Abstract

Geophysical Research LettersVolume 38, Issue 2 Hydrology and Land Surface StudiesFree Access Mapping permeability over the surface of the Earth Tom Gleeson, Tom Gleeson [email protected] Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorLeslie Smith, Leslie Smith Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorNils Moosdorf, Nils Moosdorf Institute for Biogeochemistry and Marine Chemistry, KlimaCampus, University of Hamburg, Hamburg, GermanySearch for more papers by this authorJens Hartmann, Jens Hartmann Institute for Biogeochemistry and Marine Chemistry, KlimaCampus, University of Hamburg, Hamburg, GermanySearch for more papers by this authorHans H. Dürr, Hans H. Dürr Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, NetherlandsSearch for more papers by this authorAndrew H. Manning, Andrew H. Manning U.S. Geological Survey, Denver, Colorado, USASearch for more papers by this authorLudovicus P. H. van Beek, Ludovicus P. H. van Beek Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, NetherlandsSearch for more papers by this authorA. M. Jellinek, A. M. Jellinek Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this author Tom Gleeson, Tom Gleeson [email protected] Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorLeslie Smith, Leslie Smith Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorNils Moosdorf, Nils Moosdorf Institute for Biogeochemistry and Marine Chemistry, KlimaCampus, University of Hamburg, Hamburg, GermanySearch for more papers by this authorJens Hartmann, Jens Hartmann Institute for Biogeochemistry and Marine Chemistry, KlimaCampus, University of Hamburg, Hamburg, GermanySearch for more papers by this authorHans H. Dürr, Hans H. Dürr Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, NetherlandsSearch for more papers by this authorAndrew H. Manning, Andrew H. Manning U.S. Geological Survey, Denver, Colorado, USASearch for more papers by this authorLudovicus P. H. van Beek, Ludovicus P. H. van Beek Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, NetherlandsSearch for more papers by this authorA. M. Jellinek, A. M. Jellinek Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this author First published: 21 January 2011 https://doi.org/10.1029/2010GL045565Citations: 210AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract [1] Permeability, the ease of fluid flow through porous rocks and soils, is a fundamental but often poorly quantified component in the analysis of regional-scale water fluxes. Permeability is difficult to quantify because it varies over more than 13 orders of magnitude and is heterogeneous and dependent on flow direction. Indeed, at the regional scale, maps of permeability only exist for soil to depths of 1–2 m. Here we use an extensive compilation of results from hydrogeologic models to show that regional-scale (>5 km) permeability of consolidated and unconsolidated geologic units below soil horizons (hydrolithologies) can be characterized in a statistically meaningful way. The representative permeabilities of these hydrolithologies are used to map the distribution of near-surface (on the order of 100 m depth) permeability globally and over North America. The distribution of each hydrolithology is generally scale independent. The near-surface mean permeability is of the order of ∼5 × 10−14 m2. The results provide the first global picture of near-surface permeability and will be of particular value for evaluating global water resources and modeling the influence of climate-surface-subsurface interactions on global climate change. 1. Introduction [2] Estimating and mapping regional-scale permeability is critical to examining diverse earth processes and addressing water resource problems. Land-surface, subsurface and climate models have been used to examine interactions between groundwater, soil moisture, surface water and climate [York et al., 2002; Liang and Xie, 2003; Yeh and Eltahir, 2005; Fan et al., 2007; Miguez-Macho et al., 2007; Anyah et al., 2008; Maxwell and Kollet, 2008] and the response of aquifers to climate change [Scibek and Allen, 2006]. However the integration of groundwater systems into large-scale earth system models has been limited by the lack of available parameter data, most acutely permeability data. Soil permeability (∼1–2 m depth) has been mapped over North America [Fan et al., 2007] but the permeability of lithologies underlying soil has not been systematically examined or mapped. Mapping regional-scale permeability also addresses groundwater resource concerns because permeability, along with recharge rate and hydraulic gradient, governs the flux through aquifers. Finally, permeability affects a myriad of deeper earth process [Ingebritsen et al., 2006] including volcanism and earthquakes [Wang and Manga, 2010], the formation of metallic mineral deposits and oil resources [Garven, 1995; Person et al., 1996], crustal-scale metamorphic fluid flow [Lyubetskaya and Ague, 2009] and the development of abnormal fluid pressures in basins [Neuzil, 1994]. Here we compile for the first time regional-scale permeability values for diverse lithologies in order to estimate and map near-surface permeability. 2. Methods Permeability Compilation [3] Our focus is the permeability of saturated terrestrial lithologies rather than unsaturated permeability which is non-linear and transient, or the permeability of oceanic lithologies which were previously compiled [Fisher, 1998]. We define local- and regional-scale permeability based on the scale and method of quantification. At a local scale (5 km to ensure that we are well above the scale at which heterogeneities such as discrete fractures control groundwater flow. We also define hydrolithologies as broad lithologic categories with similar hydrogeologic characteristics such as permeability. Geologic units (from geologic maps or hydrogeological models) are categorized into hydrolithologies. Our hydrolithologic categorization is consistent with current hydrogeologic modeling practice and is an extension of the ‘hydrostratigraphic’ concept commonly employed in hydrogeologic modeling of sedimentary basins [Person et al., 1996]. Figure 1Open in figure viewerPowerPoint Comparing (a) local-scale permeability (k) ranges from Freeze and Cherry [1979] and (b) calibrated regional-scale hydrogeologic models. Each open square represents a hydrolithologic unit in a calibrated model that is larger than 5 km horizontally. In Figure 1b local-scale permeability ranges are shown behind the open squares by the same colored bars. Values are grouped into hydrolithologic categories (i.e., fine grained unconsolidated). The geometric mean for each hydrolithologic category is shown as a red square with the red line representing the 1σ standard deviation for each hydrolithologic category. [4] We compiled two-hundred and thirty hydrogeologic units from calibrated models which are grouped into seven hydrolithologic categories (Table S1 and Methods in the auxiliary material). Also, two combined hydrolithologic categories (i.e., unconsolidated and siliciclastic sedimentary), used later in mapping, are defined in Table 1 as amalgamations of four hydrolithologic categories. Only hydrogeologic units that occur at shallow depths (0.2 SS, SM, CL carbonate −11.8 1.5 47 0.931 0.0008 0.142 0.019 SC crystalline −14.1 1.5 17 0.972 0.852 0.135 >0.2 MT, PA, PB, PR volcanic −12.5 1.8 33 0.933 0.043 0.134 0.136 VA,VB not assigned - - - - - - WB, IG, EV Hydrolithology (North America Map) c.g. unconsolidated −10.9 1.2 82 0.93 0.2 AD, DS, LO, SU SS f.g. unconsolidated −14.0 1.8 31 0.955 0.209 0.121 >0.2 AD, DS, LO, SU SH unconsolidated −13.0 2.0 113 0.919 0.2 SS, SM SH sil. sedimentary −15.2 2.5 20 0.942 0.265 0.154 >0.2 SS, SM MX, PY, AM, GR carbonate −11.8 1.5 47 0.931 0.0008 0.142 0.019 SC crystalline −14.1 1.5 17 0.972 0.852 0.135 >0.2 MT, PA, PB PI volcanic −12.5 1.8 33 0.933 0.043 0.134 0.136 VA,VB VI, PY not assigned - - - - - - WB, IG, EV a logk μgeo is the geometric mean logarithmic permeability; σ is the standard deviation; n is the number of hydrolithologic units; W is the Shapiro-Wilk statistic; p is the p-value for α = 0.05; K-S is the Kolmogorov-Smirnov distribution; sil. sedimentary is siliciclastic sedimentary; c.g. and f.g. are coarse- and fine-grained, respectively; values in bold fail normality test (p 2.3.CO;2. Ingebritsen, S. E., and C. E. Manning (2010), Permeability of the continental crust: Dynamic variations inferred from seismicity and metamorphism, Geofluids, 10, 193– 205. Ingebritsen, S. E., et al. (2006), Groundwater in Geological Processes, 562 pp., Cambridge Univ. Press, Cambridge, U. K. Jansen, N., et al. (2010), Dissolved silica mobilization in the conterminous USA, Chem. Geol., 270, 90– 109, doi:10.1016/j.chemgeo.2009.11.008. Jiang, X.-W., et al. (2010), Semi-empirical equations for the systematic decrease in permeability with depth in porous and fractured media, Hydrogeol. J., 18, 839– 850, doi:10.1007/s10040-010-0575-3. Liang, X., and Z. Xie (2003), Important factors in land-atmosphere interactions: Surface runoff generations and interactions between surface and groundwater, Global Planet. Change, 38, 101– 114, doi:10.1016/S0921-8181(03)00012-2. Luo, W., et al. (2010), Estimating hydraulic conductivity from drainage patterns—A case study in the Oregon Cascades, Geology, 38, 335– 338, doi:10.1130/G30816.1. Lyubetskaya, T., and J. J. Ague (2009), Modeling the magnitudes and directions of regional metamorphic fluid flow in collisional orogens, J. Petrol., 50, 1505– 1531, doi:10.1093/petrology/egp039. Maxwell, R. M., and S. J. Kollet (2008), Interdependence of groundwater dynamics and land-energy feedbacks under climate change, Nat. Geosci., 1, 665– 669, doi:10.1038/ngeo315. 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. Moosdorf, N., J. Hartmann, and H. H. Dürr (2010), Lithological composition of the North American continent and implications of lithological map resolution for dissolved silica flux modeling, Geochem. Geophys. Geosyst., 11, Q11003, doi:10.1029/2010GC003259. Neuzil, C. E. (1994), How permeable are clays and shales? Water Resour. Res., 30, 145– 150, doi:10.1029/93WR02930. Person, M., J. P. Raffensperger, S. Ge, and G. Garven (1996), Basin-scale hydrogeologic modeling, Rev. Geophys., 34, 61– 87, doi:10.1029/95RG03286. Saar, M. O., and M. Manga (2004), Depth dependence of permeability in the Oregon Cascades inferred from hydrogeologic, thermal, seismic, and magmatic modeling constraints, J. Geophys. Res., 109, B04204, doi:10.1029/2003JB002855. Scibek, J., and D. M. Allen (2006), Modeled impacts of predicted climate change on recharge and groundwater levels, Water Resour. Res., 42, W11405, doi:10.1029/2005WR004742. U.S. Geological Survey (2003), Principal aquifers of the 48 conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, U.S. Geol. Surv., Reston, Va. van Genuchten, M. T. (1980), A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 24, 133– 144. Wang, C.-Y., and M. Manga (2010), Earthquakes and Water, 218 pp., Springer, Berlin. 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. Clim., 18, 1861– 1880, doi:10.1175/JCLI3330.1. York, J. P., et al. (2002), Putting aquifers into atmospheric simulation models: An example from the Mill Creek Watershed, northeastern Kansas, Adv. Water Resour., 25, 221– 238, doi:10.1016/S0309-1708(01)00021-5. Zinn, B., and C. F. Harvey (2003), When good statistical models of aquifer heterogeneity go bad: A comparison of flow, dispersion, and mass transfer in connected and multivariate Gaussian hydraulic conductivity fields, Water Resour. Res., 39(3), 1051, doi:10.1029/2001WR001146. Citing Literature Volume38, Issue2January 2011 FiguresReferencesRelatedInformation