Transcript Diapositiva 1 - — CNR

Brocca, L. (1), Melone, F.(1), Moramarco, T.(1)
Matgen, P. (2), Wagner, W. (3)
(1) Research Institute for Geo-Hydrological Protection, CNR, Via Madonna Alta 126, 06128 Perugia, Italy
(2) Public Research Center - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg
(3) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria
[email protected]
EGU 2011, Wien
06-04-2011
Satellite soil moisture validation
satellite
pixels
in-situ
measurements
Brocca Luca
~25 km
HOW IS IT POSSIBLE TO
VALIDATE SATELLITE SOIL
MOISTURE ESTIMATES
VS
WITH IN-SITU
~50 cm
MEASUREMENTS?
~25 km
~50 cm
A= ~10-1 m2
Introduction
A = ~109 m2
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
Soil moisture temporal stability
PLOT SCALE
400-9000 m2
Brocca Luca
SMALL CATCHMENT SCALE
~50 km2
"Representative" site soil moisture (%)
50
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)
Brocca et al., 2011 (JoH, submitted)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
Application requirements
Brocca Luca
Journal of Hydrometeorology, 2010
"...different applications may require different, application-specific
metrics to define soil moisture measurement requirements"
FLOODS
transition between low and high soil moisture conditions
EVAPORATION
Introduction
drier portion of soil moisture range
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
Purposes
Brocca Luca
Analysis of 4 different approaches for the
validation and intercomparison of ASCAT and
AMSR-E soil moisture products:
1. comparison with in-situ observations
2. comparison with modelled data
3. comparison with antecedent wetness condition
estimates at catchment scale
4. improvement in runoff prediction through soil
moisture data assimilation into rainfall-runoff
modelling
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
1,2) Comparison with in-situ and
modelled data
Brocca Luca
Brocca et al., 2010 (RSE)
1. Co-location of in-situ (observed and modelled) and satellite Surface
Soil Moisture (SSM) product
2. Application of the exponential filter to satellite data, thus obtaining
the Soil Wetness Index (SWI), and optimization of the T parameter
(between 1 and 40 days)
3. Linear regression (REG) and CDF-matching application to satellite
data to remove the systematic differences with respect to in-situ (and
modelled) data
IN-SITU DATA
SATELLITE DATA
SATELLITE DATA
DATA
4. Computation of performance in terms ofIN-SITU
Correlation
Coefficient
dopo
applicazione (R)
"CDF matching"
and Root Mean Square Difference (RMSD) for SSM and
SWI products
SATELLITE DATA
It is worth of noting that if satellite soil moisture data have to be employed within
hydrological (or others) models through data assimilation the rescaling must be done.
"Data assimilation techniques are designed to correct random errors in the model and
rely on the assumption of unbiased background and observations. However, the model
simulations and data are typically different and need to be rescaled before data
assimilation."
Barbu et al., 2011 (BG)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
Antecedent wetness conditions
at catchment scale
Brocca Luca
15
Time (h)
10
0.8
8
5
0
2
20
10
15
20
Time (h)
SWI ()
0.7
25
30
0
35
10
2
4
6
8
10
12
Time (h)
14
16
0
2
4
5
10
15
30 Time (h)
20
10
20
10
12
Time 10
(h)
15
0
25
2
20
20
4
0
10
20
30
Time (h)
40
100
150
200
T=33 days
8
0
5
10
15
20
25
Time (h)
30
35
40
3 -1
5
10
50
0
0.1
14
16
18
15
Time (h)
10
0
15
Time (h)
6
6
20
8
15
0
5
10
10
10
0
5
15
Time (h)
20
0
30
25
17-Apr-1999 15:07:12
5
0
0
Discharge (m3s-1)
Rainfall (mm/h)
Discharge (m3s-1)
0
30
25
15
20
5
30
0
30
10
0
10
20
20
0
25
0
5
10
15
20
Time (h)
25
20-Nov-1999 23:02:24
10
15
Time (h)
20
0
30
25
30
10
30
0
40
35
1
40
5
2
5
20
20
25
3
4
40
4
40
5
0
20
20
2
4
0
30
20
6
10
3
10
0
5
10
15
20
Time (h)
25
30
0
35
0
60
0
45
0.8
0
8
0
5
10
15
Time (h)
0
25
50
20
100
150
200
T=80 days
0.7
0
0
0
0.5
0.5
0
0.4
0.4
0
0.3
0.3
0
0.2
0.2
0
0.1
0.1
0
0
0
50
100
150
200
T=41 days
0.7
0
50
100
0.8
150
200
T=73 days
0.7
0
0
0.6
0.6
0
0.5
0.5
0
0.4
0.4
0
0.3
0.3
0
0.2
0.2
0
0.1
0.1
0
0
50
100
150
Sobs (mm)
Methods
8
40
0
0.6
0
Purposes
6
5
40
0
0
20
18
4
0
10
5
0
Introduction
5
15-Nov-2000 12:00:00
0
6
2
5
Discharge (m3s-1)
0
30
25
28-Jan-2001 23:02:24
2
04-Dec-1998 01:55:12
0
10
21-Nov-2000 01:55:12
0
Rainfall (mm/h)
20
25
0.6
0
SWI ()
15
5
30
25
50
15
Time (h)
20
0
24-Dec-2000 22:04:48
20
0
8
10
10
0
10
0
25
20
15
Discharge (m3s-1)
Time (h)
6
0
5
4
10
15
10
30
1
Discharge (m3s-1)
40
6
0
30
10
0
25
6
0.8
3. Compare the “observed” S
with the different satellite soil
moisture products
5
15
15
3 30
0
Rainfall (mm/h)
25
0
2
Discharge (m3s-1)
20
0
20
10
20
20
Rainfall (mm/h)
15
20 (h)
Time
4
20
0
45
40
25
1
0.2
Discharge (m3s-1)
Rainfall (mm/h)
10
10
5
35
4
04-Jan-2001 12:00:00
2
0
4500
4000
19-Jan-1998 07:55:12
0
4
Rainfall (mm/h)
10
2
5
25
2 30
Rainfall (mm/h)
5
6
0
20
25
Time (h)
Discharge (m3s-1)
0
Discharge (m3s-1)
0
10
4
20
0
25
Discharge (m3s-1)
15
09-Feb-1999 18:57:36
Rainfall (mm/h)
10
30
Discharge (m3s-1)
5
Rainfall (mm/h)
20
0
Discharge (m3s-1)
Rainfall (mm/h)
0.1
20
40
5
20
23-Oct-1999 12:00:00
0
Rainfall (mm/h)
5
01-Apr-2000 01:55:12
5
03-May-1998 19:55:12
2
Rainfall (mm/h)
40
Discharge (m3s-1)
Discharge (m3s-1)
Rainfall (mm/h)
Rainfall (mm/h)
50
Discharge (m3s-1)
03-Jan-2001 00:57:36
0
0
Rainfall (mm/h)
100
40
10
3500
26-Dec-1997 15:07:12
0.3
40
Discharge (m3s-1)
0
11-Apr-2000 05:02:24
0
3000
Discharge (m3s-1)
0
0.2
2. For each event compute the
volume of rainfall and direct
runoff and then the “observed” S
which is the indicator of AWC at
catchment scale
2000
2500
Time (days)
27-Dec-2000 05:02:24
0
0
0.4
1500
15-Apr-1999 23:02:24
0.3
0
1000
Rainfall (mm/h)
500
0
0
0.5
40
0
0.4
8
50
0.6
0.5
0
T=80 days
0.7
20
Discharge (m3s-1)
0.6
0.8
T=45 days
Rainfall (mm/h)
0.7
100
Rainfall (mm/h)
0.8
SWI ()
1. From a rainfall-runoff time
series extract a number of flood
events
Rainfall (mm/h)
0
Discharge (m s )
3)
Study area
Results
200
0
50
100
150
Sobs (mm)
Conclusions
200
3)
0.8
Italy
Tiber River
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.6
SWI ()
0.5
0.8
0
50
SWI ()
0.7
Assino
0
100
150
200
T=33 days
0.8
0
50
100
150
200
T=80 days
0.7
0.8
0.6
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.1
0
0
50
100
150
200
T=41 days
0.7
0
50
100
0.8
150
200
T=73 days
0.7
0
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
Sobs (mm)
Methods
0.1
0
Australia
0
150
200
France
Study area
100
200
Sobs (mm)
Results
150
200
Nestore
0
150
100
T=31 days
0.1
Topino
50
50
0.7
0.6
0
Beck et al., 2010 (JSTARS)
0
50
100
Tramblay et al., 2010 (JoH), 2011 (NHESS)
150
200
T=47 days
Caina
0.8
0.6
Timia
100
0
0.6
0.1
50
0.1
Genna
0
0.8
0
0.7
0.6
Cerfone
Niccone
0
0.6
0.1
SWI ()
0.1
0.1
Tevere - PN
0
Purposes
0.8
0.7
0.6
Brocca et al., 2009 (JoH)
2009 (JHE)
0.8
0.5
0.7
11 catchments
130-4000 km2
Brocca Luca
T=80 days
T=80 days
ERS SCATTEROMETER
SOIL
0.7
0.6
MOISTURE
DATA
0.5
T=45 days
0.1
Introduction
EGU 2011, Wien
06-04-2011
Antecedent wetness conditions
at catchment scale
0
50
100
150
Sobs (mm)
Conclusions
200
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
MISDc: "Modello Idrologico Semi-Distribuito in continuo"
Brocca et al., 2011 (HYP)
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
geomorphological IUH
S(t)
80
S (mm)
f(t):
infiltration
s(t):
saturation
excess
directly draining areas
60
linear reservoir IUH
40
outlet
discharge
20
channel routing
diffusive linear approach
0
g(t):
percolation
0.6
0.7
0.8
0.9
1
W(t)/Wmax
FREELY AVAILABLE !!!
EMPLOYED FOR OPERATIONAL FLOOD FORECASTING IN CENTRAL ITALY
Introduction
Purposes
Methods
Study area
Results
Conclusions
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
 SWI ( t )  SWI ( t ) 
 SWI* ( t )  
  mod ( t )   mod ( t )
  SWI ( t ) 
saturation degree
 SWI* ( t )
The SWI was rescaled to match the saturation
degree, , simulated by the RR model,  mod
 mod ( t )
Brocca et al., 2010 (HESS)
 ass ( t )
assimilation time
time
 SWI* ( t )
observations
 mod ( t )
modeled saturation degree
 ass ( t )
updated saturation degree
 ass ( t )   mod ( t )  K  SWI* ( t )   mod ( t )
Introduction
Purposes
Methods
Study area
K is a constant
K=0 "perfect" model
K=1 direct insertion
Results
Conclusions
EGU 2011, Wien
06-04-2011
Advanced SCATterometer
• scatterometer (active microwave)
• C-band (5.7 GHz)
• VV polarization
• resolution 50/25 km
• daily coverage
• 2006 - ongoing
Brocca Luca
ASCAT
Change detection algorithm takes
account indirectly for surface
roughness and land cover variability
http://www.ipf.tuwien.ac.at/radar/dv/ascat/
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
Advanced Microwave Scanning
Radiometer
Brocca Luca
• radiometer (passive microwave)
• 6.9 - 10.7 - 18.7 - 36.5 GHz
• HH and VV polarization
• 74x43 km (6.9 GHz), 14x8 (36.5 GHz),
resampled at ~25 km
• daily coverage
• 2002 - ongoing
AMSR-E
SOIL MOISTURE PRODUCTS
NASA algorithm
VUA algorithm
It uses normalized polarization ratios
to take vegetation and roughness into
account through empirical
relationships. Soil moisture is
computed using the deviation of PR at
10.65 GHz from a baseline value
established from the monthly minima
at each grid cell.
Njoku et al. (2006) RSE
Introduction
Purposes
It is based on the Land parameter
retrieval model (LPRM) that is a threeparameter retrieval model (soil
moisture, vegetation water content,
and soil/canopy temperature) for
passive microwave data based on a
microwave radiative transfer model.
Owe et al. (2008) JGR
Methods
Study area
Polarization Ratio (PR)
It is based on the computation of the
polarization ratio of the AMSR-E
brightness temperatures at different
frequencies.
Pellarin et al. (2008) GRL
Results
Conclusions
EGU 2011, Wien
06-04-2011
In-situ soil moisture data
Brocca Luca
“Consistent validation of H-SAF
soil moisture satellite and model
products against ground
measurements for selected sites
in Europe”
http://hsaf.meteoam.it/
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
In-situ soil moisture data
Brocca Luca
In-situ soil moisture
data at different
depths (5, 10, 15, 30,
...) for a total of 17
sites across four
different countries
(Italy, France, Spain
and Luxembourg).
Considering both
observed and
modelled data:
29 DATA SETS
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
Rainfall-runoff data
CENTRAL ITALY
Tiber River
Brocca Luca
Data period:
2007-2008
# Catchments:
10
LUXEMBOURG
Alzette River
Drainage area:
10-1000 km2
Data period:
2007-2010
# Catchments:
10
Drainage area:
90-900 km2
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
1,2) Comparison with in-situ and
modelled data
Italy
Brocca Luca
ASCAT
AMSRE-PRI
AMSRE-NASA
AMSRE-LPRM
Vallaccia
Modelled data
5 cm depth
Introduction
Purposes
Methods
Study area
Results
Conclusions
Brocca et al., 2011
(RSE, submitted)
Introduction
Purposes
Methods
Study area
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
AMSRE-LPRM
0
0.5
SWI-REG
RELATIVE
SOIL
MOISTURE
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
IT-SPO-mod
IT-CER-mod
in-situ
and
IT-VAL-mod
1
0
AMSRE-NASA
Results
1
IT-SPO-mod
IT-CER-mod
1,2) Comparison with
IT-VAL-mod
data
0 modelled0.5
EGU 2011, Wien
06-04-2011
Brocca
0.5 Luca
Conclusions
1
AMSRE-PRI
Brocca et al., 2011
(RSE, submitted)
Introduction
Purposes
Methods
Study area
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
AMSRE-LPRM
0
0.5
SWI-REG
SOIL
MOISTURE
ANOMALIES
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
IT-SPO-mod
IT-CER-mod
in-situ
and
IT-VAL-mod
1
0
AMSRE-NASA
Results
1
IT-SPO-mod
IT-CER-mod
1,2) Comparison with
IT-VAL-mod
data
0 modelled0.5
EGU 2011, Wien
06-04-2011
Brocca
0.5 Luca
Conclusions
1
AMSRE-PRI
EGU 2011, Wien
06-04-2011
1,2) Comparison with in-situ and
modelled data
Brocca Luca
Italy
RELATIVE
SOIL
MOISTURE
CAPOFIUME
Modelled data
5 cm depth
SOIL
MOISTURE
ANOMALIES
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2011, Wien
06-04-2011
1,2) Comparison with in-situ and
modelled data
Brocca Luca
Spain
REM-K10
In-situ data
5 cm depth
ASCAT+AMSRE
Introduction
Purposes
Methods
Study area
Results
Conclusions
sat. degr
0.6
cdf
0.4
Nov090.2
0
EGU 2011, Wien
06-04-2011
1,2) Comparison with in-situ and
modelled data
Brocca Luca
Australia In-situ data 5 cm depth
Feb07
Jun07
Sep07
Dec07
Apr08
Jul08
Oct08
Jan09
May09
Aug09
Nov09
Jan09
Jan09
May09
May09
Aug09
Aug09
Nov09
Nov09
OZNET - M4
Australia-OZNY3-insitu-05cm
sat.
sat. degree
degree
1
0.8
IN-SITU
SWI-ASCATcdf
IN-SITU
0.6
SSM-AMSRE cdf
SSM-ASCAT
SWI-AMSREcdf
cdf
0.4
Nov09
Nov090.2
0
Feb07
Jun07
Sep07
Dec07
Dec07
Apr08
Apr08
Jul08
Jul08
Oct08
Oct08
ASCAT and AMSRE-LPRM
soil moisture products are in
perfect accordance
OZNET - Y3
1
sat. degree
0.8
IN-SITU
SWI-ASCATcdf
SWI-AMSREcdf
0.6
0.4
Nov09
0.2
0
Feb07
Introduction
Jun07
Sep07
Purposes
Dec07
Apr08
Methods
Jul08
Oct08
Study area
Jan09
Results
May09
Aug09
Nov09
Conclusions
3)
EGU 2011, Wien
06-04-2011
Antecedent wetness conditions
at catchment scale
100
ALZETTE
RIVER
ASCAT
AMSRE
ETT-ALZ
Brocca Luca
HES-ALZ
PON-MES
HUN-MIE
KAY-KAY
BET-DUD
LIV-BIB
MAM-MAM
USE-WOL
80
60
40
Soil Wetness Index []
20
0
100
80
60
40
20
0
100
80
60
40
20
0
0
50
100
150
0
50
100
150
0
50
100
Soil Potential Maximum Retention, S [mm]
Introduction
Purposes
Methods
Study area
Results
Conclusions
150
3)
EGU 2011, Wien
06-04-2011
Antecedent wetness conditions
at catchment scale
100
TIBER
RIVER
ASCAT
AMSRE
CANT
Brocca Luca
LUPO
MIGI
MONT
MORR
PALA
PONT
SERR
SLUC
80
60
40
Soil Wetness Index []
20
0
100
80
60
40
20
0
100
80
60
40
20
0
0
50
100
150
0
50
100
150
0
50
100
Soil Potential Maximum Retention, S [mm]
Introduction
Purposes
Methods
Study area
Results
Conclusions
150
3)
EGU 2011, Wien
06-04-2011
Antecedent wetness conditions
at catchment scale
TIBER RIVER
Brocca Luca
ALZETTE RIVER
-1
-1
ASCAT
-0.9
AMSRE
ASCAT
-0.9
-0.8
AMSRE
+0.06
-0.8
-0.7
-0.7
AVERAGE
LIN_ALZ
USE_WOL
MAM_MAM
LIV_BIB
BET_DUD
KAY_KAY
HUN_MIE
PON_MES
HES_ALZ
AVERAGE
SLUC
SERR
PONT
-0.3
PALA
-0.3
MORR
-0.4
MONT
-0.4
MIGI
-0.5
LUPO
-0.5
CANT
-0.6
BEVA
-0.6
ETT_ALZ
R
R
+0.11
ASCAT outperforms AMSRE-LPRM for the estimation of the Antecedent Wetness Conditions
at catchment scale, mainly for Italian catchments.
Introduction
Purposes
Methods
Study area
Results
Conclusions
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
Niccone
Migianella
137 km2
Central Italy
2007-2010
Relative soil moisture (m³/m³)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
R ASCAT = 0.94
R AMSRE-LPRM = 0.88
0.1
0
Jan
2007
Jun
2007
Oct
2007
SDsim
Mar
2008
Aug
2008
Jan
2009
SWI-REG ASCAT
Jun
2009
Nov
2009
Apr
2010
Sep
2010
Feb
2011
SWI-REG AMSRE-LPRM
High correlation coefficient between both ASCAT and AMSRE-LPRM satellite products with
simulate soil moisture data by MISDc model
Introduction
Purposes
Methods
Study area
Results
Conclusions
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
Niccone
Migianella
137 km2
Central Italy
2007-2010
NO
ASSIMILATION
Introduction
Purposes
Methods
Study area
Results
Conclusions
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
Niccone
Migianella
137 km2
Central Italy
2007-2010
ASCAT
ASSIMILATION
Introduction
Purposes
Methods
Study area
Results
Conclusions
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
Niccone
Migianella
137 km2
Central Italy
2007-2010
AMSRE-LPRM
ASSIMILATION
Introduction
Purposes
Methods
Study area
Results
Conclusions
4)
EGU 2011, Wien
06-04-2011
Data assimilation in rainfallrunoff modelling
Brocca Luca
0.9
9
Niccone
7
0.5
K
Volume Error
0
Introduction
Migianella
0.8
ASCAT
AMSRE-LPRM
0.75
Peak Discharge Error
Performance in
runoff prediction as
a function of K,
8
Kalman Gain
5
5
NS
0.85
0.7
0.5 0
0.2
0.4
K
0.6
137 km2
Central Italy
0.8
1
2007-2010
0.45
1
0.4
0.35
0.3
0.65 0
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.2
0.4
0
0.2
0.4
Purposes
Methods
K
K
0.6
0.8
1
0.6
0.8
1
Study area
Results
In terms of error on
peak discharge and
runoff volume the
assimilation of
ASCAT soil moisture
product provides
much better results
than AMSRE-LPRM
one
Conclusions
EGU 2011, Wien
06-04-2011
Conclusions
Brocca Luca
The application of different procedures for satellite soil
moisture products validation provide a better assessment of
their reliability
ASCAT and AMSRE-LPRM soil moisture products are found
reliable for soil moisture estimation across Europe
ASCAT seems to provide better results, mainly in terms of
runoff prediction
Soil moisture data obtained from remote sensing, even
though with low spatial resolution, can provide useful
information for hydrological applications
The proposed approaches (even improved) are going on 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 welcome 
Introduction
Purposes
Methods
Study area
Results
Conclusions
References cited
 Barbu, A.L. et al. (2011) Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model:
grassland case study, Biogeosciences Discuss., 8, 1831-1877, doi:10.5194/bgd-8-1831-2011, 2011.
 Beck, H.E. et al. (2010). Improving Curve Number based storm runoff estimates using soil moisture proxies. IEEE
JSTAR, 2(4), 1939-1404.
 Brocca, L., et al. (2009). Soil moisture temporal stability over experimental areas of central Italy. Geoderma, 148 (3-4),
364-374, doi:10.1016/j.geoderma.2008.11.004.
 Brocca, L., et al. (2009). Antecedent wetness conditions based on ERS scatterometer data. JoH, 364 (1-2), 73-87
 Brocca, L., et al. (2009). Assimilation of observed soil moisture data in storm rainfall-runoff modelling. JHE 14, 153-165.
 Brocca, L., et al. (2010). Improving runoff prediction through the assimilation of the ASCAT soil moisture product. HESS,
14, 1881-1893.
 Brocca, L., et al. (2010). ASCAT Soil Wetness Index validation through in-situ and modeled soil moisture data in central
Italy. RSE, 114 (11), 2745-2755
 Brocca, L., et al. (2010). Spatial-temporal variability of soil moisture and its estimation across scales. Water Resources
Research, 46, W02516, doi:10.1029/2009WR008016
 Brocca, L., et al. (2011). Coupling a soil water balance and an event-based rainfall-runoff model for flood frequency
estimation and real time flood forecasting. HYP, in press, doi:10.1002/hyp.8042.
 Brocca, L., et al. (2011). Soil moisture spatial-temporal variability at catchment scale. JoH, submitted.
 Brocca, L., et al. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and
validation study across Europe. RSE, submitted.
 Entekhabi, D. et al. (2010) Performance Metrics for Soil Moisture Retrievals and Application Requirements. JHM, 11,
832–840
 Njoku E.G. and Chan S.K. (2006) Vegetation and surface roughness effects on AMSR-E land observations RSE,
100(2), 190–199.
 Owe M., et al. (2008) Multi-sensor historical climatology of satellite-derived global land surface moisture. JGR, 113.
 Pellarin T., et al. (2008) Using spaceborne surface soil moisture to constrain satellite precipitation estimates over West
Africa, GRL, 35, L02813.
 Tramblay, Y. et al. (2010). Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling. JoH,
387, 176-187.
 Tramblay, Y. et al. (2011). Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil
moisture conditions estimation. NHESS, 11, 157-170.
FOR MORE INFORMATION
URL:http://www.irpi.cnr.it/it/scheda.php?cognom
e=BROCCA&nome=Luca
URL IRPI:http://www.irpi.cnr.it/it/idrologia_it.htm