Christian Massari, Luca Brocca, Angelica Tarpanelli, Luca Ciabatta, Tommaso Moramarco
Research Institute for Geo-Hydrological Protection (IRPI), National Research Council (CNR),
Perugia, Italy
http://hydrology.irpi.cnr.it
H-SAF and HEPEX workshopson coupled hydrology
1
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
Agriculture and Plant Production
Shallow Landslide Forecasting
Flood modelling and forecasting
Soil moisture in the hydrological cycle …
Assimilation of H-SAF SM products in Mediterranean catchments 2
1. Soil moisture important for the determination of the wetness conditions of the catchment before a flood event (antecedent wetness conditions) Brocca et al., 2009 (JHE), Beck et al. 2010 (AWR) Tramblay et al. 2012 Cousteau et al. 2012
3. Soil moisture used the improvement of the modelling of the catchment hydrological response (data assimilation): Francois et al. 2003 (JoM) Brocca et al., 2009 (JHE), 2010 (HESS) Matgen et al., 2012 (HYP) Chen et al. 2011 (AWR)-2014(JoM)
2. Soil moisture used for rainfall correction and estimation: Crow et al. 2011 (WRR)
Brocca et al. 2013, 2014 (JGR) Pellarin et al. (2013)
Added value of soil moisture observations…
Assimilation of H-SAF SM products in Mediterranean catchments 3
1st part of the presentation
2nd part of the presentation
3rd part of the presentation
H-SAF soil moisture products used
Assimilation of H-SAF SM products in Mediterranean catchments 4
Most of the climate
models projections for
the Mediterranean
basins have showed that
the region is very
sensitive to climate
change.
(IPCC 2012)
Assimilation of H-SAF SM products in Mediterranean catchments 5
Antecedent wetness conditions
Ponte Nuovo
For a given catchment the response to rainfall can be very different …
0
200
400
600
800
1000
1200
5/12
0.0
5/12
10.0
5/12
20.0
6/12
6.0
6/12
16.0
7/12
2.0
7/12
12.0
7/12
22.0
dis
ch
arg
e (
cm
/s)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
rain
fall
(m
m/0
.5h
)
Qp = 870 m3/s
Rc = 0.34
0
200
400
600
800
1000
1200
1/6
17.30
2/6
3.30
2/6
13.30
2/6
23.30
3/6
9.30
3/6
19.30
4/6
5.30
4/6
15.30
dis
ch
arg
e (
cm
/s)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
rain
fall
(m
m/0
.5h
)
Qp = 670 m3/s
Rc = 0.17
35 mm
85 mm
TIBERBASIN
Assimilation of H-SAF SM products in Mediterranean catchments, Massari et al. 6
The importance of the antecedent wetness conditions…
0.30.310.320.330.340.350.360.370.380.390.4
0.410.420.430.440.450.460.470.480.490.5
27/11/2003 27/11/2003 28/11/2003 28/11/2003
soil
mo
istu
re (
-)
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
dis
char
ge
(m³/
s) a
nd
rai
nfa
ll (
mm
/0.5
h)
observed soil moisture
discharge
rainfall
P
Rd
SOIL CONSERVATION SERVICE METHOD (SCS-CN)
Brocca et al. 2009 (JoH) , 2009 (JHE)
Antecedent wetness conditions
Assimilation of H-SAF SM products in Mediterranean catchments 7
… having an estimate of the wetness of the catchment before a flood event is crucial for understanding how severe will be our flood
25 2 4 5d d dS P R R PR
In the study of the antecedent wetness condition there is the question if some variables can be used to estimate S
API5
soil moisturebefore the flood event
11
nnsnnn
SWItmKSWISWI
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
Sobs (mm)
SW
I ()
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
Sobs (mm)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
Sobs (mm)
T=73 days T=31 daysT=41 days
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
SW
I ()
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
T=80 days T=47 daysT=33 days
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
SW
I ()
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200
T=45 days T=80 daysT=80 days
Tevere - PN
Cerfone
Timia
Assino
Genna
Topino
Niccone
Caina
Nestore
11 catchments
100-5000 km2
ItalyTiber River
ERS SCATTEROMETER SOIL MOISTURE DATA
Brocca et al.,2009 (JoH); 2009 (JHE)
Ttt
expK
KK
nnn
n
n
11
1
S vs Θ relation: ERS SCAT
Assimilation of H-SAF SM products in Mediterranean catchments 8
Many other studies agree that there is a linear inverse relationship between soil moisture and SBeck et al., 2010 (JSTARS) (Australia)Tramblay et al., 2010 (JoH), 2011 (NHESS) (France)Tramblay et al., 2012 (HESS) (Morocco)
Why do not embed directly the relation between the parameter S and the soil moisture into the hydrological model?
Exploiting the soil moisture importance …
Assimilation of H-SAF SM products in Mediterranean catchments 9
Assimilation of H-SAF SM products in Mediterranean catchments 10
0
20
40
60
80
100
0.6 0.7 0.8 0.9 1
W(t)/Wmax
S (
mm
)
“Observed” Soil
MoistureS-θ relationship Rainfall excess
calculationRouting IUH
2
( )( ( ) )( ( ) 2 )(t)
( )
a a
a
p t P t F P t F Se
P t F S
SCS-CN
Rainfall P
Observed rainfall
( )Q t( )t ( )S t ( )e t
Advantages:1) No need of continuous rainfall and evapotranspiration datasets.Good in poorly gauged areas.2) Parsimony and simplicity.Good for operational purposes.
“Simplified Continuous Rainfall Runoff” model (SCRRM, Massari et al. 2014, HESS)
Soil moisture estimate inside an event based model…
Early Warning System for Flood and Fire forecasting
R600
R400
SpataKantza
Pikermi
Penteli
Rafina
20° E
20° E
0°
0°
50
° N
50
° N
0 2 4 6 81km
Legend
Flow gauges
Metereological station
Rafina river network
Rafina cathcment
Elevation
High : 950 m
Low : 25 m
Area 109 km2
Application of the model in Attica (Greece)...
Assimilation of H-SAF SM products in Mediterranean catchments 11
16 flood events 2009-2013
2
1
2
1
1
ev
ev
T
obs sim
tT
obs obs
t
Q Q
NS
Q Q
MODEL PERFORMANCESInitial 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
MISDc
NS=1 perfect modelNS<0 bad performance
Lumped version of the model
The model has provided performances comparable with those obtained by a classical continuous model
RESULTS IN VALIDATION
The model was applied to 35 Italian catchments• areas ranging from 800 to 7400 km2
• Period 2010-2013 • 593 flood events• We used H07 and H14
Application of the model to 35 Italian catchments
Assimilation of H-SAF SM products in Mediterranean catchments 12
To obtain the root zone soil moisture we used the Exponential filter for H07
pixels inside the catchments were averaged for obtaining a single time series
i
i
i
i
it
T
ttexp
T
ttexpSSM
)t(SWI
Wagner et al., 1999 (RSE)
and a depth parameter zwhich accounts for the information given by the layers of H14
Application of the model to 35 Italian catchments: results
Assimilation of H-SAF SM products in Mediterranean catchments 13
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. 2014 (Hydrology)(Under review)MISDc model used as benchmark NS=0.66
MISDcNS =0.84
H14NS=0.81
H07NS =0.79
Po River at Carigliano Area=3570 km2
Application of the model to 35 Italian catchments
Assimilation of H-SAF SM products in Mediterranean catchments 14
Assimilation of H-SAF SM products in Mediterranean catchments 15
Exploiting soil moisture derived rainfall in flood modelling: SM2RAIN
Is it raining?
radar raingauge
Remote
sensing of
rainfall
TOP-DOWN
PERSPECTIVE
BOTTOM-UP PERSPECTIVECAN WE USE SOIL MOISTURE DATA TO INFER THE AMOUNT OF WATER FALLING INTO THE SOIL???
Remote
sensing of
soil moisture
Rainfall estimation: Top down vs bottom up perspective
Assimilation of H-SAF SM products in Mediterranean catchments 16
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 17
SM2RAIN concept
Assimilation of H-SAF SM products in Mediterranean catchments 18
SM2RAIN algorithm
precipitationsurface
runoff
evapotranspiration
drainage
soil water
capacity
relative saturation
Inverting for p(t):
= soil depth X porosity
Assuming: + +during rainfall
Z<Surface runoff: r(t)
Evapotranspiration: e(t)
Drainage: g(t)
Precipitation: p(t)
Brocca et al 2013, GRL
Assimilation of H-SAF SM products in Mediterranean catchments 19
SM2RAIN Performance: satellite soil moisture data (H07)
Correlation map between 5-day rainfall from GPCC and the rainfall product obtained from the application of SM2RAIN algorithm to ASCAT data(VALIDATION period 2010-2011)
Desert
Forest
Mountain Snow-Freeze
Assimilation of H-SAF SM products in Mediterranean catchments 20
SM2RAIN Performance: in situ data
12 sites, 5 countriesAt least one year of rainfall and soil moisture
data at hourly time scale.Most of the sites are located in Italy
Cerbara
Colorso
Ingegneria
ToranoBagnoli
Cordevole
Ressi
Bibeschbach
Valescure
Remedhus
Yanco
Salento
# Site Country Data Period Depth (cm) Climate
1 Bagnoli Southern Italy 2007-2008 25-35 Arid
2 Torano Southern Italy 2007-2010 25-35 Arid
3 Salento Southern Italy 2010-2014 15-25 Arid
4 Cerbara Central Italy 2011 5-15 Semiarid
5 Colorso Central Italy 2002-2004 5-15 Semiarid
6 Ingegneria Central Italy 2009-2010 2.5-7.5 Semiarid
7 Cordevole Northern Italy 2009 0-30 Humid
8 Ressi Northern Italy 2012-2013 0-30 Humid
9 Bibeschbach Luxembourg 2007-2008 4-7 Humid
10 Remedhus Spain 2007-2009 2.5-7.5 Arid
11 Valescure France 2008-2010 25-35 Humid
12 Yanco Australia 2001-2011 2.5-7.5 Arid
Mean R = 0.88
Can we use the SM2RAIN algorithm for improving flood modelling?
Exploiting the SM2RAIN derived rainfall…
Assimilation of H-SAF SM products in Mediterranean catchments 21
SCRRM model …
Assimilation of H-SAF SM products in Mediterranean catchments 22
0
20
40
60
80
100
0.6 0.7 0.8 0.9 1
W(t)/Wmax
S (
mm
)
“Observed” Soil
MoistureS-θ relationship Rainfall excess
calculationRouting IUH
2
( )( ( ) )( ( ) 2 )(t)
( )
a a
a
p t P t F P t F Se
P t F S
SCS-CN
Rainfall P
Observed rainfall
( )Q t( )t ( )e t( )S t
The model uses external source of soil moisture only for initialization
Integrated rainfall via Nudging
int 2( ) ( ) ( ) ( )obs SM RAIN obsP t P t K P t P t
We considered a modified configuration of the previous model …
Assimilation of H-SAF SM products in Mediterranean catchments 23
0
20
40
60
80
100
0.6 0.7 0.8 0.9 1
W(t)/Wmax
S (
mm
)
“Observed” Soil
MoistureS-θ relationship Rainfall excess
calculationRouting IUH
2
( )( ( ) )( ( ) 2 )(t)
( )
a a
a
p t P t F P t F Se
P t F S
SCS-CN
Rainfall P
Observed rainfallSM2RAIN rainfall
( )Q t( )t ( )e t( )S t
int 2( ) ( ) ( ) ( )obs SM RAIN obsP t P t K P t P t K=0 Pint=Pobs
K=1 Pint=PSM2RAIN
Initialization
Rainfall estimation
Catchment of Valescure - France
Assimilation of H-SAF SM products in Mediterranean catchments
France - ValescureArea: 3.83 km2
Elevations:
from 244 m to 815m
ASL
Mean slope: 56 %
(Tramblay et al. 2010)
30 minutes interval recorded data of - Rainfall- Temperature- Discharge- Soil moisture
(30 cm depth from a representative location)
24
0
20
40
5 100
2
2008-10-21
0
20
40
5 10 15 20 25 30 35 40 45 500
5
102008-10-31
0
5
5 10 150
0.5
12008-12-14
0
5
10
5 10 15 20 25 30 350
2
42008-12-30
0
5
10
5 10 15 20 25 30 35 40 45 500
2
42009-02-01
0
10
20
5 10 15 20 25 30 350
2
2009-10-20
0
5
10
5 10 15 200
1
22010-01-14
0
5
5 10 15 20 25 30 350
1
22010-02-16
0
5
10
5 10 15 20 25 30 350
1
2010-03-25
0
5
10
5 10 15 200
0.5
12010-03-30
0
10
20
5 100
0.5
12010-05-10
0
5
10
5 10 15 20 250
1
22010-11-20
0
5
10
5 10 15 20 250
1
2010-12-21
0
5
10
5 10 15 20 25 30 350
2
42010-12-22
Po
bs [m
m]
Q [m
3/s
]
time [h]
Qobs
Qcase-1
Qcase-6
Pobs
Improving
Results …
Assimilation of H-SAF SM products in Mediterranean catchments
we obtained an increase in performance from NS=0.49 to NS=0.83when soil moisture is used also for correcting rainfall during the flood event
25
Tiber River catchment @ Ponte Felcinocentral Italy
MISDc rainfall-runoff model
Ciabatta et al. (2014 in prep.)
A similar approach with satellite data
Assimilation of H-SAF SM products in Mediterranean catchments 26
Runoff simulation by using as
input
1. Observed rainfall
2. SM2RAINASCAT
3. SM2RAINASCAT +
Obs. Rainfall
Assimilation of H-SAF SM products in Mediterranean catchments 27
Data assimilation of soil moisture for improving flood predictions
In rainfall-runoff modelling …In the last decades a number 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. 2005 (GRL
Matgen et al., 2006 (IAHS) Matgen et al., 2006 (IAHS)
Lee et al. 2011 (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)
Matgen et al. (2011) (AWR)
Brocca et al. 2012 (IEEE TGRS)
Chen et al. 2014 (JoH)
Garreton et al. 2014 (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 ….
Data assimilation of soil moisture - is there a real benefit?
Assimilation of H-SAF SM products in Mediterranean catchments 28
Data assimilation of soil moisture - a complex recipe?
Data Assimilation ingredients
Bias Handling1) Variance matching2) Least square
rescaling3) Cdf matching4) Triple collocation
Filtering1) Soil water index
(Swi)2) Others3) No filtering
Rainfall runoff model1) Lumped 2) distributed3) Single layer4) Multiple layers
Assimilation technique1) Variational2) Sequential
Observations1) In situ2) Satellite data3) Land surface model data
Observation error1) Temporal variability
of the obs. error2) Spatial correlation
between the observations
3) Masking
“Cooking” techniques
Model error1) Model error covariance estimation (i.e. EnkF: ensemble size)2) What to perturb. (parameters, inputs, states etc …)3) How to perturb (amount of perturbation)
The problem is often not the ingredients but the cooking
technique …
Assimilation of H-SAF SM products in Mediterranean catchments 29
Data assimilation of soil moisture - a complex topic?
Assimilation of H-SAF SM products in Mediterranean catchments 30
Bias handling
Filtering Temporalvariability
Spatialvariability
What toperturb
Biascorrection
Ensemblesize
Ensembleverification
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 ….
We need to start a systematic study: Tiber River (Central Italy)
Assimilation of H-SAF SM products in Mediterranean catchments 31
Tiber River BasinBasin 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
Rainfall-runoff
data from 1989
at hourly time
resolution
6 sub-catchments(140-2080 km²)
Hydrological model
Assimilation of H-SAF SM products in Mediterranean catchments 32
RR model with 1 layer (Brocca et al., 2010 HESS)Lumped version
calibration period: 1989 - 2009assimilation of H07 during 2010 - 2013
yk
Nonlinearly propagates ensemble of model trajectories.
Can account for wide range of model errors (incl. non-additive).
xki state vector (eg soil moisture)
Pk state error covariance
Rk observation error covariance
Reichle et al., 2002 (MWR)
Ensemble Kalman Filter
Assimilation of H-SAF SM products in Mediterranean catchments 33
Ensemble size N=50 membersPerturbing parameters and inputs
ENSE
MB
LE T
EST
We assume observation error constant in time
1
1
2
k
k
k
k
ensk
ensp
ensk
mse
N
N
2
1
2
,
1
2
,
1( )
1( )
( )
Ni
k k k
i
Ni
k k o k
i
i
k k o k
ensp Q QN
mse Q QN
ensk Q Q
De Lannoy et al. 2006, JGR
SWI: Soil Water Index
t: time
SSMti: relative Surface Soil
Moisture [0,1]
ti: acquisition time of SSMti
T: characteristic time length
Data assimilation setup
Assimilation of H-SAF SM products in Mediterranean catchments 34
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)
RESCALING
i
i
i
i
it
T
ttexp
T
ttexpSSM
)t(SWI
Wagner et al., 1999 (RSE)
FILTERING
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
Chiani catchment: results
Assimilation of H-SAF SM products in Mediterranean catchments 35
Ascat Pixel
Centroid of the catchment
H07 pixel
Centroid Chiani catchmentArea 457 km2
01/01/90 01/01/95 01/01/00 01/01/05
0.40.60.8
NSE= 0.73462 ANSE= 0.79177 NSE(lnQ)= -6.6978 RQ= 0.74292
sat.
deg
.
(-)
0
10
20
rain
(mm
/h)
01/01/90 01/01/95 01/01/00 01/01/050
50
100
150
200
250
300
dis
ch
arg
e
(m
3/s
)
Q
obs
Qsim
Calibration 1989-2009 NS=0.73
01/01/10 01/01/11 01/01/12 01/01/13
0
50
NS= 0.72428
Rain
fall
(m
m/h
)
Sat.
deg
ree (
%)
01/01/10 01/01/11 01/01/12 01/01/130
100
200
300
400
500
Dis
ch
arg
e (
m3/s
)
Qobs
Qsim
Validation 2010-2013 NS=0.72
Chiani catchment
Assimilation of H-SAF SM products in Mediterranean catchments 36
Model
SSM H07
Model
FilteringSWI T=67 daysVariance matching
Brocca et al (2010)
Linear least square rescaling
Yilmaz and Crow (2012)
Chiani catchment: observation handling
Assimilation of H-SAF SM products in Mediterranean catchments 37
i
i
i
i
it
T
ttexp
T
ttexpSSM
)t(SWI
CDFrescaling
(Reichle and Koster, 2004)
Results: Chiani river basin data assimilation
Assimilation of H-SAF SM products in Mediterranean catchments 38
Rescaling: Linear least squaresobs= 0.05NSNoASS=0.72 NSASS=0.85
Results: Chiani river basin most important flood events
Assimilation of H-SAF SM products in Mediterranean catchments 39
Improving
Rescalinglinear leastsquare
sobs= 0.05
NSval=0.63
NSNoASS=0.66
NSASS=0.82
Results: Chiani river basin
Assimilation of H-SAF SM products in Mediterranean catchments 40
EFF
Variance matching Linear least square CDF
NS
sobs=0.05 sobs=0.05 sobs=0.03
NS=0.84 NS=0.85 NS=0.83
no
ass
no
ass
no
ass
no
ass
no
ass
no
ass
Effe=38.3Effv=43.0
Effe=41.2Effv=45.7
Effe=33.8Effv=38.9
2
2
,,
,,
100 1
ensM t
obs t
obs t
t
s
t
a s t Q
Qf
Q
EfQ
0Eff 0Eff
ImprovementsDeteriorations
Basin NS Validation
NS(Ass -LS)
NS(Ass - VM)
NS(Ass - CDF)
T (days)
TeverePonte Felcino
0.61 0.81(sobs=0.05)
0.81(sobs=0.03)
0.78(sobs=0.05)
56
Nestore Marsciano
0.87 0.91(sobs=0.07)
0.91(sobs=0.07)
0.91(sobs=0.12)
101
Chiani Morrano
0.72 0.85(sobs=0.05)
0.84(sobs=0.05)
0.83(sobs=0.03)
67
TopinoBevagna
0.41 0.40(sobs=0.09)
0.40(sobs=0.09)
0.40(sobs=0.09)
34
Marroggia Azzano
0.69 0.72(sobs=0.30)
0.71(sobs=0.30)
0.71(sobs=0.30)
62
NicconeMigianella
0.65 0.78(sobs=0.03)
0.79(sobs=0.03)
0.76(sobs=0.03)
68
Results for all cathcments
Assimilation of H-SAF SM products in Mediterranean catchments 41
Improving
Marginal orno improvement
DETERIORATION
IMPROVING
Assimilation
Conclusions
Assimilation of H-SAF SM products in Mediterranean catchments 42
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 in hydrological models
A lot of work remains to be done for:
• a better characterization of the errors to associate to satellite observations (there should be a stronger connection 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
Thanks for your attention
Christian [email protected]://hydrology.irpi.cnr.it/people/c.massari
A special thanks to Clement Albergel, Patricia De Rosnay, Silvia Puca, Simone Gabellani and Wolfgang Wagner