IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca 1 Research Institute for Geo-Hydrological Protection, Perugia, Italy Brocca L. 1 Brocca L. 1, Melone F. 1, Moramarco T. 1, Zucco G. 1, Wagner, W. 2 Soil moisture assimilation into rainfall-runoff modelling: which is the influence of the model structure? European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April 2012 European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April Institute of Photogrammetry and Remote Sensing, TU Wien, Vienna, Austria

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca 1st December 2010 very WET 90% saturation 1st December 2011 very DRY 10% saturation NORMALNOW Soil moisture importance

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca Soil moisture "appealing" Work on soil moisture to have your paper PUBLISHED PUBLISHED... and CITED CITED MOST CITED HESS PAPERS SINCE 2010 Font: SCOPUS ( )

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca Many studies performed synthetic experiments and tested different techniques and approaches for soil moisture 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., 2012 (AWR, in press) 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 Soil moisture data assimilation into rainfall-runoff modelling

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca Soil moisture data assimilation into rainfall-runoff modelling RAINFALL- RUNOFF MODEL 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

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca WHICH IS THE IMPACT OF THE MODEL STRUCTURE ON THE ASSIMILATION OF SOIL MOISTURE DATA INTO RAINFALL- RUNOFF MODELS? PURPOSESPURPOSES

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 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 Brocca et al., 2011 (HYP) Rainfall-runoff model: MISDc

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca infiltration evapotranspiration deep percolation W max rainfall Brocca et al., 2010 (HESS) Assimilation of the profile soil moisture (RZSM) ONLY  RR MODEL with 1 LAYER RZSM the MISDc model simulates the soil moisture storage of 1 layer evapotranspiration infiltration deep percolation Wsup max W max rainfall percolation THIS STUDY Assimilation of both SZSM and RZSM  RR MODEL with 2 LAYER surface layer RZSM SZSM MISDc-2L: 2-Layers RR model

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca The  SAT was rescaled to match the relative soil moisture simulated by the model,  MOD mean standard deviation BIAS handling LINEAR RESCALING

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca ykyk Nonlinearly propagates ensemble of model trajectories. Can account for wide range of model errors (incl. non-additive). x k i state vector (eg soil moisture) P k state error covariance R k observation error covariance Propagation t k-1 to t k : x k i- = f(x k-1 i+ ) + e k i e = model error Update at t k : x k i+ = x k i- + G k (y k i - x k i- ) for each ensemble member i=1…N G k = P k (P k + R k ) -1 with P k computed from ensemble spread Reichle et al., 2002 (MWR) Ensemble Kalman Filter

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca Study area Niccone Migianella 137 km 2 Central Italy

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca ASCAT soil moisture product

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca SIM.ASS. NS7584 |  Qp | 3924 |  Rd | 4421 Eff39 start of flood events Brocca et al., 2010 (HESS) Niccone Migianella 137 km 2 Central Italy EGU 2010: first results (4 floods)

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca Niccone Migianella 137 km 2 Central Italy improving EGU 2012: (21 floods)

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca MISDc-2L: EnKF Brocca et al., 2012 (IEEE TGRS) Niccone Migianella 137 km 2 Central Italy

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca RZSM ASSIMILATION SZSM ASSIMILATION NS=86%NS=79% NS (no assimilation)=76% (MISDc-2L) The assimilation of RZSM has a higher impact on runoff prediction, and better results Niccone Migianella 137 km 2 Central Italy SZSM vs RZSM assimilation

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca 1.OPEN LOOP  "true" Q  "true" SZSM  "true" RZSM 2.add ERROR on forcing data and model parameters 3.perturb "true" SZSM and RZSM with Gaussian error 4.assimilation of the perturbed "true" SZSM and RZSM with the assumed Gaussian error and with a revisit time of 1 day (50 simulations) TRUE discharge TRUE RZSM TRUE SZSM Synthetic experiment

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca SZSM ASSIMILATION RZSM ASSIMILATION The results of the synthetic experiments confirm the findings obtained with real-data Synthetic experiment

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca For the MISDc-2L structure, SZSM and RZSM are not linearly related. Therefore, EnKF fails to correctly update the states Modelled SZSM vs RZSM

IntroductionPurposesMethods Study area ResultsConclusions EGU 2012 Wien 24 th April 2012 Brocca Luca 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 CONCLUSIONSCONCLUSIONS Thursday, 26 Apr 2012 POSTER: EGU Improving hypothesis testing through the application of flexible model structures F. Fenicia, D. Kavetski, G. Schoups, M.P. Clark, H.H.G. Savenije, and L. PfisterEGU

References 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. (2010). Improving runoff prediction through the assimilation of the ASCAT soil moisture product. HESS, 14, Brocca, L., et al. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. HYP, 25, Brocca, L., et al. (2012). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE TGRS, 50(7), Chen, F. et al. (2011). Improving hydrologic predictions of catchment model via assimilation of surface soil moisture. AWR, 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, 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. FOR FURTHER INFORMATION FOR FURTHER INFORMATION URL: URL IRPI: This presentation is available for download at: