauwels et al., 2001, 2002 (JoH, HP)
Anctil et al., 2008 (JoH)
Crow et al (GRL
Matgen et al., 2006 (IAHS)
Lee et al (AWR)
Reichle et al (2008)
Matgen et al. (2011) (AWR)
Crow and Ryu (2009) (HESS)
Brocca et al., 2010 (HESS)
Chen et al. (2011) (AWR)
Montzka et al (2011) (JoH)
Brocca et al (IEEE TGRS)
Chen et al (JoH)
Garreton et al (HESSD)
However, few studies (to our knowledge) demonstrated the real value of assimilating true soil moisture data for improving runoff prediction and there are still many controversial issues to be solved ….
Assimilation of H-SAF SM products in Mediterranean catchments

30. The problem is often not the ingredients but the cooking technique …
Data assimilation of soil moisture - a complex recipe?
Data Assimilation ingredients
Observations
In situ
Satellite data
Land surface model data
Rainfall runoff model
Lumped
distributed
Single layer
Multiple layers
Assimilation technique
Variational
Sequential
The problem is often not the ingredients but the cooking technique …
“Cooking” techniques
Bias Handling
Variance matching
Least square rescaling
Cdf matching
Triple collocation
Filtering
Soil water index (Swi)
Others
No filtering
Observation error
Temporal variability of the obs. error
Spatial correlation between the observations
Masking
Given etc… one should obtain the same resutls however.. Depending how one cooks the ingredients…
Model error
Model error covariance estimation (i.e. EnkF: ensemble size)
What to perturb. (parameters, inputs, states etc …)
How to perturb (amount of perturbation)
Assimilation of H-SAF SM products in Mediterranean catchments

31. Data assimilation of soil moisture - a complex topic?
Bias
handling
Filtering
Temporal
variability
Spatial
variability
What to
perturb
Bias
correction
Ensemble
size
Ensemble
verification
Given a model and the EnKF as a
assimilation technique we can obtain 2300
different results
The task can be even more difficult if we consider different catchment sizes and climatic conditions ….
Assimilation of H-SAF SM products in Mediterranean catchments

32. Toward a systematic study: Tiber River (Central Italy)
Tiber River Basin
Basin
Area (km2)
Tevere at Ponte Felcino
2080
Nestore at Marsciano
725
Chiani at Morrano
457
Topino at Bevagna
440
Marroggia at Azzano
258
Niccone at Migianella
137
6 sub-catchments
( km²)
Rainfall-runoff data from 1989 at hourly time resolution
Assimilation of H-SAF SM products in Mediterranean catchments 32

33. assimilation of H07 during 2010 - 2013
Hydrological model
RR model with 1 layer (Brocca et al., 2010 HESS)
Lumped version
calibration period:
assimilation of H07 during
Assimilation of H-SAF SM products in Mediterranean catchments

34. Ensemble Kalman Filteryk
Nonlinearly propagates ensemble of model trajectories.
Can account for wide range of model errors (incl. non-additive).
Reichle et al., 2002 (MWR)
xki state vector (eg soil moisture)
Pk state error covariance
Rk observation error covariance
Ensemble size N=50 members
Perturbing parameters and inputs
ENSEMBLE TEST
De Lannoy et al. 2006, JGR
We assume observation error constant in time
Assimilation of H-SAF SM products in Mediterranean catchments

35. Data assimilation setup
FLAGGING AND AVERAGING OF THE OBSERVATION
Data removed when quality flags of H07 >1
Averaging pixels fallen inside the catchment using the weighted linear inverse method
FILTERING
SWI: Soil Water Index
t: time
SSMti: relative Surface Soil Moisture [0,1]
ti: acquisition time of SSMti
T: characteristic time length
Wagner et al., 1999 (RSE)
RESCALING
1) Variance matching (Brocca et al 2010, HESS)
2) Linear least square rescaling (Yilmaz and Crow 2012, JM)
3) CDF matching (Reichle and Koster, 2004)
Assimilation of H-SAF SM products in Mediterranean catchments 35

36. Centroid of the catchment
Chiani catchment: results
Ascat Pixel
Centroid of the catchment
H07 pixel
Centroid
Chiani catchment
Area 457 km2
The satellite measure only the first centimeters of the soil while the model simulate the soil moisture of the root zoneWe have an observation and the outcome of the model whi
Assimilation of H-SAF SM products in Mediterranean catchments 36

37. Chiani catchment Calibration 1989-2009 NS=0.73
Validation NS=0.72
Calibration NS=0.73
Assimilation of H-SAF SM products in Mediterranean catchments 37

38. Linear least square rescaling
Chiani catchment: observation handling
CDF
rescaling
(Reichle and Koster, 2004)
Model
Filtering
SWI T=67 days
Model
SSM H07
Linear least square rescaling
Yilmaz and Crow (2012)
Variance matching
Brocca et al (2010)
The satellite measure only the first centimeters of the soil while the model simulate the soil moisture of the root zoneWe have an observation and the outcome of the model whi
Assimilation of H-SAF SM products in Mediterranean catchments 38

39. Results: Chiani river basin data assimilation
Rescaling: Linear least square
sobs= 0.05
NSNoASS=0.72
NSASS=0.85
Assimilation of H-SAF SM products in Mediterranean catchments 39

40. Results: Chiani river basin most important flood events
Improving
Rescaling linear least square
sobs= 0.05
NSval=0.63
NSNoASS=0.66
NSASS=0.82
Assimilation of H-SAF SM products in Mediterranean catchments 40

41. Variance matching Linear least square CDF EFF NS
Results: Chiani river basin
Variance matching
Linear least square
CDF
Effe=38.3
Effv=43.0
Effe=41.2
Effv=45.7
Effe=33.8
Effv=38.9
EFF
sobs=0.05
no ass
sobs=0.05
no ass
sobs=0.03
no ass
NS=0.84
NS=0.85
NS=0.83 NS
Deteriorations
Improvements
no ass
no ass
no ass
Assimilation of H-SAF SM products in Mediterranean catchments 41

42. Results for all cathcments Assimilation
Basin NS
Validation
(Ass -LS)
(Ass - VM)
(Ass - CDF) T
(days)
sobs
Tevere
Ponte Felcino
0.61
0.81
0.78 56
0.03
Nestore Marsciano
0.87
0.91
101
0.07
Chiani Morrano
0.72
0.85
0.84
0.83 67
0.05
Topino
Bevagna
0.41
0.40 34
0.09
Marroggia Azzano
0.69
0.71 62
0.30
Niccone Migianella
0.65
0.79
0.76 68
DETERIORATION
IMPROVING
Improving
Marginal or
no improvement
Assimilation of H-SAF SM products in Mediterranean catchments 42

43. A lot of work remain to be done for:
Conclusions
H-SAF soil moisture products may offer a great benefit in rainfall-runoff modelling in Mediterranean catchments and can be used directly both in RR modelling for operational purposes and as additional information for improving flood modelling in the contest of data assimilation
A lot of work remain to be done for:
a better characterization of the errors to associate to the satellite observations (there should be a stronger connections between users and developers)
the understanding the effect of the RR model structure, rescaling, filtering on the results of the data assimilation
Exploring the potentiality for a larger range of climatic conditions and catchment characteristics
Assimilation of H-SAF SM products in Mediterranean catchments 43

44. Thanks for your attention
Christian Massari 44