1. (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 luca.brocca@irpi.cnr.ithttp://hydrology.irpi.cnr.it/people/l.brocca

2. 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 = 0.17 35 mm 85 mm TIBER BASIN Soil moisture importance

3. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca MOST CITED HESS PAPERS published in 2010-2011 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

4. 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

5. 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

6. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca PLOT SCALE 400-9000 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)

7. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Available soil moisture data set ASCAT (2007-...) SMOS (2009-...) AMSR-E (2002-...) Windsat (2003-...) SATELLITE SENSORS NWP MODELS, REANALYSIS,... http://hsaf.meteoam.it/ SM-ASS-1 is the H-SAF volumetric soil moisture (root-zone) by scatterometer assimilation in a NWP model (ECMWF).

8. 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

9. 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 0-289 cm depth 4 layer ROOT-ZONE SOIL MOISTURE (~ 0-100 cm) Exponential filter Weighted average of the first 3 layer (0-100 cm) Data assimilation WACMOS

10. 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 2007 - ongoing ASCAT Soil moisture products Change detection algorithm takes account indirectly for surface roughness and land cover variability. Wagner et al., 1999 (RSE) 2007-2010 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) 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 - ongoing AMSR-E 2007-2010

11. 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 (1978-2010) global soil moisture product (http://wacmos.itc.nl/?q=node/5)http://wacmos.itc.nl/?q=node/5 Liu et al., 2011 (HESS) Soil moisture products 1995-2010 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 (http://hsaf.meteoam.it/description-sm-ass- 1.php).http://hsaf.meteoam.it/description-sm-ass- 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)

12. 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 !!! http://hydrology.irpi.cnr.it/tools-and-files/misdc r(t): rainfall Rainfall-runoff model: MISDc Brocca et al., 2011 (HYP)

13. 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

14. 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=0.001-150 km²

15. 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)=0.947 - R(AMSRE)=0.885 - R(ECMWF)=0.974 R(ASCAT)=0.891 - R(AMSRE)=0.932 - R(ECMWF)=0.965 R(ASCAT)=0.797 - R(AMSRE)=0.893 - R(ECMWF)=0.968 Bibeshbach 10.7 km 2 Luxembourg

16. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Niccone Migianella 137 km 2 Central Italy 2007-2010 Runoff simulation NS: Nash-Sutcliffe NS=100%  perfect model! Assimilation of ASCAT improving

17. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Niccone Migianella 137 km 2 Central Italy 2007-2010 Runoff simulation NS: Nash-Sutcliffe NS=100%  perfect model! Assimilation of AMSRE improving the error on peak discharge and volume increase

18. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca Niccone Migianella 137 km 2 Central Italy 2007-2010 Runoff simulation NS: Nash-Sutcliffe NS=100%  perfect model! Assimilation of ECMWF slight decrease in model performance

19. 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 2007-2010 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

20. 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 (2009-2010)  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

21. IntroductionIntroductionPurposesPurposesMethodsMethods Study area ResultsResultsConclusionsConclusions EGU Leonardo 2011 Bratislava 25 th Sep 2011 Brocca Luca WACMOS soil moisture product FEW DATA 1995-2000 NO DATA 2001-2004 FEW DATA 2005-2006 The agreement with simulated data is good

22. 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

23. 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

24. References cited FOR FURTHER INFORMATION URL: http://hydrology.irpi.cnr.it/people/l.brocca/ URL IRPI: http://hydrology.irpi.cnr.it/http://hydrology.irpi.cnr.it/people/l.brocca/http://hydrology.irpi.cnr.it/  Albergel et al. (2010). Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France, HESS, 14, 2177-2191.  Aubert, 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. (2009). Soil moisture temporal stability over experimental areas of central Italy. GEOD, 148 (3-4), 364-374.  Brocca, L., et al. (2009). Antecedent wetness conditions based on ERS scatterometer data. JoH, 364 (1-2), 73-87  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. (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 frequency estimation and flood forecasting. HYP, 25, 2801-2813.  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, 817408.  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 Watershed and its applications in satellite soil moisture product 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.  De Rosnay, P. et al. (2011). Extended Kalman filter soil moisture analysis in the IFS. ECMWF newsletter, 127. 12-16.  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, 305-319.  Koster, R.D. et al. (2011). Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nature Geosciences, 3 613-616.  Liu, Y.Y. et al. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. HESS 15, 425-436.  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, 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. This presentation is available for download at: http://hydrology.irpi.cnr.it/repository/public/presentations/2011/egu-leonardo-2011-bratislava/ http://hydrology.irpi.cnr.it/repository/public/presentations/2011/egu-leonardo-2011-bratislava/