christian massari, luca brocca, angelica tarpanelli, luca ... · christian massari, luca brocca,...

Christian Massari, Luca Brocca, Angelica Tarpanelli, Luca Ciabatta, Tommaso Moramarco

Research Institute for Geo-Hydrological Protection (IRPI), National Research Council (CNR),

Perugia, Italy

http://hydrology.irpi.cnr.it

H-SAF and HEPEX workshopson coupled hydrology

1

Soil moisture plays a critical role in the hydrological cycle since it determines the partitioning of precipitation into runoff, evaporation and groundwater recharge) but also in atmosphere studies

Numerical Weather Forecasting

Climate Prediction

Agriculture and Plant Production

Shallow Landslide Forecasting

Flood modelling and forecasting

Soil moisture in the hydrological cycle …

Assimilation of H-SAF SM products in Mediterranean catchments 2

1. Soil moisture important for the determination of the wetness conditions of the catchment before a flood event (antecedent wetness conditions) Brocca et al., 2009 (JHE), Beck et al. 2010 (AWR) Tramblay et al. 2012 Cousteau et al. 2012

3. Soil moisture used the improvement of the modelling of the catchment hydrological response (data assimilation): Francois et al. 2003 (JoM) Brocca et al., 2009 (JHE), 2010 (HESS) Matgen et al., 2012 (HYP) Chen et al. 2011 (AWR)-2014(JoM)

2. Soil moisture used for rainfall correction and estimation: Crow et al. 2011 (WRR)

Brocca et al. 2013, 2014 (JGR) Pellarin et al. (2013)

Added value of soil moisture observations…


1st part of the presentation

2nd part of the presentation

3rd part of the presentation

H-SAF soil moisture products used


Most of the climate

models projections for

the Mediterranean

basins have showed that

the region is very

sensitive to climate

change.

(IPCC 2012)


Antecedent wetness conditions

Ponte Nuovo

For a given catchment the response to rainfall can be very different …

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Qp = 670 m3/s

Rc = 0.17

35 mm

85 mm

TIBERBASIN

Assimilation of H-SAF SM products in Mediterranean catchments, Massari et al. 6

The importance of the antecedent wetness conditions…

0.30.310.320.330.340.350.360.370.380.390.4

0.410.420.430.440.450.460.470.480.490.5

27/11/2003 27/11/2003 28/11/2003 28/11/2003

soil

mo

istu

re (

-)

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dis

char

ge

(m³/

s) a

nd

rai

nfa

ll (

mm

/0.5

h)

observed soil moisture

discharge

rainfall

P

Rd

SOIL CONSERVATION SERVICE METHOD (SCS-CN)

Brocca et al. 2009 (JoH) , 2009 (JHE)

Antecedent wetness conditions


… having an estimate of the wetness of the catchment before a flood event is crucial for understanding how severe will be our flood

25 2 4 5d d dS P R R PR

In the study of the antecedent wetness condition there is the question if some variables can be used to estimate S

API5

soil moisturebefore the flood event

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nnsnnn

SWItmKSWISWI

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Tevere - PN

Cerfone

Timia

Assino

Genna

Topino

Niccone

Caina

Nestore

11 catchments

100-5000 km2

ItalyTiber River

ERS SCATTEROMETER SOIL MOISTURE DATA

Brocca et al.,2009 (JoH); 2009 (JHE)

Ttt

expK

KK

nnn

n

n

11

1

S vs Θ relation: ERS SCAT


Many other studies agree that there is a linear inverse relationship between soil moisture and SBeck et al., 2010 (JSTARS) (Australia)Tramblay et al., 2010 (JoH), 2011 (NHESS) (France)Tramblay et al., 2012 (HESS) (Morocco)

Why do not embed directly the relation between the parameter S and the soil moisture into the hydrological model?

Exploiting the soil moisture importance …



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W(t)/Wmax

S (

mm

)

“Observed” Soil

MoistureS-θ relationship Rainfall excess

calculationRouting IUH

2

( )( ( ) )( ( ) 2 )(t)

( )

a a

a

p t P t F P t F Se

P t F S

SCS-CN

Rainfall P

Observed rainfall

( )Q t( )t ( )S t ( )e t

Advantages:1) No need of continuous rainfall and evapotranspiration datasets.Good in poorly gauged areas.2) Parsimony and simplicity.Good for operational purposes.

“Simplified Continuous Rainfall Runoff” model (SCRRM, Massari et al. 2014, HESS)

Soil moisture estimate inside an event based model…

Early Warning System for Flood and Fire forecasting

R600

R400

SpataKantza

Pikermi

Penteli

Rafina

20° E

20° E

0°

0°

50

° N

50

° N

0 2 4 6 81km

Legend

Flow gauges

Metereological station

Rafina river network

Rafina cathcment

Elevation

High : 950 m

Low : 25 m

Area 109 km2

Application of the model in Attica (Greece)...


16 flood events 2009-2013

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1

2

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1

ev

ev

T

obs sim

tT

obs obs

t

Q Q

NS

Q Q

MODEL PERFORMANCESInitial conditions from NS

MISDc model (Brocca et al. 2011)

0.55

ERA-Land 0.56

ASCAT 0.48

AMSR-e 0.47

In situ 0.43

Mean Nash-Sutcliffe Index

MISDc

NS=1 perfect modelNS<0 bad performance

Lumped version of the model

The model has provided performances comparable with those obtained by a classical continuous model

RESULTS IN VALIDATION

The model was applied to 35 Italian catchments• areas ranging from 800 to 7400 km2

• Period 2010-2013 • 593 flood events• We used H07 and H14

Application of the model to 35 Italian catchments


To obtain the root zone soil moisture we used the Exponential filter for H07

pixels inside the catchments were averaged for obtaining a single time series

i

i

i

i

it

T

ttexp

T

ttexpSSM

)t(SWI

Wagner et al., 1999 (RSE)

and a depth parameter zwhich accounts for the information given by the layers of H14

Application of the model to 35 Italian catchments: results


Results in terms of Nash-Sutcliffe efficiency index

Good

Poor

Mean NS equal to 0.58 Mean NS equal to 0.61

H14 H07

Massari et al. 2014 (Hydrology)(Under review)MISDc model used as benchmark NS=0.66

MISDcNS =0.84

H14NS=0.81

H07NS =0.79

Po River at Carigliano Area=3570 km2

Application of the model to 35 Italian catchments



Exploiting soil moisture derived rainfall in flood modelling: SM2RAIN

Is it raining?

radar raingauge

Remote

sensing of

rainfall

TOP-DOWN

PERSPECTIVE

BOTTOM-UP PERSPECTIVECAN WE USE SOIL MOISTURE DATA TO INFER THE AMOUNT OF WATER FALLING INTO THE SOIL???

Remote

sensing of

soil moisture

Rainfall estimation: Top down vs bottom up perspective


RAINFALL SOIL MOISTURE

The soil moisture variations are strongly related to the amount of rainfall

falling into the soil. Therefore, we can use soil moisture observations for

estimating rainfall by considering the “soil as a natural raingauge”.


SM2RAIN concept


SM2RAIN algorithm

precipitationsurface

runoff

evapotranspiration

drainage

soil water

capacity

relative saturation

Inverting for p(t):

= soil depth X porosity

Assuming: + +during rainfall

Z<Surface runoff: r(t)

Evapotranspiration: e(t)

Drainage: g(t)

Precipitation: p(t)

Brocca et al 2013, GRL


SM2RAIN Performance: satellite soil moisture data (H07)

Correlation map between 5-day rainfall from GPCC and the rainfall product obtained from the application of SM2RAIN algorithm to ASCAT data(VALIDATION period 2010-2011)

Desert

Forest

Mountain Snow-Freeze


SM2RAIN Performance: in situ data

12 sites, 5 countriesAt least one year of rainfall and soil moisture

data at hourly time scale.Most of the sites are located in Italy

Cerbara

Colorso

Ingegneria

ToranoBagnoli

Cordevole

Ressi

Bibeschbach

Valescure

Remedhus

Yanco

Salento

# Site Country Data Period Depth (cm) Climate

1 Bagnoli Southern Italy 2007-2008 25-35 Arid

2 Torano Southern Italy 2007-2010 25-35 Arid

3 Salento Southern Italy 2010-2014 15-25 Arid

4 Cerbara Central Italy 2011 5-15 Semiarid

5 Colorso Central Italy 2002-2004 5-15 Semiarid

6 Ingegneria Central Italy 2009-2010 2.5-7.5 Semiarid

7 Cordevole Northern Italy 2009 0-30 Humid

8 Ressi Northern Italy 2012-2013 0-30 Humid

9 Bibeschbach Luxembourg 2007-2008 4-7 Humid

10 Remedhus Spain 2007-2009 2.5-7.5 Arid

11 Valescure France 2008-2010 25-35 Humid

12 Yanco Australia 2001-2011 2.5-7.5 Arid

Mean R = 0.88

Can we use the SM2RAIN algorithm for improving flood modelling?

Exploiting the SM2RAIN derived rainfall…


SCRRM model …


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S (

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“Observed” Soil



2

( )( ( ) )( ( ) 2 )(t)

( )

a a

a

p t P t F P t F Se

P t F S

SCS-CN

Rainfall P

Observed rainfall

( )Q t( )t ( )e t( )S t

The model uses external source of soil moisture only for initialization

Integrated rainfall via Nudging

int 2( ) ( ) ( ) ( )obs SM RAIN obsP t P t K P t P t

We considered a modified configuration of the previous model …


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S (

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“Observed” Soil



2

( )( ( ) )( ( ) 2 )(t)

( )

a a

a

p t P t F P t F Se

P t F S

SCS-CN

Rainfall P

Observed rainfallSM2RAIN rainfall

( )Q t( )t ( )e t( )S t

int 2( ) ( ) ( ) ( )obs SM RAIN obsP t P t K P t P t K=0 Pint=Pobs

K=1 Pint=PSM2RAIN

Initialization

Rainfall estimation

Catchment of Valescure - France

Assimilation of H-SAF SM products in Mediterranean catchments

France - ValescureArea: 3.83 km2

Elevations:

from 244 m to 815m

ASL

Mean slope: 56 %

(Tramblay et al. 2010)

30 minutes interval recorded data of - Rainfall- Temperature- Discharge- Soil moisture

(30 cm depth from a representative location)

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Po

bs [m

m]

Q [m

3/s

]

time [h]

Qobs

Qcase-1

Qcase-6

Pobs

Improving

Results …

Assimilation of H-SAF SM products in Mediterranean catchments

we obtained an increase in performance from NS=0.49 to NS=0.83when soil moisture is used also for correcting rainfall during the flood event

25

Tiber River catchment @ Ponte Felcinocentral Italy

MISDc rainfall-runoff model

Ciabatta et al. (2014 in prep.)

A similar approach with satellite data


Runoff simulation by using as

input

1. Observed rainfall

2. SM2RAINASCAT

3. SM2RAINASCAT +

Obs. Rainfall


Data assimilation of soil moisture for improving flood predictions

In rainfall-runoff modelling …In the last decades a number studies performed data assimilation

experiments and tested different techniques and approaches for soil

moisture assimilation within rainfall-runoff modelling …

In situ soil moisture … Satellite soil moisture …Loumagne et al., 2001 (HSJ) Houser et al. (1998) (WRR)

Aubert et al., 2003 (JoH) Pauwels et al., 2001, 2002 (JoH, HP)

Anctil et al., 2008 (JoH) Crow et al. 2005 (GRL

Matgen et al., 2006 (IAHS) Matgen et al., 2006 (IAHS)

Lee et al. 2011 (AWR) Reichle et al (2008)

Matgen et al. (2011) (AWR) Crow and Ryu (2009) (HESS)

Brocca et al., 2010 (HESS)

Chen et al. (2011) (AWR)

Montzka et al (2011) (JoH)

Matgen et al. (2011) (AWR)

Brocca et al. 2012 (IEEE TGRS)

Chen et al. 2014 (JoH)

Garreton et al. 2014 (HESSD)

However, few studies (to our knowledge)

demonstrated the real value of assimilating

true soil moisture data for improving runoff

prediction and there are still many

controversial issues to be solved ….

Data assimilation of soil moisture - is there a real benefit?


Data assimilation of soil moisture - a complex recipe?

Data Assimilation ingredients

Bias Handling1) Variance matching2) Least square

rescaling3) Cdf matching4) Triple collocation

Filtering1) Soil water index

(Swi)2) Others3) No filtering

Rainfall runoff model1) Lumped 2) distributed3) Single layer4) Multiple layers

Assimilation technique1) Variational2) Sequential

Observations1) In situ2) Satellite data3) Land surface model data

Observation error1) Temporal variability

of the obs. error2) Spatial correlation

between the observations

3) Masking

“Cooking” techniques

Model error1) Model error covariance estimation (i.e. EnkF: ensemble size)2) What to perturb. (parameters, inputs, states etc …)3) How to perturb (amount of perturbation)

The problem is often not the ingredients but the cooking

technique …


Data assimilation of soil moisture - a complex topic?


Bias handling

Filtering Temporalvariability

Spatialvariability

What toperturb

Biascorrection

Ensemblesize

Ensembleverification

Given a model and the EnKF as a

assimilation technique we can obtain 2300

different results

The task can be even more difficult if we consider different

catchment sizes and climatic conditions ….

We need to start a systematic study: Tiber River (Central Italy)


Tiber River BasinBasin Area (km2)

Tevere at Ponte Felcino 2080

Nestore at Marsciano 725

Chiani at Morrano 457

Topino at Bevagna 440

Marroggia at Azzano 258

Niccone at Migianella 137

Rainfall-runoff

data from 1989

at hourly time

resolution

6 sub-catchments(140-2080 km²)

Hydrological model


RR model with 1 layer (Brocca et al., 2010 HESS)Lumped version

calibration period: 1989 - 2009assimilation of H07 during 2010 - 2013

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

Reichle et al., 2002 (MWR)

Ensemble Kalman Filter


Ensemble size N=50 membersPerturbing parameters and inputs

ENSE

MB

LE T

EST

We assume observation error constant in time

1

1

2

k

k

k

k

ensk

ensp

ensk

mse

N

N

2

1

2

,

1

2

,

1( )

1( )

( )

Ni

k k k

i

Ni

k k o k

i

i

k k o k

ensp Q QN

mse Q QN

ensk Q Q

De Lannoy et al. 2006, JGR

SWI: Soil Water Index

t: time

SSMti: relative Surface Soil

Moisture [0,1]

ti: acquisition time of SSMti

T: characteristic time length

Data assimilation setup


1) Variance matching (Brocca et al 2010, HESS)

2) Linear least square rescaling (Yilmaz and Crow 2012, JM)

3) CDF matching (Reichle and Koster, 2004)

RESCALING

i

i

i

i

it

T

ttexp

T

ttexpSSM

)t(SWI

Wagner et al., 1999 (RSE)

FILTERING

FLAGGING AND AVERAGING OF THE OBSERVATION

Data removed when quality flags of H07 >1 Averaging pixels fallen inside the catchment using the weighted linear inverse method

Chiani catchment: results


Ascat Pixel

Centroid of the catchment

H07 pixel

Centroid Chiani catchmentArea 457 km2

01/01/90 01/01/95 01/01/00 01/01/05

0.40.60.8

NSE= 0.73462 ANSE= 0.79177 NSE(lnQ)= -6.6978 RQ= 0.74292

sat.

deg

.

(-)

0

10

20

rain

(mm

/h)

01/01/90 01/01/95 01/01/00 01/01/050

50

100

150

200

250

300

dis

ch

arg

e

(m

3/s

)

Q

obs

Qsim

Calibration 1989-2009 NS=0.73

01/01/10 01/01/11 01/01/12 01/01/13

0

50

NS= 0.72428

Rain

fall

(m

m/h

)

Sat.

deg

ree (

%)

01/01/10 01/01/11 01/01/12 01/01/130

100

200

300

400

500

Dis

ch

arg

e (

m3/s

)

Qobs

Qsim

Validation 2010-2013 NS=0.72

Chiani catchment


Model

SSM H07

Model

FilteringSWI T=67 daysVariance matching

Brocca et al (2010)

Linear least square rescaling

Yilmaz and Crow (2012)

Chiani catchment: observation handling


i

i

i

i

it

T

ttexp

T

ttexpSSM

)t(SWI

CDFrescaling

(Reichle and Koster, 2004)

Results: Chiani river basin data assimilation


Rescaling: Linear least squaresobs= 0.05NSNoASS=0.72 NSASS=0.85

Results: Chiani river basin most important flood events


Improving

Rescalinglinear leastsquare

sobs= 0.05

NSval=0.63

NSNoASS=0.66

NSASS=0.82

Results: Chiani river basin


EFF

Variance matching Linear least square CDF

NS

sobs=0.05 sobs=0.05 sobs=0.03

NS=0.84 NS=0.85 NS=0.83

no

ass

no

ass

no

ass

no

ass

no

ass

no

ass

Effe=38.3Effv=43.0

Effe=41.2Effv=45.7

Effe=33.8Effv=38.9

2

2

,,

,,

100 1

ensM t

obs t

obs t

t

s

t

a s t Q

Qf

Q

EfQ

0Eff 0Eff

ImprovementsDeteriorations

Basin NS Validation

NS(Ass -LS)

NS(Ass - VM)

NS(Ass - CDF)

T (days)

TeverePonte Felcino

0.61 0.81(sobs=0.05)

0.81(sobs=0.03)

0.78(sobs=0.05)

56

Nestore Marsciano

0.87 0.91(sobs=0.07)

0.91(sobs=0.07)

0.91(sobs=0.12)

101

Chiani Morrano

0.72 0.85(sobs=0.05)

0.84(sobs=0.05)

0.83(sobs=0.03)

67

TopinoBevagna

0.41 0.40(sobs=0.09)

0.40(sobs=0.09)

0.40(sobs=0.09)

34

Marroggia Azzano

0.69 0.72(sobs=0.30)

0.71(sobs=0.30)

0.71(sobs=0.30)

62

NicconeMigianella

0.65 0.78(sobs=0.03)

0.79(sobs=0.03)

0.76(sobs=0.03)

68

Results for all cathcments


Improving

Marginal orno improvement

DETERIORATION

IMPROVING

Assimilation

Conclusions


H-SAF soil moisture products may offer a great benefit in rainfall-runoff modelling in Mediterranean catchments and can be used directly both in RR modelling for operational purposes and as additional information for improving flood modelling in

the contest of data assimilation in hydrological models

A lot of work remains to be done for:

• a better characterization of the errors to associate to satellite observations (there should be a stronger connection between users and developers)

• the understanding the effect of the RR model structure, rescaling, filtering on the results of the data assimilation

• exploring the potentiality for a larger range of climatic conditions and catchment characteristics

Thanks for your attention

Christian [email protected]://hydrology.irpi.cnr.it/people/c.massari

A special thanks to Clement Albergel, Patricia De Rosnay, Silvia Puca, Simone Gabellani and Wolfgang Wagner

christian massari, luca brocca, angelica tarpanelli, luca ... · christian massari, luca brocca,...

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