Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5)

Michael A. Brunke * Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Michael A. Brunke in
Current site
Google Scholar
PubMed
Close
,
Patrick Broxton * Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Patrick Broxton in
Current site
Google Scholar
PubMed
Close
,
Jon Pelletier Department of Geosciences, The University of Arizona, Tucson, Arizona

Search for other papers by Jon Pelletier in
Current site
Google Scholar
PubMed
Close
,
David Gochis National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by David Gochis in
Current site
Google Scholar
PubMed
Close
,
Pieter Hazenberg * Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Pieter Hazenberg in
Current site
Google Scholar
PubMed
Close
,
David M. Lawrence National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by David M. Lawrence in
Current site
Google Scholar
PubMed
Close
,
L. Ruby Leung Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by L. Ruby Leung in
Current site
Google Scholar
PubMed
Close
,
Guo-Yue Niu Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Guo-Yue Niu in
Current site
Google Scholar
PubMed
Close
,
Peter A. Troch Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Peter A. Troch in
Current site
Google Scholar
PubMed
Close
, and
Xubin Zeng * Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Xubin Zeng in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.9° latitude × 1.25° longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0307.s1.

Corresponding author address: Michael A. Brunke, Department of Atmospheric Sciences, The University of Arizona, P.O. Box 210081, Tucson, AZ 85721-0081. E-mail: brunke@atmo.arizona.edu

Abstract

One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.9° latitude × 1.25° longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0307.s1.

Corresponding author address: Michael A. Brunke, Department of Atmospheric Sciences, The University of Arizona, P.O. Box 210081, Tucson, AZ 85721-0081. E-mail: brunke@atmo.arizona.edu

Supplementary Materials

    • Supplemental Materials (DOCX 11.63 MB)
Save
  • 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
  • Canadell, J., R. B. Jackson, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E.-D. Schulze, 1996: Maximum rooting depth of vegetation types at the global scale. Oecologia, 108, 583595, doi:10.1007/BF00329030.

    • Search Google Scholar
    • Export Citation
  • Chen, J. L., M. Rodell, C. R. Wilson, and J. S. Famiglietti, 2005: Low degree spherical harmonic influences on Gravity Recovery and Climate Experiment (GRACE) water storage estimates. Geophys. Res. Lett., 32, L14405, doi:10.1029/2005GL022964.

    • Search Google Scholar
    • Export Citation
  • Chen, X., and Q. Hu, 2004: Groundwater influences on soil moisture and surface evaporation. J. Hydrol., 297, 285300, doi:10.1016/j.jhydrol.2004.04.019.

    • Search Google Scholar
    • Export Citation
  • Clauser, C., and E. Huenges, 1995: Thermal conductivity of rocks and minerals. Rock Physics and Phase Relations: A Handbook of Physical Constants, T. J. Ahrens, Ed., Amer. Geophys. Union, 105–126.

  • Decker, M., and X. Zeng, 2009: Impact of modified Richards equation on global soil moisture simulation in the Community Land Model (CLM3.5). J. Adv. Model. Earth Syst., 1, 5, doi:10.3894/JAMES.2009.1.5.

    • Search Google Scholar
    • Export Citation
  • Dietrich, W. E., R. Reiss, M.-L. Hsu, and D. R. Montgomery, 1995: A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol. Processes, 9, 383400, doi:10.1002/hyp.3360090311.

    • Search Google Scholar
    • Export Citation
  • Eltahir, E. A. B., and P. J.-F. Yeh, 1999: On the assymetric response of aquifer water level to floods and droughts in Illinois. Water Resour. Res., 35, 11991217, doi:10.1029/1998WR900071.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Farouki, O. T., 1981: The thermal properties of soils in cold regions. Cold Reg. Sci. Technol., 5, 6775, doi:10.1016/0165-232X(81)90041-0.

    • Search Google Scholar
    • Export Citation
  • Gochis, D. J., E. R. Vivoni, and C. J. Watts, 2010: The impact of soil depth on land surface energy and water fluxes in the North American monsoon region. J. Arid Environ., 74, 564571, doi:10.1016/j.jaridenv.2009.11.002.

    • Search Google Scholar
    • Export Citation
  • Gulden, L. E., E. Rosero, Z.-L. Yang, M. Rodell, C. S. Jackson, G.-Y. Niu, P. J.-F. Yeh, and J. Famiglietti, 2007: Improving land-surface model hydrology: Is an explicit aquifer model better than a deeper soil profile? Geophys. Res. Lett., 34, L09402, doi:10.1029/2007GL029804.

    • Search Google Scholar
    • Export Citation
  • Gutowski, W. J., C. J. Vörösmarty, M. Person, Z. Ötles, B. Fekete, and J. York, 2002: A coupled land-atmosphere simulation program (CLASP): Calibration and validation. J. Geophys. Res., 107, 4283, doi:10.1029/2001JD000392.

    • Search Google Scholar
    • Export Citation
  • Hazenberg, P., Y. Fang, P. Broxton, D. Gochis, G.-Y. Niu, J. D. Pelletier, P. A. Troch, and X. Zeng, 2015: A hybrid-3D hillslope hydrological model for use in Earth system models. Water Resour. Res., 51, 82188239, doi:10.1002/2014WR016842.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., G.-Y. Niu, and Z.-L. Yang, 2009: Impacts of vegetation and groundwater dynamics on warm season precipitation over the central United States. J. Geophys. Res., 114, D06109, doi:10.1029/2008JD010756.

    • Search Google Scholar
    • Export Citation
  • Koirala, S., P. J.-F. Yeh, Y. Hirabayashi, S. Kanae, and T. Oki, 2014: Global-scale land surface hydrologic modeling with the representation of water table dynamics. J. Geophys. Res. Atmos., 119, 7589, doi:10.1002/2013JD020398.

    • Search Google Scholar
    • Export Citation
  • Landerer, F. W., and S. C. Swenson, 2012: Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res., 48, W04531, doi:10.1029/2011WR011453.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and A. G. Slater, 2008: Incorporating organic soil into a global climate model. Climate Dyn., 30, 145160, doi:10.1007/s00382-007-0278-1.

    • Search Google Scholar
    • Export Citation
  • Lee, J.-E., R. S. Oliveira, T. E. Dawson, and I. Fung, 2005: Root functioning modifies seasonal climate. Proc. Natl. Acad. Sci. USA, 102, 17 57617 581, doi:10.1073/pnas.0508785102.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., M. Huang, Y. Qian, and X. Liang, 2011: Climate–soil–vegetation control on groundwater table dynamics and its feedbacks in a climate model. Climate Dyn., 36, 5781, doi:10.1007/s00382-010-0746-x.

    • Search Google Scholar
    • Export Citation
  • Liang, X., Z. Xie, and M. Huang, 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
  • 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: 1. Water table observations and equilibrium water table simulations. J. Geophys. Res., 112, D13108, doi:10.1029/2006JD008112.

    • Search Google Scholar
    • Export Citation
  • Miller, D. A., and R. A. White, 1998: A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interact., 2, 126, doi:10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nepstad, D. C., and Coauthors, 1994: The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures. Nature, 372, 666669, doi:10.1038/372666a0.

    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, and L. E. Gulden, 2005: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res., 110, D21106, doi:10.1029/2005JD006111.

    • 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
  • Oleson, K. W., and Coauthors, 2013: Technical description of version 4.5 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-503+STR, 420 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.2/clm/CLM45_Tech_Note.pdf.]

  • Orellana, F., P. Verma, S. P. Loheide II, and E. Daly, 2012: Monitoring and modeling water-vegetation interactions in groundwater-dependent ecosystems. Rev. Geophys., 50, RG2003, doi:10.1029/2011RG000383.

    • Search Google Scholar
    • Export Citation
  • Pelletier, J. D., 2013: A robust, two-parameter method for the extraction of drainage networks from high-resolution digital elevation models (DEMs): Evaluation using synthetic and real-world DEMs. Water Resour. Res., 49, 7589, doi:10.1029/2012WR012452.

    • Search Google Scholar
    • Export Citation
  • Pelletier, J. D., and C. Rasmussen, 2009: Geomorphically based predictive mapping of soil thickness in upland watersheds. Water Resour. Res., 45, W09417, doi:10.1029/2008WR007319.

    • Search Google Scholar
    • Export Citation
  • Pelletier, J. D., and Coauthors, 2016: A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. J. Adv. Model. Earth Syst., doi:10.1002/2015MS000526, in press.

    • Search Google Scholar
    • Export Citation
  • Qian, T., A. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. J. Hydrometeor., 7, 953975, doi:10.1175/JHM540.1.

    • Search Google Scholar
    • Export Citation
  • Roering, J. J., 2008: How well can hillslope evolution models “explain” topography? Simulating soil transport and production with high-resolution topographic data. Geol. Soc. Amer. Bull., 120, 12481262, doi:10.1130/B26283.1.

    • Search Google Scholar
    • Export Citation
  • Romero-Saltos, H., L. da Silveira Lobo Sternberg, M. Z. Moreira, and D. C. Nepstad, 2005: Rainfall exclusion in an eastern Amazonian forest alters soil water movement and depth of water uptake. Amer. J. Bot., 92, 443455, doi:10.3732/ajb.92.3.443.

    • Search Google Scholar
    • Export Citation
  • Rossatto, D. R., L. de Carvalho Ramos Silva, R. Villalobos-Vega, L. da Silveira Lobo Sternberg, and A. C. Franco, 2012: Depth of water uptake in woody plants relates to groundwater level and vegetation structure along a topographic gradient in a neotropical savanna. Environ. Exp. Bot., 77, 259266, doi:10.1016/j.envexpbot.2011.11.025.

    • Search Google Scholar
    • Export Citation
  • Shabbir, G., A. Maqsood, and C. A. Majid, 2000: Thermophysical properties of consolidated porous rocks. J. Phys., 33D, 658661, doi:10.1088/0022-3727/33/6/311.

    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., 2012: GRACE monthly land water mass grids NETCDF RELEASE 5.0. version 5.0. PO.DAAC, data accessed 11 December 2014, doi:10.5067/TELND-NC005.

  • Swenson, S. C., and J. Wahr, 2006: Post-processing removal of correlated errors in GRACE data. Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285.

    • Search Google Scholar
    • Export Citation
  • Tesfa, T. K., D. G. Tarboton, D. G. Chandler, and J. P. McNamara, 2009: Modeling soil depth from topographic and land cover attributes. Water Resour. Res., 45, W10438, doi:10.1029/2008WR007474.

    • Search Google Scholar
    • Export Citation
  • Woolhiser, D. A., R. W. Fedora, R. E. Smith, and S. A. Stohoff, 2006: Estimating infiltration in the Upper Split Wash Watershed, Yucca Mountain, Nevada. J. Hydrol. Eng., 11, 123133, doi:10.1061/(ASCE)1084-0699(2006)11:2(123).

    • 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.

    • Search Google Scholar
    • Export Citation
  • Yeh, P. J.-F., M. Irizarry, and E. A. B. Eltahir, 1998: Hydroclimatology of Illinois: A comparison of monthly evaporation estimates based on atmospheric water balance and soil water balance. J. Geophys. Res., 103, 19 82319 837, doi:10.1029/98JD01721.

    • 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 the Mill Creek Watershed, northeastern Kansas. Adv. Water Resour., 25, 221238, doi:10.1016/S0309-1708(01)00021-5.

    • Search Google Scholar
    • Export Citation
  • Zeng, X., 2001: Global vegetation root distribution for land modeling. J. Hydrometeor., 2, 525530, doi:10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zeng, X., and M. Decker, 2009: Improving the numerical solution of soil moisture–based Richards equation for land models with a deep or a shallow water table. J. Hydrometeor., 10, 308319, doi:10.1175/2008JHM1011.1.

    • Search Google Scholar
    • Export Citation
  • Zeng, X., Y.-J. Dai, R. E. Dickinson, and M. Shaikh, 1998: The role of root distribution for climate simulation over land. Geophys. Res. Lett., 25, 45334536, doi:10.1029/1998GL900216.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1121 357 40
PDF Downloads 783 252 24