introductionpurposesmethods study area resultsconclusions egu 2012 wien 24 th april 2012 brocca luca...

IntroductionIntroduction PurposesPurposes MethodsMethods Study areaStudy area ResultsResults ConclusionsConclusions

EGU 2012EGU 2012WienWien

2424thth April 2012 April 2012Brocca LucaBrocca Luca

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/

––

22Institute of Photogrammetry and Remote Sensing, TU Wien, Vienna, AustriaInstitute of Photogrammetry and Remote Sensing, TU Wien, Vienna, Austria




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




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)




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




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




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




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

http://hydrology.irpi.cnr.it/tools-and-files/misdc









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




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




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




Study areaStudy areaStudy areaStudy area

Niccone

Migianella

137 km2

Central Italy




ASCAT soil moisture productASCAT soil moisture productASCAT soil moisture productASCAT soil moisture product




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

QQ

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)




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)




MISDc-2L: EnKFMISDc-2L: EnKFMISDc-2L: EnKFMISDc-2L: EnKF

Brocca et al., 2012 (IEEE TGRS)

Niccone

Migianella

137 km2

Central Italy

2007-2010




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




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




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




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




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

http://meetingorganizer.copernicus.org/EGU2012/EGU2012-11557.pdf

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

introductionpurposesmethods study area resultsconclusions egu 2012 wien 24 th april 2012 brocca luca...

Documents