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1 Research Institute for Geo-Hydrological Protection, Perugia, Italy

European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April 2012. Soil moisture assimilation into rainfall-runoff modelling: which is the influence of the model structure?. Brocca L. 1 , Melone F. 1 , Moramarco T. 1 , Zucco G. 1 , Wagner, W. 2.

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1 Research Institute for Geo-Hydrological Protection, Perugia, Italy

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  1. European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April 2012 Soil moisture assimilation into rainfall-runoff modelling: which is the influence of the model structure? Brocca L.1, Melone F.1, Moramarco T.1, Zucco G.1, Wagner, W.2 1Research Institute for Geo-Hydrological Protection, Perugia, Italy 2Institute of Photogrammetry and Remote Sensing, TU Wien, Vienna, Austria luca.brocca@irpi.cnr.it http://hydrology.irpi.cnr.it/

  2. 1st December 2011very DRY NORMAL NOW 10% saturation Soil moisture importance 1st December 2010very WET 90% saturation

  3. Soil moisture "appealing" MOST CITED HESS PAPERS SINCE 2010Font: SCOPUS (2012-04-16) Work on soil moisture to have your paper PUBLISHED ... and CITED

  4. 1981 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) Soil moisture data assimilationinto rainfall-runoff modelling Many studies performed synthetic experiments and tested different techniques and approaches for soil moisture assimilation into rainfall-runoff modelling. 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. • Spatial Mismatch: i.e. point ("in-situ") or coarse (satellite) measurements are compared with model predicted average quantities in space REPRESENTATIVENESS • 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 • 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 • 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

  5. Input/output data Model parameter values Model structure Technique (EKF, EnKF, PF, ...) Accuracy BIAS handling (CDF match, ...) Spatial/temporal resolution DATA ASSIMILATION Error modelling (OBS, MOD) Layer depth OBSERVATIONS Soil moisture data assimilationinto rainfall-runoff modelling COMPONENTS SUB-COMPONENTS RAINFALL-RUNOFF MODEL

  6. PURPOSES WHICH IS THE IMPACT OF THE MODEL STRUCTURE ON THE ASSIMILATION OF SOIL MOISTURE DATA INTO RAINFALL-RUNOFF MODELS?

  7. EVENT-BASED RAINFALL-RUNOFF MODEL (MISD) SOIL WATER BALANCE MODEL upstream discharge rainfall excess SCS-CN e(t):evapotranspiration s(t):saturationexcess subcatchments geomorphological IUH f(t):infiltration directly draining areaslinear reservoir IUH outlet discharge Wmax W(t) S(t) W(t) channel routingdiffusive linear approach g(t):percolation Rainfall-runoff model: MISDc MISDc: "Modello Idrologico Semi-Distribuito in continuo" r(t):rainfall S: soil potential maximum retentionW(t)/Wmax: saturation degree FREELY AVAILABLE !!! http://hydrology.irpi.cnr.it/tools-and-files/misdc Brocca et al., 2011 (HYP)

  8. THIS STUDY Assimilation of both SZSM and RZSM  RR MODEL with 2 LAYER rainfall rainfall evapotranspiration evapotranspiration RZSM RZSM SZSM surface layer infiltration infiltration Wsupmax percolation Wmax Wmax deep percolation deep percolation MISDc-2L: 2-Layers RR model Brocca et al., 2010 (HESS) Assimilation of the profile soil moisture (RZSM) ONLY  RR MODEL with 1 LAYER the MISDc model simulates the soil moisture storage of 1 layer

  9. BIAS handling LINEAR RESCALING standard deviation mean The SAT was rescaled to match the relative soil moisture simulated by the model, MOD

  10. Propagation tk-1to tk: xki- = f(xk-1i+) + eki e = model error Update at tk: xki+ = xki- + Gk(yki - xki- ) for each ensemble member i=1…N Gk = Pk(Pk + Rk)-1 with Pk computed from ensemble spread Ensemble Kalman Filter Nonlinearlypropagates ensemble of model trajectories. Can account for wide range of model errors (incl. non-additive). yk Reichle et al., 2002 (MWR) xki state vector (eg soil moisture) Pkstate error covariance Rkobservation error covariance

  11. Study area Niccone Migianella 137 km2 Central Italy

  12. ASCAT soil moisture product

  13. EGU 2010: first results (4 floods) Niccone Migianella 137 km2 Central Italy 2007-2008 start of flood events 3 4 2 1 Brocca et al., 2010 (HESS)

  14. improving EGU 2012: 2007-2010 (21 floods) Niccone Migianella 137 km2 Central Italy 2007-2010

  15. MISDc-2L: EnKF Niccone Migianella 137 km2 Central Italy 2007-2010 Brocca et al., 2012 (IEEE TGRS)

  16. NS (no assimilation)=76% (MISDc-2L) NS=79% NS=86% SZSM vs RZSM assimilation Niccone Migianella 137 km2 Central Italy 2007-2010 SZSM ASSIMILATION RZSM ASSIMILATION The assimilation of RZSM has a higher impact on runoff prediction, and better results

  17. TRUE RZSM TRUE SZSM TRUE discharge Synthetic experiment • OPEN LOOP "true" Q "true" SZSM "true" RZSM • add ERROR on forcing data and model parameters • perturb "true" SZSM and RZSM with Gaussian error • assimilation of the perturbed "true" SZSM and RZSM with the assumed Gaussian error and with a revisit time of 1 day (50 simulations)

  18. Synthetic experiment SZSM ASSIMILATION RZSM ASSIMILATION The results of the synthetic experiments confirm the findings obtained with real-data

  19. Modelled SZSM vs RZSM For the MISDc-2L structure, SZSM and RZSM are not linearly related. Therefore, EnKF fails to correctly update the states

  20. CONCLUSIONS The assimilation of satellite soil moisture product provides an improvement in runoff prediction The rainfall-runoff model structure has an important role in determining the results of the data assimilation The assimilation of SZSM has low impact on runoff prediction The optimization of the rainfall-runoff model structure through the implementation of a flexible modelling approach (SUPERFLEX) will be the object of future investigations

  21. Questions? References • 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. (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. This presentation is available for download at: http://hydrology.irpi.cnr.it/repository/public/presentations/2012/egu-2012-l.-brocca FOR FURTHER INFORMATIONURL: http://hydrology.irpi.cnr.it/people/l.broccaURL IRPI: http://hydrology.irpi.cnr.it Thanks for your attention

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