Luca Brocca seminario trento

[email protected] http://hydrology.irpi.cnr.it

Luca Broccaand many others

Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy

Floods

Landslides

RainfallIn situ validation

USE OF SATELLITE SOIL MOISTURE DATA FOR HYDROLOGICAL

APPLICATIONS

Monday, September 23, 13

mailto:[email protected]

mailto:[email protected]

http://hydrology.irpi.cnr.it/l.brocca

http://hydrology.irpi.cnr.it/l.brocca

25 September 2013Trento

Importance of soil moisture

Campo et al. (2006, HYP)

Casentino basincentral Italy

30% increase of soil moisture produces a 8-fold increase of peak discharge!



Importance of soil moisture

Campo et al. (2006, HYP)

Casentino basincentral Italy

30% increase of soil moisture produces a 8-fold increase of peak discharge!

Runoff generationBrocca et al. (2010, HESS)

Landslide triggeringBrocca et al. (2012, RS)

Numerical Weather PredictionDharssi et al. (2011, HESS)

Erosion modelling

Nearing et al. (2005, CAT)

Plant productionBolten et al. (2010, JSTARS)

Water quality modellingHan et al. (2012, HYP)



Soil moisture is needed by all GEO Social Benefit Areas and was ranked the second top priority parameter (behind precipitation) in a year 2010 GEO report on "Critical Earth Observation Priorities".

http://sbageotask.larc.nasa.gov/US-09-01a_SummaryBrochure.pdf

Need for soil moisture







Soil moisture monitoring



IN SITU(TDR, FDR, Gravimetric, Geophysical methods)

REMOTE SENSING (AMSR-E, SAR, Scatterometer,

ASCAT, SMOS, SMAP...)

HYDROLOGICAL MODELLING




Experimental Catchment




LaboratoryExperimental Catchment

TDR and FDR continuous monitoring probes

TDR spot measurements





International Soil Moisture

Networkhttp://www.ipf.tuwien.ac.at/insitu/



Coarse-resolution soil moisture product



Typical catchment size for hydrological studies.




~25 kmsatellitepixels

Typical catchment size for hydrological studies.

HYDROLOGIST

too coarse for hydrological applications !




PLOT SCALE 400-9000 m2

CEN

TRA

L IT

ALY

Brocca et al., 2009 (GEOD)

Soil moisture temporal stability




CEN

TRA

L IT

ALY


SMALL CATCHMENT SCALE ~50 km2

Brocca et al., 2010 (WRR)

CATCHMENT SCALE~250 km2

Brocca et al., 2012 (JoH)





CEN

TRA

L IT

ALY


SMALL CATCHMENT SCALE ~50 km2

Brocca et al., 2010 (WRR)

CATCHMENT SCALE~250 km2

Brocca et al., 2012 (JoH)

USA

Cosh et al., 2006 (JoH)

AFRICA

de Rosnay et al., 2009 (JoH)

ASIA

Zhao et al., 2010 (HYP)




1990 2000 20101980

Remote sensing of soil moisture



1990 2000 20101980

SSM/I

Nimbus-7 Aqua

AMSR-E

TMI

TRMM

Coriolis

Windsat

METOP-A

ASCAT

SCAT

ERS-1&2

SMOS

SMAP

SMMR DMSP; F8-F16

AMSU

Remote sensing of soil moisture



PURPOSE

VALIDATION OF SATELLITE SOIL MOISTURE PRODUCTS WITH IN SITU OBSERVATIONS ACROSS EUROPE

USE OF SATELLITE SOIL MOISTURE PRODUCT FOR:

1. RAINFALL ESTIMATION

2. FLOOD PREDICTION AND FORECASTING

3. LANDSLIDE MOVEMENT PREDICTION



Validation of remote sensing

soil moisture products



Wagner et al., 1999 (RSE)

Satellite soil moisture product

• scatterometer (active microwave)• C-band (5.7 GHz)• VV polarization• resolution 50/25 km• daily coverage• 2007 - ongoing

ASCAT2007- …

Change detection algorithm takes account indirectly for surface roughness and land cover variability.



Owe et al., 2008 (JGR)

AMSR-E2002-2011

LPRM algorithm three-parameter retrieval model (soil moisture, vegetation water content, and soil/canopy temperature) for passive microwave data based on a

• 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 - 2011

Satellite soil moisture product



“Consistent validation of H-SAF soil moisture satellite and model products against ground measurements for selected sites in Europe”

http://hsaf.meteoam.it/

In situ soil moisture network



In-situ soil moisture data at different depths (5, 10, 15, 30, ...) for a total of 17 sites across four different countries (Italy, France, Spain and Luxembourg).

Considering both observed and modelled data:

29 DATA SETS

In situ soil moisture network



ItalyVallaccia

5 cm

ASCAT

In situ validation



ItalyVallaccia

5 cm

AMSRE-LPRMAMSRE-NASA

AMSRE-PRIASCAT

In situ validation



ItalyVallaccia

5 cm

AMSRE-LPRMAMSRE-NASA

AMSRE-PRIASCAT

Regression matching CDF matching

In situ validation



Correlation coefficient between

all satellite products and ground data sets

Brocca et al., 2011 (RSE)

In situ validation



Correlation coefficient between

all satellite products and ground data sets

Brocca et al., 2011 (RSE)

Average R-values ~0.80

In situ validation



In situ validation (mountain regions)

Brocca et al., 2013 (VZJ)

ITALIAN ALPS



In situ validation (Africa)

MOROCCO

Tramblay et al., 2012 (HESS)



In situ validation (USA)

… and in very interesting places !!!

HAWAII



SM2RAIN



R-metric



RAINFALL

Doing Hydrology Backward

Brocca et al., 2013 (GRL)Monday, September 23, 13


RAINFALL SOIL MOISTUREInfiltration

evapotranspiration

Doing Hydrology Backward

Brocca et al., 2013 (GRL)Monday, September 23, 13


Soil water balance equation

precipitation runoffEvapo-

transpiration drainage

soil depth

relative saturation

SM2RAIN






soil depth

relative saturation

Inverting for p(t):

SM2RAIN






soil depth

relative saturation

Assuming: + +

Inverting for p(t):

SM2RAIN



Three sites in Italy, Spain and France with hourly rainfall and soil moisture observations are

selected

SM2RAIN testing: in-situ data




selected

Estimation of daily rainfall for 1-year data





selected

Estimation of daily rainfall for 1-year data

Italy

Spain

France

NS=0.82

NS=0.89

NS=0.81




Results – ASCAT SWI

Estimation of 4-day rainfall

for 4-year data

Italy

Spain

NS=0.57

NS=0.62



1) ASCAT TU-Wien (FTP)

2) AMSR-E LPRM - asc, desc, asc+desc (VUA)

3) ESA – CCI SM product

4) ERA-Land (ECMWF)

and

1) TRMM 3B42v7 (standard satellite rainfall product)

ASCAT GRID ~ 12.5 km

SM2RAIN testing: Italy

Brocca et al., 2013 (EGU poster)Monday, September 23, 13


Correlation MAPS

AMSR-E ASCAT

ESA-CCI

ERA-Land

TRMM



Europe … Africa



Global scale

Brocca et al. (in preparation, NGS)Monday, September 23, 13


Global scale

Brocca et al. (in preparation, NGS)

SM2RAIN(ASCAT) vs TRMM



Antecedent Wetness Conditions




Brocca et al. 2009 (JoH) 2009 (JHE)Monday, September 23, 13


P




P

Rd




P

Rd


SOIL CONSERVATION

SERVICE METHOD (SCS-CN)Brocca et al. 2009 (JoH) 2009 (JHE)



P

Rd

θ


SOIL CONSERVATION

SERVICE METHOD (SCS-CN)Brocca et al. 2009 (JoH) 2009 (JHE)



Tevere - PN

Cerfone

Timia

Assino

Genna

Topino

Niccone

Caina

Nestore

11 catchments100-5000 km2

ItalyTiber River

ERS SCATTEROMETER SOIL MOISTURE DATA

S vs θ relation: ERS SCAT

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



Tevere - PN

Cerfone

Timia

Assino

Genna

Topino

Niccone

Caina

Nestore

11 catchments100-5000 km2

ItalyTiber River

ERS SCATTEROMETER SOIL MOISTURE DATA

S vs θ relation: ERS SCAT

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

Beck et al., 2010 (JSTARS)Tramblay et al., 2010 (JoH), 2011 (NHESS)

AustraliaFrance



ALZETTE RIVER

Average T~17 days

S vs θ relation: ASCAT vs AMSR-E



TIBER RIVER ALZETTE RIVER




+0.04+0.14

TIBER RIVER ALZETTE RIVER




S vs θ relation: whole ITALY

Analysis for 50 river basins across the

whole Italian territory



S vs θ relation: whole ITALYTanaro River – Masio (4500 km²)

API5 ASCAT SWI

Analysis for 50 river basins across the

whole Italian territory



Tramblay et al., 2012 (HESS)

S vs θ relation: Morocco



Simplified

SOIL MOISTURE

θ

Massari et al., 2013 (HESSD)

Continuous RR

modelling



Simplified

SOIL MOISTURE

θ

S = a(1 - θ)

S

SOIL CONSERVATION SERVICE METHOD (SCS-

CN)

θ

S


Continuous RR

modelling



Simplified

SOIL MOISTURE

θ

S = a(1 - θ)

S


CN)

θ

S

EVENT-BASED RAINFALL-RUNOFF

MODEL


Continuous RR

modelling



DIS

CH

AR

GE

Simplified

SOIL MOISTURE

θ

S = a(1 - θ)

S


CN)

θ

S


MODEL


Continuous RR

modelling



SCRRM: Greece

Early Warning System for

Flood and Fire forecasting




SCRRM: Greece

Early Warning System for

Flood and Fire forecasting


ASCAT

AMSR-E



Soil moistureassimilation into rainfall-runoff

modelling



MISDc: "Modello Idrologico Semi-Distribuito in continuo"

W(t) S(t)

outlet discharge

upstream discharge

directly draining areaslinear reservoir IUH


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 MODEL

S: soil potential maximum retentionW(t)/Wmax: saturation degree

r(t):rainfall

Brocca et al., 2011 (HYP)

Rainfall-runoff model: MISDc



MISDc: "Modello Idrologico Semi-Distribuito in continuo"

W(t) S(t)

outlet discharge

upstream discharge

directly draining areaslinear reservoir IUH


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 MODEL

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: MISDc


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

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


Model implemented for real time application for the

Umbria Region Civil Protection Warning

System:UPPER TIBER RIVER

Flood event of January 2010

Jan-2010

http://www.cfumbria.it/

Real time flood forecasting






SWI: Soil Water Indext: timeti: acquisition time of SSMtiSSMti : relative surface soil moisture [0,1]T: characteristic time length

Soil Water Index (SWI)




SWI: Soil Water Indext: timeti: acquisition time of SSMtiSSMti : relative surface soil moisture [0,1]T: characteristic time length

SSM

SWI

Soil Water Index (SWI)



1981

Soil moisture data assimilation



Many studies performed synthetic experiments and tested different techniques and approaches for soil moisture assimilation into rainfall-runoff modelling.





Aubert et al., 2003 (JoH)Francois et al., 2003 (JHM)Chen et al., 2011 (AWR)

Matgen et al., 2012 (AWR)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.





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

Aubert et al., 2003 (JoH)Francois et al., 2003 (JHM)Chen et al., 2011 (AWR)

Matgen et al., 2012 (AWR)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.




time

rela

tive

soil

moi

stur

e

observations

modeled soil moisture

updated soil moisture

Brocca et al., 2010 (HESS), 2012 (IEEE TGRS)

observations




G is a constantG=0 "perfect" modelG=1 direct insertion

time

rela

tive

soil

moi

stur

e

observations

modeled soil moisture

updated soil moisture

Brocca et al., 2010 (HESS), 2012 (IEEE TGRS)

Kalman GAINmodel error

obs error

observations




RAINFALL-RUNOFF MODEL

COMPONENTS





DATA ASSIMILATION

COMPONENTS





DATA ASSIMILATION

COMPONENTS

OBSERVATIONS





SUB-COMPONENTS

Input/output data

Model parameter values

Model structure

DATA ASSIMILATION

COMPONENTS

Technique (EKF, EnKF, PF, ...)

BIAS handling (CDF match, ...)

Error modelling (OBS, MOD)

OBSERVATIONS

Accuracy

Spatial/temporal resolution

Layer depth




NicconeMigianella137 km2

Central Italy

Migianella experimental basin



Runoff prediction

Brocca et al., 2012 (IEEE TGRS)



improving

Runoff prediction

Brocca et al., 2012 (IEEE TGRS)



Runoff prediction



Runoff prediction

STRONG IMPROVEMENT



without assimilation

with assimilation


with assimilation

model starts one months before the first events

Unknown initial conditions

Brocca et al., 2010 (HESS) – Open access




with assimilation


with assimilation

model starts one months before the first events

SIM. ASS.NS 36 83|εQp| 43 18|εRd| 59 24Eff 62

Unknown initial conditions

Brocca et al., 2010 (HESS) – Open access



Further applications …



South Italy - Fiumarella(33 km²)

USA - Lucky Hills(0.04 km²)

Luxembourg - Bibesbach(10.7 km²)

France - Valescure(3.9 km²)

Central Italy - Assino(165 km²)

Central Italy - Niccone(137 km²)

Further applications …



Summarizing …



the assimilation of the ECMWF product has a slight impact due to the limited time period (2009-2010)

Summarizing …




for central Italy basins the assimilation of ASCAT and AMSR-E provide a significant improvement in model performance

Summarizing …





in south Italy a slight improvement can be yet seen

Summarizing …






in France no improvement can be obtained due to the difficulties of satellite data to retrieve soil moisture over mountain areas

Summarizing …







in Luxembourg the impact is limited due to the presence of snow

Summarizing …







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

Summarizing …



Landslide prediction



Torgiovannetto landslide

Near Assisi

Rock slope (abandoned stone quarry)

First slide in 2003

Landslide monitoring (extensometer, inclinometer)

Meteorological monitoring (rainfall and temperature)



Rainfall versus displacements rate

Graziani et al., 2009



Data set

Soil moisture is estimated through ASCAT and

considering an Antecedent Precipitation Index

October 2007 – July 2009

Brocca et al., 2012 (RS) – Open access



Rainfall events extraction



................

Rainfall events extraction



Multiple regression

1h max rainfall

Total rainfallDisplacements

Antecedent Precipitation Index (N=20 g)

Soil Water Index (T=75 g)



Multiple regression

1h max rainfall

Total rainfallDisplacements

Antecedent Precipitation Index (N=20 g)

Soil Water Index (T=75 g)

1) only rainfall (Pmax-1h e Ptot)

2) rainfall + API20

3) rainfall + SWI75

4) rainfall + API20 + SWI75



OBSERVED

ESTIMATED

1) only rainfall (Pmax-1h e Ptot)

Prediction of landslide movements



2) rainfall + API20

OBSERVED

ESTIMATED




3) rainfall + SWI75

OBSERVED

ESTIMATED





4) rainfall + API20 + SWI75

OBSERVED

ESTIMATED



Landslide forecasting Operational system

TORGIOVANNETTO

soil moisture

rain

fall

LANDWARNrainfall + soil moisture thresholds



MALARIA OUTBREAKSNUMERICAL WEATHERPREDICTION

Capecchi, Albergel, De Rosnay, …

FLOOD DETECTION

Lacava, Temimi, ...

Montanari, Montosi, ...

Further soil moisture applications

EROSION

Todisco, Mannocchi, Bagarello, Ferro, ...Soil moisture*Rainfall intensity

Soil

loss

es



Conclusions

(finally )



Remote sensing soil moisture products are found accurate for soil moisture estimation across Europe (and worldwide)

Conclusions




The use of soil moisture products might improve flood and landslide prediction

Conclusions





Satellite soil moisture product can be also employed as additional tool for rainfall estimation

Soil moisture data obtained from coarse-resolution sensors can provide useful information for many applications, new important challenges and opportunities for the use of these new sources of data are opened

Conclusions





Satellite soil moisture product can be also employed as additional tool for rainfall estimation

Soil moisture data obtained from coarse-resolution sensors can provide useful information for many applications, new important challenges and opportunities for the use of these new sources of data are opened

Conclusions

Who is interested to obtain satellite soil moisture data can contact me for getting information. Please do not hesitate.


References cited Aubert, D. et al. (2003). Sequential assimilation of soil moisture and

streamflow data ... JoH., 280,145-161. Bolten, J.D. et al. (2010). Evaluating the utility of remotely-sensed

soil moisture for agricultural monitoring. JSTARS, 3, 57-66. 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). Assimilation of observed soil moisture data

in storm rainfall-runoff modelling. JHE 14, 153-165. Brocca, L., et al. (2010). Improving runoff prediction through the

assimilation of the ASCAT soil moisture... 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 forecasting. HYP, 25, 2801-2813.

FOR 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/2013/seminatio-trento-l.-brocca

Brocca, L. et al. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors ... across Europe. RSE, 115, 3390-3408. Brocca, L., et al. (2012). Soil moisture spatial-temporal variability at catchment scale. JoH, 422-423, 63-75. Brocca, L., et al. (2012). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff ... IEEE TGRS, 50(7), 1-14. Brocca, L., et al. (2012). Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study ... RS, 4, 1232-1244. Brocca, L., et al. (…). Soil moisture estimation in alpine catchments through modelling and satellite observations. submitted to VZJ. Brocca, L., et al. (...). A new method for rainfall estimation through soil moisture observations. submitted to GRL 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 and its applications... 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. Dharssi, I. et al. (2011). Operational assimilation of ASCAT surface soil wetness at the Met Office, HESS, 15, 2729-2746. 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. Han, E., et al. (2012). Application of data assimilation with the RZQM for soil moisture profile estimation. HYP, 26, 1707–1719. Jackson, T. et al. (1981). 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 Geo, 3 613-616. 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, 44, 49-65. Nearing, M. et al. (2005). Modeling response of soil erosion and runoff to changes in precipitation and cover. CAT, 61, 131-154. Owe M., et al. (2008). Multi-sensor historical climatology of satellite-derived global land surface moisture. JGR, 113, F01002. Tramblay, Y., et al. (2012). Estimation of antecedent wetness conditions for flood modelling in Northern Morocco. submitted to HESS. 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, 2507-2519.


http://hydrology.irpi.cnr.it/repository/public/presentations/2012/plinius-2012-l.-brocca






References cited Aubert, D. et al. (2003). Sequential assimilation of soil moisture and

streamflow data ... JoH., 280,145-161. Bolten, J.D. et al. (2010). Evaluating the utility of remotely-sensed

soil moisture for agricultural monitoring. JSTARS, 3, 57-66. 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). Assimilation of observed soil moisture data

in storm rainfall-runoff modelling. JHE 14, 153-165. Brocca, L., et al. (2010). Improving runoff prediction through the

assimilation of the ASCAT soil moisture... 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 forecasting. HYP, 25, 2801-2813.

Thanks for your attention FOR 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/2013/seminatio-trento-l.-brocca

Brocca, L. et al. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors ... across Europe. RSE, 115, 3390-3408. Brocca, L., et al. (2012). Soil moisture spatial-temporal variability at catchment scale. JoH, 422-423, 63-75. Brocca, L., et al. (2012). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff ... IEEE TGRS, 50(7), 1-14. Brocca, L., et al. (2012). Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study ... RS, 4, 1232-1244. Brocca, L., et al. (…). Soil moisture estimation in alpine catchments through modelling and satellite observations. submitted to VZJ. Brocca, L., et al. (...). A new method for rainfall estimation through soil moisture observations. submitted to GRL 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 and its applications... 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. Dharssi, I. et al. (2011). Operational assimilation of ASCAT surface soil wetness at the Met Office, HESS, 15, 2729-2746. 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. Han, E., et al. (2012). Application of data assimilation with the RZQM for soil moisture profile estimation. HYP, 26, 1707–1719. Jackson, T. et al. (1981). 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 Geo, 3 613-616. 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, 44, 49-65. Nearing, M. et al. (2005). Modeling response of soil erosion and runoff to changes in precipitation and cover. CAT, 61, 131-154. Owe M., et al. (2008). Multi-sensor historical climatology of satellite-derived global land surface moisture. JGR, 113, F01002. Tramblay, Y., et al. (2012). Estimation of antecedent wetness conditions for flood modelling in Northern Morocco. submitted to HESS. 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, 2507-2519.

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Luca Brocca seminario trento

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