(Source: Western et al. (2002))

1. Soil Moisture Data Assimilation at Multiple Scales and Estimation of Representative Field Scale Soil Moisture Characteristics EunjinHan Co-chairs: Dr. Merwade and Dr. Heathman November 15, 2011

2. Background / Motivation • Significance of soil moisture • Partitioning of water and energy at the land surface (Source: Western et al. (2002))

3. Background / Motivation • Significance of soil moisture (contd.) • Weather forecast and climate change • e.g. Soil moisture change → changes in surface albedo, the evaporative fraction, and potential for cloud formation and precipitation, evaporation /transpiration recirculation ratio (Pielke et al. 2010) • Flood or drought prediction • e.g. Accurate estimation of antecedent soil moisture condition • → improved runoff prediction • Irrigation planning • Crop yield prediction • Soil erosion • Water quality management

4. Background / Motivation • Different scales of soil moisture observations Remotely sensed soil moisture In-situ soil moisture measurement

5. Background / Motivation • Why surface SM data assimilation? • Improved predictions of profile SM by combining information from surface SM observations and hydrologic models Needs for : • Further investigations on SM data assimilation at field or watershed scales • Resolving scaling issues in soil moisture estimations • (in-situ point measurements → RS data)

6. Research Objectives I. Explore the effects of surface SM data assimilation on hydrological responses at the field scale II. Investigate the effects of surface SM data assimilation on hydrological responses at the watershed scale III. Link two different scales of SM estimates by upscaling point SM measurements to field averages

7. Research Objectives • Hypothesis I: Assimilating surface SM observations can improve predictions of profile SM, resulting in improved model performance of the hydrological system at local scales. • Approaches: • Field scale SM data assimilation with RZWQM (1D model) • Watershed scale SM data assimilation with SWAT • (semi-distributed model) • Hypothesis II: Representative SM characteristics can be estimated using SM observation at a single location. • Approaches: • Estimation of field averages and variances using point SM measurements and a Cumulative Distribution Function matching method

8. Study Area • Upper Cedar Creek Watershed, IN For objective II For objective I & III AS1 AS2

9. Objective I – Experiment Setup • Field scale application of surface SM assimilation • RZWQM: physically based, 1D, agricultural model • Applied EnKF to RZWQM using in-situ SM observations at AS1 and AS2 • Compared the results from EnKF with simple direct insertion method and open loop simulations • Explored the effect of ensemble size and update interval on model output Soil profile (numerical layers) 1 cm 2 cm 5 cm 5 cm 7 cm 11 cm AS1 15 cm SM measurement using Hydra Probe 20 cm 19 cm : 40 cm AS2 60 cm

10. Objective I - Methodology • Ensemble Kalman Filter? • Optimal recursive data processing algorithm Analysis (Source: Reichle et al. 2002) Prediction (RZWQM. SWAT) Prediction (RZWQM. SWAT)

11. Objective I - Methodology • Ensemble Kalman Filter? (contd.) • Nonlinear System model : Process noise, P(w)~ (0, Q) : Measurement noise, P(v)~(0,R) : Measurement * Error Covariance Matrix: Prediction step Analysis step

12. Objective I - Results • Results of surface SM assimilation • Improved SM estimation in the upper dynamic layers (5 & 20cm) with surface SM assimilation, while less improvement in the deep layers (40 & 60cm) • Performance: EnKF > DIR > Open loop • Local heterogeneity issue at AS1 (40cm depth)

13. Objective I - Results • Results of surface SM assimilation (contd.)

14. Objective I - Summary • Results & Discussions • Effect of update interval (1 ~ 14 days) • - More frequent update, more improvements (except deep layers at AS1) • - Surface SM is more sensitive to the update interval changes • Effect of ensemble size (50 ~ 500) • - Improvements even with 50 ensembles at upper layers at AS1 and AS2 • - Little sensitivity of EnKF to ensemble size • Bias correction issues • - Systematic biases exist in the model predictions • - Violation of EnKF assumption (Gaussian distribution) • ▪ Han, E., Merwade, V. and Heathman, G. C. (2011), Application of data assimilation with the Root Zone Water Quality Model for soil moisture profile estimation in the upper Cedar Creek, Indiana. Hydrological Processes. doi: 10.1002/hyp.8292

15. Objective II – Experiment Setup • Watershed scale application of surface SM assimilation • To Investigate the effect of surface SM assimilation on different hydrologic responses (ET, surface runoff, lateral flow etc.) • To examine how the surface SM assimilation compensate for errors in the hydrologic predictions due to inaccurate rainfall • To explore the effect of spatially varying input (landuse and soil types) on data assimilation results • Experimental design with SWAT • True state • “prior” • (no assimilation) • EnKF • Rainfall data from NCDC and NSERL raingauge network • Rainfall data only from NCDC • Intentionally poor initial condition • Same set up as “prior” • Updated soil water content using the observed surface soil moisture (~5cm) Our imperfect knowledge of the true system

16. Objective II - Results • RMSE of surface and profile SM Day of Year

17. Objective II - Results • Summary of SWAT SM assimilation • Improvements in surface and profile SM predictions with the EnKF→ showing potentials for future Remotely sensed SM data • Varying influence of the EnKF on other subsequent hydrological responses (SHALLST, GW_Q, QDAY, LATQ, ET, CNDAY) • Minimal improvement in streamflow predictions • - Importance of accurate precipitation and antecedent SM condition • - Weak model vertical coupling strength in SWAT • - Sub-optimal update with the EnKF due to the model non-linearity • and bounded nature of SM violating Gaussian assumption • - Limited sensitivity of surface runoff prediction to the change in SM • both with SCS-CN and Green Ampt method

18. Objective II – Results • Results : Impact of spatially varying input on surface SM DAY=258, Open loop DAY=258, EnKF DAY=259, Open loop (Precipitation) DAY=259, EnKF (Precipitation) Error (true-simulation) DAY=260, Open loop DAY=260, EnKF

19. Objective II - Results • Summary of SWAT SM assimilation (contd.) • Impact of spatially varying input (landuse and soil type) • - e.g. greater errors with FRSD(forest) and Hw(HSG-A) than AGRR or GnB2(HSG-C) • Inaccurate SM prediction → crop growth → LAI → canopy interception of precipitation → inaccurate net-precipitation → inaccurate surface runoff and infiltration • Surface SM assimilation can help adjusting long-lasting errors with certain soil types or landuse (cf. calibration ) • ▪ Han, E., Merwade, V. and Heathman, G. C. Implementation of Surface Soil Moisture Data Assimilation with Watershed Scale Distributed Hydrological Model, Journal of Hydrology, (pending revision)

20. Objective III – Experiment Setup • Estimation of field averages using single point measurements → upscaling approach • Limitations of temporal stability analysis • To find appropriate upscaling method(s) → observation operator • To explore whether the observation operator from CDF matching is transferable in time and space • Estimation of variances using single point measurements AS1: 2.23 ha AS2: 2.71 ha

21. Objective III - Methodology • Cumulative Distribution Function (CDF) matching method • cdfm: CDF of field avg. • cdfp: CDF of permanent sensor data • x: original data from permanent sensor • x1: transformed data of permanent sensor RS soil moisture • Observation operator – removing the systematic difference between two different data sets. • Polynomial fit to the ranked observed SM values and the corresponding differences Model prediction soil moisture from permanent sensor Field average (Source: Drusch et al. (2005))

22. Objective III - Results *M0: Original data from permanent sensor, M1: linear relationship, M2: absolute mean difference, M3: relative mean difference (most temporally stable site), M4: relative mean difference (permanent sensor), M5: CDF matching

23. Objective III - Results • Cumulative Distribution Function (CDF) matching method • M1: linear relationship, • M3: relative mean difference (most temporally stable site, #17 for AS1 and #21 for AS2) • M4: relative mean difference (permanent sensor) • M5: CDF matching

24. Objective III - Results • Upscaling of point measurements to field averages • CDF matching produces best results • Relative mean difference method using the most temporally stable site (TSA offset method) can produce reasonable estimates, but results in bias with 20cm data • Relative mean difference method using the permanent site does not work most of the time → weakness of temporal stability analyses • Temporal & spatial transferability of observation operators • Not temporally transferable for both AS1 and AS2 • → Possible reasons: rainfall characteristics, crop type (soybean vs. corn) • Spatially transferable, but with caution • Vertically transferable

25. Objective III - Results • Estimation of STDEV using point measurements • Dynamic variation of STDEV can be predicted successfully from single point measurements using the CDF matching method (AS1- 5cm, 2009) (AS1- 5cm, 2010) (AS1- 20cm, 2010) R=0.835 R=0.943 R=0.970 • ▪ Han, E., Merwade, V. and Heathman, G. C. Application of Observation Operators for Field-scale Soil Moisture Averages and Variances in Agricultural Landscapes, Journal of Hydrology, (in review)

26. Synthesis / Future Research • Proved potential of SM data assimilation at field and watershed scale hydrologic modeling → further applications for water quality or crop growth/yield prediction • Issues with the EnKF • - Estimation of system and observation error statistics • - Bias correction • Issues streamflow prediction • - Obtaining more accurate precipitation • - Improving the current rainfall-runoff mechanism to effectively • reflect SM changes from the data assimilation • Successful estimation of field averages and variances using single point measurements in agricultural landscape • → implication for future validation of RS SM products, data assimilation • and hydrological model calibration • - Expanding to watershed scale upscaling • - Effect of length of temporal sampling → temporally transferable • observation operator?

27. Acknowledgements • Dr. VenkateshMerawe • Dr. Gary Heathman • Dr. RaoGovindaraju • Dr. Laura Bowling • National Soil Erosion Research Laboratory • (Scott McAfee, Jim Frankenberger)

28. References • Drusch, M., Wood, E.F. and Gao, H., 2005. Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture. Geophysical Research Letters, 32(15). • Pielke, R., Niyogi, D., Otto, J.-C., and Dikau, R. (2010). "The Role of Landscape Processes within the Climate System Landform - Structure, Evolution, Process • Control." Springer Berlin / Heidelberg, 67-85. • Reichle, R. H., Walker, J. P., Koster, R. D., and Houser, P. R. (2002). Extended versus Ensemble Kalman Filtering for Land Data Assimilation. Journal of Hydrometeorology, American Meteorological Society, 728.

29. Thank you !