Reintroducing radiometric surface temperature into the <scp>P</scp>enman‐<scp>M</scp>onteith formulation

Mallick, Kaniska; Boegh, Eva; Trebs, Ivonne; Alfieri, Joseph G.; Kustas, William P.; Prueger, John H.; Niyogi, Dev; Das, Narendra N.; Drewry, D.; Hoffmann, Lucien; Jarvis, Andrew

doi:10.1002/2014wr016106

Cited by 58 publications

(116 citation statements)

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“…The prime focus of STIC (Mallick et al, 2014;Mallick et al, 2015;Mallick et al, 2016) is based on physical integration of 15 TR into the Penman-Monteith (PM) equation, which is fundamentally constrained to account for the necessary feedbacks between ET, TR, DA, gA, and gC (Monteith, 1965). Monteith (1981) highlighted the fact that the biophysical conductances (i.e., gA and gC) regulating ET are heavily temperature dependent, after which a stream of research demonstrated the dominant control of TR into gC and associated canopy-scale aerodynamics (Moffett and Gorelick, 2012;Blonquist et al, 2009).…”

Section: Et Mapping Is: How Can State-of-the-art Seb Models Overcome mentioning

confidence: 99%

“…Monteith (1981) highlighted the fact that the biophysical conductances (i.e., gA and gC) regulating ET are heavily temperature dependent, after which a stream of research demonstrated the dominant control of TR into gC and associated canopy-scale aerodynamics (Moffett and Gorelick, 2012;Blonquist et al, 2009). Somewhat surprisingly, the idea of integrating TR into the PM model was never attempted because of complexities associated with gC 20 parameterization (Bell et al, 2015;Matheny et al, 2014), until the concept of STIC was formulated (Mallick et al, 2014;Mallick et al, 2015). The recent version of STIC, STIC1.2, combines PM with the Shuttleworth-Wallace (SW) model (Shuttleworth and Wallace, 1985) to estimate the source/sink height temperature and vapour pressure (T0 and e0) (Mallick et al, 2016).…”

Section: Et Mapping Is: How Can State-of-the-art Seb Models Overcome mentioning

confidence: 99%

“…This could largely be attributed to the fact that the model is only used for understanding ecosystem-scale ET partitioning and their biophysical controls at the eddy covariance (EC) footprints (Mallick et al, 2015;Mallick et al, 2016), where all the necessary forcing variables were measured at the flux tower sites. In this paper, we present the first ever implementation of the STIC1.2 model using MODIS LST and associated land surface products, and its validation in thirteen core AmeriFlux sites Hydrol.…”

Section: Et Mapping Is: How Can State-of-the-art Seb Models Overcome mentioning

confidence: 99%

“…The key model structures of SEBS and MOD16 are briefly explained in subsections 2.1.2 and 2.1.3. 5 2.1.1 STIC1.2 STIC1.2 is the most recent version of the original STIC formulation (Mallick et al, 2014;Mallick et al, 2015), which is a onedimensional physically-based SEB model that treats the vegetation-substrate complex as a single unit (Fig. 1).…”

Section: Rn = Rs (1-αo) + εOrld -Rlumentioning

confidence: 99%

“…(5), the two biophysical conductances (gA and gC) are unknown and the STIC1.2 methodology is based on finding analytical solutions for the two unknown conductances to directly estimate ET (Mallick et al, 2014;Mallick et al, 2015). The need for such analytical estimation of these conductances is motivated by the fact that gA and gC can neither be measured at the canopy or larger spatial scales, and there is not an appropriate model of gA and gC that currently exists (Matheny et al, 2014;van Dijk et al, 2015b).…”

Section: Respectively 15mentioning

confidence: 99%

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Regional evapotranspiration from image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous United States

Bhattarai¹,

Mallick²,

Brunsell³

et al. 2017

Preprint

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Abstract. Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman-15Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystemscale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that physically integrates Moderate Resolution Imaging Spectroradiometer (MODIS)-derived TR and ancillary land surface variables in conjunction with NLDAS (North American 20Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth-Wallace framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at thirteen core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day 25 cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of -4 %. These error statistics show higher accuracies than a widely-used SEB-based Surface Energy Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-535 Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 11 September 2017 c Author(s) 2017. CC BY 4.0 License. 2Balance System (SEBS) and PM-based MOD16 ET, which were found to overestimate (PBIAS = 28 %) and underestimate ET (PBIAS = -26 %), respectively. The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapour pressure deficit (DA), and vegetation cover. A consistent performance 5 of STIC1.2 in a broad range of hydrological and biome categories as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient points to its potential for near real time ET mapping from regional to continental scales.

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Section: Et Mapping Is: How Can State-of-the-art Seb Models Overcome mentioning

confidence: 99%

Section: Et Mapping Is: How Can State-of-the-art Seb Models Overcome mentioning

confidence: 99%

Section: Et Mapping Is: How Can State-of-the-art Seb Models Overcome mentioning

confidence: 99%

Section: Rn = Rs (1-αo) + εOrld -Rlumentioning

confidence: 99%

Section: Respectively 15mentioning

confidence: 99%

See 3 more Smart Citations

Regional evapotranspiration from image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous United States

Bhattarai¹,

Mallick²,

Brunsell³

et al. 2017

Preprint

Self Cite

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Near‐surface turbulence as a missing link in modeling evapotranspiration‐soil moisture relationships

Haghighi

Kirchner

2017

Water Resources Research

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Despite many efforts to develop evapotranspiration (ET) models with improved parametrizations of resistance terms for water vapor transfer into the atmosphere, estimates of ET and its partitioning remain prone to bias. Much of this bias could arise from inadequate representations of physical interactions near nonuniform surfaces from which localized heat and water vapor fluxes emanate. This study aims to provide a mechanistic bridge from land‐surface characteristics to vertical transport predictions, and proposes a new physically based ET model that builds on a recently developed bluff‐rough bare soil evaporation model incorporating coupled soil moisture‐atmospheric controls. The newly developed ET model explicitly accounts for (1) near‐surface turbulent interactions affecting soil drying and (2) soil‐moisture‐dependent stomatal responses to atmospheric evaporative demand that influence leaf (and canopy) transpiration. Model estimates of ET and its partitioning were in good agreement with available field‐scale data, and highlight hidden processes not accounted for by commonly used ET schemes. One such process, nonlinear vegetation‐induced turbulence (as a function of vegetation stature and cover fraction) significantly influences ET‐soil moisture relationships. Our results are particularly important for water resources and land use planning of semiarid sparsely vegetated ecosystems where soil surface interactions are known to play a critical role in land‐climate interactions. This study potentially facilitates a mathematically tractable description of the strength (i.e., the slope) of the ET‐soil moisture relationship, which is a core component of models that seek to predict land‐atmosphere coupling and its feedback to the climate system in a changing climate.

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Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems

et al. 2018

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Accurate quantification of terrestrial evapotranspiration and ecosystem productivity is of significant merit to better understand and predict the response of ecosystem energy, water, and carbon budgets under climate change. Existing diagnostic models have different focus on either water or carbon flux estimates with various model complexity and uncertainties induced by distinct representation of the coupling between water and carbon processes. Here, we propose a diagnostic model to estimate evapotranspiration and gross primary production that is based on biophysical mechanism yet simple for practical use. This is done by coupling the carbon and water fluxes via canopy conductance used in the Penman–Monteith–Leuning equation (named as PML_V2 model). The PML_V2 model takes Moderate Resolution Imaging Spectrometer leaf area index and meteorological variables as inputs. The model was tested against evapotranspiration and gross primary production observations at 9 eddy‐covariance sites in Australia, which are spread across wide climate conditions and ecosystems. Results indicate that the simulated evapotranspiration and gross primary production by the PML_V2 model are in good agreement with the measurements at 8‐day timescale, indicated by the cross site Nash–Sutcliffe efficiency being 0.70 and 0.66, R2 being 0.80 and 0.75, and root mean square error being 0.96 mm d−1 and 1.14 μmol m−2 s−1 for evapotranspiration and gross primary production, respectively. As the PML_V2 model only requires readily available climate and Moderate Resolution Imaging Spectrometer vegetation dynamics data and has few parameters, it can potentially be applied to estimate evapotranspiration and carbon assimilation simultaneously at long‐term and large spatial scales.

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Reintroducing radiometric surface temperature into the Penman‐Monteith formulation

Cited by 58 publications

References 89 publications

Regional evapotranspiration from image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous United States

Regional evapotranspiration from image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous United States

Near‐surface turbulence as a missing link in modeling evapotranspiration‐soil moisture relationships

Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems

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