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Jeffrey Walker

Estimating Soil Moisture Profile Dynamics From Near-Surface Soil Moisture Measurements and Standard Meteorological Data. Jeffrey Walker. Department of Civil, Surveying and Environmental Engineering The University of Newcastle AUSTRALIA Supervisor: Co-Supervisor:

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Jeffrey Walker

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  1. Estimating Soil Moisture Profile Dynamics From Near-Surface Soil Moisture Measurements and Standard Meteorological Data Jeffrey Walker Department of Civil, Surveying and Environmental Engineering The University of Newcastle AUSTRALIA Supervisor: Co-Supervisor: Garry Willgoose Jetse Kalma

  2. Importance of Soil Moisture • Meteorology • Evapotranspiration - partitioning of available energy into sensible and latent heat exchange • Hydrology • Rainfall Runoff - infiltration rate; water supply • Agriculture • Crop Yield - pre-planting moisture; irrigation scheduling; insects & diseases; de-nitrification • Sediment Transport - runoff producing zones • Climate Studies

  3. Background to Soil Moisture R e m o t e S e n s i n g S a t e l l i t e S u r f a c e S o i l M o i s t u r e L o g g e r S o i l M o i s t u r e M o d e l f  (z) [ q , D ( ) , ( ) ]      s S o i l M o i s t u r e S e n s o r s

  4. Research Objective • Develop a methodology for making improved estimates of the soil moisture profile dynamics Efforts focussed on: • Identification of an appropriate soil moisture profile estimation algorithm • Remote Sensing for surface soil moisture - volume scattering • Observation depth = f(frequency, moisture, look angle, polarisation) • Assessment of assimilation techniques • Importance of increased observation depth • Effect of satellite repeat time • Computational efficiency - moisture model/assimilation • Collection of an appropriate data set for algorithm evaluation • Proving the usefulness of near-surface soil moisture data

  5. Seminar Outline • Identification of an appropriate methodology for estimation soil moisture profile dynamics • Near-surface soil moisture measurement • One-dimensional desktop study • Model development • Simplified soil moisture model • Simplified covariance estimation • Field applications • One-dimensional • Three-dimensional • Conclusions and Future direction

  6. Literature Review • Regression Approach • Uses typical data and land use - location specific • Knowledge Based Approach • Uses a-priori knowledge on the hydrological behaviour of soils • Inversion Approach • Mainly applied to passive microwave • Water Balance Approach • Uses a water balance model with surface observations as input

  7. Water Balance Approach • Updated 2-layer model by direct insertion of observations - Jackson et al. (1981), Ottle and Vidal-Madjar (1994) • Fixed head boundary condition on 1D Richards eq. -Bernard et al. (1981), Prevot et al. (1984), Bruckler and Witono (1989) • Updated 1D Richards equation with Kalman filter -Entekhabi et al. (1994) • Updated 2-layer basin average model with Kalman filter -Georgakakos and Baumer (1996) • Updated 3-layer TOPLATS model with: direct insertion; statistical correction; Newtonian nudging (Kalman filter); and statistical interpolation -Houser et al. (1998)

  8. Soil Moisture Profile Estimation Algorithm • Initialisation Phase • Use a knowledge-based approach • Lapse rate; Hydraulic equilibrium; Root density; Field capacity; Residual soil moisture • Dynamic Phase (Water Balance Model) • Forecast soil moisture with meteorological data • Update soil moisture forecast with observations • Direct insertion approach • Dirichlet boundary condition • Kalman filter approach

  9. Direct-Insertion Kalman-Filtering Data Assimilation Observation Depth

  10. The (Extended) Kalman-Filter • Forecasting Equations States: Xn+1= An Xn + Un Covariances: n+1= An n AnT+ Q • Observation equation Z = H X + V

  11. Active or Passive? • Passive • Measures the naturally emitted radiation from the earth - Brightness Temperature • Resolution - 10’s km  100 km (applicable to GCM’s) • Active • Sends out a signal and measures the return - Backscattering Coefficient • More confused by roughness, topography and vegetation • Resolution - 10’s m (applicable to partial area hydrology and agriculture)

  12. The Modified IEM • Modified reflectivities • Dielectric profile • m = 12 gives varying profile to depth 3mm • Radar observation depth 1/10  1/4 of the wavelength

  13. Radar Observation Depth

  14. Evol /Esur = ? • Addressed through error analysis of backscattering equation • 2% change in mc  0.15 - 1 dB, wet  dry • Radar calibration  1 - 2 dB • 1.5 dB  0.17

  15. Application of the Models rms = 25 mm correlation length = 60 mm incidence angle = 23o moisture content  9% v/v vv polarisation hh polarisation

  16. 1D Desktop Study • 1D soil moisture and heat transfer • Moisture Equation • Matric Head form of Richard’s eq. • Assumes: • Isothermal conditions (decoupled from temperature) • Vapour flux is negligible • Temperature Equation • Function of soil moisture • Assumes: • Effect from differential heat of wetting is negligible • Effect from vapour flux is negligible

  17. Temperature Dependence Low Soil Moisture (5%) • Microwave remote sensing is a function of dielectric constant High Soil Moisture (40%)

  18. Synthetic Data Initial conditions Boundary conditions

  19. Direct-Insertion Every Hour

  20. Kalman-Filter Update Every Hour

  21. Kalman-Filter Update Every 5 Days

  22. Quasi Profile Observations

  23. Kalman-Filter Update Every 5 Days

  24. Volumetric Moisture Transformation

  25. Summary of Results • Continuous Dirichlet boundary condition • Moisture 5 - 8 days Temperature >20 days • 10 cm update depth • Required Dirichlet boundary condition for 1 hour • Required Dirichlet boundary condition for 24 hours • Moisture Transformation

  26. A Simplified Moisture Model • Computationally efficient -based model • Capillary rise during drying events • Gravity drainage during wetting events • Lateral redistribution • No assumption of water table • Amenable to the Kalman-filter • Buckingham Darcy Equation q = K+K • Approximate Buckingham Darcy Equation q = KVDF+K where VDF = Vertical Distribution Factor

  27. Vertical Distribution Factor • Special cases Uniform Infiltration Exfiltration • Proposed VDF

  28. Model Comparison • Exfiltration (0.5 cm/day) • Infiltration (10 mm/hr)

  29. Kalman-Filter Update Every 5 Days

  30. KF Modification for 3D Modelling • Implicit Scheme 1n+1 Xn+1 + 1n+1 = 2n Xn + 2n • State Forecasting Xn+1 = An Xn + Un where An = [1n+1]-1 [2n] Un = [1n+1]-1 [2n – 1n+1] • Covariance Forecasting n+1= An n AnT+ Q

  31. KF Modification for 3D Modelling • Covariance Forecast Auto-regressive smooth of 1 and 2 1n+1 =  1n+ (1 –  ) 1n+1 Estimate correlations from:  = AAT where A = [1]-1 [2] Reduce  to correlation matrix i,j = ewhere

  32. Correlation Estimate

  33. Modified Kalman-Filter Application

  34. Field Application

  35. Meteorological Station

  36. 1D Model Calibration/Evaluation

  37. 1D Profile Retrieval - 1/5 Days

  38. 3D Model Calibration 3D Model Evaluation

  39. 3D Profile Retrieval • All observations • Single Observation

  40. Summary of Results

  41. Conclusions • Radar observation depth model has been developed which gives results comparable to those suggested in literature • Modified IEM backscattering model has been developed to infer the soil moisture profile over the observation depth • Computationally efficient spatially distributed soil moisture forecasting model has been developed • Computationally efficient method for forecasting of the model covariances has been developed

  42. Conclusions • Require an assimilation scheme with the characteristics of the Kalman-filter (ie. a scheme which can potentially alter the entire profile) • Require as linear forecasting model as possible to ensure stable updating with the Kalman-filter (ie. -based model rather than a -based model) • Updating of model is only as good as the models representation of the soil physics • Usefulness of near-surface soil moisture observations for improving the soil moisture estimation has been verified

  43. Future Direction • Addition of a root sink term to the simplified soil moisture forecasting model • Improved specification of the forecast system state variances • Application of the soil moisture profile estimation algorithm with remote sensing observations, published soils and elevation data, and routinely collected met data • Use point measurements to interpret the near-surface soil moisture observations for applying observations to the entire profile - may alleviate the decoupling problem

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