1 Research Institute for Geo-Hydrological Protection, Perugia, Italy
description
Transcript of 1 Research Institute for Geo-Hydrological Protection, Perugia, Italy
IntroductionIntroduction PurposesPurposes MethodsMethods Study areaStudy area ResultsResults ConclusionsConclusions
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11Research Institute for Geo-Hydrological Protection, Perugia, ItalyResearch Institute for Geo-Hydrological Protection, Perugia, Italy
Brocca L.Brocca L.11, Melone F.1, Moramarco T.1, Zucco G.1, Wagner, W.2
[email protected] http://hydrology.irpi.cnr.it/
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22Institute of Photogrammetry and Remote Sensing, TU Wien, Vienna, AustriaInstitute of Photogrammetry and Remote Sensing, TU Wien, Vienna, Austria
IntroductionIntroduction PurposesPurposes MethodsMethods Study areaStudy area ResultsResults ConclusionsConclusions
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1st December 20101st December 2010very WETvery WET
90% saturation
1st December 20111st December 2011very DRYvery DRY
10% saturation
NORMALNORMAL NOWNOW
Soil moisture importanceSoil moisture importanceSoil moisture importanceSoil moisture importance
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Soil moisture "appealing"Soil moisture "appealing"Soil moisture "appealing"Soil moisture "appealing"
Work on soil moisture Work on soil moisture to have your paper to have your paper PUBLISHEDPUBLISHED ... and ... and
CITEDCITED
MOST CITED HESS PAPERS SINCE 2010MOST CITED HESS PAPERS SINCE 2010Font: SCOPUS (2012-04-16)Font: SCOPUS (2012-04-16)
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Many studies performed synthetic experiments and tested different techniques and approaches for soil moisture assimilation into rainfall-runoff modelling.
1.1. Spatial MismatchSpatial Mismatch: i.e. point ("in-situ") or coarse (satellite) measurements are compared with model predicted average quantities in space REPRESENTATIVENESS
2.2. Time ResolutionTime 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
3.3. Layer DepthLayer 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
4.4. AccuracyAccuracy: 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
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)
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.
1981
Soil moisture data assimilationSoil moisture data assimilationinto rainfall-runoff modellinginto rainfall-runoff modellingSoil moisture data assimilationSoil moisture data assimilationinto rainfall-runoff modellinginto rainfall-runoff modelling
IntroductionIntroduction PurposesPurposes MethodsMethods Study areaStudy area ResultsResults ConclusionsConclusions
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Soil moisture data assimilationSoil moisture data assimilationinto rainfall-runoff modellinginto rainfall-runoff modellingSoil moisture data assimilationSoil moisture data assimilationinto rainfall-runoff modellinginto rainfall-runoff modelling
RAINFALL-RAINFALL-RUNOFF MODELRUNOFF MODEL
SUB-COMPONENTSSUB-COMPONENTS
Input/output dataInput/output data
Model parameter valuesModel parameter values
Model structureModel structure
DATA DATA ASSIMILATIONASSIMILATION
COMPONENTSCOMPONENTS
Technique (EKF, EnKF, PF, ...)Technique (EKF, EnKF, PF, ...)
BIAS handling (CDF match, ...)BIAS handling (CDF match, ...)
Error modelling (OBS, MOD)Error modelling (OBS, MOD)
OBSERVATIONSOBSERVATIONS
AccuracyAccuracy
Spatial/temporal resolutionSpatial/temporal resolution
Layer depthLayer depth
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WHICH IS THE IMPACT OF THE WHICH IS THE IMPACT OF THE MODEL MODEL STRUCTURESTRUCTURE ON THE ASSIMILATION OF ON THE ASSIMILATION OF SOIL MOISTURE DATA INTO RAINFALL-SOIL MOISTURE DATA INTO RAINFALL-
RUNOFF MODELS?RUNOFF MODELS?
PURPOSESPURPOSESPURPOSESPURPOSES
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0
20
40
60
80
100
0.6 0.7 0.8 0.9 1
W(t)/Wmax
S (
mm
)
MISDc: "Modello Idrologico Semi-Distribuito in continuo"MISDc: "Modello Idrologico Semi-Distribuito in continuo"
W(t)W(t) S(t)S(t)
outlet discharge
upstream discharge
directly draining areaslinear reservoir IUH
EVENT-BASED EVENT-BASED RAINFALL-RUNOFF RAINFALL-RUNOFF
MODEL (MISD)MODEL (MISD)
subcatchmentsgeomorphological IUH
channel routingdiffusive linear approach
rainfall excessSCS-CN
e(t):evapotranspiration
f(t):infiltration
g(t):percolation
WmaxW(t)
s(t):saturationexcess
SOIL WATER BALANCE SOIL WATER BALANCE MODELMODEL
S: soil potential maximum retentionW(t)/Wmax: saturation degree
S: soil potential maximum retentionW(t)/Wmax: saturation degree
FREELY AVAILABLE !!!http://hydrology.irpi.cnr.it/tools-and-files/misdc
r(t):rainfall
Brocca et al., 2011 (HYP)
Rainfall-runoff model: MISDcRainfall-runoff model: MISDcRainfall-runoff model: MISDcRainfall-runoff model: MISDc
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infiltration
evapotranspiration
deep percolation
Wmax
rainfall
Brocca et al., 2010 (HESS)
Assimilation of the profile soil moisture (RZSM) ONLY RR MODEL with 1 LAYER
RZSMthe MISDc model simulates the soil moisture storage of 1 layer
evapotranspiration
infiltration
deep percolation
Wsupmax
Wmax
rainfall
percolation
THIS STUDY
Assimilation of both SZSM and RZSM RR MODEL with 2 LAYER
surface layer
RZSM
SZSM
MISDc-2L: 2-Layers RR modelMISDc-2L: 2-Layers RR modelMISDc-2L: 2-Layers RR modelMISDc-2L: 2-Layers RR model
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Jan-2007
May-2007
Sep-2007
Jan-2008
May-2008
Sep-2008
Jan-2009
May-2009
Sep-2009
Jan-2010
May-2010
Sep-2010
Jan-2011
rela
tive
so
il m
ois
ture
The SAT was rescaled to match the relative soil moisture simulated by the model, MOD
)t( )t()t(
)t()t()t( MODMOD
SAT
SATSAT*SAT
*SAT
SAT
MOD
meanstandard deviation
BIAS handlingBIAS handlingBIAS handlingBIAS handling
LINEAR RESCALINGLINEAR RESCALINGLINEAR RESCALINGLINEAR RESCALING
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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
Propagation tk-1 to tk:
xki- = f(xk-1
i+) + eki
e = model error
Update at tk:
xki+ = xk
i- + Gk(yki - xk
i- )
for each ensemble member i=1…N
Gk = Pk (Pk + Rk)-1
with Pk computed from ensemble spread
Reichle et al., 2002 (MWR)
Ensemble Kalman FilterEnsemble Kalman FilterEnsemble Kalman FilterEnsemble Kalman Filter
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Study areaStudy areaStudy areaStudy area
Niccone
Migianella
137 km2
Central Italy
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ASCAT soil moisture productASCAT soil moisture productASCAT soil moisture productASCAT soil moisture product
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SIM. ASS.
NS 75 84
|Qp| 39 24
|Rd| 44 21
Eff 39
start of flood events
1
2
34
tt,obst,sim
tt,obst,ass
QQEff 2
2
1100
Brocca et al., 2010 (HESS)
Niccone
Migianella
137 km2
Central Italy
2007-2008
EGU 2010: first results (4 floods)EGU 2010: first results (4 floods)EGU 2010: first results (4 floods)EGU 2010: first results (4 floods)
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Niccone
Migianella
137 km2
Central Italy
2007-2010
improving
EGU 2012: 2007-2010 (21 floods)EGU 2012: 2007-2010 (21 floods)EGU 2012: 2007-2010 (21 floods)EGU 2012: 2007-2010 (21 floods)
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MISDc-2L: EnKFMISDc-2L: EnKFMISDc-2L: EnKFMISDc-2L: EnKF
Brocca et al., 2012 (IEEE TGRS)
Niccone
Migianella
137 km2
Central Italy
2007-2010
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RZSM RZSM ASSIMILATIONASSIMILATION
SZSM SZSM ASSIMILATIONASSIMILATION
NS=86%NS=79%
NS (no assimilation)=76% (MISDc-2L)
The assimilation of RZSM has a higher impact on runoff prediction, and better resultsThe assimilation of RZSM has a higher impact on runoff prediction, and better results
Niccone
Migianella
137 km2
Central Italy
2007-2010
SZSM vs RZSM assimilationSZSM vs RZSM assimilationSZSM vs RZSM assimilationSZSM vs RZSM assimilation
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1. OPEN LOOP "true" Q "true" SZSM "true" RZSM
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)
TRUE dischargeTRUE discharge
TRUE RZSMTRUE RZSMTRUE SZSMTRUE SZSM
Synthetic experimentSynthetic experimentSynthetic experimentSynthetic experiment
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SZSM ASSIMILATIONSZSM ASSIMILATION RZSM ASSIMILATIONRZSM ASSIMILATION
The results of the synthetic experiments confirm the findings obtained The results of the synthetic experiments confirm the findings obtained with real-datawith real-data
Synthetic experimentSynthetic experimentSynthetic experimentSynthetic experiment
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For the MISDc-2L structure, SZSM and RZSM are not linearly related. For the MISDc-2L structure, SZSM and RZSM are not linearly related. Therefore, EnKF fails to correctly update the statesTherefore, EnKF fails to correctly update the states
Modelled SZSM vs RZSMModelled SZSM vs RZSMModelled SZSM vs RZSMModelled SZSM vs RZSM
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The assimilation of The assimilation of satellite soil moisture productsatellite soil moisture product provides an improvement in runoff predictionprovides an improvement in runoff prediction
The The rainfall-runoff model structurerainfall-runoff model structure has an important has an important role in determining the results of the role in determining the results of the data assimilationdata assimilation
The assimilation of The assimilation of SZSMSZSM has has low impactlow impact on runoff on runoff predictionprediction
The optimization of the rainfall-runoff model structure The optimization of the rainfall-runoff model structure through the implementation of a flexible modelling through the implementation of a flexible modelling approach (approach (SUPERFLEXSUPERFLEX) will be the object of future ) will be the object of future investigationsinvestigations
CONCLUSIONSCONCLUSIONSCONCLUSIONSCONCLUSIONS
Thursday, 26 Apr 2012POSTER: EGU2012-11557
Improving hypothesis testing through the application of flexible model structuresF. Fenicia, D. Kavetski, G. Schoups, M.P. Clark, H.H.G. Savenije, and L. Pfister
ReferencesReferencesAubert, 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 surface-hydrological 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.
FOR FURTHER INFORMATIONFOR FURTHER INFORMATIONURL: http://hydrology.irpi.cnr.it/people/l.brocca
URL IRPI: http://hydrology.irpi.cnr.it
This presentation is available for download at: http://hydrology.irpi.cnr.it/repository/public/presentations/2012/egu-2012-l.-brocca