• Anyah, R. O., C. P. Weaver, G. Miguez-Macho, Y. Fan, and A. Robock, 2008: Incorporating water table dynamics in climate modeling: 3. Simulated groundwater influence on coupled land–atmosphere variability. J. Geophys. Res., 113, D07103, doi:10.1029/2007JD009087.

    • Search Google Scholar
    • Export Citation
  • Ashby, S. F., and R. D. Falgout, 1996: A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow simulations. Nucl. Sci. Eng., 124 (1), 145159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baatz, R., H. R. Bogena, H.-J. H. Franssen, J. A. Huisman, W. Qu, C. Montzka, and H. Vereecken, 2014: Calibration of a catchment scale cosmic-ray probe network: A comparison of three parameterization methods. J. Hydrol., 516, 231244, doi:10.1016/j.jhydrol.2014.02.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldauf, M., A. Seifert, J. Förstner, D. Majewski, M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 38873905, doi:10.1175/MWR-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Best, M. J., and Coauthors, 2015: The plumbing of land surface models: Benchmarking model performance. J. Hydrometeor., 16, 14251442, doi:10.1175/JHM-D-14-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bick, T., and Coauthors, 2016: Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale. Quart. J. Roy. Meteor. Soc., 142, 14901504, doi:10.1002/qj.2751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bierkens, M., and B. van den Hurk, 2007: Groundwater convergence as a possible mechanism for multi-year persistence in rainfall. Geophys. Res. Lett., 34, L02402, doi:10.1029/2006GL028396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bogena, H. R., J. A. Huisman, R. Baatz, H.-J. H. Franssen, and H. Vereecken, 2013: Accuracy of the cosmic-ray soil water content probe in humid forest ecosystems: The worst case scenario. Water Resour. Res., 49, 57785791, doi:10.1002/wrcr.20463.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brubaker, K. L., and D. Entekhabi, 1996: Analysis of feedback mechanisms in land–atmosphere interaction. Water Resour. Res., 32, 13431357, doi:10.1029/96WR00005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Butts, M., and Coauthors, 2014: Embedding complex hydrology in the regional climate system—Dynamic coupling across different modelling domains. Adv. Water Resour., 74, 166184, doi:10.1016/j.advwatres.2014.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campoy, A., A. Ducharne, F. Cheruy, F. Hourdin, J. Polcher, and J. C. Dupont, 2013: Response of land surface fluxes and precipitation to different soil bottom hydrological conditions in a general circulation model. J. Geophys. Res. Atmos., 118, 10 72510 739, doi:10.1002/jgrd.50627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 2014: Modeling seasonal snowpack evolution in the complex terrain and forested Colorado headwaters region: A model intercomparison study. J. Geophys. Res. Atmos., 119, 13 79513 819, doi:10.1002/2014JD022167.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, Y., R. E. Dickinson, and Y.-P. Wang, 2004: A two-big-leaf model for canopy temperature, photosynthesis and stomata conductance. J. Climate, 17, 22812299, doi:10.1175/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Decker, M., A. J. Pitman, and J. P. Evans, 2013: Groundwater constraints on simulated transpiration variability over southeastern Australian forests. J. Hydrometeor., 14, 543559, doi:10.1175/JHM-D-12-058.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and S. Manabe, 1988: The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Climate, 1, 523547, doi:10.1175/1520-0442(1988)001<0523:TIOPEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diederich, M., A. Ryzhkov, C. Simmer, P. Zhang, and S. Trömel, 2015a: Use of specific attenuation for rainfall measurement at X-band radar wavelengths. Part I: Radar calibration and partial beam blockage estimation. J. Hydrometeor., 16, 487502, doi:10.1175/JHM-D-14-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diederich, M., A. Ryzhkov, C. Simmer, P. Zhang, and S. Trömel, 2015b: Use of specific attenuation for rainfall measurement at X-band radar wavelengths. Part II: Rainfall estimates and comparison with rain gauges. J. Hydrometeor., 16, 503516, doi:10.1175/JHM-D-14-0067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., A. J. Dolman, and N. Sato, 1999: The pilot phase of the Global Soil Wetness Project. Bull. Amer. Meteor. Soc., 80, 851878, doi:10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eder, F., M. Schmidt, T. Damian, K. Träumner, and M. Mauder, 2015: Mesoscale eddies affect near-surface turbulent exchange: Evidence from lidar and tower measurements. J. Appl. Meteor. Climatol., 54, 189206, doi:10.1175/JAMC-D-14-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ek, M. B., and A. A. M. Holtslag, 2004: Influence of soil moisture on boundary layer cloud development. J. Hydrometeor., 5, 8699, doi:10.1175/1525-7541(2004)005<0086:IOSMOB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 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
  • Eltahir, E. A. B., 1998: A soil moisture–rainfall feedback mechanism 1. Theory and observations. Water Resour. Res., 34, 765776, doi:10.1029/97WR03499.

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

  • Findell, K. L., and E. A. B. Eltahir, 1997: An analysis of the soil moisture–rainfall feedback, based on direct observations from Illinois. Water Resour. Res., 33, 725735, doi:10.1029/96WR03756.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franz, T. E., M. Zreda, T. P. A. Ferre, R. Rosolem, C. Zweck, S. Stillman, X. Zeng, and W. J. Shuttleworth, 2012: Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources. Water Resour. Res., 48, W08515, doi:10.1029/2012WR011871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasper, F., K. Goergen, P. Shrestha, M. Sulis, J. Rihani, M. Geimer, and S. Kollet, 2014: Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment—A case study on JUQUEEN (IBM Blue Gene/Q). Geosci. Model Dev., 7, 25312543, doi:10.5194/gmd-7-2531-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2955, doi:10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleeson, T., L. Smith, N. Moosdorf, J. Hartmann, H. H. Dürr, A. H. Manning, and A. M. Jellinek, 2011: Mapping permeability over the surface of the Earth. Geophys. Res. Lett., 38, L02401, doi:10.1029/2010GL045565.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gochis, D. J., W. Yu, and D. N. Yates, 2013: The WRF-Hydro model technical description and user's guide, version 1.0. NCAR Tech. Doc., 120 pp. [Available online at https://www.ral.ucar.edu/projects/wrf_hydro.]

  • Graf, A., J. Werner, M. Langensiepen, A. van de Boer, M. Schmidt, M. Kupisch, and H. Vereecken, 2013: Validation of a minimum microclimate disturbance chamber for net ecosystem flux measurements. Agric. For. Meteor., 174–175, 114, doi:10.1016/j.agrformet.2013.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson-Sellers, A., A. J. Pitman, P. K. Love, P. Irannejad, and T. H. Chen, 1995: The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phase 2 and 3. Bull. Amer. Meteor. Soc., 76, 489503, doi:10.1175/1520-0477(1995)076<0489:TPFIOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacquemin, B., and J. Noilhan, 1990: Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound.-Layer Meteor., 52, 93134, doi:10.1007/BF00123180.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1990: The step-mountain coordinate-physical package. Mon. Wea. Rev., 118, 14291443, doi:10.1175/1520-0493(1990)118<1429:TSMCPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, J., N. L. Miller, and N. Schegel, 2010: Sensitivity study of four land surface schemes in the WRF Model. Adv. Meteor., 2010, 167436, doi:10.1155/2010/167436.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, J. E., and C. S. Woodward, 2001: Newton–Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems. Adv. Water Resour., 24, 763774, doi:10.1016/S0309-1708(00)00075-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., R. Furrer, C. Tebaldi, J. Cermak, and G. A. Meehl, 2010: Challenges in combining projections from multiple climate models. J. Climate, 23, 27392758, doi:10.1175/2009JCLI3361.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kollet, S. J., and R. M. Maxwell, 2006: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model. Adv. Water Resour., 29, 945958, doi:10.1016/j.advwatres.2005.08.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kollet, S. J., and R. M. Maxwell, 2008: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour. Res., 44, W02402, doi:10.1029/2007WR006004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kollet, S. J., and Coauthors, 2017: The Integrated Hydrologic Model Intercomparison Project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resour. Res., 53, 867890, doi:10.1002/2016WR019191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., M. J. Suarez, R. W. Higgins, and H. M. V. den Dool, 2003: Observational evidence that soil moisture variations affect precipitation. Geophys. Res. Lett., 30, 1241, doi:10.1029/2002GL016571.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, doi:10.1126/science.1100217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590610, doi:10.1175/JHM510.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larsen, M. A. D., J. C. Refsgaard, M. Drews, M. B. Butts, K. H. Jensen, J. H. Christensen, and O. B. Christensen, 2014: Results from a full coupling of the HIRHAM regional climate model and the MIKE SHE hydrological model for a Danish catchment. Hydrol. Earth Syst. Sci., 18, 47334749, doi:10.5194/hess-18-4733-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, D., P. Thornton, K. Oleson, and G. Bonan, 2007: The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: Impacts on land–atmosphere interaction. J. Hydrometeor., 8, 862880, doi:10.1175/JHM596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, D. H., and L. M. Abriola, 1999: Use of the Richards equation in land surface parameterizations. J. Geophys. Res., 104, 27 51927 526, doi:10.1029/1999JD900951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lo, M.-H., and J. S. Famiglietti, 2010: Effect of water table dynamics on land surface hydrologic memory. J. Geophys. Res., 115, 21562202, doi:10.1029/2010JD014191.

    • Search Google Scholar
    • Export Citation
  • Los, S. O., G. P. Weedon, P. R. J. North, J. D. Kaduk, C. M. Taylor, and P. M. Cox, 2006: An observation-based estimate of the strength of rainfall–vegetation interactions in the Sahel. Geophys. Res. Lett., 33, L16402, doi:10.1029/2006GL027065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., and L. M. Kueppers, 2012: Surface energy partitioning over four dominant vegetation types across the United States in a coupled regional climate model (Weather Research and Forecasting Model 3–Community Land Model 3.5). J. Geophys. Res., 117, D06111, doi:10.1029/2011JD016991.

    • Search Google Scholar
    • Export Citation
  • Mauder, M., M. Cuntz, C. Drüe, A. Graf, C. Rebmann, H. P. Schmid, M. Schmidt, and R. Steinbrecher, 2013: A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric. For. Meteor., 169, 122135, doi:10.1016/j.agrformet.2012.09.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maxwell, R. M., 2013: A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling. Adv. Water Resour., 53, 109117, doi:10.1016/j.advwatres.2012.10.001.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maxwell, R. M., and S. J. Kollet, 2008: Interdependence of groundwater dynamics and land-energy feedbacks under climate change. Nat. Geosci., 1, 665669, doi:10.1038/ngeo315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maxwell, R. M., F. Chow, and S. J. Kollet, 2007: The groundwater–land-surface–atmosphere connection: Soil moisture effects on the atmospheric boundary layer in fully-coupled simulations. Adv. Water Resour., 30, 24472466, doi:10.1016/j.advwatres.2007.05.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maxwell, R. M., J. K. Lundquist, J. D. Mirocha, S. G. Smith, C. S. Woodward, and A. F. B. Tompson, 2011: Development of a coupled groundwater–atmosphere model. Mon. Wea. Rev., 139, 96116, doi:10.1175/2010MWR3392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maxwell, R. M., and Coauthors, 2014: Surface–subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resour. Res., 50, 15311549, doi:10.1002/2013WR013725.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miguez-Macho, G., and Y. Fan, 2012: The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands. J. Geophys. Res., 117, D15113, doi:10.1029/2012JD017539.

    • Search Google Scholar
    • Export Citation
  • Miguez-Macho, G., Y. Fan, C. P. Weaver, R. Walko, and A. Robock, 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
  • Milan, M., D. Schuettemeyer, T. Bick, and C. Simmer, 2014: A sequential ensemble prediction system at convection-permitting scales. Meteor. Atmos. Phys., 123, 1731, doi:10.1007/s00703-013-0291-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, L. E. Gulden, and H. Su, 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., C. Paniconi, P. A. Troch, R. L. Scott, M. Durcik, X. Zeng, T. Huxman, and D. C. Goodrich, 2014: An integrated modelling framework of catchment-scale ecohydrological processes: 1. Model description and tests over an energy-limited watershed. Ecohydrology, 7, 427439, doi:10.1002/eco.1362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niyogi, D. S., and S. Raman, 1997: Comparison of stomatal resistance simulated by four different schemes using FIFE observations. J. Appl. Meteor. Climatol., 36, 903917, doi:10.1175/1520-0450(1997)036<0903:COFDSR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.

    • Search Google Scholar
    • Export Citation
  • Orth, R., and S. I. Seneviratne, 2012: Analysis of soil moisture memory from observations in Europe. J. Geophys. Res., 117, D15115, doi:10.1029/2011JD017366.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39, 151177, doi:10.1029/1999RG000072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahman, M., M. Sulis, and S. J. Kollet, 2014: The concept of dual-boundary forcing in land surface–subsurface interactions of the terrestrial hydrologic and energy cycles. Water Resour. Res., 50, 85318548, doi:10.1002/2014WR015738.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahman, M., M. Sulis, and S. J. Kollet, 2015: The subsurface–land surface–atmosphere connection under convective conditions. Adv. Water Resour., 83, 240249, doi:10.1016/j.advwatres.2015.06.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahman, M., M. Sulis, and S. J. Kollet, 2016: Evaluating the dual-boundary forcing concept in subsurface–land surface interactions of the hydrological cycle. Hydrol. Processes, 30, 15631573, doi:10.1002/hyp.10702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raschendorfer, M., 2001: The new turbulence parameterization of LM. COSMO Newsletter, No. 1, Consortium for Small-Scale Modeling, Offenbach, Germany, 89–97. [Available online at http://www.cosmo-model.org/content/model/documentation/newsLetters/newsLetter01/newsLetter_01.pdf.]

  • Rigon, R., G. Bertoldi, and T. M. Over, 2006: GEOtop: A distributed hydrological model with coupled water and energy budgets. J. Hydrometeor., 7, 371388, doi:10.1175/JHM497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakaguchi, K., and X. Zeng, 2009: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3. 5). J. Geophys. Res., 114, D01107, doi:10.1029/2008JD010834.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., C. D. Peters-Lidard, S. V. Kumar, C. Alonge, and W. K. Tao, 2009: A modeling and observational framework for diagnosing local land–atmosphere coupling on diurnal time scales. J. Hydrometeor., 10, 577599, doi:10.1175/2009JHM1066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., C. D. Peters-Lidard, and S. V. Kumar, 2011: Diagnosing the sensitivity of local land–atmosphere coupling via the soil moisture–boundary layer interaction. J. Hydrometeor., 12, 766786, doi:10.1175/JHM-D-10-05014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaap, M. G., and F. J. Leij, 1998: Database-related accuracy and uncertainty of pedotransfer functions. Soil Sci., 163, 765779, doi:10.1097/00010694-199810000-00001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller, 2004: The role of increasing temperature variability in European summer heatwaves. Nature, 427, 332336, doi:10.1038/nature02300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwitalla, T., H.-S. Bauer, V. Wulfmeyer, and F. Aoshima, 2011: High-resolution simulation over central Europe: Assimilation experiments during COPS IOP 9c. Quart. J. Roy. Meteor. Soc., 137, 156175, doi:10.1002/qj.721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., J. A. Berry, G. J. Collatz, C. B. Field, and F. G. Hall, 1992a: Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens. Environ., 42, 187216, doi:10.1016/0034-4257(92)90102-P.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., M. D. Heiser, and F. G. Hall, 1992b: Relations between surface conductance and spectral vegetation indices at intermediate (100 m2 to 15 km2) length scales. J. Geophys. Res., 97, 19 03319 059, doi:10.1029/92JD01096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., and Coauthors, 1995: Effects of spatial variability in topography, vegetation cover and soil moisture on area-averaged surface fluxes: A case study using the FIFE 1989 data. J. Geophys. Res., 100, 25 60725 629, doi:10.1029/95JD02205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land–atmosphere coupling and climate change in Europe. Nature, 443, 205209, doi:10.1038/nature05095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, C., J. Niu, and M. S. Phanikumar, 2013: Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface–land surface processes model. Water Resour. Res., 49, 25522572, doi:10.1002/wrcr.20189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shrestha, P., M. Sulis, M. Masbou, S. J. Kollet, and C. Simmer, 2014: A scale-consistent terrestrial systems modeling platform based on COSMO, CLM, and ParFlow. Mon. Wea. Rev., 142, 34663483, doi:10.1175/MWR-D-14-00029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shrestha, P., M. Sulis, C. Simmer, and S. J. Kollet, 2015: Impacts of grid resolution on surface energy fluxes simulated with an integrated surface–groundwater flow model. Hydrol. Earth Syst. Sci., 19, 43174326, doi:10.5194/hess-19-4317-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmer, C., and Coauthors, 2015: Monitoring and modeling the terrestrial system from pores to catchments: The transregional collaborative research center on patterns in the soil–vegetation–atmosphere system. Bull. Amer. Meteor. Soc., 96, 17651787, doi:10.1175/BAMS-D-13-00134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, doi:10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, M. B., D. J. Seo, V. I. Koren, S. M. Reed, Z. Zhang, Q. Y. Duan, F. Moreda, and S. Cong, 2004: The Distributed Model Intercomparison Project (DMIP): Motivation and experiment design. J. Hydrol., 298, 426, doi:10.1016/j.jhydrol.2004.03.040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sulis, M., S. B. Meyerhoff, C. Paniconi, R. M. Maxwell, M. Putti, and S. J. Kollet, 2010: A comparison of two physics-based numerical models for simulating surface water–groundwater interactions. Adv. Water Resour., 33, 456467, doi:10.1016/j.advwatres.2010.01.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sulis, M., C. Paniconi, M. Marrocu, D. Huard, and D. Chaumont, 2012: Hydrologic response to multimodel climate output using a physically based model of groundwater/surface water interactions. Water Resour. Res., 48, W12510, doi:10.1029/2012WR012304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sulis, M., M. Langensiepen, P. Shrestha, A. Schickling, C. Simmer, and S. J. Kollet, 2015: Evaluating the influence of plant-specific physiological parameterizations on the partitioning of land surface energy fluxes. J. Hydrometeor., 16, 517533, doi:10.1175/JHM-D-14-0153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, J. Y., and W. J. Riley, 2013a: Impacts of a new bare-soil evaporation formulation on site, regional, and global surface energy and water budgets in CLM4. J. Adv. Model. Earth Syst., 5, 558571, doi:10.1002/jame.20034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, J. Y., and W. J. Riley, 2013b: A new top boundary condition for modeling surface diffusive exchange of a generic volatile tracer: Theoretical analysis and application to soil evaporation. Hydrol. Earth Syst. Sci., 17, 873893, doi:10.5194/hess-17-873-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, C. M., and T. Lebel, 1998: Observational evidence of persistent convective-scale rainfall patterns. Mon. Wea. Rev., 126, 15971607, doi:10.1175/1520-0493(1998)126<1597:OEOPCS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, C. M., A. Gounou, F. Guichard, P. P. Harris, R. J. Ellis, F. Couvreux, and M. D. Kauwe, 2011: Frequency of Sahelian storm initiation enhanced over mesoscale soil-moisture patterns. Nat. Geosci., 4, 430433, doi:10.1038/ngeo1173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teuling, A. J., S. I. Seneviratne, C. Williams, and P. A. Troch, 2006: Observed timescales of evapotranspiration response to soil moisture. Geophys. Res. Lett., 33, L23403, doi:10.1029/2006GL028178.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teuling, A. J., and Coauthors, 2010: Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geo., 3, 722727, doi:10.1038/ngeo950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Valcke, S., 2013: The OASIS3 coupler: A European climate modelling community software. Geosci. Model Dev., 6, 373388, doi:10.5194/gmd-6-373-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Genuchten, M. T., and D. R. Nielsen, 1985: On describing and predicting the hydraulic properties of unsaturated soils. Ann. Geophys., 3 (5), 615628.

    • Search Google Scholar
    • Export Citation
  • Williams, J. L., and R. M. Maxwell, 2011: Propagating subsurface uncertainty to the atmosphere using fully coupled stochastic simulations. J. Hydrometeor., 12, 690701, doi:10.1175/2011JHM1363.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, J. L., R. M. Maxwell, and L. D. Monache, 2013: Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model. J. Adv. Model. Earth Syst., 5, 785800, doi:10.1002/jame.20051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z.-L., R. E. Dickinson, A. Henderson-Sellers, and A. J. Pitman, 1995: Preliminary study of spin-up processes in land surface models with the first stage data of Project for Intercomparison of Land Surface Parameterization Schemes Phase 1(a). J. Geophys. Res., 100, 16 55316 578, doi:10.1029/95JD01076.

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

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

    • Crossref
    • Search Google Scholar
    • Export Citation
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Coupling Groundwater, Vegetation, and Atmospheric Processes: A Comparison of Two Integrated Models

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  • 1 Meteorological Institute, University of Bonn, Bonn, Germany
  • | 2 Meteorological Institute, University of Bonn, Bonn, and Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund Aachen, Bonn, Cologne, and Jülich (ABC/J), Jülich, Germany
  • | 3 Institute for Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, and Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund Aachen, Bonn, Cologne, and Jülich (ABC/J), Jülich, Germany
  • | 4 Integrated GroundWater Modeling Center, Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado
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Abstract

This study compares two modeling platforms, ParFlow.WRF (PF.WRF) and the Terrestrial Systems Modeling Platform (TerrSysMP), with a common 3D integrated surface–groundwater model to examine the variability in simulated soil–vegetation–atmosphere interactions. Idealized and hindcast simulations over the North Rhine–Westphalia region in western Germany for clear-sky conditions and strong convective precipitation using both modeling platforms are presented. Idealized simulations highlight the strong variability introduced by the difference in land surface parameterizations (e.g., ground evaporation and canopy transpiration) and atmospheric boundary layer (ABL) schemes on the simulated land–atmosphere interactions. Results of the idealized simulations also suggest a different range of sensitivity in the two models of land surface and atmospheric parameterizations to water-table depth fluctuations. For hindcast simulations, both modeling platforms simulate net radiation and cumulative precipitation close to observed station data, while larger differences emerge between spatial patterns of soil moisture and convective rainfall due to the difference in the physical parameterization of the land surface and atmospheric component. This produces a different feedback by the hydrological model in the two platforms in terms of discharge over different catchments in the study area. Finally, an analysis of land surface and ABL heat and moisture budgets using the mixing diagram approach reveals different sensitivities of diurnal atmospheric processes to the groundwater parameterizations in both modeling platforms.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Mauro Sulis, msulis@uni-bonn.de

Abstract

This study compares two modeling platforms, ParFlow.WRF (PF.WRF) and the Terrestrial Systems Modeling Platform (TerrSysMP), with a common 3D integrated surface–groundwater model to examine the variability in simulated soil–vegetation–atmosphere interactions. Idealized and hindcast simulations over the North Rhine–Westphalia region in western Germany for clear-sky conditions and strong convective precipitation using both modeling platforms are presented. Idealized simulations highlight the strong variability introduced by the difference in land surface parameterizations (e.g., ground evaporation and canopy transpiration) and atmospheric boundary layer (ABL) schemes on the simulated land–atmosphere interactions. Results of the idealized simulations also suggest a different range of sensitivity in the two models of land surface and atmospheric parameterizations to water-table depth fluctuations. For hindcast simulations, both modeling platforms simulate net radiation and cumulative precipitation close to observed station data, while larger differences emerge between spatial patterns of soil moisture and convective rainfall due to the difference in the physical parameterization of the land surface and atmospheric component. This produces a different feedback by the hydrological model in the two platforms in terms of discharge over different catchments in the study area. Finally, an analysis of land surface and ABL heat and moisture budgets using the mixing diagram approach reveals different sensitivities of diurnal atmospheric processes to the groundwater parameterizations in both modeling platforms.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Mauro Sulis, msulis@uni-bonn.de
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