• Anderson, M. C., , Norman J. M. , , Diak G. 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, 195216, doi:10.1016/S0034-4257(96)00215-5.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Neale C. M. U. , , Li F. , , Norman J. M. , , Kustas W. P. , , Jayanthi H. , , and Chavez J. , 2004a: Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens. Environ., 92, 447464, doi:10.1016/j.rse.2004.03.019.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Norman J. M. , , Mecikalski J. R. , , Torn R. D. , , Kustas W. P. , , and Basara J. B. , 2004b: A multi-scale remote sensing model for disaggregating regional flues to micrometeorological scales. J. Hydrometeor., 5, 343363, doi:10.1175/1525-7541(2004)005<0343:AMRSMF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Norman J. M. , , Kustas W. P. , , Li F. , , Prueger J. H. , , and Mecikalski J. R. , 2005: Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape using SMACEX. J. Hydrometeor., 6, 892909, doi:10.1175/JHM465.1.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Kustas W. P. , , and Norman J. M. , 2007a: Upscaling tower and aircraft fluxes from local to continental scales using thermal remote sensing. Agron. J., 99, 240254, doi:10.2134/agronj2005.0096S.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Norman J. M. , , Kustas W. P. , , Li F. , , Prueger J. H. , , and Mecikalski J. R. , 2007b: A climatological study of evapotranspiration and moisture stress across the continental United States: 1. Model formulation. J. Geophys. Res., 112, D11112, doi:10.1029/2006JD007507.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., and et al. , 2012: Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign. Adv. Water Resour., 50, 162177, doi:10.1016/j.advwatres.2012.06.005.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Hain C. , , Otkin J. , , Zhan X. , , Mo K. , , Svoboda M. , , Wardlow B. , , and Pimstein A. , 2013a: An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. Drought Monitor classifications. J. Hydrometeor., 14, 10351056, doi:10.1175/JHM-D-12-0140.1.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Kustas W. P. , , and Hain C. R. , 2013b: Mapping surface fluxes and moisture conditions from field to global scales using ALEXI/DisALEXI. Remote Sensing of Energy Fluxes and Soil Moisture Content, G. P. Petropoulos, Ed., CRC Press, 207–232, doi:10.1201/b15610-11.

  • Cammalleri, C., , Anderson M. C. , , Ciraolo G. , , D'Urso G. , , Kustas W. P. , , La Loggia G. , , and Minacapilli M. , 2012: Applications of a remote sensing–based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations. Remote Sens. Environ., 124, 502515, doi:10.1016/j.rse.2012.06.009.

    • Search Google Scholar
    • Export Citation
  • Cammalleri, C., , Anderson M. C. , , Gao F. , , Hain C. R. , , and Kustas W. P. , 2013: A data fusion approach for mapping daily evapotranspiration at field scale. Water Resour. Res., 49, 46724686, doi:10.1002/wrcr.20349.

    • Search Google Scholar
    • Export Citation
  • Cammalleri, C., , Anderson M. C. , , Gao F. , , Hain C. R. , , and Kustas W. P. , 2014: Mapping daily Evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agric. For. Meteor., 186, 111, doi:10.1016/j.agrformet.2013.11.001.

    • Search Google Scholar
    • Export Citation
  • Chen, F., , and Dudhia J. , 2001: Coupling an advanced land surface hydrology model with the Penn State/NCAR MM5 modeling system. Part 1: Model description and implementation. Mon. Wea. Rev., 129, 569586, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and et al. , 1996: Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 72517268, doi:10.1029/95JD02165.

    • Search Google Scholar
    • Export Citation
  • Cosgrove, B. A., and et al. , 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation
  • Dickinson, R. E., , Henderson-Sellers A. , , and Kennedy P. J. , 1993: Biosphere–Atmosphere Transfer Scheme (BATS) Version 1e as coupled to the NCAR Community Climate Model. NCAR Tech. Note NCAR-TN-387+STR, 88 pp., doi:10.5065/D67W6959.

  • Dirmeyer, P. A., and et al. , 2012: Evidence for enhanced land–atmosphere feedback in a warming climate. J. Hydrometeor., 13, 981995, doi:10.1175/JHM-D-11-0104.1.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., , Mitchell K. E. , , Lin Y. , , Rogers E. , , Grummann P. , , Koren V. , , Gayno G. , , and Tarpley J. D. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., , and Miguez-Macho G. , 2011: A simple hydrologic framework for simulating wetlands in climate and Earth system models. Climate Dyn., 37, 253278, doi:10.1007/s00382-010-0829-8.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., , Miguez-Macho G. , , Weaver C. P. , , Walko R. , , and Robock A. , 2007: Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations. J. Geophys. Res., 112, D10125, doi:10.1029/2006JD008111.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., , Li H. , , and Miguez-Macho G. , 2013: Global patterns of groundwater table depth. Science, 339, 940943, doi:10.1126/science.1229881.

    • Search Google Scholar
    • Export Citation
  • Fry, J., and et al. , 2011: Completion of the 2006 National Land Cover Database for the conterminous United States. Photogramm. Eng. Remote Sens.,77, 858–864.

  • Gedney, N., , and Cox P. M. , 2003: The sensitivity of global climate model simulations to the representation of soil moisture heterogeneity. J. Hydrometeor., 4, 12651275, doi:10.1175/1525-7541(2003)004<1265:TSOGCM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gedney, N., , Cox P. M. , , Betts R. A. , , Boucher O. , , Huntingford C. , , and Stott P. A. , 2006: Detection of a direct carbon dioxide effect in continental river runoff records. Nature, 439, 835–838, doi:10.1038/nature04504.

    • Search Google Scholar
    • Export Citation
  • Haddeland, I., , Skaugen T. , , and Lettenmaier D. P. , 2006: Anthropogenic impacts on continental surface water fluxes. Geophys. Res. Lett.,33, L08406, doi:10.1029/2006GL026047.

  • Hain, C. R., , Mecikalski J. R. , , and Anderson M. C. , 2009: Retrieval of an available water-based soil moisture proxy from thermal infrared remote sensing. Part I: Methodology and validation. J. Hydrometeor., 10, 665683, doi:10.1175/2008JHM1024.1.

    • Search Google Scholar
    • Export Citation
  • Hain, C. R., , Crow W. T. , , Mecikalski J. R. , , Anderson M. C. , , and Holmes T. , 2011: An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling. J. Geophys. Res., 116, D15107, doi:10.1029/2011JD015633.

    • Search Google Scholar
    • Export Citation
  • Hansen, M. C., , DeFries R. S. , , Townshend J. R. G. , , and Sohlberg R. , 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21, 13311364, doi:10.1080/014311600210209.

    • Search Google Scholar
    • Export Citation
  • Harding, K. J., , and Snyder P. K. , 2012: Modeling the atmospheric response to irrigation in the Great Plains. Part II: The precipitation or irrigated water and changes in precipitation recycling. J. Hydrometeor., 13, 16871703, doi:10.1175/JHM-D-11-099.1.

    • Search Google Scholar
    • Export Citation
  • Jedlovec, G. J., , Haines S. L. , , and LaFontaine F. J. , 2008: Spatial and temporal varying thresholds for cloud detection in GOES imager. IEEE Trans. Geosci. Remote Sens., 46, 17051717, doi:10.1109/TGRS.2008.916208.

    • Search Google Scholar
    • Export Citation
  • Jung, M., and et al. , 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951954, doi:10.1038/nature09396.

    • Search Google Scholar
    • Export Citation
  • Koch, S., , Bauwe A. , , and Lennartz B. , 2013: Application of the SWAT model for a tile-drained lowland catchment in north-eastern Germany on subbasin scale. Water Resour. Manage., 27, 791805, doi:10.1007/s11269-012-0215-x.

    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and et al. , 2006: Land information system: An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21, 14021415, doi:10.1016/j.envsoft.2005.07.004.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , Xie Z. , , and Huang M. , 2003: A new parameterization for surface and groundwater interactions and its impact on water budgets with the variable infiltration capacity (VIC) land surface model. J. Geophys. Res., 108, 8613, doi:10.1029/2002JD003090.

    • Search Google Scholar
    • Export Citation
  • Long, D., , Longuevergne L. , , and Scanlon B. , 2014: Uncertainty in evapotranspiration from land surface modeling, remote sensing and GRACE satellites. Water Resour. Res., 50, 11311151, doi:10.1002/2013WR014581.

    • Search Google Scholar
    • Export Citation
  • Maxwell, R. M., , and Miller N. L. , 2005: On the development of a coupled land surface and groundwater model. J. Hydrometeor., 6, 233247, doi:10.1175/JHM422.1.

    • Search Google Scholar
    • Export Citation
  • McNaughton, K. J., , and Spriggs T. W. , 1986: A mixed-layer model for regional evaporation. Bound.-Layer Meteor., 34, 243262, doi:10.1007/BF00122381.

    • Search Google Scholar
    • Export Citation
  • Miguez-Macho, G., , Fan Y. , , Weaver C. P. , , Walko R. , , and Robock A. , 2007: Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation. J. Geophys. Res., 112, D13108, doi:10.1029/2006JD008112.

    • Search Google Scholar
    • Export Citation
  • Miguez-Macho, G., , Li H. , , and Fan Y. , 2008: Simulated water table and soil moisture climatology over North America. Bull. Amer. Meteor. Soc., 89, 663672, doi:10.1175/BAMS-89-5-663.

    • Search Google Scholar
    • Export Citation
  • Moody, E. G., , King M. D. , , Platnick S. , , Schaaf C. B. , , and Gao F. , 2005: Spatially complete global spectral surface albedos: Value-added datasets derived from Terra MODIS land products. IEEE Trans. Geosci. Remote Sens., 43, 144158, doi:10.1109/TGRS.2004.838359.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., , Heinsch F. A. , , Zhao M. , , and Running S. W. , 2007: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ., 111, 519536, doi:10.1016/j.rse.2007.04.015.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., , Zhao M. , , Kimball J. S. , , McDowell N. G. , , and Running S. W. , 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 17811800, doi:10.1016/j.rse.2011.02.019.

    • Search Google Scholar
    • Export Citation
  • Myneni, R. B., and et al. , 2002: Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ., 83, 214231, doi:10.1016/S0034-4257(02)00074-3.

    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., , Yang Z.-L. , , Dickinson R. E. , , Gulden L. E. , , and Su H. , 2007: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J. Geophys. Res., 112, D07103, doi:10.1029/2006JD007522.

    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and et al. , 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, doi:10.1029/2010JD015139.

    • Search Google Scholar
    • Export Citation
  • Norman, J. M., , Kustas W. P. , , and Humes K. S. , 1995: A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperatures. Agric. For. Meteor., 77, 263293, doi:10.1016/0168-1923(95)02265-Y.

    • Search Google Scholar
    • Export Citation
  • Ozdogan, M., , and Gutman G. , 2008: A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US. Remote Sens. Environ., 112, 35203537, doi:10.1016/j.rse.2008.04.010.

    • Search Google Scholar
    • Export Citation
  • Ozdogan, M., , Rodell M. , , Beaudoing H. K. , , and Toll D. L. , 2010: Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J. Hydrometeor., 11, 171184, doi:10.1175/2009JHM1116.1.

    • Search Google Scholar
    • Export Citation
  • Pervez, M. S., , and Brown J. F. , 2010: Mapping irrigated lands at 250-m scale by merging MODIS data and national agricultural statistics. Remote Sens., 2, 23882412, doi:10.3390/rs2102388.

    • 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, 39793992, doi:10.1002/hyp.8387.

    • Search Google Scholar
    • Export Citation
  • Price, J. C., 1983: Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect. Remote Sens. Environ., 13, 353361, doi:10.1016/0034-4257(83)90036-6.

    • Search Google Scholar
    • Export Citation
  • Rover, J., , Wright C. K. , , Euliss N. H. , , Mushet D. M. , , and Wylie B. K. , 2011: Classifying the hydrologic function of prairie potholes with remote sensing and GIS. Wetlands, 31, 319327, doi:10.1007/s13157-011-0146-y.

    • Search Google Scholar
    • Export Citation
  • Sutanudjaja, E. H., , van Beek L. P. H. , , de Jong S. M. , , van Geer F. C. , , and Bierkens M. F. P. , 2014: Calibrating a large-extent high-resolution coupled groundwater–land surface model using soil moisture and discharge data. Water Resour. Res., 50, 687–705, doi:10.1002/2013WR013807.

    • Search Google Scholar
    • Export Citation
  • Wei, H., , Xia Y. , , Mitchell K. , , and Ek M. , 2013: Improvement of the Noah land surface model for warm season processes: Evaluation of water and energy flux simulation. Hydrol. Processes, 27, 297303, doi:10.1002/hyp.9214.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 2012a: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, doi:10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 2012b: Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res., 117, D03110, doi:10.1029/2011JD016051.

    • Search Google Scholar
    • Export Citation
  • Yang, Z.-L., and et al. , 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. J. Geophys. Res., 116, D12110, doi:10.1029/2010JD015140.

    • Search Google Scholar
    • Export Citation
  • Yeh, P. J. F., , and Eltahir E. A. B. , 2005: Representation of water table dynamics in a land surface scheme. Part I: Model development. J. Climate, 18, 18611880, doi:10.1175/JCLI3330.1.

    • 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, 386408, doi:10.1002/2013WR014194.

    • Search Google Scholar
    • Export Citation
  • York, J. P., , Person M. , , Gutowski W. J. , , and Winter T. C. , 2002: Putting aquifers into atmospheric simulation models: An example from the Mill Creek watershed, northeastern Kansas. Adv. Water Resour., 25, 221238, doi:10.1016/S0309-1708(01)00021-5.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 93 93 18
PDF Downloads 46 46 8

Diagnosing Neglected Soil Moisture Source–Sink Processes via a Thermal Infrared–Based Two-Source Energy Balance Model

View More View Less
  • 1 Earth System Science Interdisciplinary Center, University of Maryland College Park, College Park, Maryland
  • | 2 Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland
  • | 3 Water Resources Division, Civil Engineering Department, Middle East Technical University, Ankara, Turkey
© Get Permissions
Restricted access

Abstract

In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction, and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, efforts to parameterize these processes within land surface models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy flux predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the top-down remote sensing retrieval. Based on this concept, the authors introduce the Atmosphere–Land Exchange Inverse (ALEXI) Source–Sink for Evapotranspiration (ASSET) index derived from comparisons between ALEXI remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM, version 3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies, and tile drainage density within the contiguous United States verify the ability of ASSET to identify regions where neglected soil water source–sink processes may be impacting modeled surface energy fluxes. Consequently, ASSET appears to provide valuable information for ongoing efforts to improve the parameterization of new water source–sink processes within modern LSMs.

Corresponding author address: Christopher R. Hain, Earth System Science Interdisciplinary Center, University of Maryland, College Park, 5825 University Research Ct., Suite 4001, College Park, MD 20740. E-mail: chris.hain@noaa.gov

Abstract

In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction, and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, efforts to parameterize these processes within land surface models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy flux predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the top-down remote sensing retrieval. Based on this concept, the authors introduce the Atmosphere–Land Exchange Inverse (ALEXI) Source–Sink for Evapotranspiration (ASSET) index derived from comparisons between ALEXI remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM, version 3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies, and tile drainage density within the contiguous United States verify the ability of ASSET to identify regions where neglected soil water source–sink processes may be impacting modeled surface energy fluxes. Consequently, ASSET appears to provide valuable information for ongoing efforts to improve the parameterization of new water source–sink processes within modern LSMs.

Corresponding author address: Christopher R. Hain, Earth System Science Interdisciplinary Center, University of Maryland, College Park, 5825 University Research Ct., Suite 4001, College Park, MD 20740. E-mail: chris.hain@noaa.gov
Save