(1)IRPI-CNR, Perugia, Italy (2)IPF-TUWIEN, Vienna, Austria (3)ECMWF, Reading, UK (4)CRP - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg (5)UMR-6012.

Slides:

Advertisements

Similar presentations

1 CODATA 2006 October 23-25, 2006, Beijing Cryospheric Data Assimilation An Integrated Approach for Generating Consistent Cryosphere Data Set Xin Li World.

Advertisements

Multi-sensor and multi-scale data assimilation of remotely sensed snow observations Konstantinos Andreadis 1, Dennis Lettenmaier 1, and Dennis McLaughlin.

R. Reichle 1 * and C. Draper 1,2 With contributions from: G. De Lannoy, R. de Jeu, Q. Liu, V. Naeimi, R. Parinussa, W. Wagner AMSR-E Technical Interchange.

Lucinda Mileham, Dr Richard Taylor, Dr Martin Todd

Forest Hydrology: Lect. 18

Vienna 15 th April 2015 Luca Brocca(1), Clement Albergel(2), Christian Massari(1), Luca Ciabatta(1), Tommaso Moramarco (1), Patricia de Rosnay(2) (1) Research.

International Centre for Integrated Mountain Development Kathmandu, Nepal Mandira Singh Shrestha Satellite rainfall application in the Narayani basin CORDEX.

Remote Sensing, Land Surface Modelling and Data Assimilation Christoph Rüdiger, Jeffrey Walker The University of Melbourne Jetse Kalma The University.

European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April 2012 European Geosciences Union General Assembly 2012 Vienna, Austria,

Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez.

Remote Sensing of Hydrological Variables over the Red Arkansas Eric Wood Matthew McCabe Rafal Wojcik Hongbo Su Huilin Gao Justin Sheffield Princeton University.

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Global one-meter soil moisture fields from satellellite Ralf Lindau.

1 Streamflow Data Assimilation - Field requirements and results -

Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe.

Globally distributed evapotranspiration using remote sensing and CEOP data Eric Wood, Matthew McCabe and Hongbo Su Princeton University.

Outline Background, climatology & variability Role of snow in the global climate system Indicators of climate change Future projections & implications.

Can we use microwave satellite data to monitor inundation at high spatial resolution? Competing issues Passive (high repeat) data have poor (50km) spatial.

Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM Xiwu Zhan 1, Paul Houser 2, Sujay Kumar 1 Kristi Arsenault 1, Brian Cosgrove 3 1 UMBC-GEST/NASA-GSFC;

Implementation of the fully-distributed and physically-based FEST-WB model A. Amengual Grup de Meteorologia, Departament de Física, Universitat de les.

Data-assimilation in flood forecasting for the river Rhine between Andernach and Düsseldorf COR-JAN VERMEULEN.

Slide 1/32NOAA Soil Moisture/Soil Temperature Workshop, Oak Ridge, TN, 3-5 March, 2009 Value of Ground Network Observations in Development of Satellite.

Experiments with the microwave emissivity model concerning the brightness temperature observation error & SSM/I evaluation Henning Wilker, MIUB Matthias.

Recent advances in remote sensing in hydrology

Streamflow Predictability Tom Hopson. Conduct Idealized Predictability Experiments Document relative importance of uncertainties in basin initial conditions.

Princeton University Development of Improved Forward Models for Retrievals of Snow Properties Eric. F. Wood, Princeton University Dennis. P. Lettenmaier,

Retrieving Snowpack Properties From Land Surface Microwave Emissivities Based on Artificial Neural Network Techniques Narges Shahroudi William Rossow NOAA-CREST.

1 Exploiting Multisensor Spectral Data to Improve Crop Residue Cover Estimates for Management of Agricultural Water Quality Magda S. Galloza 1, Melba M.

Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.

The discharge prediction at a river site is fundamental for water resources management and flood risk prevention. Over the last decade, the possibility.

William Crosson, Ashutosh Limaye, Charles Laymon National Space Science and Technology Center Huntsville, Alabama, USA Soil Moisture Retrievals Using C-

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca 1 Research Institute for Geo-Hydrological Protection,

PASSIVE MICROWAVE TECHNIQUES FOR HYDROLOGICAL APPLICATIONS by : P. Ferrazzoli Tor Vergata University Roma, Italy

Remote sensing for surface water hydrology RS applications for assessment of hydrometeorological states and fluxes –Soil moisture, snow cover, snow water.

Dr. Naira Chaouch Research scientist, NOAA-CREST Nir Krakauer, Marouane Temimi, Adao Matonse (CUNY) Elliot Schneiderman, Donald Pierson, Mark Zion (NYCDEP)

Understanding hydrologic changes: application of the VIC model Vimal Mishra Assistant Professor Indian Institute of Technology (IIT), Gandhinagar

The NOAA Hydrology Program and its requirements for GOES-R Pedro J. Restrepo Senior Scientist Office of Hydrologic Development NOAA’s National Weather.

NASA Snow and Ice Products NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November 28-December.

INNOVATIVE SOLUTIONS for a safer, better world Capability of passive microwave and SNODAS SWE estimates for hydrologic predictions in selected U.S. watersheds.

The lower boundary condition of the atmosphere, such as SST, soil moisture and snow cover often have a longer memory than weather itself. Land surface.

AOM 4643 Principles and Issues in Environmental Hydrology.

Parameterisation by combination of different levels of process-based model physical complexity John Pomeroy 1, Olga Semenova 2,3, Lyudmila Lebedeva 2,4.

AGU Fall Meeting 2008 Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation Kostas Andreadis, and Dennis Lettenmaier.

Hydrologic Data Assimilation with a Representer-Based Variational Algorithm Dennis McLaughlin, Parsons Lab., Civil & Environmental Engineering, MIT Dara.

Developing Synergistic Data Assimilation Approaches for Passive Microwave and Thermal Infrared Soil Moisture Retrievals Christopher R. Hain SPoRT Data.

Developing Consistent Earth System Data Records for the Global Terrestrial Water Cycle Alok Sahoo 1, Ming Pan 2, Huilin Gao 3, Eric Wood 2, Paul Houser.

VERIFICATION OF A DOWNSCALING SEQUENCE APPLIED TO MEDIUM RANGE METEOROLOGICAL PREDICTIONS FOR GLOBAL FLOOD PREDICTION Nathalie Voisin, Andy W. Wood and.

EVALUATION OF A GLOBAL PREDICTION SYSTEM: THE MISSISSIPPI RIVER BASIN AS A TEST CASE Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier Civil and.

The SCM Experiments at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Progress Meeting 12./

Sarah Abelen and Florian Seitz Earth Oriented Space Science and Technology (ESPACE) IAPG, TUM Geodätische Woche 2010 Contributions of different water storage.

ASCAT Soil Moisture Workshop 2011 Data Assimilation 2 (Applied Data Assimiliation) Contact: Luca Brocca

 It is not representative of the whole water flow  High costs of installation and maintenance  It is not uniformly distributed in the world  Inaccessibility.

DOWNSCALED SOIL MOISTURE DATA FROM THE METOP ASCAT SCATTEROMETER FOR HYDROLOGICAL USERS IN EUROPE Stefan Hasenauer (1), Barbara Zeiner (2), Alexander Jann.

Luca Brocca, Christian Massari, Luca Ciabatta, Tommaso Moramarco Research Institute for Geo-Hydrological Protection (IRPI-CNR), Perugia, Italy …and many.

Sandeep Bisht Assistant Director Basin planning

H-SAF soil moisture products based on METOP-ASCAT scatterometer data Stefan Hasenauer (1), Wolfgang Wagner (1), Barbara Zeiner (2), Alexander Jann (2),

Role of ECVs in climate-carbon feedback assessment Claire Magand Patricia Cadule, Jean-Louis Dufresne Institut Pierre Simon Laplace CMUG Integration Meeting,

Soil Moisture: Synergistic approach for the merge of thermal and ASCAT information 2nd User Training Workshop of Land Surface Analysis Satellite Application.

Alexander Loew1, Mike Schwank2

Assimilation of H-SAF Soil moisture products

RELIABLE VALIDATION OF SOIL MOISTURE

European Geosciences Union General Assembly 2015

The 2015 Workshop at MOISST: The Dawn of the Soil Moisture Information Age? Stillwater 2nd June 2015 New information content from soil moisture data:

Kostas Andreadis and Dennis Lettenmaier

ECV Requirements Wolfgang Wagner.

Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model Elizabeth Clark1, Paul Bates2, Matthew Wilson3, Delwyn.

Kostas M. Andreadis1, Dennis P. Lettenmaier1

SMOS Soil Moisture Validation With U.S. In Situ Networks

Development and Evaluation of a Forward Snow Microwave Emission Model

Improved Forward Models for Retrievals of Snow Properties

Ben Zaitchik, Matt Rodell, Rolf Reichle, Rasmus Houborg, Bailing Li,

Presentation transcript:

(1)IRPI-CNR, Perugia, Italy (2)IPF-TUWIEN, Vienna, Austria (3)ECMWF, Reading, UK (4)CRP - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg (5)UMR-6012 ESPACE, Nice, France (6)VUA, Amsterdam, The Netherlands Brocca, L. Brocca, L. 1, Melone, F. 1, Moramarco, T. 1, Zucco, G. 1 Wagner, W. 2, Hasenauer, S. 2, Dorigo, W. 2, De Rosnay, P. 3, Albergel, C. 3, Matgen, P. 4, Martin, C. 5, De Jeu, R. 6 EGU Leonardo Conference Series on the Hydrological Cycle Floods in 3D: Processes, Patterns, Predictions

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Ponte Nuovo Many studies highlighted the importance of soil moisture for flood forecasting Q p = 870 m 3 /s R c = 0.34 Q p = 670 m 3 /s R c = mm 85 mm TIBER BASIN Soil moisture importance

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca MOST CITED HESS PAPERS published in Soil moisture "appealing" Number of papers published on HESS in the last 2 years:880 With "soil moisture" within the title: 58 (6.6%) With "soil moisture" within the title/abstract/keywords: 158 (18%) Font: SCOPUS (23rd Nov 2011) Work on soil moisture to have your paper PUBLISHED PUBLISHED... and CITED CITED

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Many studies performed synthetic experiments and tested different techniques and approaches for soil moisture assimilation into rainfall-runoff modelling. Soil moisture data assimilation into rainfall-runoff modelling 1.Spatial Mismatch 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 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 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 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., 2011 (AWR, under review) Brocca et al., 2010 (HESS) Brocca et al., 2011 (IEEE TGRS, in press) 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

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Soil moisture data assimilation into rainfall-runoff modelling ~25 km satellitepixels Typical catchment size for hydrological studies. HYDROLOGIST

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca PLOT SCALE m 2 Soil moisture scaling properties CENTRAL ITALY Brocca et al., 2009 (GEOD) SMALL CATCHMENT SCALE ~50 km 2 Brocca et al., 2010 (WRR) CATCHMENT SCALE ~250 km 2 Brocca et al., 2011 (JoH, mod.rev.) USA Cosh et al., 2006 (JoH) AFRICA De Rosnay et al., 2009 (JoH) ASIA Zhao et al., 2010 (HYP)

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Available soil moisture data set ASCAT ( ) SMOS ( ) AMSR-E ( ) Windsat ( ) SATELLITE SENSORS NWP MODELS, REANALYSIS,... SM-ASS-1 is the H-SAF volumetric soil moisture (root-zone) by scatterometer assimilation in a NWP model (ECMWF).

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Purposes Assimilation of different soil moisture product into rainfall-runoff modelling for several catchment in Europe and USA PRODUCTS ASCAT-TUWIEN (0-5 cm) AMSRE-LPRM (0-5 cm) H-SAF-SMASS1 (0-100 cm) WACMOS CATCHMENTS Central Italy South Italy LuxembourgFranceUSA

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Soil moisture data assimilation Rainfall-runoff model: MISDc Rescaling SOIL MOISTURE PRODUCTS ASCAT AMSR-E 0-5 cm depth SM-ASS-1 ECMWF cm depth 4 layer ROOT-ZONE SOIL MOISTURE (~ cm) Exponential filter Weighted average of the first 3 layer (0-100 cm) Data assimilation WACMOS

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca scatterometer (active microwave) C-band (5.7 GHz) VV polarization resolution 50/25 km daily coverage ongoing ASCAT Soil moisture products Change detection algorithm takes account indirectly for surface roughness and land cover variability. Wagner et al., 1999 (RSE) LPRM algorithm three-parameter retrieval model (soil moisture, vegetation water content, and soil/canopy temperature) for passive microwave data based on a microwave radiative transfer model. Owe et al., 2008 (JGR) radiometer (passive microwave) GHz HH and VV polarization 74x43 km (6.9 GHz), 14x8 (36.5 GHz), resampled at ~25 km daily coverage ongoing AMSR-E

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca WACMOS Water Cycle Multimission Observation Strategy (WACMOS): Merging of passive and active soil moisture product to derive a long-term ( ) global soil moisture product ( Liu et al., 2011 (HESS) Soil moisture products SMASS1-ECMWF SM-ASS-1 has been produced continuously by assimilation of ASCAT soil moisture in the IFS and is available for 01 July 2008 to 30 September 2010 ( 1.php). 1.php Albergel et al., 2010 (HESS) De Rosnay et al., 2011 (ECMWF news) 2009-Sep-2010 Hydrology - Satellite Application Facility H-SAF project (EUMETSAT)

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca MISDc: "Modello Idrologico Semi-Distribuito in continuo" W(t)S(t) outlet discharge upstream discharge directly draining areas linear reservoir IUH EVENT-BASED RAINFALL-RUNOFF MODEL (MISD) subcatchments geomorphological IUH channel routing diffusive linear approach rainfall excess SCS-CN e(t): evapotranspiration f(t): infiltration g(t): percolation W max W(t) s(t): saturation excess SOIL WATER BALANCE MODEL S: soil potential maximum retention W(t)/W max : saturation degree FREELY AVAILABLE !!! r(t): rainfall Rainfall-runoff model: MISDc Brocca et al., 2011 (HYP)

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca G is a constant G=0 "perfect" model G=1 direct insertion time moisture relative soil moisture observations modeled soil moisture updated soil moisture Brocca et al., 2010 (HESS), 2011 (Proc. SPIE) Nudging scheme Kalman GAIN model error obs error assimilation time

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Study catchments South Italy - Fiumarella USA - Lucky Hills Luxembourg - Bibesbach France - Valescure Central Italy - AssinoCentral Italy - Niccone Area= km²

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Valescure 3.9 km 2 France Modelled (MISDc) versus satellite and ECMWF soil moisture products Niccone 137 km 2 Central Italy R(ASCAT)= R(AMSRE)= R(ECMWF)=0.974 R(ASCAT)= R(AMSRE)= R(ECMWF)=0.965 R(ASCAT)= R(AMSRE)= R(ECMWF)=0.968 Bibeshbach 10.7 km 2 Luxembourg

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Niccone Migianella 137 km 2 Central Italy Runoff simulation NS: Nash-Sutcliffe NS=100%  perfect model! Assimilation of ASCAT improving

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Niccone Migianella 137 km 2 Central Italy Runoff simulation NS: Nash-Sutcliffe NS=100%  perfect model! Assimilation of AMSRE improving the error on peak discharge and volume increase

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Niccone Migianella 137 km 2 Central Italy Runoff simulation NS: Nash-Sutcliffe NS=100%  perfect model! Assimilation of ECMWF slight decrease in model performance

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Runoff simulation G=0  NO ASSIMILATION G=1  DIRECT INSERTION Niccone Migianella 137 km 2 Central Italy IMPROVEMENT In terms of error on peak discharge and runoff volume the assimilation of ASCAT soil moisture product provides much better results than AMSRE and ECMWF In terms of Nash- Sutcliffe efficiency, both ASCAT and AMSR-E provide an improvement in runoff prediction. IMPROVEMENT

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Data assimilation summary  the assimilation of the ECMWF product has a slight impact due to the limited time period ( )  for central Italy basins the assimilation of ASCAT and AMSR-E provide a significant improvement in model performance  in south Italy a slight improvement can be yet seen  in France no improvement can be obtained due to the difficulties of satellite data to retrieve soil moisture over mountain areas  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

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca WACMOS soil moisture product FEW DATA NO DATA FEW DATA The agreement with simulated data is good

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca WACMOS assimilation  the impact is limited due to the low temporal resolution before 2007  the longer time period allows a more robust and interesting assessment of the assimilation performance

IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Conclusions The ASCAT-TUWIEN, AMSRE-LPRM and ECMWF soil moisture products provide a good agreement with modelled data for the investigated catchments The performance of soil moisture data assimilation for improving runoff prediction depends on climatic and terrain conditions Soil moisture data obtained from coarse-resolution sensors can provide useful information for hydrological applications, new important challenges and opportunities for the use of these new sources of data in rainfall-runoff modelling are opened The proposed approaches (even improved) are going to be applied for a larger number of catchments and regions. Who would like to contribute by sharing rainfall-runoff and soil moisture data is highly welcome The proposed approaches (even improved) are going to be applied for a larger number of catchments and regions. Who would like to contribute by sharing rainfall-runoff and soil moisture data is highly welcome

References cited FOR FURTHER INFORMATION URL: URL IRPI:  Albergel et al. (2010). Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France, HESS, 14,  Aubert, D. et al. (2003). Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall runoff model. JoH., 280,  Brocca, L., et al. (2009). Soil moisture temporal stability over experimental areas of central Italy. GEOD, 148 (3-4),  Brocca, L., et al. (2009). Antecedent wetness conditions based on ERS scatterometer data. JoH, 364 (1-2),  Brocca, L., et al. (2010). Improving runoff prediction through the assimilation of the ASCAT soil moisture product. HESS, 14,  Brocca, L., et al. (2010). Spatial-temporal variability of soil moisture and its estimation across scales. WRR, 46,W  Brocca, L., et al. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. HYP, 25,  Brocca, L., et al. (2011). Soil moisture spatial-temporal variability at catchment scale. JoH, moderate revision.  Brocca, L., et al. (2011). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE TGRS, in press.  Brocca, L., et al. (2011). What perspective in remote sensing of soil moisture for hydrological applications by coarse-resolution sensors. Proc. SPIE, 8174,  Chen, F. et al. (2011). Improving hydrologic predictions of catchment model via assimilation of surface soil moisture. AWR,  Cosh, M.H. et al. (2006). Temporal stability of surface soil moisture in the Little Washita River Watershed and its applications in satellite soil moisture product validation. JoH, 323,  de Rosnay, P. et al. (2009). Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali. JoH, 375,  De Rosnay, P. et al. (2011). Extended Kalman filter soil moisture analysis in the IFS. ECMWF newsletter,  Francois, C. et al. (2003). Sequential assim. of ERS-1 SAR data into a coupled land surface-hydrological model using an extended Kalman filter. JHM 4(2), 473–487.  Jackson, T. et al. (2001). Soil moisture updating and microwave remote sensing for hydrological simulation. HSJ, 26, 3,  Koster, R.D. et al. (2011). Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nature Geosciences,  Liu, Y.Y. et al. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. HESS 15,  Matgen, P. et al. (2011). 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, under review.  Owe M., et al. (2008). Multi-sensor historical climatology of satellite-derived global land surface moisture. JGR, 113.  Wagner, W., et al. (1999). A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data, RSE 70,  Zhao, Y. et al. (2010). Controls of surface soil moisture spatial patterns and their temporal stability in a semi-arid steppe. HYP, 24, This presentation is available for download at: