Transcript Diapositiva 1 - — CNR

European Geosciences Union General EGU 2012
Wien
Assembly 2012
24th April 2012
Brocca Luca
Vienna, Austria, 22  27 April 2012
Soil moisture
assimilation into
rainfall-runoff
modelling:
which is the
influence of the
model structure?
Brocca L.1, Melone F.1, Moramarco T.1, Zucco G.1, Wagner, W.2
1Research
2Institute
Institute for Geo-Hydrological Protection, Perugia, Italy
of Photogrammetry and Remote Sensing, TU Wien, Vienna, Austria
[email protected]
Introduction
Purposes
Methods
Study area
http://hydrology.irpi.cnr.it/
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
Soil moisture importance
1st December 2010
very WET
1st December 2011
very DRY
90% saturation
NORMAL
NOW
10% saturation
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
Soil moisture "appealing"
MOST CITED HESS PAPERS SINCE 2010
Font: SCOPUS (2012-04-16)
Work on soil moisture to
have your paper
PUBLISHED ... and
CITED 
Introduction
Purposes
Methods
Study area
Results
Conclusions
Soil moisture data assimilation
into rainfall-runoff modelling
EGU 2012
Wien
24th April 2012
Brocca Luca
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., 2012 (AWR, in press)
Brocca et al., 2010 (HESS)
Brocca et al., 2012 (IEEE TGRS)
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
Soil moisture data assimilation
into rainfall-runoff modelling
COMPONENTS
EGU 2012
Wien
24th April 2012
Brocca Luca
SUB-COMPONENTS
Input/output data
RAINFALLRUNOFF MODEL
Model parameter values
Model structure
Technique (EKF, EnKF, PF, ...)
DATA
ASSIMILATION
BIAS handling (CDF match, ...)
Error modelling (OBS, MOD)
Accuracy
OBSERVATIONS
Spatial/temporal resolution
Layer depth
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
PURPOSES
WHICH IS THE IMPACT OF THE MODEL
STRUCTURE ON THE ASSIMILATION OF
SOIL MOISTURE DATA INTO RAINFALLRUNOFF MODELS?
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
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
MISDc-2L: 2-Layers RR model
EGU 2012
Wien
24th April 2012
Brocca Luca
Brocca et al., 2010 (HESS)
THIS STUDY
Assimilation of the profile
soil moisture (RZSM) ONLY
 RR MODEL with 1 LAYER
Assimilation of both SZSM
and RZSM
 RR MODEL with 2 LAYER
rainfall
rainfall
evapotranspiration
evapotranspiration
infiltration
infiltration
SZSM
Wsupmax
RZSM
percolation
Wmax
RZSM
Wmax
deep percolation
Introduction
Purposes
deep percolation
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
BIAS handling
LINEAR RESCALING
standard deviation
 SAT ( t )   SAT ( t ) 
 SAT* ( t )  
  MOD ( t )   MOD ( t )
   SAT ( t ) 
mean
The  SAT was rescaled to match the relative soil moisture simulated by the model,  MOD
 MOD
1
 SAT*
relative soil moisture
0.9
0.8
0.7
0.6
 SAT
0.5
0.4
0.3
0.2
0.1
0
Jan2007
May2007
Introduction
Sep2007
Jan2008
Purposes
May2008
Sep2008
Methods
Jan2009
May2009
Study area
Sep2009
Jan2010
Results
May2010
Sep2010
Jan2011
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
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
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
Study area
Niccone
Migianella
137 km2
Central Italy
Introduction
Purposes
Methods
Study area
Results
Conclusions
ASCAT soil moisture product
Introduction
Purposes
Methods
Study area
Results
EGU 2012
Wien
24th April 2012
Brocca Luca
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
EGU 2010: first results (4 floods)
start of flood
3
events
2
Niccone
Migianella
4
137 km2
1
Central Italy
2007-2008
Brocca et al., 2010 (HESS)


Eff  100 1 


Purposes
Methods
Study area
ass ,t
 Qobs ,t
sim ,t
 Qobs ,t
t
t
SIM.
ASS.
NS
75
84
|Qp|
39
24
|Rd|
44
21
Eff
Introduction
 Q
 Q
Results
39
Conclusions




2 


2
EGU 2012: 2007-2010 (21 floods)
EGU 2012
Wien
24th April 2012
Brocca Luca
Niccone
Migianella
137 km2
Central Italy
2007-2010
improving
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
MISDc-2L: EnKF
Niccone
Migianella
137 km2
Central Italy
2007-2010
Brocca et al., 2012 (IEEE TGRS)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
SZSM vs RZSM assimilation
Niccone
Migianella
137 km2
Central Italy
2007-2010
NS (no assimilation)=76% (MISDc-2L)
SZSM
ASSIMILATION
NS=79%
RZSM
ASSIMILATION
NS=86%
The assimilation of RZSM has a higher impact on runoff prediction, and better results
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
Synthetic experiment
1. OPEN LOOP
 "true" Q
 "true" SZSM
 "true" RZSM
TRUE RZSM
TRUE SZSM
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)
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
Synthetic experiment
SZSM ASSIMILATION
RZSM ASSIMILATION
The results of the synthetic experiments confirm the findings obtained
with real-data
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
Modelled SZSM vs RZSM
For the MISDc-2L structure, SZSM and RZSM are not linearly related.
Therefore, EnKF fails to correctly update the states
Introduction
Purposes
Methods
Study area
Results
Conclusions
EGU 2012
Wien
24th April 2012
Brocca Luca
CONCLUSIONS
The assimilation of satellite soil moisture product
provides an improvement in runoff prediction
The rainfall-runoff model structure has an important
role in determining the results of the data assimilation
The assimilation of SZSM has low impact on runoff
prediction
The optimization of the rainfall-runoff model structure
through the implementation of a flexible modelling
approach (SUPERFLEX) will be the object of future
investigations
Thursday, 26 Apr 2012
POSTER: EGU2012-11557
Improving hypothesis testing through the application of flexible model structures
F. Fenicia, D. Kavetski, G. Schoups, M.P. Clark, H.H.G. Savenije, and L. Pfister
Introduction
Purposes
Methods
Study area
Results
Conclusions
References
 Aubert, D. et al. (2003). Sequential assimilation of soil moisture and streamflow data in a conceptual
rainfall runoff model. JoH., 280,145-161.
 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. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood
forecasting. HYP, 25, 2801-2813.
 Brocca, L., et al. (2012). Assimilation of surface and root-zone ASCAT soil moisture products into
rainfall-runoff modelling. IEEE TGRS, 50(7), 1-14.
 Chen, F. et al. (2011). Improving hydrologic predictions of catchment model via assimilation of
surface soil moisture. AWR, 34 526-535.
 Francois, C. et al. (2003). Sequential assimilation of ERS-1 SAR data into a coupled land surfacehydrological model using EKF. JHM 4(2), 473–487.
 Jackson, T. et al. (1981). Soil moisture updating and microwave remote sensing for hydrological
simulation. HSJ, 26, 3, 305-319.
 Matgen, P. et al. (2012). 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, in press.
 Reichle R H et al. (2002). Hydrologic data assimilation with the ensemble Kalman filter. MWR, 130:
103–114.
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