1. ASCAT Soil Moisture Workshop 2011 Data Assimilation 2 (Applied Data Assimiliation) Contact: Luca Brocca luca.brocca@irpi.cnr.it

2. 2 Soil moisture importance The soil moisture governs the partition of rainfall into runoff and infiltration. Moreover, it influences the partitioning of the incoming energy into latent and sensible heat components. Soil moisture, thus provides a key link between the water and energy balances. Numerical Weather Forecasting Climate Prediction Shallow Landslide Forecasting Agriculture and Plant Production FLOOD PREDICTION AND FORECASTING Introduction

3. 3 Antecedent wetness conditions Ponte Nuovo Many studies highlighted the importance of the antecedent wetness conditions to determine the catchment hydrological response:  Merz and Bloschl, 2009 (WRR)  Brocca et al., 2009 (JHE), 2010 (HESS) 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 Introduction

4. 4 Antecedent wetness conditions ARNO CATCHMENT: fast transition from dry conditions to flood season 321 m 3 /s !!! Campo et al., 2006 (HYP) 40 m 3 /s Introduction

5. 5 Coarse-resolution soil moisture products Typical catchment size for hydrological studies. ~25 km satellitepixels "Space-borne microwave radiometers and scatterometers have a too coarse spatial resolution and, hence, they do not meet spatial requirements for hydrological applications" (e.g. Wang et al., 2011 (HESS)). For hydrological applications, the coarse spatial resolution of radiometers and scatterometers (~25 km) has initially prevented their use and more attention was given to the high resolution (~10 m) Synthetic Aperture Radar (SAR) sensors. Introduction

6. 6 Soil moisture scaling properties PLOT SCALE 400-9000 m 2 SMALL CATCHMENT SCALE ~50 km 2 CATCHMENT SCALE ~250 km 2 Brocca et al., 2009 (GEOD) Brocca et al., 2010 (WRR) Brocca et al., 2011 (JoH, mod.rev.) Also randomly selecting only 5 locations the areal mean soil moisture temporal pattern can be estimated with high accuracy Introduction

7. 7 Soil moisture scaling properties Due to the temporal stability and the high temporal dynamics of soil moisture, the temporal sampling appears more important than the spatial one Coarse-resolution soil moisture products derived by satellite sensors, as ASCAT, can be efficiently used for hydrological applications New important challenges and opportunities for the use of this new sources of data in operational hydrology are opened Introduction

8. 8 25 km (satellite) versus 0.025 m (in situ) Introduction

9. 9 Introduction Italy Vallaccia Modelled data 5 cm depth AMSRE-LPRM AMSRE-NASA AMSRE-PRI ASCAT

10. 10 25 km (satellite) versus 0.025 m (in situ) Introduction Correlation coefficient between all satellite products and ground data sets Brocca et al., 2011 (RSE)

11. 11 Outline 1.Brief introduction to the studies investigating soil moisture data assimilation into rainfall-runoff modelling 2.Description of the employed data assimilation approaches 3.Data assimilation results for the Tiber River Basin 4.Open issues and conclusions Introduction

12. 12 Use of soil moisture data for hydrological applications Calibration/validation rainfall- runoff modelling: "in-situ" Wooldridge et al., 2003 (EMS) Koren et al., 2008 (JAWRA) remote sensing Parajka et al., 2006, 2009 (HESS) Sinclair and Pegram, 2010 (HESS) Antecedent wetness conditions: "in-situ" Pfister et al., 2003 (JHH) Huang et al., 2008 (HYP) Brocca et al., 2009 (JHE) Zehe et al., 2010 (HESS) Tramblay et al., 2010 (JoH) remote sensing Goodrich et al., 1994 (WRR) Jacobs et al., 2003 (JAWRA) Brocca et al., 2009 (JoH) Beck et al., 2010 (IEEE JST) Data assimilation: "in-situ"remote sensing Loumagne et al., 2001 (HSJ)Pauwels et al., 2001, 2002 (JoH, HP) Aubert et al., 2003 (JoH)Francois et al., 2003 (JHM) Anctil et al., 2008 (JoH)Crow et al. 2005 (GRL) Brocca et al., 2010 (HESS)Matgen et al., 2006 (IAHS) Antecedent wetness conditions: "in-situ" Pfister et al., 2003 (JHH) Huang et al., 2008 (HYP) Brocca et al., 2009 (JHE) Zehe et al., 2010 (HESS) Tramblay et al., 2010 (JoH) remote sensing Goodrich et al., 1994 (WRR) Jacobs et al., 2003 (JAWRA) Brocca et al., 2009 (JoH) Beck et al., 2010 (IEEE JSTAR) Calibration/validation rainfall- runoff modelling: "in-situ" Wooldridge et al., 2003 (EMS) Koren et al., 2008 (JAWRA) remote sensing Parajka et al., 2006, 2009 (HESS) Sinclair and Pegram, 2010 (HESS) Data assimilation: "in-situ"remote sensing Loumagne et al., 2001 (HSJ)Pauwels et al., 2001, 2002 (JoH, HP) Aubert et al., 2003 (JoH)Francois et al., 2003 (JHM) Anctil et al., 2008 (JoH)Crow et al. 2005 (GRL) Brocca et al., 2010 (HESS)Matgen et al., 2006 (IAHS) Introduction

13. 13 Soil moisture data assimilation into rainfall-runoff modelling However, only a small number of studies demonstrated the value of assimilating REAL in situ and remotely sensed soil moisture data to improve runoff prediction (see also Crow and Ryu, 2009, HESS) 1.Aubert et al., 2003 (JoH) 2.Francois et al., 2003 (JHM) 3.Chen et al., 2011 (AWR) 4.Matgen et al., 2011 (AWR, under review) 5.Brocca et al., 2010 (HESS); Brocca et al., 2011 (IEEE TGRS, under review) Many studies performed synthetic experiments and tested different techniques and approaches for soil moisture assimilation within rainfall-runoff modelling Introduction

14. 14 Previous studies (1) Chen et al, 2011 (AWR) 1.Ensemble Kalman Filter 2.In-situ data (Micronet network) 3.Daily time step (SWAT RR model) Cobb Creek Watershed (341 km 2 ) - USA LINEAR RESCALING "Assimilation of actual surface soil moisture data had limited success in the upper layers only and was generally unsuccessful in improving stream flow prediction.... [ ]... mainly due to the SWAT decoupling between surface and root-zone layer that limits the ability of the EnKF to update the soil moisture states of deeper layers." Introduction

15. 15 Previous studies (2) 1.Particle Filter 2.In-situ and ASCAT soil moisture data 3.Hourly time step (FLEX model) LINEAR RESCALING Bibeschbach (10.6 km 2 ) - Luxembourg Matgen et al, 2011 (AWR, under review) Marginal improvement in runoff prediction, Efficiency score of 5% Introduction

16. 16 Why? 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 Introduction

17. 17 Soil moisture data assimilation into rainfall-runoff modelling 1.Rainfall-runoff model: MISDc 2.Linear rescaling 3.Data assimilation technique: NUDGING SCHEME Methods

18. 18 Soil Water Index – Exponential filter Methods Simple differential model for describing the exchange of soil moisture between surface layer (  s ) and the “reservoir”(  ) T: characteristic time length Wagner et al., 1999 (RSE)

19. 19 Soil Water Index Wagner et al., 1999 (RSE) SWI:Soil Water Index t:time t i :acquisition time of SSM ti SSM t i :relative surface soil moisture [0,1] T: characteristic time length SSM SWI Methods

20. 20 MISDc rainfall-runoff model MISDc: "Modello Idrologico Semi-Distribuito in continuo" 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 W(t)S(t) S: soil potential maximum retention W(t)/W max : saturation degree FREELY AVAILABLE !!! r(t): rainfall Brocca et al., 2011 (HYP) Methods

21. 21 MISDc: applicationsLuxembourg North Italy France VALESCURE ALZETTE CORDEVOLE NICCONE Central Italy Methods

22. 22 MISDc: Real-time flood forecastng 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/Methods

23. 23 MISDc-2L: 2-layer rainfall-runoff model infiltration evapotranspiration deep percolation W max rainfall Brocca et al., 2010 (HESS) Assimilation of the profile soil moisture (SWI) ONLY  RR MODEL with 1 LAYER SWI the MISDc model simulates the soil moisture storage of 1 layer evapotranspiration infiltration deep percolation Wsup max W max rainfall percolation Brocca et al., 2010 (IEEE TGRS) Assimilation of both SSM and SWI  RR MODEL with 2 LAYER surface layer SWI SSM Investigation of the impact on discharge prediction of the assimilation of surface and root-zone soil moisture into rainfall-runoff modelling Methods

24. 24 Linear rescaling The SWI was rescaled to match the relative soil moisture, , simulated by MISDc,  mod Methods mean standard deviation

25. 25 G is a constant G=0 "perfect" model G=1 direct insertion time moisture relative soil moisture observations modeled soil moisture updated soil moisture Nudging scheme Kalman GAIN model error obs error assimilation time Brocca et al., 2010 (HESS)

26. 26 Ensemble Kalman Filter Methods 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)

27. 27 Tiber River Basin Study Area Rainfall-runoff data from 1989 with sub-hourly time resolution Nov-2005 Dec-2000 Dec-2008 Jan-2010 Chiani at Ponticelli Whole basin (4500 km²) + 5 sub-catchments (100-658 km²)

28. 28 Alzette River Basin Study Area Rainfall-runoff data from 2004 with sub-hourly time resolution Alzette at Hesperange (279 km²)

29. 29 Data assimilation examples Results 1.Soil Water Index (SWI) assimilation for five Tiber River subcatchments  impact of catchment size 2.SWI assimilation for the whole Upper Tiber River Basin at Monte Molino (5200 km²)  impact of RR model calibration 3.SWI assimilation for the Alzette River at Hesperange (279 km²)  impact of climatic and soil conditions Nudging scheme 1.SWI and Surface Soil Moisture (SSM) assimilation for Niccone catchment at Migianella  impact of the assimilated quantity 2.SWI and SSM assimilation for a synthetic experiment  impact of the assimilated quantity (ideal conditions) Ensemble Kalman Filter (EnKF)

30. 30 ASCAT SWI versus modelled soil moisture Results: Nudging Very high correlation between the ASCAT derived SWI and modeled soil moisture data R>0.95 Tiber catchment (S.Lucia)Assino catchment Niccone catchmentTimia catchment Brocca et al., 2010 (HESS)

31. 31 SWI assimilation: Tiber subcatchments Results: Nudging Niccone at Migianella (137 km²) SIM.ASS. NS7584 |  Qp | 3924 |  Rd | 4421 Eff39 start of flood events 1 2 3 4 Brocca et al., 2010 (HESS) relative soil moisture (-)

32. 32 Cumulated runoff Niccone at Migianella (137 km 2 ) Assino at Serrapartucci (165 km 2 ) Tiber at S.Lucia (658 km 2 ) Results: Nudging Brocca et al., 2010 (HESS) The simulated discharge with SWI assimilation (blue line) is much closer to observations (green line) than the without assimilation (red line)

33. 33 Results summary Results: Nudging SMALL improvement Eff<10% HIGH improvement Eff>35% Area <200 km 2 Area>500 km 2 The improving in runoff prediction is higher for smaller catchments !!! Brocca et al., 2010 (HESS)

34. 34 SWI assimilation: Tiber River at M.Molino Results: Nudging 200820052010 200820052010 NS=0.59 flooding "old" calibration (1994-1998) Discharge overestimation for flood with very high initial soil moisture conditions (January 2010) NS=0.89 "new" calibration (2005-2010)

35. 35 SWI assimilation: Tiber River at M.Molino Results: Nudging SIM.ASS. NS8688 Eff8 "old" calibration (1994-1998) "new" calibration (2005-2010) SIM.ASS. NS5271 Eff40 The improving in runoff prediction is higher for "bad" calibrated RR model

36. 36 Unknown initial conditions Results: Nudging IC NS EFF SIM.ASS. 0698650 0.2798620 0.486871 0.686888 0.8848613 1.0848613 mean818618 Unknown initial conditions For unknown initial conditions, the SWI assimilation significantly improves runoff prediction

37. 37 SWI assimilation: Tiber vs Alzette River Results: Nudging Niccone Migianella 137 km 2 Central Italy 2007-2010 Alzette Hesperange 292 km 2 Luxembourg 2007-2008 NO ASSIMILATION NS=80% NS=86% overestimation Brocca et al., 2011 (SPIE)

38. 38 SWI assimilation: Tiber vs Alzette River Results: Nudging ASCAT ASSIMILATION NS=87% NS=85% improvingslightly worse slightly better Niccone Migianella 137 km 2 Central Italy 2007-2010 Alzette Hesperange 292 km 2 Luxembourg 2007-2008 Brocca et al., 2011 (SPIE)

39. 39 SWI assimilation: Tiber vs Alzette River Results: Nudging AMSR-E ASSIMILATION NS=86% NS=85% improvingslightly better Niccone Migianella 137 km 2 Central Italy 2007-2010 Alzette Hesperange 292 km 2 Luxembourg 2007-2008 Brocca et al., 2011 (SPIE)

40. 40 Tiber vs Alzette River: summary Results: Nudging Performance in runoff prediction as a function of G, the Kalman Gain NO ASSIMILATION DIRECT INSERTION For central Italy, in terms of error on peak discharge and runoff volume the assimilation of ASCAT soil moisture product provides much better results than AMSRE For Luxembourg, the impact of data assimilation is very limited, likely due to soil freezing Brocca et al., 2011 (SPIE)

41. 41 ASCAT SSM (SZSM) and SWI (RZSM) vs modelled soil moisture Results: EnKF Brocca et al., 2011 (IEEE TGRS) Good correlation between the ASCAT derived SZSM and modeled soil moisture data for the surface layer (5 cm) Niccone Migianella 137 km 2 Central Italy 2007-2010

42. 42 SZSM and RZSM assimilation Results: EnKF Brocca et al., 2011 (IEEE TGRS) RZSM ASSIMILATION SZSM ASSIMILATION NS=86%NS=79% NS (no assimilation)=76% The assimilation of SWI has a higher impact on runoff prediction, and better results Niccone Migianella 137 km 2 Central Italy 2007-2010

43. 43 Synthetic experiments Results: EnKF Brocca et al., 2011 (IEEE TGRS) 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

44. 44 Synthetic experiments Results: EnKF Brocca et al., 2011 (IEEE TGRS) SZSM ASSIMILATION RZSM ASSIMILATION The results of the synthetic experiments confirm the findings obtained with real-data

45. 45 Modelled SZSM vs RZSM Results: EnKF Brocca et al., 2011 (IEEE TGRS) For the MISDc-2L structure, SZSM and RZSM are not linearly related. Therefore, EnKF fails to correctly update the states

46. 46 Open issues 1.Better characterization of modelling errors (perturbation factors, correlation structure, bias handling,...) 2.Better characterization of observation errors (autocorrelation structure, temporal and spatial variability,...) 3.Rainfall-runoff model structure optimization for the assimilation of surface soil moisture data 4.Improvement of the data assimilation approach 5.Joint assimilation of surface and root-zone soil moisture 6.Joint assimilation of soil moisture and discharge 7.Assimilation performance in different climatic, soil, land- use settings Open issues

47. 47 Conclusions soil moisture data obtained from ASCAT 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 Conclusions SIMPLY TRY! 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

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EMS, 18(1), 35-48.  Zehe, E. et al. (2010). Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater in the Ore... HESS, 14, 873-889. FOR FURTHER INFORMATION URL:http://www.irpi.cnr.it/it/scheda.php?cognome=BROCCA&nome=Luca URL IRPI:http://www.irpi.cnr.it/it/idrologia_it.htmhttp://www.irpi.cnr.it/it/scheda.php?cognome=BROCCA&nome=Lucahttp://www.irpi.cnr.it/it/idrologia_it.htm