Transcript Diapositiva 1

EGU Leonardo
Conference Series on
the Hydrological Cycle
Floods in 3D:
Processes, Patterns,
Predictions
Brocca, L.1, Melone, F.1, Moramarco, T.1, Zucco, G.1
Wagner, W.2, Hasenauer, S.2, Dorigo, W.2, De Rosnay, P.3, Albergel, C.3,
Matgen, P.4, Martin, C.5, De Jeu, R.6
(1)
IRPI-CNR, Perugia, Italy
(2)
IPF-TUWIEN, Vienna, Austria
(3)
ECMWF, Reading, UK
(4)
CRP - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg
(5)
UMR-6012 ESPACE, Nice, France
(6)
VUA, Amsterdam, The Netherlands
[email protected]
http://hydrology.irpi.cnr.it/people/l.brocca
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture importance
discharge (cm/s)
1
1.5
800
2
600
2.5
3
400
3.5
Qp = 870 m3/s
Rc = 0.34
200
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
discharge (cm/s)
TIBER
BASIN
Ponte Nuovo
0.5
1000
c
1.5
800
2
600
2.5
3
400
3.5
4
200
4.5
0
Introduction
Purposes
Methods
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
Study area
Results
rainfall (mm/0.5h)
Many studies highlighted the importance
of soil moisture for flood forecasting
0
35
mm
Conclusions
rainfall (mm/0.5h)
1200
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture "appealing"
Font: SCOPUS (23rd Nov 2011)
Number of papers published on HESS in the last 2 years:
With "soil moisture" within the title/abstract/keywords:
With "soil moisture" within the title:
880
158 (18%)
58 (6.6%)
MOST CITED HESS PAPERS published in 2010-2011
Work on soil moisture to
have your paper
PUBLISHED ... and
CITED 
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture data assimilation into
rainfall-runoff modelling
Many studies performed synthetic experiments and tested different techniques and
approaches for soil moisture assimilation into rainfall-runoff modelling.
1981
However, very few studies employed real-data ... and the improvement in runoff prediction
obtained by the assimilation of soil moisture data is usually very limited.
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, in press)
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
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture data assimilation into
rainfall-runoff modelling
satellite
pixels
Typical catchment
size for
hydrological
studies.
~25 km
HYDROLOGIST
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
PLOT SCALE
400-9000 m2
SMALL CATCHMENT
SCALE ~50 km2
CATCHMENT SCALE
~250 km2
50
"Representative" site soil moisture (%)
CENTRAL ITALY
Soil moisture scaling properties
Castel Rigone
Casale Belfiore
Val di Rosa
45
40
35
30
25
20
20
30
40
50
Mean soil moisture (%)
Brocca et al., 2009 (GEOD)
Brocca et al., 2010 (WRR)
Brocca et al., 2011 (JoH, mod.rev.)
USA
AFRICA
ASIA
Cosh et al., 2006 (JoH)
Introduction
Purposes
De Rosnay et al., 2009 (JoH)
Methods
Study area
Zhao et al., 2010 (HYP)
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Available soil moisture data set
SATELLITE SENSORS
ASCAT (2007-...)
SMOS (2009-...)
NWP MODELS,
REANALYSIS, ...
layer: 1
layer: 2
70
60
0.2
50
0.4
40
LAT
LAT
70
60
0.2
50
0.4
40
0.6
0.6
30
30
-20
0
20
LON
layer: 3
40
-20
0.2
50
0.4
40
Windsat (2003-...)
40
70
60
LAT
AMSR-E (2002-...)
LAT
70
0
20
LON
layer: 4
60
0.2
50
0.4
40
0.6
30
0.6
30
-20
0
20
LON
40
-20
0
20
LON
40
SM-ASS-1 is the H-SAF
volumetric soil moisture
(root-zone) by scatterometer
assimilation in a NWP model
(ECMWF).
http://hsaf.meteoam.it/
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Purposes
Assimilation of different soil moisture product into
rainfall-runoff modelling for several catchment in
Europe and USA
PRODUCTS
CATCHMENTS
ASCAT-TUWIEN (0-5 cm)
AMSRE-LPRM (0-5 cm)
H-SAF-SMASS1 (0-100 cm)
WACMOS
Central Italy
South Italy
Luxembourg
France
USA
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture data assimilation
Rainfall-runoff model: MISDc
SOIL MOISTURE PRODUCTS
AMSR-E
SM-ASS-1
ECMWF
ASCAT
WACMOS
0-289 cm depth
4 layer
0-5 cm depth
Exponential filter
Weighted
average of the
first 3 layer
(0-100 cm)
Data
assimilation
Rescaling
ROOT-ZONE SOIL MOISTURE
(~ 0-100 cm)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture products
• scatterometer (active microwave)
• C-band (5.7 GHz)
• VV polarization
• resolution 50/25 km
• daily coverage
• 2007 - ongoing
ASCAT
2007-2010
Change detection algorithm takes account indirectly for surface roughness and land cover variability.
Wagner et al., 1999 (RSE)
• 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
2007-2010
LPRM algorithm three-parameter 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)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Soil moisture products
WACMOS
1995-2010
Water Cycle Multimission Observation Strategy (WACMOS): Merging of passive and active soil moisture
product to derive a long-term (1978-2010) global soil moisture product (http://wacmos.itc.nl/?q=node/5)
Liu et al., 2011 (HESS)
SMASS1-ECMWF
2009-Sep-2010
SM-ASS-1 has been produced continuously by
assimilation of ASCAT soil moisture in the IFS
and is available for 01 July 2008 to 30 September
2010 (http://hsaf.meteoam.it/description-sm-ass1.php).
Hydrology - Satellite Application Facility
H-SAF project (EUMETSAT)
Albergel et al., 2010 (HESS)
De Rosnay et al., 2011 (ECMWF news)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Rainfall-runoff model: MISDc
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
s(t):
saturation
excess
Wmax
W(t)
100
W(t)
subcatchments
geomorphological IUH
S(t)
80
S (mm)
f(t):
infiltration
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 !!!
http://hydrology.irpi.cnr.it/tools-and-files/misdc
Brocca et al., 2011 (HYP)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
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 )
G is a constant
G=0 "perfect" model
G=1 direct insertion
model error
Kalman GAIN
obs error
Brocca et al., 2010 (HESS), 2011 (Proc. SPIE)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Study catchments
Central Italy - Niccone Central Italy - Assino
Area=0.001-150 km²
Luxembourg - Bibesbach
France - Valescure
South Italy - Fiumarella
USA - Lucky Hills
Introduction
Purposes
Methods
Study area
Results
Conclusions
Modelled (MISDc) versus satellite and
ECMWF soil moisture products
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Niccone
137 km2
Central Italy
R(ASCAT)=0.947 - R(AMSRE)=0.885 - R(ECMWF)=0.974
Valescure
3.9 km2
France
R(ASCAT)=0.891 - R(AMSRE)=0.932 - R(ECMWF)=0.965
Bibeshbach
10.7 km2
Luxembourg
R(ASCAT)=0.797 - R(AMSRE)=0.893 - R(ECMWF)=0.968
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Runoff simulation
Niccone
Migianella
137 km2
Central Italy
2007-2010
improving
Assimilation of ASCAT
Introduction
Purposes
Methods
Study area
NS: Nash-Sutcliffe
NS=100%  perfect model!
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Runoff simulation
Niccone
Migianella
the error on peak discharge and volume increase
137 km2
Central Italy
2007-2010
improving
Assimilation of AMSRE
Introduction
Purposes
Methods
Study area
NS: Nash-Sutcliffe
NS=100%  perfect model!
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Runoff simulation
Niccone
Migianella
slight decrease in model performance
137 km2
Central Italy
2007-2010
Assimilation of ECMWF
Introduction
Purposes
Methods
Study area
NS: Nash-Sutcliffe
NS=100%  perfect model!
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Runoff simulation
IMPROVEMENT
G=0  NO ASSIMILATION
In terms of error on
peak discharge and
runoff volume the
assimilation of ASCAT
soil moisture product
provides much better
results than AMSRE
and ECMWF
In terms of NashSutcliffe efficiency, both
ASCAT and AMSR-E
provide an
improvement in runoff
prediction.
G=1  DIRECT INSERTION
IMPROVEMENT
Niccone
Migianella
137 km2
IMPROVEMENT
Central Italy
2007-2010
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Data assimilation summary

the assimilation of the ECMWF product
has a slight impact due to the limited
time period (2009-2010)

for central Italy basins the assimilation
of ASCAT and AMSR-E provide a
significant improvement in model
performance

in south Italy a slight improvement can
be yet seen

in France no improvement can be
obtained due to the difficulties of
satellite data to retrieve soil moisture
over mountain areas

in Luxembourg the impact is limited due
to the presence of snow

in USA (arid catchment) soil moisture
temporal variability is limited thus the
assimilation do not have a significant
impact
Introduction
Purposes
Methods
Study area
Results
Conclusions
WACMOS soil moisture product
FEW
DATA
1995-2000
FEW
NO DATA
2001-2004
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
DATA
2005-2006
The agreement with simulated data is good
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
WACMOS assimilation

the impact is
limited due to
the low
temporal
resolution
before 2007

the longer time
period allows a
more robust
and interesting
assessment of
the assimilation
performance
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU Leonardo 2011
Bratislava
25th Sep 2011
Brocca Luca
Conclusions
The ASCAT-TUWIEN, AMSRE-LPRM and ECMWF soil moisture
products provide a good agreement with modelled data for the
investigated catchments
The performance of soil moisture data assimilation for improving
runoff prediction depends on climatic and terrain conditions
Soil moisture data obtained from coarse-resolution sensors can
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
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 
Introduction
Purposes
Methods
Study area
Results
Conclusions
References cited
 Albergel et al. (2010). Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France,
HESS, 14, 2177-2191.
 Aubert, D. et al. (2003). Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall runoff model. JoH., 280,145161.
 Brocca, L., et al. (2009). Soil moisture temporal stability over experimental areas of central Italy. GEOD, 148 (3-4), 364-374.
 Brocca, L., et al. (2009). Antecedent wetness conditions based on ERS scatterometer data. JoH, 364 (1-2), 73-87
 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). Spatial-temporal variability of soil moisture and its estimation across scales. WRR, 46,W02516.
 Brocca, L., et al. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. HYP, 25, 2801-2813.
 Brocca, L., et al. (2011). Soil moisture spatial-temporal variability at catchment scale. JoH, moderate revision.
 Brocca, L., et al. (2011). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE TGRS, in
press.
 Brocca, L., et al. (2011). What perspective in remote sensing of soil moisture for hydrological applications by coarse-resolution sensors.
Proc. SPIE, 8174, 817408.
 Chen, F. et al. (2011). Improving hydrologic predictions of catchment model via assimilation of surface soil moisture. AWR, 34 526-535.
 Cosh, M.H. et al. (2006). Temporal stability of surface soil moisture in the Little Washita River Watershed and its applications in satellite soil
moisture product validation. JoH, 323, 168-177.
 de Rosnay, P. et al. (2009). Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali. JoH, 375, 241-252.
 De Rosnay, P. et al. (2011). Extended Kalman filter soil moisture analysis in the IFS. ECMWF newsletter, 127. 12-16.
 Francois, C. et al. (2003). Sequential assim. of ERS-1 SAR data into a coupled land surface-hydrological model using an extended Kalman
filter. JHM 4(2), 473–487.
 Jackson, T. et al. (2001). Soil moisture updating and microwave remote sensing for hydrological simulation. HSJ, 26, 3, 305-319.
 Koster, R.D. et al. (2011). Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nature Geosciences,
3 613-616.
 Liu, Y.Y. et al. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals.
HESS 15, 425-436.
 Matgen, P. et al. (2011). Can ASCAT-derived soil wetness indices reduce predictive uncertainty in well-gauged areas? A
 comparison with in situ observed soil moisture in an assimilation application. AWR, under review.
 Owe M., et al. (2008). Multi-sensor historical climatology of satellite-derived global land surface moisture. JGR, 113.
 Wagner, W., et al. (1999). A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data, RSE 70, 191-207.
 Zhao, Y. et al. (2010). Controls of surface soil moisture spatial patterns and their temporal stability in a semi-arid steppe. HYP, 24, 25072519.
This presentation is available for download at:
http://hydrology.irpi.cnr.it/repository/public/presentations/2011/egu-leonardo-2011-bratislava/
FOR FURTHER INFORMATION
URL: http://hydrology.irpi.cnr.it/people/l.brocca/
URL IRPI: http://hydrology.irpi.cnr.it/