This paper reviews methods for estimating evaporation from landscapes, regions and larger geographic extents, with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Particular attention is given to the validation of such approaches against ground based flux measurements. An assessment of some 30 published validations shows an average root mean squared error value of about 50 W m -2 and relative errors of 15-30%. The comparison also shows that more complex physical and analytical methods are not necessarily more accurate than empirical and statistical approaches. While some of the methods were developed for specific land covers (e.g. irrigation areas only) we also review methods developed for other disciplines, such as hydrology and meteorology, where continuous estimates in space and in time are needed, thereby focusing on physical and analytical methods as empirical methods are usually limited by in situ training data. This review also provides a discussion of temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evaporation. Improved temporal scaling procedures are required to extrapolate instantaneous estimates to daily and longer time periods and gap-filling procedures are needed when temporal scaling is affected by intermittent satellite coverage. It is also noted that analysis of multi-resolution data from different satellite/sensor systems (i.e. data fusion) will assist in the development of spatial scaling and aggregation approaches, and that several biological processes need to be better characterized in many current land surface models. Nomenclature B Coefficient in Eq. 3 (-) C BNBulk turbulent transfer coefficient (-) C p Specific heat of air at constant pressure (J kg -1 K -1 ) D Zero plane displacement height (m) E, E a Actual evaporation rate (mm day -1 ) E n Normalised actual evaporation (mm day -1 ) E p Potential evaporation rate (mm day -1 ) E PT Priestley-Taylor evaporation rate (mm day -1 ) E w Equilibrium evaporation rate (mm day -1 ) e a Actual vapour pressure of the air (Pa) e a * Saturated vapour pressure of the air (Pa) e s * Saturated vapour pressure at T s (Pa) e u * Saturated vapour pressure at T u (Pa) f c Fractional vegetation cover (-) G Soil heat flux (W m -2 ) G s Surface conductance (m s -1 ) g bBulk leaf boundary layer conductance (m s -1 ) H Sensible heat flux (W m -2 ) H c Sensible heat flux to/from canopy (W m -2 ) H s Sensible heat flux to/from soil (W m -2 ) K;Downwelling shortwave radiation flux (W m -2 ) K:Upwelling shortwave radiation flux (W m -2 ) k Von Karman's constant (0.4) kB -1 Dimensionless ratio used to calculate r ex L Monin-Obukhov length (m) L;Downwelling longwave radiation flux (W m -2 ) L:Upwelling longwave radiation flux (W m -2 ) n Exponent in Eq. 3 r a Aerodynamic resistance (s m -1 ) r c Canopy resistance (s m -1 ) r cp Canopy resistance at potential transpiration (s m -1 ) r ex Excess (supplementary, extra) resistance (s m -1 ) R n N...

[1] The water table fluctuation method for determining recharge from precipitation and water table measurements was originally developed on an event basis. Here a new multievent time series approach is presented for inferring groundwater recharge from longterm water table and precipitation records. Additional new features are the incorporation of a variable specific yield based upon the soil moisture retention curve, proper accounting for the Lisse effect on the water table, and the incorporation of aquifer drainage so that recharge can be detected even if the water table does not rise. A methodology for filtering noise and non-rainfall-related water table fluctuations is also presented. The model has been applied to 2 years of field data collected in the Tomago sand beds near Newcastle, Australia. It is shown that gross recharge estimates are very sensitive to time step size and specific yield. Properly accounting for the Lisse effect is also important to determining recharge.Citation: Crosbie, R. S., P. Binning, and J. D. Kalma (2005), A time series approach to inferring groundwater recharge using the water table fluctuation method, Water Resour. Res., 41, W01008,

The Kalman filter assimilation technique is applied to a simplified soil moisture model for retrieval of the soil moisture profile from near-surface soil moisture measurements. First, the simplified soil moisture model is developed, based on an approximation to the Buckingham-Darcy equation. This model is then used in a 12-month one-dimensional field application, with updating at 1-, 5-, 10-, and 20-day intervals. The data used are for the Nerrigundah field site, New South Wales, Australia. This study has identified (i) the importance of knowing the depth over which the near-surface soil moisture measurements are representative (i.e., observation depth), (ii) soil porosity and residual soil moisture content as the most important soil parameters for correct retrieval of the soil moisture profile, (iii) the importance of a soil moisture model that represents the dominant soil physical processes correctly, and (iv) an appropriate forecasting model as far more important than the temporal resolution of near-surface soil moisture measurements. Although the soil moisture model developed here is a good approximation to the Richards equation, it requires a root water uptake term or calibration to an extreme drying event to model extremely dry periods at the field site correctly.

The Kalman filter assimilation technique is applied to a simplified soil moisture model for retrieval of the soil moisture profile from near-surface soil moisture measurements. First, the simplified soil moisture model is developed, based on an approximation to the Buckingham-Darcy equation. This model is then used in a 12month one-dimensional field application, with updating at 1-, 5-, 10-, and 20-day intervals. The data used are for the Nerrigundah field site, New South Wales, Australia. This study has identified (i) the importance of knowing the depth over which the near-surface soil moisture measurements are representative (i.e., observation depth), (ii) soil porosity and residual soil moisture content as the most important soil parameters for correct retrieval of the soil moisture profile, (iii) the importance of a soil moisture model that represents the dominant soil physical processes correctly, and (iv) an appropriate forecasting model as far more important than the temporal resolution of near-surface soil moisture measurements. Although the soil moisture model developed here is a good approximation to the Richards equation, it requires a root water uptake term or calibration to an extreme drying event to model extremely dry periods at the field site correctly.

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