Estimating the moisture dependence of root zone water loss using conditionally averaged precipitation

Salvucci, Guido D.

doi:10.1029/2000wr900336

Cited by 89 publications

(102 citation statements)

References 14 publications

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“…The interpretation offered in Salvucci (2001) is that, under the assumption of stationarity, conditional averaging of P on W must reflect evapotranspiration at low soil moisture and runoff at high soil moisture, so consistent with (4), DP/DW 5 DE/DW 1 DQ/DW. Such results do not necessarily imply that coupling with the atmosphere is not playing a role: assuming the SVAT model simulations reflect what would be measured, the soil moisture and surface fluxes produced by the model would reflect land-atmosphere interaction.…”

Section: Sensitivity For Disabling Two-way Coupling Of Evapotranspiramentioning

confidence: 99%

“…3 has been previously demonstrated in an uncoupled stationary soil moisture balance model (Salvucci 2001). Using observed precipitation and other meteorological measurements to drive a soil-vegetation-atmosphere transfer (SVAT) model to estimate soil moisture and surface fluxes, Salvucci (2001) constructed conditional averages of P, E, and Q on soil moisture and obtained a U-shaped profile of DP/DW.…”

Section: Sensitivity For Disabling Two-way Coupling Of Evapotranspiramentioning

confidence: 99%

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An Idealized Prototype for Large-Scale Land–Atmosphere Coupling

et al. 2013

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A process-based, semianalytic prototype model for understanding large-scale land-atmosphere coupling is developed here. The metric for quantifying the coupling is the sensitivity of precipitation P to soil moisture W, DP/DW. For a range of prototype parameters typical of conditions found over tropical or summertime continents, the sensitivity measure exhibits a broad minimum at intermediate soil moisture values. This minimum is attributed to a trade-off between evaporation (or evapotranspiration) E and large-scale moisture convergence across the range of soil moisture states. For water-limited, low soil moisture conditions, DP/DW is dominated by evaporative sensitivity DE/DW, reflecting high potential evaporation E p arising from relatively warm surface conditions and a moisture-deficient atmospheric column under dry surface conditions. By contrast, under high soil moisture (or energy limited) conditions, DE/DW becomes slightly negative as E p decreases. However, because convergence and precipitation increase strongly with decreasing (drying) moisture advection, while soil moisture slowly saturates, DP/DW is large. Variation of key parameters is shown to impact the magnitude of DP/DW, for example, increasing the time scale for deep convective adjustment lowers DP/DW at a given W, especially on the moist side of the profile where convergence dominates. While the prototype's applicability for direct quantitative comparison with either observations or models is clearly limited, it nonetheless demonstrates how the complex interplay of surface turbulent and column radiative fluxes, deep convection, and horizontal and vertical moisture transport influences the coupling of the land surface and atmosphere that may be expected to occur in either more realistic models or observations.

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Section: Sensitivity For Disabling Two-way Coupling Of Evapotranspiramentioning

confidence: 99%

Section: Sensitivity For Disabling Two-way Coupling Of Evapotranspiramentioning

confidence: 99%

An Idealized Prototype for Large-Scale Land–Atmosphere Coupling

et al. 2013

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“…[6] The basic idea presented here, by Salvucci [2001] and by Sun et al [2011] is as follows: Using conditional averaging, the relation between soil moisture and precipitation (P) can be studied in place of the relation between soil moisture and evapotranspiration (ET), and drainage plus runoff (D+RO, which we will here denote as Q). The benefit of doing this is that very few long term and/or spatially distributed data sets exist for evapotranspiration and drainage, but many exist for precipitation and soil moisture.…”

Section: Methodsmentioning

confidence: 99%

“…The measure is formed by conditionally averaging the tendency term by the state, which Salvucci [2001] showed to be zero for stationary systems. This measure has distinct advantages over calibration metrics.…”

Section: Introductionmentioning

confidence: 99%

“…[3] In a recent paper [Sun et al, 2011], we have presented a method for estimating land surface model parameters by maximizing a measure [Salvucci, 2001] of stationarity of model predicted tendencies (the time derivative of land surface moisture and temperature states with time). The measure is formed by conditionally averaging the tendency term by the state, which Salvucci [2001] showed to be zero for stationary systems.…”

Section: Introductionmentioning

confidence: 99%

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An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations

Salvucci

Entekhabi

2011

Geophys. Res. Lett.

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[1] Atmospheric models include land surface parameterizations of heat and moisture fluxes. Most parameterizations derive from early models of layered soil and vegetation canopy that account for liquid, vapor and heat diffusion, radiation processes, and surface turbulence. The number of parameters in these models ranges roughly from 10 to 50. Some parameters are known to vary over a small range and/or not significantly impact predictions. Others are highly influential (e.g., Leaf Area Index), but are currently well estimated from satellite data. Many parameters, however, are highly influential, vary over a large dynamic range, cannot be estimated from satellite data, and cannot be readily upscaled from in-situ measurements. For such parameters (e.g., maximum stomatal conductance and soil hydraulic conductivity), values are assigned based on look-up tables sorted by land cover and soil texture. Here we present a method for estimating these parameters by minimizing a measure of nonstationarity of model-predicted moisture state variable tendencies. This method has advantages over calibration: 1) it does not require flux data (e.g., evapotranspiration); and 2) the tendency terms are evaluated at the model grid and thus yield parameters that are effective for that scale. The method is demonstrated with the Noah Land Surface Model, using remotely-sensed soil moisture, at a site in California. Preliminary results indicate that the method is robust and performs better than both: 1) calibration to soil moisture observations, which can lead to large, compensating errors in drainage and evaporation; and 2) minimizing the sum of squares of innovations of soil moisture updates. Citation: Salvucci, G. D., and D. Entekhabi (2011), An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations, Geophys. Res. Lett., 38, L16404,

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Estimation of land surface water and energy balance parameters using conditional sampling of surface states

et al. 2014

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Numerical models of heat and moisture diffusion in the soil-vegetation-atmosphere continuum are linked through the moisture flux from the surface to the atmosphere. This mass flux represents a heat exchange as latent heat flux, coupling water, and energy balance equations. In this paper, a new approach for estimating key parameters governing moisture and heat diffusion equation and the closure function which links these equations, is introduced. Parameters of the system are estimated by developing objective functions that link atmospheric forcing, surface states, and unknown parameters. This approach is based on conditional averaging of heat and moisture diffusion equations on land surface temperature and moisture states, respectively. A single objective function is expressed that measures moisture and temperaturedependent errors solely in terms of observed forcings and surface states. This objective function is minimized with respect to the parameters to identify evaporation and drainage models and estimate water and energy balance flux components. The approach is calibration free (surface flux observations are not required), it is not hampered by missing data and does not require continuous records. Uncertainty of parameter estimates is obtained from the inverse of Hessian of the objective function, which is an approximation of the error covariance matrix. Uncertainty analysis and analysis of the covariance approximation, guides the formulation of a well-posed estimation problem. Accuracy of this method is examined through its application over three different field sites. This approach can be applied to diverse climates and land surface conditions with different spatial scales, using remotely sensed measurements.

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Estimating the moisture dependence of root zone water loss using conditionally averaged precipitation

Cited by 89 publications

References 14 publications

An Idealized Prototype for Large-Scale Land–Atmosphere Coupling

An Idealized Prototype for Large-Scale Land–Atmosphere Coupling

An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations

Estimation of land surface water and energy balance parameters using conditional sampling of surface states

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