Characterizing the Effect of Vegetation Dynamics on the Bulk Heat Transfer Coefficient to Improve Variational Estimation of Surface Turbulent Fluxes

Abdolghafoorian, Abedeh; Farhadi, Leila; Bateni, Sayed M.; Margulis, Steve; Xu, Tongren

doi:10.1175/jhm-d-16-0097.1

Cited by 30 publications

(32 citation statements)

References 60 publications

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“…The augmented VDA method was tested over four different sites by comparing the estimated value of heat fluxes with "in situ" measurement of heat fluxes. Results demonstrate that by taking C HN as a function of LAI, the rootmean-square-error (and bias) of sensible and latent heat flux estimates across the four sites are reduced on average by 31% (61%) and 21% (37%), respectively [66].…”

Section: E Uq Framework: a Guide To Well-posed Estimation Problemmentioning

confidence: 96%

“…Since during the growing season, bare soil may turn into a fully vegetated surface only within a few weeks, assuming the neutral bulk heat transfer coefficient C HN , as a monthly constant parameter may result in a significant amount of error in the estimation of surface fluxes. Abdolghafoorian et al [66] augmented the VDA method by characterizing the dynamic effect of vegetation phenology on C HN and formulating C HN as a function of LAI as proposed by Farhadi et al [34]. The augmented VDA method was tested over four different sites by comparing the estimated value of heat fluxes with "in situ" measurement of heat fluxes.…”

Section: E Uq Framework: a Guide To Well-posed Estimation Problemmentioning

confidence: 99%

“…In this study, we take the advantage of the UQ framework as a new tool to assess whether the new parametric form of C HN (i.e., taking C HN as a function of LAI in lieu of a constant parameter) [66], may also lead the estimation problem to a well-posed form. Fig.…”

Section: E Uq Framework: a Guide To Well-posed Estimation Problemmentioning

confidence: 99%

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Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

Abdolghafoorian

Farhadi

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Variational data assimilation (VDA) is an effective technique for the estimation of land surface heat fluxes. In this method, sequences of remotely sensed land surface temperature measurements are assimilated into a dynamic surface energy balance model to estimate the key unknown parameters of the turbulent heat fluxes. Despite the advantages of the VDA technique in the retrieval of land surface heat fluxes, it suffers from a key limitation, which is its tendency to be ill posed. Moreover, unlike ensemble-based schemes, the VDA method itself does not provide estimates of the predictive uncertainty of estimated parameters and, thus, retrieved fluxes. This research addresses these shortcomings by proposing an uncertainty quantification (UQ) framework for the VDA technique. The proposed framework utilizes uncertainty analysis and analysis of error covariance approximation as a tool to quantify the uncertainty of estimated parameters and to guide the formulation of a well-posed estimation problem. It provides a calibration-free tool to assess the performance of the VDA technique in retrieving land surface heat fluxes over a range of land surfaces and climatic conditions. The UQ framework suggests that the VDA approach performs poorly over wet and highly vegetated land surface regions and when the difference between land surface and air temperature is low. Moreover, it reveals that characterizing the effect of vegetation dynamics on the bulk heat transfer coefficient reduces the correlation between unknown parameters and, hence, leads to a more robust estimation of parameters.Index Terms-Land surface hydrology, uncertainty analysis, variational data assimilation (VDA).

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Section: E Uq Framework: a Guide To Well-posed Estimation Problemmentioning

confidence: 96%

Section: E Uq Framework: a Guide To Well-posed Estimation Problemmentioning

confidence: 99%

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Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

Abdolghafoorian

Farhadi

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The bulk heat transfer coefficient (C H ) is related to the neutral bulk heat transfer coefficient (C HN ) and the atmospheric stability correction function (f) via (Abdolghafoorian et al, 2017;Bateni, Entekhabi, & Castelli, 2013;Xu et al, 2014Xu et al, , 2016) The bulk heat transfer coefficient (C H ) is related to the neutral bulk heat transfer coefficient (C HN ) and the atmospheric stability correction function (f) via (Abdolghafoorian et al, 2017;Bateni, Entekhabi, & Castelli, 2013;Xu et al, 2014Xu et al, , 2016)…”

Section: System Modelmentioning

confidence: 99%

“…Variational data assimilation (VDA) methods estimate the key unknowns of the surface energy balance (SEB) equations (i.e., neutral bulk heat transfer coefficient, C HN , and evaporative fraction, EF) by assimilating LST observations into the heat diffusion or force-restore equations (Abdolghafoorian et al, 2017;Bateni, Entekhabi, & Jeng, 2013;Bateni, Entekhabi, & Castelli, 2013;Bateni et al, 2014;Bateni & Entekhabi, 2012a;Bateni & Liang, 2012;Crow & Kustas, 2005;He et al, 2018;Qin et al, 2007;Sini et al, 2008;Xu et al, 2014Xu et al, , 2016Xu, Bateni, et al, 2015;Xu, He, et al, 2019). The unknown parameters of the VDA approaches (i.e., C HN and EF) are obtained by minimizing the misfit between the LST observations and estimations from the force-restore or heat diffusion equation.…”

Section: Introductionmentioning

confidence: 99%

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Bateni

et al. 2019

Earth and Space Science

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Estimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land).

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Estimation of Turbulent Heat Fluxes by Assimilation of Land Surface Temperature Observations From GOES Satellites Into an Ensemble Kalman Smoother Framework

Bateni

Neale

et al. 2018

JGR Atmospheres

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In different studies, land surface temperature (LST) observations have been assimilated into the variational data assimilation (VDA) approaches to estimate turbulent heat fluxes. The VDA methods yield accurate turbulent heat fluxes, but they need an adjoint model, which is difficult to derive and code. They also cannot directly calculate the uncertainty of their estimates. To overcome the abovementioned drawbacks, this study assimilates LST data from Geostationary Operational Environmental Satellite into the ensemble Kalman smoother (EnKS) data assimilation system to estimate turbulent heat fluxes. EnKS does not need to derive the adjoint term and directly generates statistical information on the accuracy of its predictions. It uses the heat diffusion equation to simulate LST. EnKS with the state augmentation approach finds the optimal values for the unknown parameters (i.e., evaporative fraction and neutral bulk heat transfer coefficient, CHN) by minimizing the misfit between LST observations from Geostationary Operational Environmental Satellite and LST estimations from the heat diffusion equation. The augmented EnKS scheme is tested over six Ameriflux sites with a wide range of hydrological and vegetative conditions. The results show that EnKS can predict not only the model parameters and turbulent heat fluxes but also their uncertainties over a variety of land surface conditions. Compared to the variational method, EnKS yields suboptimal turbulent heat fluxes. However, suboptimality of EnKS is small, and its results are comparable to those of the VDA method. Overall, EnKS is a feasible and reliable method for estimation of turbulent heat fluxes.

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Characterizing the Effect of Vegetation Dynamics on the Bulk Heat Transfer Coefficient to Improve Variational Estimation of Surface Turbulent Fluxes

Cited by 30 publications

References 60 publications

Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Estimation of Turbulent Heat Fluxes by Assimilation of Land Surface Temperature Observations From GOES Satellites Into an Ensemble Kalman Smoother Framework

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