Editorial Type:
Article
Article Type:
Research Article

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

Abedeh Abdolghafoorian Department of Civil and Environmental Engineering, George Washington University, Washington, D.C.

Search for other papers by Abedeh Abdolghafoorian in
Current site
Google Scholar
PubMed Close
,
Leila Farhadi Department of Civil and Environmental Engineering, George Washington University, Washington, D.C.

Search for other papers by Leila Farhadi in
Current site
Google Scholar
PubMed Close
,
Sayed M. Bateni Department of Civil and Environmental Engineering, and Water Resources Research Center, University of Hawai‘i at Mānoa, Honolulu, Hawaii

Search for other papers by Sayed M. Bateni in
Current site
Google Scholar
PubMed Close
,
Steve Margulis Department of Civil and Environmental Engineering, University of California Los Angeles, Los Angeles, California

Search for other papers by Steve Margulis in
Current site
Google Scholar
PubMed Close
, and
Tongren Xu State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, China

Search for other papers by Tongren Xu in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2017
DOI:
https://doi.org/10.1175/JHM-D-16-0097.1
Page(s):
321–333
Restricted access

Abstract

Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into a variational data assimilation (VDA) framework has been the subject of numerous studies. The VDA approaches are focused on the estimation of two key parameters that regulate the partitioning of available energy between sensible and latent heat fluxes. These parameters are neutral bulk heat transfer coefficient CHN and evaporative fraction (EF). The CHN mainly depends on the roughness of the surface and varies on the time scale of changing vegetation phenology. The existing VDA methods assumed that the variations in vegetation phenology over the period of one month are negligible and took CHN as a monthly constant parameter. However, during the growing season, bare soil may turn into a fully vegetated surface within a few weeks. Thus, assuming a constant CHN may result in a significant error in the estimation of surface fluxes, especially in regions with a high temporal variation in vegetation cover. In this study the VDA approach is advanced by taking CHN as a function of leaf area index (LAI). This allows the characterization of the dynamic effect of vegetation phenology on CHN. The performance of the new VDA model is tested over three sites in the United States and one site in China. The results show that the new model outperforms the previous one and reduces the root-mean-square error (and bias) in sensible and latent heat flux estimates across the four sites on average by 31% (61%) and 21% (37%), respectively.

Corresponding author e-mail: Leila Farhadi, lfarhadi@gwu.edu

Abstract

Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into a variational data assimilation (VDA) framework has been the subject of numerous studies. The VDA approaches are focused on the estimation of two key parameters that regulate the partitioning of available energy between sensible and latent heat fluxes. These parameters are neutral bulk heat transfer coefficient CHN and evaporative fraction (EF). The CHN mainly depends on the roughness of the surface and varies on the time scale of changing vegetation phenology. The existing VDA methods assumed that the variations in vegetation phenology over the period of one month are negligible and took CHN as a monthly constant parameter. However, during the growing season, bare soil may turn into a fully vegetated surface within a few weeks. Thus, assuming a constant CHN may result in a significant error in the estimation of surface fluxes, especially in regions with a high temporal variation in vegetation cover. In this study the VDA approach is advanced by taking CHN as a function of leaf area index (LAI). This allows the characterization of the dynamic effect of vegetation phenology on CHN. The performance of the new VDA model is tested over three sites in the United States and one site in China. The results show that the new model outperforms the previous one and reduces the root-mean-square error (and bias) in sensible and latent heat flux estimates across the four sites on average by 31% (61%) and 21% (37%), respectively.

Corresponding author e-mail: Leila Farhadi, lfarhadi@gwu.edu
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Abdolghafoorian, A., and Farhadi L. , 2016: Uncertainty quantification in land surface hydrologic modeling: Toward an integrated variational data assimilation framework. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 2628–2637, doi:10.1109/JSTARS.2016.2553444.

    • Search Google Scholar
    • Export Citation

  • Alfieri, J. G., Xiao X. , Niyogi D. , Pielke R. A. , Chen F. , and LeMone M. A. , 2009: Satellite-based modeling of transpiration from the grasslands in the Southern Great Plains, USA. Global Planet. Change, 67, 78–86, doi:10.1016/j.gloplacha.2008.12.003.

    • Search Google Scholar
    • Export Citation

  • Allen, R., Irmak A. , Trezza R. , Hendrickx J. M. H. , Bastiaanssen W. , and Kjaersgaard J. , 2011: Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrol. Processes, 25, 4011–4027, doi:10.1002/hyp.8408.

    • Search Google Scholar
    • Export Citation

  • Anderson, M. C., Norman J. M. , Diak J. R. , Kustas W. P. , and Mecikalski J. R. , 1997: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195–216, doi:10.1016/S0034-4257(96)00215-5.

    • Search Google Scholar
    • Export Citation

  • Andreas, E. L, and Murphy B. , 1986: Bulk transfer coefficients for heat and momentum over leads and polynyas. J. Phys. Oceanogr., 16, 1875–1883, doi:10.1175/1520-0485(1986)016<1875:BTCFHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Andrews, T., Forster P. M. , and Gregory J. M. , 2009: A surface energy perspective on climate change. J. Climate, 22, 2557–2570, doi:10.1175/2008JCLI2759.1.

    • Search Google Scholar
    • Export Citation

  • Ann-Sofi, S., Ulf S. , Erik H. , and Cecilia J. , 2007: Critical re-evaluation of the bulk transfer coefficient for sensible heat over the ocean during unstable and neutral conditions. Quart. J. Roy. Meteor. Soc., 133, 227–250, doi:10.1002/qj.6.

    • Search Google Scholar
    • Export Citation

  • Baldocchi, D., and Coauthors, 2001: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Amer. Meteor. Soc., 82, 2415–2434, doi:10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bastiaanssen, W. G. M., Menenti M. , Feddes R. A. , and Holtslag A. A. M. , 1998: A remote sensing Surface Energy Balance Algorithm for Land (SEBAL). 1. Formulation J. Hydrol., 212–213, 198–212, doi:10.1016/S0022-1694(98)00253-4.

    • Search Google Scholar
    • Export Citation

  • Bateni, S. M., and Entekhabi D. , 2012: Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters. Water Resour. Res., 48, W08521, doi:10.1029/2011WR011542.

    • Search Google Scholar
    • Export Citation

  • Bateni, S. M., Entekhabi D. , and Castelli F. , 2013a: Mapping evaporation and estimation of surface control of evaporation using remotely sensed land surface temperature from a constellation of satellites. Water Resour. Res., 49, 950–968, doi:10.1002/wrcr.20071.

    • Search Google Scholar
    • Export Citation

  • Bateni, S. M., Entekhabi D. , and Jeng D.-S. , 2013b: Variational assimilation of land surface temperature and the estimation of surface energy balance components. J. Hydrol., 481, 143–156, doi:10.1016/j.jhydrol.2012.12.039.

    • Search Google Scholar
    • Export Citation

  • Bateni, S. M., Entekhabi D. , Margulis S. A. , Castelli F. , and Kergoat L. , 2014: Coupled estimation of surface heat fluxes and vegetation dynamics from remotely sensed land surface temperature and fraction of photosynthetically active radiation. Water Resour. Res., 50, 8420–8440, doi:10.1002/2013WR014573.

    • Search Google Scholar
    • Export Citation

  • Behera, S. K., Srivastava P. , Pathre U. V. , and Tuli R. , 2010: An indirect method of estimating leaf area index in Jatropha curcas L. using LAI-2000 Plant Canopy Analyzer. Agric. For. Meteor., 150, 307–311, doi:10.1016/j.agrformet.2009.11.009.

    • Search Google Scholar
    • Export Citation

  • Betts, A. K., and Ball J. H. , 1998: FIFE surface climate and site-average dataset 1987–89. J. Atmos. Sci., 55, 1091–1108, doi:10.1175/1520-0469(1998)055<1091:FSCASA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Boulet, G., Olioso A. , Ceschia E. , Marloie O. , Coudert B. , Rivalland V. , Chirouze J. , and Chehbouni G. , 2012: An empirical expression to relate aerodynamic and surface temperatures for use within single-source energy balance models. Agric. For. Meteor., 161, 148–155, doi:10.1016/j.agrformet.2012.03.008.

    • Search Google Scholar
    • Export Citation

  • Brutsaert, W., 1979: Heat and mass transfer to and from surfaces with dense vegetation or similar permeable roughness. Bound.-Layer Meteor., 16, 365–388, doi:10.1007/BF02220492.

    • Search Google Scholar
    • Export Citation

  • Campbell, G. S., 1985: Soil Physics with BASIC: Transport Models for Soil-Plant Systems. Elsevier, 149 pp.

  • Campo, L., Castelli F. , Entekhabi D. , and Caparrini F. , 2009: Land–atmosphere interactions in an high resolution atmospheric simulation coupled with a surface data assimilation scheme. Nat. Hazards Earth Syst. Sci., 9, 1613–1624, doi:10.5194/nhess-9-1613-2009.

    • Search Google Scholar
    • Export Citation

  • Caparrini, F., Castelli F. , and Entekhabi D. , 2003: Mapping of land–atmosphere heat fluxes and surface parameters with remote sensing data. Bound.-Layer Meteor., 107, 605–633, doi:10.1023/A:1022821718791.

    • Search Google Scholar
    • Export Citation

  • Caparrini, F., Castelli F. , and Entekhabi D. , 2004a: Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resour. Res., 40, W12515, doi:10.1029/2004WR003358.

    • Search Google Scholar
    • Export Citation

  • Caparrini, F., Castelli F. , and Entekhabi D. , 2004b: Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences. J. Hydrometeor., 5, 145–159, doi:10.1175/1525-7541(2004)005<0145:EOSTFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Colaizzi, P. D., Evett S. R. , Howell T. A. , and Tolk J. A. , 2006: Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Trans. ASABE, 49, 1409–1417, doi:10.13031/2013.22056.

    • Search Google Scholar
    • Export Citation

  • Cragoa, R., and Brutsaert W. , 1996: Daytime evaporation and the self-preservation of the evaporative fraction and the Bowen ratio. J. Hydrol., 178, 241–255, doi:10.1016/0022-1694(95)02803-X.

    • Search Google Scholar
    • Export Citation

  • Crow, W. T., and Kustas W. P. , 2005: Utility of assimilating surface radiometric temperature observations for evaporative fraction and heat transfer coefficient retrieval. Bound.-Layer Meteor., 115, 105–130, doi:10.1007/s10546-004-2121-0.

    • Search Google Scholar
    • Export Citation

  • Fang, H., Wei S. , and Liang S. , 2012: Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sens. Environ., 119, 43–54, doi:10.1016/j.rse.2011.12.006.

    • Search Google Scholar
    • Export Citation

  • Farhadi, L., Entekhabi D. , Salvucci G. , and Sun J. , 2014: Estimation of land surface water and energy balance parameters using conditional sampling of surface states. Water Resour. Res., 50, 1805–1822, doi:10.1002/2013WR014049.

    • Search Google Scholar
    • Export Citation

  • Farhadi, L., Entekhabi D. , and Salvucci G. , 2016: Mapping land water and energy balance relations through conditional sampling of remote sensing estimates of atmospheric forcing and surface states. Water Resour. Res., 52, 2737–2752, doi:10.1002/2015WR017680.

    • Search Google Scholar
    • Export Citation

  • Fensholt, R., Sandholt I. , and Rasmussen M. S. , 2004: Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens. Environ., 91, 490–507, doi:10.1016/j.rse.2004.04.009.

    • Search Google Scholar
    • Export Citation

  • Gentine, P., Entekhabi D. , Chehbouni A. , Boulet G. , and Duchemin B. , 2007: Analysis of evaporative fraction diurnal behaviour. Agric. For. Meteor., 143, 13–29, doi:10.1016/j.agrformet.2006.11.002.

    • Search Google Scholar
    • Export Citation

  • Hasager, C. B., and Jensen N. O. , 1999: Surface-flux aggregation in heterogeneous terrain. Quart. J. Roy. Meteor. Soc., 125, 2075–2102, doi:10.1002/qj.49712555808.

    • Search Google Scholar
    • Export Citation

  • Hirota, T., Pomeroy J. W. , Granger R. J. , and Maule C. P. , 2002: An extension of the force-restore method to estimating soil temperature at depth and evaluation for frozen soils under snow. J. Geophys. Res., 107, 4767, doi:10.1029/2001JD001280.

    • Search Google Scholar
    • Export Citation

  • Hopmans, J. W., Šimunek J. , and Bristow K. L. , 2002: Indirect estimation of soil thermal properties and water flux using heat pulse probe measurements: Geometry and dispersion effects. Water Resour. Res., 38, 1006, doi:10.1029/2000WR000071.

    • Search Google Scholar
    • Export Citation

  • Jensen, N. O., and Hummelshøj P. , 1995: Derivation of canopy resistance for water vapour fluxes over a spruce forest, using a new technique for the viscous sublayer resistance. Agric. For. Meteor., 73, 339–352, doi:10.1016/0168-1923(94)05083-I.

    • Search Google Scholar
    • Export Citation

  • Kalma, J. D., McVicar T. R. , and McCabe M. F. , 2008: Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys., 29, 421–469, doi:10.1007/s10712-008-9037-z.

    • Search Google Scholar
    • Export Citation

  • Kubota, A., and Sugita M. , 1994: Radiometrically determined skin temperature and scalar roughness to estimate surface heat flux. Part I: Parameterization of radiometric scalar roughness. Bound.-Layer Meteor., 69, 397–416, doi:10.1007/BF00718127.

    • Search Google Scholar
    • Export Citation

  • Lewellen, D. C., and Lewellen W. S. , 2002: Entrainment and decoupling relations for cloudy boundary layers. J. Atmos. Sci., 59, 2966–2986, doi:10.1175/1520-0469(2002)059<2966:EADRFC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lewis, P., Gómez-Dans J. , Kaminski T. , Settle J. , Quaife T. , Gobron N. , Styles J. , and Berger M. , 2012: An Earth Observation Land Data Assimilation System (EO-LDAS). Remote Sens. Environ., 120, 219–235, doi:10.1016/j.rse.2011.12.027.

    • Search Google Scholar
    • Export Citation

  • Lhomme, J. P., Chehbouni A. , and Monteny B. , 2000: Sensible heat flux–radiometric surface temperature relationship over sparse vegetation: Parameterizing B-1. Bound.-Layer Meteor., 97, 431–457, doi:10.1023/A:1002786402695.

    • Search Google Scholar
    • Export Citation

  • Li, X., and Coauthors, 2013: Heihe watershed allied telemetry experimental research (HiWater) scientific objectives and experimental design. Bull. Amer. Meteor. Soc., 94, 1145–1160, doi:10.1175/BAMS-D-12-00154.1.

    • Search Google Scholar
    • Export Citation

  • Mahrt, L., and Vickers D. , 2004: Bulk formulation of the surface heat flux. Bound.-Layer Meteor., 110, 357–379, doi:10.1023/B:BOUN.0000007244.42320.1e.

    • Search Google Scholar
    • Export Citation

  • Massman, W. J., 1999: A model study of kBH −1 for vegetated surfaces using “localized near-field” Lagrangian theory. J. Hydrol., 223, 27–43, doi:10.1016/S0022-1694(99)00104-3.

    • Search Google Scholar
    • Export Citation

  • Matsushima, D., 2005: Relations between aerodynamic parameters of heat transfer and thermal-infrared thermometry in the bulk surface formulation. J. Meteor. Soc. Japan, 83, 373–389, doi:10.2151/jmsj.83.373.

    • Search Google Scholar
    • Export Citation

  • McNaughton, K. G., and Hurk B. J. J. M. , 1995: A “Lagrangian” revision of the resistors in the two-layer model for calculating the energy budget of a plant canopy. Bound.-Layer Meteor., 74, 261–288, doi:10.1007/BF00712121.

    • Search Google Scholar
    • Export Citation

  • Perez, P. J., Castellvi F. , Ibañez M. , and Rosell J. I. , 1999: Assessment of reliability of Bowen ratio method for partitioning fluxes. Agric. For. Meteor., 97, 141–150, doi:10.1016/S0168-1923(99)00080-5.

    • Search Google Scholar
    • Export Citation

  • Peters-Lidard, C. D., Kumar S. V. , Mocko D. M. , and Tian Y. , 2011: Estimating evapotranspiration with land data assimilation systems. Hydrol. Processes, 25, 3979–3992, doi:10.1002/hyp.8387.

    • Search Google Scholar
    • Export Citation

  • Polonio, D., and Soler M. R. , 2000: Surface fluxes estimation over agricultural areas. Comparison of methods and the effects of land surface inhomogeneity. Theor. Appl. Climatol., 67, 65–79, doi:10.1007/s007040070016.

    • Search Google Scholar
    • Export Citation

  • Qualls, R., and Brutsaert W. , 1996: Effect of vegetation density on the parameterization of scalar roughness to estimate spatially distributed sensible heat fluxes. Water Resour. Res., 32, 645–652, doi:10.1029/95WR03097.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Entekhabi D. , and McLaughlin D. B. , 2001a: Downscaling of radio brightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach. Water Resour. Res., 37, 2353–2364, doi:10.1029/2001WR000475.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., McLaughlin D. B. , and Entekhabi D. , 2001b: Variational data assimilation of microwave radio brightness observations for land surface hydrology applications. IEEE Trans. Geosci. Remote Sens., 39, 1708–1718, doi:10.1109/36.942549.

    • Search Google Scholar
    • Export Citation

  • Richardson, A. D., and Coauthors, 2006: A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes. Agric. For. Meteor., 136, 1–18, doi:10.1016/j.agrformet.2006.01.007.

    • Search Google Scholar
    • Export Citation

  • Shokri, N., Lehmann P. , Vontobel P. , and Or D. , 2008: Drying front and water content dynamics during evaporation from sand delineated by neutron radiography. Water Resour. Res., 44, W06418, doi:10.1029/2007WR006385.

    • Search Google Scholar
    • Export Citation

  • Sini, F., Boni G. , Caparrini F. , and Entekhabi D. , 2008: Estimation of large-scale evaporation fields based on assimilation of remotely sensed land temperature. Water Resour. Res., 44, W06410, doi:10.1029/2006WR005574.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and Coauthors, 2012: An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci., 5, 691–696, doi:10.1038/ngeo1580.

    • Search Google Scholar
    • Export Citation

  • Su, Z., 2002: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci., 6, 85–100, doi:10.5194/hess-6-85-2002.

    • Search Google Scholar
    • Export Citation

  • Sugita, M., and Brutsaert W. , 1990: Regional surface fluxes from remotely sensed skin temperature and lower boundary layer measurements. Water Resour. Res., 26, 2937–2944, doi:10.1029/WR026i012p02937.

    • Search Google Scholar
    • Export Citation

  • Timmermans, J., Su Z. , van der Tol C. , Verhoef A. , and Verhoef W. , 2013: Quantifying the uncertainty in estimates of surface–atmosphere fluxes through joint evaluation of the SEBS and SCOPE models. Hydrol. Earth Syst. Sci., 17, 1561–1573, doi:10.5194/hess-17-1561-2013.

    • Search Google Scholar
    • Export Citation

  • Verhoef, A., De Bruin H. R. , and Van Den Hurk B. J. J. M. , 1997: Some practical notes on the parameter kB−1 for sparse vegetation. J. Appl. Meteor., 36, 560–572, doi:10.1175/1520-0450(1997)036<0560:SPNOTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wu, X., Zhou J. , Wang H. , Li Y. , and Zhong B. , 2015: Evaluation of irrigation water use efficiency using remote sensing in the middle reach of the Heihe River, in the semi-arid northwestern China. Hydrol. Processes, 29, 2243–2257, doi:10.1002/hyp.10365.

    • Search Google Scholar
    • Export Citation

  • Xu, T. R., Bateni S. M. , Liang S. , Entekhabi D. , and Mao K. B. , 2014: Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites. J. Geophys. Res. Atmos., 119, 10 780–10 798, doi:10.1002/2014JD021814.

    • Search Google Scholar
    • Export Citation

  • Yilmaz, M. T., Anderson M. C. , Zaitchik B. , Hain C. R. , Crow W. T. , Ozdogan M. , Chun J. A. , and Evans J. , 2014: Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River basin. Water Resour. Res., 50, 386–408, doi:10.1002/2013WR014194.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1904 874 41
PDF Downloads 400 79 7