Transcript DATA ASSIMILATION 2 - — CNR

ASCAT Soil Moisture Workshop 2011
Data Assimilation 2
(Applied Data Assimiliation)
Contact:
Luca Brocca
[email protected]
Introduction
Soil moisture importance
The soil moisture governs the partition of rainfall into
runoff and infiltration. Moreover, it influences the
partitioning of the incoming energy into latent and
sensible heat components. Soil moisture, thus provides
a key link between the water and energy balances.
Numerical Weather Forecasting
Climate Prediction
Shallow Landslide Forecasting
Agriculture and Plant Production
FLOOD PREDICTION AND FORECASTING
2
1.5
800
2
600
2.5
3
400
200
3.5
Qp = 870 m3/s
Rc = 0.34
4
4.5
0
5
5/12 5/12 5/12 6/12 6/12 7/12 7/12 7/12
1200 0.0 10.0 20.0 6.0 16.0 2.0 12.0 22.0 0
85
0.5
mm
Qp = 670 m3/s
1000
1
R = 0.17
TIBER
BASIN
Ponte Nuovo
1
c
800
1.5
2
600
2.5
3
400
200
rainfall (mm/0.5h)
 Merz and Bloschl, 2009 (WRR)
 Brocca et al., 2009 (JHE), 2010 (HESS)
0.5
1000
discharge (cm/s)
Many studies highlighted the importance
of the antecedent wetness conditions to
determine the catchment hydrological
response:
0
35
mm
3.5
4
rainfall (mm/0.5h)
1200
discharge (cm/s)
Introduction
Antecedent wetness conditions
4.5
0
5
1/6 2/6 2/6 2/6 3/6 3/6 4/6 4/6
17.30 3.30 13.30 23.30 9.30 19.30 5.30 15.30
3
Introduction
Antecedent wetness conditions
ARNO
Campo et al., 2006 (HYP) CATCHMENT:
fast transition
from dry
conditions to
flood season
321 m3/s !!!
40 m3/s
Evapotraspirazione
Calore
sens.
Precipitazione
Radiazione
Volume superficiale
Vegetazione
Suolo a prevalente
comportamento
capillare
Infiltrazione
Suzione
Assorbimento
Percolazione
Suolo a prevalente
comportamento
gravitazionale
Acquifero
Deflusso
superficiale
Deflusso
ipodermico
Deflusso
di base
4
Introduction
Coarse-resolution soil moisture products
satellite
pixels
~25 km
For hydrological applications, the coarse
spatial resolution of radiometers and
scatterometers (~25 km) has initially
prevented their use and more attention was
given to the high resolution (~10 m)
Synthetic Aperture Radar (SAR) sensors.
Typical catchment
size for hydrological
studies.
"Space-borne microwave
radiometers and scatterometers
have a too coarse spatial
resolution and, hence, they do
not meet spatial requirements
for hydrological applications"
(e.g. Wang et al., 2011 (HESS)). 5
Introduction
Soil moisture scaling properties
PLOT SCALE
400-9000 m2
SMALL CATCHMENT SCALE
~50 km2
"Representative" site soil moisture (%)
50
Also randomly selecting only 5
locations the areal mean soil
moisture temporal pattern can
be estimated with high accuracy
Castel Rigone
Casale Belfiore
Val di Rosa
45
40
35
30
25
20
20
30
40
50
Mean soil moisture (%)
CATCHMENT SCALE
~250 km2
Brocca et al., 2009 (GEOD)
Brocca et al., 2010 (WRR)
6
Brocca et al., 2011 (JoH, mod.rev.)
Introduction
Soil moisture scaling properties
Due to the temporal stability and the high temporal
dynamics of soil moisture, the temporal sampling
appears more important than the spatial one
Coarse-resolution soil moisture products derived by
satellite sensors, as ASCAT, can be efficiently used for
hydrological applications
New important challenges and opportunities for the
use of this new sources of data in operational
hydrology are opened
7
Introduction
25 km (satellite) versus 0.025 m (in situ)
8
Introduction
25 km (satellite) versus 0.025 m (in situ)
Italy
ASCAT
AMSRE-PRI
Vallaccia
Modelled data
5 cm depth
ASCAT
AMSRE-NASA
AMSRE-LPRM
9
Brocca et al., 2011
(RSE)
0.5
1
1
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
0
0
0.5
0.5
SWI-CDF
SSM-CDF
1
1
AMSRE-LPRM
0
0.5
SWI-REG
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
ASCAT
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
0
SSM-REG
Correlation
coefficient between
all satellite products
and ground data sets
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
Introduction
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
25 km
(satellite)
versus
0.0250 m (in situ)
0
0.5
1
0.5
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
AMSRE-NASA
1
AMSRE-PRI
10
Introduction
Outline
1. Brief introduction to the studies investigating soil
moisture data assimilation into rainfall-runoff modelling
2. Description of the employed data assimilation
approaches
3. Data assimilation results for the Tiber River Basin
4. Open issues and conclusions
11
Introduction
Use of soil moisture data for hydrological
applications
Antecedent wetness
Antecedent wetness
conditions:
conditions:
"in-situ"
"in-situ"
Pfister
et al., 2003 (JHH)
Pfister
al.,2008
2003(HYP)
(JHH)
Huang etetal.,
Huangetetal.,
al.,2009
2008(JHE)
(HYP)
Brocca
Brocca
et 2010
al., 2009
(JHE)
Zehe
et al.,
(HESS)
Zehe et al.,
2010
(HESS)
Tramblay
et al.,
2010
(JoH)
Tramblay
et
al.,
2010
(JoH)
remote sensing
remote sensing
Goodrich
et al., 1994 (WRR)
Goodrich
et 2003
al., 1994
(WRR)
Jacobs et al.,
(JAWRA)
Jacobsetetal.,
al.,2009
2003(JoH)
(JAWRA)
Brocca
Brocca
et 2010
al., 2009
(JoH)
Beck
et al.,
(IEEE
JSTAR)
Beck et al., 2010 (IEEE JST)
Data assimilation:
Data assimilation:
"in-situ"
"in-situ" et al., 2001 (HSJ)
Loumagne
Loumagne
al., (JoH)
2001 (HSJ)
Aubert
et al.,et
2003
Aubert
2003
(JoH)
Anctil
et et
al.,al.,
2008
(JoH)
Anctil et
et al.,
al., 2010
2008 (HESS)
(JoH)
Brocca
Brocca et al., 2010 (HESS)
Calibration/validation rainfallCalibration/validation
rainfallrunoff
modelling:
runoff modelling:
"in-situ"
"in-situ" et al., 2003 (EMS)
Wooldridge
Wooldridge
et al.,
2003 (EMS)
Koren
et al., 2008
(JAWRA)
Koren et al., 2008 (JAWRA)
remote sensing
remoteetsensing
Parajka
al., 2006, 2009 (HESS)
Parajka
et
2006, 2010
2009 (HESS)
Sinclair and al.,
Pegram,
(HESS)
Sinclair and Pegram, 2010 (HESS)
remote sensing
remote
sensing
Pauwels et al., 2001, 2002
(JoH,
HP)
PauwelsFrancois
et al., 2001,
2002
(JoH,
HP)
et al., 2003 (JHM)
Francois
al.,2005
2003(GRL)
(JHM)
Crow etetal.
Crow
et al.
2005
(GRL)
Matgen
et al.,
2006
(IAHS)
Matgen et al., 2006 (IAHS)
12
Introduction
Soil moisture data assimilation
into rainfall-runoff modelling
Many studies performed synthetic experiments and tested different
techniques and approaches for soil moisture assimilation within
rainfall-runoff modelling
However, only a small number of studies demonstrated the value of
assimilating REAL in situ and remotely sensed soil moisture data to
improve runoff prediction (see also Crow and Ryu, 2009, HESS)
1.
2.
3.
4.
5.
Aubert et al., 2003 (JoH)
Francois et al., 2003 (JHM)
Chen et al., 2011 (AWR)
Matgen et al., 2011 (AWR, under review)
Brocca et al., 2010 (HESS);
Brocca et al., 2011 (IEEE TGRS, under review)
13
Introduction
Previous studies (1)
1. Ensemble Kalman
Filter
2. In-situ data
(Micronet network)
LINEAR
RESCALING
3. Daily time step
(SWAT RR model)
Chen et al, 2011 (AWR)
Cobb Creek Watershed (341 km2) - USA
"Assimilation of actual surface soil
moisture data had limited success in the
upper layers only and was generally
unsuccessful in improving stream flow
prediction. ... [ ] ... mainly due to the
SWAT decoupling between surface and
root-zone layer that limits the ability of the
EnKF to update the soil moisture states
of deeper layers."
14
Introduction
Previous studies (2)
1. Particle Filter
2. In-situ and ASCAT
soil moisture data
LINEAR
RESCALING
3. Hourly time step
(FLEX model)
Bibeschbach (10.6 km2) Luxembourg
Marginal improvement in
runoff prediction, Efficiency
score of 5%
Matgen et al, 2011 (AWR, under review)
15
Introduction
Why?
1. Spatial Mismatch: i.e. point ("in-situ") or coarse (satellite)
measurements are compared with model predicted average quantities
in space
 REPRESENTATIVENESS
2. Time Resolution: only recently soil moisture estimates from satellite
data are available with a daily (or less) temporal resolution (even if
with a coarse spatial resolution) which is required for RR applications
 DATA AVAILABILITY
3. Layer Depth: only the first 2-5 cm are investigated by remote sensing
whereas in RR models a "bucket" layer of 1-2 m is usually simulated
 ONLY SURFACE LAYER
4. Accuracy: the reliability at the catchment scale of soil moisture
estimates obtained through both in-situ measurements and satellite
data is frequently poor
 TOO LOW QUALITY
16
Methods
Soil moisture data assimilation
into rainfall-runoff modelling
1. Rainfall-runoff model:
MISDc
2. Linear rescaling
3. Data assimilation technique:
NUDGING SCHEME
17
Methods
Soil Water Index – Exponential filter
Simple differential model for describing the exchange of soil moisture
between surface layer (s) and the “reservoir”()
T: characteristic time length
Wagner et al., 1999 (RSE)
18
Methods
Soil Water Index
SWI ( t ) 

i
 t  ti 

SSM ti exp 
T 

 t  ti 
exp 

T


i

SWI:
t:
ti:
SSMti :
Soil Water Index
time
acquisition time of SSMti
relative surface soil
moisture [0,1]
T:
characteristic time length
SSM
SWI
Wagner et al., 1999 (RSE)
19
Methods
MISDc rainfall-runoff model
MISDc: "Modello Idrologico Semi-Distribuito in continuo"
EVENT-BASED
RAINFALL-RUNOFF
MODEL (MISD)
SOIL WATER BALANCE
MODEL
e(t):
evapotranspiration
upstream
discharge
r(t):
rainfall
rainfall excess
SCS-CN
S: soil potential maximum retention
W(t)/Wmax: saturation degree
Wmax
W(t)
100
W(t)
subcatchments
directly draining areas
60
linear reservoir IUH
40
outlet
discharge
20
g(t):
percolation
geomorphological IUH
S(t)
80
S (mm)
f(t):
infiltration
s(t):
saturation
excess
channel routing
diffusive linear approach
0
0.6
0.7
0.8
0.9
1
W(t)/Wmax
FREELY AVAILABLE !!!
Brocca et al., 2011 (HYP)
20
Methods
MISDc: applications
ALZETTE
02/07/07
80
sat. degree
floods
rainfall
60
40
20
0
0
5
10
20
0
0
5
10
Qobs
40
discharge
40
12
Qobs
10
Qsim
8
(m 3s-1)
SD
AWC
R
60
rain
(mm/h)
sat. deg. (%)
rain (mm/h)
80
6
4
Luxembourg
Qsim
2
(m 3s-1)
30
0
1
2
20
3
40
60
20
10
0
1
2
3 4 5
200
6
7
400
8
9
10
600
1112
13
800
Time (h)
14
15 16 17 18
1000
1200
19
rain
(mm/h)
NS= 0.83744 |ErrQp|= 0.18363 |ErrVol|= 0.16433
sat. deg. (%)
rain (mm/h)
01/01/08
01/01/05
discharge
CORDEVOLE
NS= 0.86371 |ErrQp|= 0.19872 |ErrVol|= 0.11296
80
100
Time (h)
4
120
140
160
180
North
Italy
20
21 22
1400
France
VALESCURE
80
Central
Italy
sat. degree
floods
rainfall
60
40
20
NS= 0.85256 |ErrQp|= 0.15566 |ErrVol|= 0.15953
0
50
Qobs
8
sat. deg. (%)
rain (mm/h)
01/01/90
0
rain
(mm/h)
sat. deg. (%)
rain (mm/h)
01/01/09
01/01/95
02/07/97
80
SD
AWC
R
60
40
20
0
Qsim
0
6
10
Qobs
4
2
0
1
2
3
50
4
100
5
150
6
200
7
250
Time (h)
8
300
350
9
400
10
450
(m 3s-1)
40
discharge
(m 3s-1)
discharge
02/07/92
NICCONE
rain
(mm/h)
NS= 0.88043 |ErrQp|= 0.22062 |ErrVol|= 0.26521
01/01/08
Qsim
30
20
10
0
1 2
3
4
100
5
6
200
7
8 9 10
11 12 13 14 15 16 17 18 19
20 21 22 23
300
400
500
600
700
800
Time (h)
24
900
21
Methods
MISDc: Real-time flood forecastng
Jan-2010
Flood event of
January 2010
Model implemented for real
time application for the
Umbria Region Civil
Protection Warning System:
UPPER TIBER RIVER
http://www.cfumbria.it/
22
Methods
MISDc-2L: 2-layer rainfall-runoff model
Brocca et al., 2010 (HESS)
Brocca et al., 2010 (IEEE TGRS)
Assimilation of the profile
soil moisture (SWI) ONLY
 RR MODEL with 1 LAYER
Assimilation of both SSM
and SWI
 RR MODEL with 2 LAYER
rainfall
rainfall
evapotranspiration
evapotranspiration
infiltration
infiltration
SSM
Wsupmax
SWI
percolation
Wmax
SWI
Wmax
deep percolation
deep percolation
Investigation of the impact on discharge prediction of the assimilation of surface
and root-zone soil moisture into rainfall-runoff modelling
23
Methods
Linear rescaling
standard deviation
 SWI ( t )  SWI ( t ) 
 SWI* ( t )  
  m od( t )   m od( t )
  SWI ( t ) 
mean
The SWI was rescaled to match the relative soil moisture, , simulated by MISDc,  mod
 m od
1
relative soil moisture
0.9
0.8
0.7
0.6
 SWI*
SWI
0.5
0.4
0.3
0.2
0.1
0
Jan2007
May2007
Sep2007
Jan2008
May2008
Sep2008
Jan2009
May2009
Sep2009
Jan2010
May2010
Sep2010
Jan2011
24
Nudging scheme
relative soil moisture
 mod ( t )
 ass ( t )
 SWI* ( t )
observations
 mod ( t )
modeled soil moisture
 ass ( t )
updated soil moisture
 SW I* (t )
assimilation time
time
ass ( t )  mod( t )  G  SWI* ( t )  mod( t )
Kalman GAIN
G is a constant
G=0 "perfect" model
G=1 direct insertion
model error
obs error
Brocca et al., 2010 (HESS)
25
Methods
Ensemble Kalman Filter
Nonlinearly propagates
ensemble of model trajectories.
Can account for wide range of
model errors (incl. non-additive).
yk
Reichle et al., 2002 (MWR)
xki state vector (eg soil moisture)
Pk state error covariance
Rk observation error covariance
Propagation tk-1 to tk:
xki- = f(xk-1i+) + eki
e = model error
Update at tk:
xki+ = xki- + Gk(yki - xki- )
for each ensemble member i=1…N
Gk = Pk (Pk + Rk)-1
with Pk computed from ensemble spread
26
Study Area
Tiber River Basin
Dec-2000
Rainfall-runoff
data from
1989 with
sub-hourly
time
resolution
Nov-2005
Dec-2008
Chiani at Ponticelli
Whole basin
Jan-2010
(4500 km²)
+
5 sub-catchments
(100-658 km²)27
Study Area
Alzette River Basin
Rainfall-runoff
data from
2004 with
sub-hourly
time
resolution
Alzette at
Hesperange
(279 km²)
28
Results
Data assimilation examples
Nudging scheme
1. Soil Water Index (SWI) assimilation for five Tiber River
subcatchments  impact of catchment size
2. SWI assimilation for the whole Upper Tiber River Basin at Monte
Molino (5200 km²)  impact of RR model calibration
3. SWI assimilation for the Alzette River at Hesperange (279 km²) 
impact of climatic and soil conditions
Ensemble Kalman Filter (EnKF)
1. SWI and Surface Soil Moisture (SSM) assimilation for Niccone
catchment at Migianella  impact of the assimilated quantity
2. SWI and SSM assimilation for a synthetic experiment  impact
of the assimilated quantity (ideal conditions)
29
Results: Nudging
ASCAT SWI versus modelled soil moisture
Very high
correlation
between
the ASCAT
derived
SWI and
modeled
soil
moisture
data
Tiber catchment (S.Lucia)
Assino catchment
Niccone catchment
Timia catchment
R>0.95
Brocca et al., 2010 (HESS)
30
start of flood events
relative soil moisture (-)
Results: Nudging
SWI assimilation: Tiber subcatchments
2
1
3
4
Niccone at
Migianella
(137 km²)


Eff  100 1 



SIM.
ASS.
NS
75
84
|Qp|
39
24
|Rd|
44
21
Eff
Brocca et al., 2010 (HESS)
t Qass ,t  Qobs,t 2 

2


Q

Q
t sim ,t obs,t 
39
31
Assino at Serrapartucci (165 km2)
Niccone at Migianella (137 km2)
1600
Qobs
1400
cumulated runoff (m³)
cumulated runoff (m³)
Qsim
1200
Qass
1000
800
600
400
200
0
1
11
21
31
41
51
61
71
81
2000
1800
1600
1400
1200
1000
800
600
400
200
0
91 101 111
Qobs
Qsim
Qass
1
11
21
time (hour)
31
41
51
61
71
81
91 101
time (hour)
Tiber at S.Lucia (658 km2)
The simulated discharge
with SWI assimilation
(blue line) is much closer
to observations (green
line) than the without
assimilation (red line)
cumulated runoff (m³)
Results: Nudging
Cumulated runoff
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
Qobs
Qsim
Qass
1
Brocca et al., 2010 (HESS)
11
21
31 41
51
61
71
time (hour)
81
91 101 111
32
Results: Nudging
Basin
Index
Area>500 km2
Area <200 km2
Results summary
NIC
NS
|e Qp |
|e Rd |
Eff
NS
|e Qp |
|e Rd |
Eff
NS
|e Qp |
|e Rd |
Eff
NS
|e Qp |
|e Rd |
Eff
NS
|e Qp |
|e Rd |
Eff
ASS
CHI
TIM
TEV
Synthetic experiment
Observed data
R error*
Par error*
IC error**
sim.
ass.
sim.
ass.
sim.
ass.
sim.
ass.
75
39
44
/
62
28
33
/
55
28
33
/
60
48
18
/
76
42
19
/
84
24
21
39
76
29
22
36
72
20
26
44
63
48
13
8
78
39
24
5
50
42
44
/
53
35
35
/
38
38
38
/
55
52
24
/
69
43
23
/
69
35
27
28
68
34
26
24
59
34
34
32
57
48
21
4
71
40
25
7
37
49
57
/
46
34
37
/
43
34
40
/
34
46
33
/
68
40
28
/
65
34
34
32
66
31
26
24
63
28
32
29
43
43
26
7
71
38
29
6
36
43
59
/
58
33
34
/
42
38
44
/
27
52
48
/
71
39
31
/
83
18
24
62
76
30
22
40
71
22
27
49
62
50
15
41
77
35
32
12
HIGH improvement
Eff>35%
SMALL improvement
Eff<10%
The improving in runoff prediction is higher for smaller catchments !!!
Brocca et al., 2010 (HESS)
33
sat. deg. (%)
rain (mm/h)
SWI assimilation: Tiber River at M.Molino
0
5
2005
2008
rain
(mm/h)
0
flooding
2010
"old" calibration
(1994-1998)
2000
Qobs
1500
Qsim
NS= 0.89057 |ErrQp|= 21.002 ErrQp= 8.2777 |ErrVol|= 19.4972 ErrVol= -19.4972
1000
01/01/06
100
01/01/08
01/01/10
500
2
3
4
50 1
0
100
200
300
7
6
5
400
Time (h)
500
8
600
9
700
Discharge
overestimation for flood
with very high initial soil
moisture conditions
(January 2010)
800
0
0
5
1600
2005
2008
2010
"new" calibration
(2005-2010)
NS=0.89
1400
1200
rain
(mm/h)
sat. deg. (%)
rain (mm/h)
Discharge (m3s -1)
NS=0.59
Discharge (m3s -1)
Results: Nudging
50
Qobs
1000
Qsim
800
600
400
2
200
3
4
1
0
100
200
300
7
6
5
400
Time (h)
500
8
600
9
700
800
34
0
Discharge (m 3s-1)
5
1500
Qobs
Qsim NO ASS
1000
Q
sim
ASS
NS
500
5
0
rain
(mm/h)
"old" calibration (1994-1998)
50
100
150
200
250
Time (h)
300
350
400
ASS.
52
71
Eff
4
3
2
1
SIM.
40
450
0
5
rain
(mm/h)
"new" calibration (2005-2010)
Discharge (m 3s-1)
Results: Nudging
SWI assimilation: Tiber River at M.Molino
Qobs
Q
1000
sim
NO ASS
Qsim ASS
NS
500
0
2
1
50
100
150
200
5
4
3
250
Time (h)
300
350
400
Eff
SIM.
ASS.
86
88
8
450
The improving in runoff prediction is higher for "bad" calibrated RR model35
Results: Nudging
Unknown initial conditions
NS
Unknown initial
conditions
EFF
IC
SIM.
ASS.
0
69
86
50
0.2
79
86
20
0.4
86
87
1
0.6
86
88
8
0.8
84
86
13
1.0
84
86
13
mean
81
86
18
For unknown initial conditions, the SWI assimilation significantly improves
36
runoff prediction
Results: Nudging
SWI assimilation: Tiber vs Alzette River
NO ASSIMILATION
Niccone
overestimation
Migianella
137 km2
NS=80%
Central Italy
2007-2010
Alzette
Hesperange
292 km2
NS=86%
Luxembourg
2007-2008
Brocca et al., 2011 (SPIE)
37
Results: Nudging
SWI assimilation: Tiber vs Alzette River
ASCAT ASSIMILATION
Niccone
improving
Migianella
137 km2
NS=87%
Central Italy
2007-2010
Alzette
Hesperange
292 km2
NS=85%
slightly worse
slightly better
Luxembourg
2007-2008
Brocca et al., 2011 (SPIE)
38
Results: Nudging
SWI assimilation: Tiber vs Alzette River
AMSR-E ASSIMILATION
Niccone
improving
Migianella
137 km2
NS=86%
Central Italy
2007-2010
Alzette
Hesperange
292 km2
NS=85%
slightly better
Luxembourg
2007-2008
Brocca et al., 2011 (SPIE)
39
Results: Nudging
Tiber vs Alzette River: summary
Performance in
runoff prediction
as a function of G,
the Kalman Gain
NO ASSIMILATION
DIRECT INSERTION
For central
Italy, in terms
of error on
peak discharge
and runoff
volume the
assimilation of
ASCAT soil
moisture
product
provides much
better results
than AMSRE
Brocca et al., 2011 (SPIE)
For Luxembourg, the impact of data assimilation is
40
very limited, likely due to soil freezing
Results: EnKF
ASCAT SSM (SZSM) and SWI (RZSM) vs
modelled soil moisture
Niccone
Migianella
137 km2
Central Italy
2007-2010
Good correlation between the
ASCAT derived SZSM and
modeled soil moisture data for
the surface layer (5 cm)
Brocca et al., 2011 (IEEE TGRS)
41
Results: EnKF
SZSM and RZSM assimilation
Niccone
Migianella
137 km2
Central Italy
2007-2010
SZSM
ASSIMILATION
NS (no assimilation)=76%
NS=79%
Brocca et al., 2011 (IEEE TGRS)
RZSM
ASSIMILATION
NS=86%
The assimilation of SWI has a higher impact on
runoff prediction, and better results 42
Results: EnKF
Synthetic experiments
1. OPEN LOOP
 "true" Q
 "true" SZSM
 "true" RZSM
TRUE SZSM
TRUE RZSM
TRUE discharge
2. add ERROR on forcing data and model parameters
3. perturb "true" SZSM and RZSM with Gaussian error
4. assimilation of the perturbed "true" SZSM and RZSM with
the assumed Gaussian error and with a revisit time of 1
day (50 simulations)
Brocca et al., 2011 (IEEE TGRS)
43
Results: EnKF
Synthetic experiments
SZSM ASSIMILATION
RZSM ASSIMILATION
The results of the synthetic experiments confirm the findings obtained
with real-data
Brocca et al., 2011 (IEEE TGRS)
44
Results: EnKF
Modelled SZSM vs RZSM
For the MISDc-2L structure, SZSM and RZSM are not linearly related.
Therefore, EnKF fails to correctly update the states
Brocca et al., 2011 (IEEE TGRS)
45
Open issues
Open issues
1. Better characterization of modelling errors (perturbation
factors, correlation structure, bias handling, ...)
2. Better characterization of observation errors
(autocorrelation structure, temporal and spatial
variability, ...)
3. Rainfall-runoff model structure optimization for the
assimilation of surface soil moisture data
4. Improvement of the data assimilation approach
5. Joint assimilation of surface and root-zone soil moisture
6. Joint assimilation of soil moisture and discharge
7. Assimilation performance in different climatic, soil, landuse settings
46
Conclusions
Conclusions
soil moisture data obtained from ASCAT provide useful
information for hydrological applications
new important challenges and opportunities for the use
of these new sources of data in rainfall-runoff modelling
are opened
SIMPLY TRY!
The proposed approaches (even improved) are going to be
applied for a larger number of catchments and regions.
Who would like to contribute by sharing rainfall-runoff and soil
moisture data is highly welcome 
47
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FOR FURTHER INFORMATION
URL:http://www.irpi.cnr.it/it/scheda.php?cognome=BROCCA&nome=Luca
URL IRPI:http://www.irpi.cnr.it/it/idrologia_it.htm
48