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This research focuses on developing improved methods for estimating soil moisture profiles using near-surface soil moisture measurements and meteorological data. The study aims to enhance agricultural planning, hydrological analysis, and climate studies by accurately monitoring soil moisture dynamics. The project includes algorithm development, remote sensing applications, and data assimilation techniques. The goal is to provide valuable insights for optimizing irrigation scheduling, crop yield prediction, and water resource management.
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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
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
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
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
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
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
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)
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
Direct-Insertion Kalman-Filtering Data Assimilation Observation Depth
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
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)
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
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
Application of the Models rms = 25 mm correlation length = 60 mm incidence angle = 23o moisture content 9% v/v vv polarisation hh polarisation
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
Temperature Dependence Low Soil Moisture (5%) • Microwave remote sensing is a function of dielectric constant High Soil Moisture (40%)
Synthetic Data Initial conditions Boundary conditions
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
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 = KVDF+K where VDF = Vertical Distribution Factor
Vertical Distribution Factor • Special cases Uniform Infiltration Exfiltration • Proposed VDF
Model Comparison • Exfiltration (0.5 cm/day) • Infiltration (10 mm/hr)
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
KF Modification for 3D Modelling • Covariance Forecast Auto-regressive smooth of 1 and 2 1n+1 = 1n+ (1 – ) 1n+1 Estimate correlations from: = AAT where A = [1]-1 [2] Reduce to correlation matrix i,j = ewhere
3D Model Calibration 3D Model Evaluation
3D Profile Retrieval • All observations • Single Observation
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
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
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