Application of the Priestley–Taylor Approach in a Two-Source Surface Energy Balance Model

Agam, Nurit; Kustas, William P.; Anderson, Martha C.; Norman, John M.; Colaizzi, Paul D.; Howell, T. A.; Prueger, John H.; Meyers, Tilden P.; Wilson, Thaddeus

doi:10.1175/2009jhm1124.1

Cited by 115 publications

(109 citation statements)

References 65 publications

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“…Although this study had strong advection, the vapor pressure deficits were not extremely large and therefore a PTC was assigned a value of 1.3 [23]. Under stress conditions, the TSEB model iteratively reduces a PTC from its initial value, as described below.…”

Section: Two-source Energy Balance (Tseb) Model Formulationmentioning

confidence: 99%

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

Kustas

Alfieri

Anderson

et al. 2012

Advances in Water Resources

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Evett, Steven R.; Neale, Christopher M. U.; French, Andrew N.; Hipps, Lawrence E.; Chávez, José L.; Copeland, Karen S.; and Howell, Terry A., "Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area" (2012 a b s t r a c tApplication and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.Published by Elsevier Ltd.

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Section: Two-source Energy Balance (Tseb) Model Formulationmentioning

confidence: 99%

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

Kustas

Alfieri

Anderson

et al. 2012

Advances in Water Resources

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“…The most common value of the Priestley-Taylor coefficient (close to 1.3) has indeed been challenged for natural vegetation and sites with strong vapour pressure deficit values where root zone moisture does not limit transpiration (Agam et al, 2010). According to Colaizzi et al (2014), potential transpiration using the PenmanMonteith equation showed better performances compared to the Priestley-Taylor equation.…”

Section: Introductionmentioning

confidence: 99%

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

Boulet

Mougenot

Lhomme

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract.Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil-vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m −2 from values of the order of 50-80 W m −2 ), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution.

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“…The canopy transpiration term used to compute latent heat (LE C ) is calculated using a modified Priestley-Taylor approximation [28]. The plant transpiration is related to the canopy net radiation divergence RN C as: (9) where is the Priestley-Taylor coefficient equivalent to 1.3 which can be down-scaled depending on the vegetative stress [17,14,29] and discussed further below, f c is the fraction of green vegetation,  is the slope of saturated vapor pressure curve with respect to temperature; and  is the psychometric constant (0.066 kPa•°C -1 ). The soil latent heat (LE S ) is computed as the residual of the canopy latent heat and latent heat of the system (LE):…”

Section: Two-source Energy Balance Modelmentioning

confidence: 99%

A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

et al. 2013

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Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. 3332The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.

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Application of the Priestley–Taylor Approach in a Two-Source Surface Energy Balance Model

Cited by 115 publications

References 65 publications

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

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