The Randolph Glacier Inventory:a globally complete inventory of glaciers

W. Tad PFEFFER,1 Anthony A. ARENDT,2 Andrew BLISS,2 Tobias BOLCH,3,4

J. Graham COGLEY,5 Alex S. GARDNER,6 Jon-Ove HAGEN,7 Regine HOCK,2,8

Georg KASER,9 Christian KIENHOLZ,2 Evan S. MILES,10 Geir MOHOLDT,11

Nico MÖLG,3 Frank PAUL,3 Valentina RADIĆ,12 Philipp RASTNER,3 Bruce H. RAUP,13

Justin RICH,2 Martin J. SHARP,14 THE RANDOLPH CONSORTIUM15

1Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA2Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA

3Department of Geography, University of Zürich, Zürich, Switzerland4Institute for Cartography, Technische Universität Dresden, Dresden, Germany5Department of Geography, Trent University, Peterborough, Ontario, Canada

E-mail: gcogley@trentu.ca6Graduate School of Geography, Clark University, Worcester, MA, USA

7Department of Geosciences, University of Oslo, Oslo, Norway8Department of Earth Sciences, Uppsala University, Uppsala, Sweden

9Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria10Scott Polar Research Institute, University of Cambridge, Cambridge, UK

11Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego,La Jolla, CA, USA

12Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia,Canada

13National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA14Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada

15A complete list of Consortium authors is in the Appendix

ABSTRACT. The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines ofglaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of theIntergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI wascreated with limited resources in a short period. Priority was given to completeness of coverage, but alimited, uniform set of attributes is attached to each of the ��198000 glaciers in its latest version, 3.2.Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as726 800� 34000 km2. The uncertainty, about�5%, is derived from careful single-glacier and basin-scaleuncertainty estimates and comparisons with inventories that were not sources for the RGI. The maincontributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover.These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem.Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be com-bined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers.The RGI has already proved its value in the generation of significantly improved aggregate estimates ofglacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

KEYWORDS: Antarctic glaciology, Arctic glaciology, glacier delineation, glacier mapping, remotesensing, tropical glaciology

1. INTRODUCTIONThe Randolph Glacier Inventory (RGI) is a collection ofdigital outlines of the world’s glaciers, excluding theGreenland and Antarctic ice sheets. The RGI was developedin a short (1–2 year) period with limited resources in order tomeet the needs of the Fifth Assessment Report (AR5) of theIntergovernmental Panel on Climate Change (IPCC) forestimates of recent and future glacier mass balance. Prioritywas given to complete coverage rather than to extensivedocumentary detail. The rationale of the RGI (Casey, 2003;Cogley, 2009a; Ohmura, 2009) is that fewer attributes and, ifunavoidable, locally reduced accuracy of delineation(Section 3) would be a price worth paying for the complete

coverage that is essential for global-scale assessments.However, of the few attributes attached to each RGI vectoroutline, some are analytically valuable. For example theterminus-type attribute and the complete coverage combineto yield the first estimate of the global proportion oftidewater glaciers.

Complete coverage is desirable for a broad range ofglobal-scale investigations. Volume–area scaling (e.g. Bahrand others, 1997) and other procedures to derive glaciervolume (e.g. Farinotti and others, 2009; Linsbauer andothers, 2012) require detailed knowledge of glacier areas,and in some cases a digital elevation model (DEM) as well.Glacier outlines are needed in geodetic estimation of

Journal of Glaciology, Vol. 60, No. 221, 2014 doi: 10.3189/2014JoG13J176 537

volume changes by comparison of DEMs and altimetry, andof mass changes after the application of appropriatecorrections (e.g. Berthier and others, 2010; Gardner andothers, 2011). Glacier outlines are also crucial for theinitialization of projections of glacier mass change (e.g.Gregory and Oerlemans, 1998; Bahr and others, 2009;Marzeion and others, 2012; Radić and others, 2014).

1.1. HistoryA global glacier inventory was first proposed, in the form ofnational lists of glaciers, during planning for the Inter-national Geophysical Year 1957–58. Progress was initiallyslow, but accelerated under the leadership of F. Müllerduring the 1970s, when the product became known as theWorld Glacier Inventory (WGI). The status of the WGI wasassessed by the World Glacier Monitoring Service (WGMS,1989). A digital version has been available since 1995 fromthe National Snow and Ice Data Center (NSIDC), Boulder,Colorado, USA (http://nsidc.org/data/glacier_inventory/). Itcombined the WGI with the Eurasian Glacier Inventory ofBedford and Haggerty (1996) but still covered only �25% ofthe global glacierized area. The extended WGI-XF inventoryof Cogley (2009a) increased the global coverage to �48%.Incompleteness aside, the WGI and its variants do notinclude glacier outlines, making them difficult to use for theglobal assessment of glacier change. A different initiativewas launched in 1995 as GLIMS (Global Land Ice Measure-ments from Space). GLIMS, led initially by H.H. Kieffer andlater by J.S. Kargel, is a consortium of regional investigatorscontributing glacier outlines in a digital vector format (Raupand others, 2007; Kargel and others, in press). The GLIMSinventory, which has an extensive set of attributes, hasgrown steadily, but it too remains incomplete. In mid-2013,its coverage was �58% of global glacierized area, althoughit has multitemporal coverage of several thousand glaciers.

Thus, half a century of work has yielded only two globallycomplete digital depictions of glacier extent: GGHYDRO(Cogley, 2003), a raster product compiled in the early 1980s,and the vectors of the Digital Chart of the World (DCW) ofDanko (1992). Like DCW, GGHYDRO is highly general-ized, but it has been used in several global-scale assessments(e.g. Raper and Braithwaite, 2006; Hock and others, 2009;Radić and Hock, 2010). Other assessments (e.g. Meier andBahr, 1996; Ohmura, 2004; Dyurgerov and Meier, 2005;Raper and Braithwaite, 2005) have relied on separateindirect estimates of global aggregate information. Not allassessments have included the peripheral glaciers aroundthe two ice sheets.

In recognition of the importance of baseline informationfor the assessment of glacier changes, the idea of a completeinventory of the world’s glaciers has been strongly endorsedby international organizations such as the World Meteoro-logical Organization (2004).

Free access to the US Geological Survey’s archive ofLandsat imagery (Wulder and others, 2012) has bothstimulated and facilitated the mapping of glacier extent atregional and broader scales. The failure of the scan-linecorrector of Landsat 7’s Enhanced Thematic Mapper Plus(ETM+) sensor in May 2003 (Markham and others, 2004)limited the subsequent usefulness of that archive, but othersensors have offered a further stimulus by making possible,by stereographic or interferometric mapping, the construc-tion of DEMs and thus the subdivision of ice bodies alongdrainage divides and the calculation of topographic and

hypsometric glacier attributes. These products include theAdvanced Spaceborne Thermal Emission and ReflectionRadiometer (ASTER) global DEM and the DEM of the ShuttleRadar Topography Mission of February 2000. Their advan-tages and disadvantages are discussed by Frey and Paul(2012). In addition, several collaborative internationalprojects, such as GlobGlacier (Paul and others, 2009a) andGlaciers_cci (Paul and others, 2012), have facilitatedinventory-related data collection and production. Finally,the most immediate stimulus for rapid assembly of the RGIwas the need for a globally complete inventory to meet thegoals of the recently completed contribution of WorkingGroup I to AR5 of the IPCC.

The RGI was planned by an ad hoc group that assembledfour times between December 2010 and December 2011.One of the meeting venues, Randolph, New Hampshire,USA, gave its name to the resulting product. The members ofthe group pooled material already in their possession andapproached other colleagues individually, and solicitedadditional contributions from the Cryolist (http://cryolist.org)and GLIMS communities and other sources. More than60 colleagues from 18 countries contributed, making theRGI a truly global initiative.

1.2. Distribution of the RGIThe RGI is distributed through the GLIMS/NSIDC website(http://www.glims.org/RGI/randolph.html), and is accom-panied by technical documentation (Arendt and others,2013). To allow some contributors time to report their ownresults, access was initially granted on the understandingthat the inventory be used only for purposes related to AR5.This constraint has now been removed. It is intended that indue course the RGI outlines will be merged into the GLIMSdatabase. Planning of this merger is in progress (Raup andothers, 2013).

The version described here, RGI 3.2, reflects the correc-tion of topological, georeferencing and interpretative errorsdetected in RGI 1.0, released in February 2012, and RGI 2.0,released in June 2012, as well as the application of qualitycontrols described in Section 2. Versions 3.0 and 3.1 wereinterim releases. Version 3.2 substitutes improved outlines ina number of regions, adds a small number of outlines frompreviously omitted regions, and features a uniform set ofattributes (Section 2.4). Auxiliary information includes out-lines of a set of 19 first-order and 89 second-order regions(see Section 2.3 and the Supplementary Information (http://www.igsoc.org/hyperlink/13j176/13j176supp.pdf) which in-cludes maps showing the second-order regions).

2. METHODS AND QUALITY CONTROL2.1. Glacier outline generation and data sourcesParts of the RGI were compiled at several institutions, but theinventory was assembled in its entirety at the University ofAlaska Fairbanks. The earliest version consisted of DCWoutlines, which were then replaced by outlines from GLIMSwhere available. Further additions of new or replacementoutlines from contributors were assimilated, checked and ifnecessary revised using standard vector-editing tools. Alloutlines were transformed when necessary to the WorldGeodetic System 1984 (WGS84) datum.

Most of the glacier outlines in the RGI with known dateswere derived from satellite imagery acquired in 1999 orlater. Various Landsat platforms, principally Landsat 5 TM

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and Landsat 7 ETM+, were the primary sources, and imageryfrom ASTER, IKONOS and SPOT 5 high-resolution stereo(HRS) sensors was also used. In northernmost Greenland, theRGI draws on the ice mask of the Greenland MappingProject (Howat and others, 2014). For most regions auto-matic or semi-automatic routines were used to map glaciersbased on the distinctive spectral reflectance signatures ofsnow and ice in simple and normalized band-ratio maps(e.g. Le Bris and others, 2011). Some outlines wereproduced by manual digitizing on satellite imagery or onscanned digital copies of maps.

Glacier complexes (unsubdivided ice bodies) weresubdivided into glaciers either by visual identification offlow divides or with semi-automated algorithms for detec-tion of divides in a DEM (Bolch and others, 2010; Kienholzand others, 2013). These algorithms use standard watersheddelineation tools to build a preliminary map of ice‘flowsheds’ which are then merged, based on chosenthresholds for the proximity of their termini, to form glaciers.The Bolch algorithm was applied in western Canada, parts ofAlaska, Greenland and parts of High Mountain Asia. TheKienholz algorithm was developed, calibrated and quality-checked in Alaska and Arctic Canada South, and wasapplied without quality assessment in several other regions,where errors due to subdivision will be governed primarilyby the quality of the DEM.

Some of our source inventories have been published onlyrecently, including Nuth and others (2013) for Svalbard,Rastner and others (2012) for Greenland, and Bliss andothers (2013) for the Antarctic and Subantarctic. These threesources represent 5%, 12% and 18% of global glacier extentrespectively. During assembly of the RGI, many GLIMScontributions of restricted extent were replaced by outlinesfrom sources with more extensive coverage. However, theRGI outlines in British Columbia, the Caucasus and Icelandcome entirely, and those in China mostly, from GLIMS, andthere are also limited contributions in Central Asia andAlaska. GLIMS has therefore contributed at least 10% of theRGI coverage. The RGI still draws on the DCW for �1% ofglobal ice extent in northern Afghanistan, Tajikistan andKyrgyzstan. In the Pyrenees, Iran and parts of North andCentral Asia, the RGI records ‘nominal glaciers’ for whichonly location and area are available. They are representedby circles of the area reported in the WGI or WGI-XF, andaccount for

(Section 4.2), and in the Antarctic and Subantarctic, but notyet in other regions, it distinguishes between glaciers and icecaps. The GlacType attribute implements table 1 of Paul andothers (2009b), although only the terminus-type code isassigned values (land-, marine-, lake- or shelf-terminating) inRGI 3.2. As yet, lake-terminating glaciers have been dis-tinguished from land-terminating glaciers only in Alaska, theSouthern Andes and Antarctica. First- and second-orderregion numbers are given by O1Region and O2Region. Amore precise location is given by the longitude CenLon andlatitude CenLat of the glacier; these attributes also appear inGLIMSId. Most of the locations are centroids of glacierpolygons; �1% lie outside their polygons. The glacier area is

given by Area, which is calculated in Cartesian coordinateson a cylindrical equal-area projection of the authalic sphereof the WGS84 ellipsoid, or, for nominal glaciers, is acceptedfrom the source inventory. The date of the source (Fig. 2) isgiven by BgnDate if it is known, or by a range BgnDate,EndDate. About 45% of the glaciers by number, accountingfor �25% of global glacierized area, lack date information.

3. ACCURACY3.1. Data qualityAccurate recognition of glaciers in satellite imagery can bechallenging (Paul and others, 2013). Maps can be similarlydifficult to work with, and sometimes offer additionaldifficulties such as poor georeferencing and inadequatedatum information. The accuracy of the result depends onthe resolution and quality of the source imagery or map, thescale at which the analyst traces the glacier outlines, and hisor her skill at identifying glacier surfaces consistently (e.g.Bhambri and Bolch, 2009).

The glacier outlines in Figure 3 are from RGI 3.2 and aresuperimposed on Google Earth images. In each panel theoutlines are of inadequate quality and need substantialreworking. Those in Figure 3a are at the correct locations,but many are too large by >50%. The outlines include ice-free glacier forefields, mountain rock walls, and rockglaciers. The outlines in Figure 3b are rather precise on theeast side of the image and north of the drainage divide, butglaciers south of it and in the west are either missing,represented by nominal circles from the WGI, or digitized ina highly generalized way. In Figure 3c the outlines showsome generalization, with missing nunataks, but the largestproblem is the wrong geolocation. There is a systematic shiftof all outlines relative to the satellite image in thebackground (which also shows seasonal snow cover, apotential cause of misinterpretation). Figure 3d illustrates theomission of many glaciers, and also outlines that arewrongly placed and highly generalized.

Fig. 2. Frequency distribution of known dates and date ranges in theRGI by glacierized area (red line) and glacier number (yellowhistogram). Glaciers with date ranges are assigned with uniformprobability to each year of the range. Undated glaciers are notrepresented. Those in China date from the 1970s and 1980s and inAntarctica from the 1960s to 2000s. Most other undated glaciers areknown to have been measured on Landsat ETM+, ASTER or SPOT5imagery, i.e. of the late 1990s or later.

Fig. 1. First-order regions of the RGI, with glaciers shown in red. Region numbers are those of Table 2. Cylindrical equidistant projection.

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The problems illustrated here are addressed in Sections3.2 and 3.3. Mislocated outlines have only limited effect onmeasurements of glacier area, but can introduce seriouserrors into procedures that rely on absolute positioning (e.g.co-registration to other datasets such as DEMs). Unlessmislocations can be traced to systematic (and correctable)errors (e.g. due to misprojection or to errors in datum trans-formations), the only realistic way to correct them is to supplymore accurate outlines. One highly anticipated source ofimproved outlines is the second Chinese Glacier Inventory.

3.2. An error modelBecause the sources of the RGI outlines are so diverse, it isnot practical to estimate uncertainties source by source.Instead, we develop a simple model of uncertainty in glacierarea, drawing on published estimates that have uncertaintiesattached. Small areas are harder to measure accurately thanlarge areas (Fig. 4) because the measurement error tends tobe inversely proportional to the length of the glacier margin.Estimates for collections of glaciers would ideally be calcu-lated by summing the errors of glacier complexes rather thanthose of individual glaciers, because errors at flow dividessum to zero when both sides of the divide are included.However, Figure 4 shows no clear difference betweensingle-glacier and multiple-glacier errors. We fit a power

law by least squares to the relationship between fractionaluncertainty and glacier area and assume as a first guess thatthis captures the contributions of inaccurate treatment ofdebris cover, omission of nunataks, inclusion of seasonalsnow, and inaccurate mapping. This yields the relation

eðsÞ ¼ ke1sp ð1Þbetween the error e and area s (both in km2); p is 0.70, ande1 = 0.039 is the estimated fractional error in a measured areaof 1 km2. The correction factor k is discussed in Section 3.3.For each glacier in the RGI, the standard error is thearithmetic sum of e(s) as calculated for the glacier and all ofits nunataks. Regional uncertainties presented below are alsosimple sums of all single-glacier area errors, which weexpect, consistent with the simple reasoning that leads to Eqn(1), to be fully correlated.

3.3. A case study: South AmericaIn earlier versions of the RGI, various factors obliged us toselect satellite images of South America that in some casesshowed extensive seasonal snow. Because it is time-consuming, the visual inspection needed to correct auto-matically generated outlines for the seasonal snow was notalways possible. In RGI 3.2 we have corrected much of theresulting overestimate of glacier area by identifying images

Fig. 3. Images illustrating difficulties in the estimation of glacierized area for four regions in High Mountain Asia: (a) Hindu Kush,(b) northern Tien Shan, (c) Karakoram and (d) eastern Nyainqentanglha. All outlines are overlaid on Google Earth satellite images.

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of better quality, but because seasonal snow was apparent inall available images our estimated ice cover in SouthAmerica, 31 670 km2, may still be too large. There is noauthoritative estimate for comparison; WGMS (1989) givesestimates for the South American nations which sum to25 900 km2, but they are mostly undated and all involvesome guesswork for unsurveyed regions.

We therefore assembled repeated measurements of theglacierized area of 37 South American subregions from theliterature and compared them to corresponding sums of areasof RGI glaciers (Fig. 5). Where possible, we interpolatedlinearly from the bracketing dates of the independentlymeasured areas from the literature to the date of the RGIimage covering the subregion; where necessary, we extrapo-lated linearly, by from 0.5 to 9 years. (See www.igsoc.org/hyperlink/13j176/13j176supp.pdf for references.)

In five subregions where there are multiple independentmeasurements, the measurements differ, with standarddeviations ranging from 1.2% to as great as 38%. Afteraveraging these multiple measurements, the deviations�= (S – Sind)/Sind between RGI areas S and independentareas Sind range from –68% (–63 km

2) to +149% (+150 km2).The RGI total, 25 842 km2, is 5.4% greater than theindependent total, 24 506 km2, and the mean and medianof the absolute deviations |�| are 30% and 13% respectively.

We conclude that area errors of tens of percent or moreare possible in either or both of the RGI and the independent

areas. Equation (1) (with k=1) probably represents the bestcase, in which researchers have estimated uncertaintiesexplicitly, although in a variety of ways. We are aware offurther biases, due to one or more of the problems illustratedin Figure 3, in regions other than South America. Forexample, Gardelle and others (2013) found an averagediscrepancy in area of only –1.1% between RGI 2.0 andseven of their eight SPOT 5 scenes in South Asia, but in theeighth, in southeastern Tibet, RGI 2.0 has an extent 88%greater than their estimate. Many RGI contributions, notablyin Alaska, Arctic Canada North and South, Greenland,Svalbard, the Russian Arctic, Central Europe and parts ofSouth Asia West, are significantly more accurate than suchworst-case examples. However, for the RGI as a whole wedo not know how strongly the probability distribution of theerrors is skewed by gross errors, so it seems prudent to scaleEqn (1), which captures an important part of the errorstructure, to reflect the likely non-normality of the errordistribution. The sample of errors in Figure 4 and the sampleof differences in Figure 5 both fail tests for goodness of fit tothe normal distribution. Departures from normality are notsurprising if we consider the difficulty of correcting forerratically variable seasonal snow, and equivalently thedifficulty of recognizing debris-covered ice. It is also truethat widely variable methods reduce the comparability ofuncertainties estimated by different analysts.

How to correct Eqn (1) is not obvious, because the mainsources of uncertainty, being heterogeneous across regionsand data sources, are not amenable to handling withconventional statistics. Moreover, although Figure 5 suggeststhat Eqn (1) is likely to underestimate the uncertainty, afurther consideration is that we have applied Eqn (1) toglaciers, rather than to glacier complexes as would bepreferable because of the cancellation of errors at divides; atest calculation based on glacier complexes in the RussianArctic yielded an area error of only �1.2% as against �2.8%for glaciers. As an interim measure pending further investi-gation, we set the correction factor k in Eqn (1) to 3 (anumber chosen arbitrarily).

4. RESULTS AND APPLICATIONS OF THEINVENTORY4.1. Glacier numbersThe RGI contains outlines for �198 000 glaciers. We stressthat the total number of glaciers in the world, or in anyregion, is an arbitrary quantity. The fineness of subdivision ofglacier complexes into glaciers varies from region to region,influenced by particular objectives and available resources.For example, glacier complexes in the Antarctic aresubdivided less finely than elsewhere. Moreover, subdivisionof ice bodies can produce doubtful results when the bestavailable DEM or map is poor; glaciers tend to fragment in awarming climate; and the total number is strongly deter-mined by the choice of minimum-area threshold (Bahr andRadić, 2012).

The spatial resolution of the source image or map willimpose a minimum-area threshold for the identification ofglaciers, but the choice of threshold is also influenced by theavailability of resources. The labour needed for manual digi-tizing or for visual correction of automatically obtained out-lines increases dramatically as the area threshold is reduced,and the time available for compiling a typical regionalinventory can easily be overwhelmed by large numbers of

Fig. 4. Published estimates of the uncertainty of area measurementsof single glaciers (diamonds) and collections of glaciers (dots). Seehttp://www.igsoc.org/hyperlink/13j176/13j176supp.pdf for a list ofsources. Solid line: best-fitting relationship between measured areaand its standard error from Eqn (1) in text (with k=1). Dashed line:relationship adopted for estimation of RGI errors (from Eqn (1)with k=3).

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http://www.igsoc.org/hyperlink/13j176/13j176supp.pdf

very small glaciers. For example, in their glacier inventory ofBritish Columbia (not part of the RGI), Schiefer and others(2008) adopted a threshold of 0.1 km2 and an additionalthreshold based on glacier elevation ranges. By comparisonsto subregional inventories with lower thresholds, theyestimated that their thresholds excluded �69% of the actualglaciers by number, but only�7% of total glacierized area. InSection 4.2 we discuss the impact of omission of glaciers onthe RGI estimates of glacier numbers and areas.

4.2. Glacier areasThe total glacierized area in the RGI is 726 800 km2 (Table 2).The region with the most ice is the Antarctic andSubantarctic (132 900 km2), followed by Arctic CanadaNorth with 104 900 km2. The regions with the least ice arethe Caucasus and Middle East (1140 km2) and New Zealand(1160 km2). Of the total extent, 44% is in the Arctic regions(Arctic Canada North, Arctic Canada South, Greenland,Svalbard and Russian Arctic) and 18% in the Antarctic andSubantarctic. High Mountain Asia (i.e. Central Asia, SouthAsia West and South Asia East) accounts for 16% and Alaska

for 12%. The uncertainty in total regional area depends onthe distribution of glacier areas, which we discuss below.According to Eqn (1), regional uncertainty is as small as�1.9% in the Antarctic and Subantarctic, where Bliss andothers (2013) assumed an uncertainty of �5%, and exceeds�10% in five regions with relatively small glaciers.

The total ice-covered area in RGI 3.2 is similar to earlierestimates (Table 3). Excluding Greenland and Antarctica, thelargest deviation from the RGI in Table 3 is +7% (the areasdetermined by Meier and Bahr (1996) and Dyurgerov andMeier (2005)), consistent with an uncertainty of the order of�3.5%. The analysis in Section 3, however, suggests asomewhat greater uncertainty. The RGI area is the smallestof the entries. This is partly because some of the earlierestimates include 6000–8000 km2 of ice in the Subantarctic,which in the RGI is included in the Antarctic andSubantarctic region and thus excluded from this column ofthe table. Glacier shrinkage between older and newersources of regional information may also account for someof the difference. When the Greenland and Antarcticglaciers are included, earlier estimates differ from the RGI

Fig. 5. A comparison of RGI glacierized areas S for subregions in South America with equivalent measurements Sind from independentstudies (listed at http://www.igsoc.org/hyperlink/13j176/13j176supp.pdf). The subregions, their approximate latitudes and independentlyobtained glacierized areas (km2) are listed at left. Orange dots: independent measurements (up to three per region); blue dots: averages ofindependent measurements (as listed at left, but uniformly zero in the graph); red crosses: RGI measurements (horizontal error bars aresmaller than symbol size for most subregions).

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by as much as –6% and +8%. These differences mainlyreflect substantial improvement in the sources of informa-tion. Rastner and others (2012) identified considerably moreperipheral ice in Greenland than most previous investiga-tions, and Bliss and others (2013) relied mainly on theAntarctic Digital Database (ADD Consortium, 2000), a moreaccurate source than those of earlier estimates.

Tidewater glaciers account for 39% of the total RGIextent. This statistic, reported earlier by Gardner and others(2013) using RGI 3.0, is valuable because it is the firstentirely measurement-based estimate of the potential im-portance of tidewater glaciers among the world’s glaciers. Itappears to confirm that the under-representation of calvingglaciers and frontal ablation in mass-balance measurementprogrammes is more serious than calculated by Cogley(2009b), in whose dataset only 3% by area of the glacierswith glaciological measurements were tidewater glaciers.Including glaciers with geodetic measurements, the propor-tion rose to 16%, still well below the new RGI estimate.

In contrast to ice-sheet modelling, most modelling ofglacier mass balance necessarily focuses on the climatic

mass balance because the modelling of frontal losses due todynamics is relatively underdeveloped, and more seriouslybecause basic information required for the assessment ofpossible rapid dynamic responses of tidewater glaciers isonly starting to be collected. Concrete evidence of rapiddynamic response is provided by Burgess and others (2013)for Alaska, where only 14% of the region’s glacier area isdrained through tidewater outlets, but calving accounts for36% of the regional loss rate. We expect that improvedestimates of the extent of tidewater glaciers will aid in thedetermination of dynamical mass losses.

The proximity of glaciers in Greenland and Antarctica tothe ice sheets requires care to avoid double-counting oromission by different groups working on large-scale cryo-spheric analyses. For example, gravimetric measurementsof mass change by the Gravity Recovery and ClimateExperiment (GRACE) satellites have low spatial resolutionand are unable to distinguish the peripheral glaciers fromthe ice sheets. In Antarctica the implications of proximityare reduced, but not eliminated, because the RGI excludesthe mainland. In Greenland the RGI includes all theperipheral glaciers, each of which is assigned a connectiv-ity level CL (through RGIFlag; see Table S2 (http://www.igsoc.org/hyperlink/13j176/13j176supp.pdf)). Thethree values of CL represent glaciers that are unconnected,weakly connected or strongly connected to the ice sheet;the basis and methodology for these assignments areexplained by Rastner and others (2012). We follow theirsuggestion that the strongly connected CL2 glaciers, withan extent of 40 400 km2, be regarded as part of theGreenland ice sheet, yielding an area of 1 718000 km2

for the ice sheet including the CL2 glaciers. (The CL2glaciers are nevertheless included in the RGI shapefile forGreenland.) The areas of the CL0 (unconnected;65 500 km2) and CL1 (weakly connected; 24 200 km2)glaciers sum to 89 700 km2.

Table 2. Summary of the RGI, Version 3.2

All glaciers Tidewater glaciers Nominal glaciers

Region Number Area Error* Area Number Area

km2 % km2 km2

01 Alaska 26944 86715 5.3 11781 0 002 Western Canada and US 15215 14559 9.5 0 0 003 Arctic Canada North 4538 104873 3.2 49111 0 004 Arctic Canada South 7347 40894 4.9 3030 0 005 Greenland Periphery 19323 89721 5.0 31106 0 006 Iceland 568 11060 2.6 0 0 007 Svalbard and Jan Mayen 1615 33922 3.5 14884 0 008 Scandinavia 2668 2851 9.3 0 4

The frequency distribution of glacier areas is shown inFigure 6. Acknowledging that some very small glaciers maybe omitted by virtue of the imposition of minimum-areathresholds, it is nevertheless clear that the area distribution isdominated by large glaciers. Glaciers with areas between�4 and �1500 km2 account for two-thirds of the ice cover.

The distribution of standard errors in Figure 6 is based onEqn (1) with k=3 (Fig. 4). As follows from the form of Eqn (1)and the frequency distribution of the areas, smaller glacierscontribute more to the total uncertainty than do largerglaciers. In the smallest-size bin of Figure 6 the standarderror of total bin area implied by Eqn (1) is 44%, while in thelargest-size bin it is 0.9%. Of the uncertainty of 4.7%(34 000 km2) in the total area, two-thirds is contributed byglaciers with areas between �1 and �500 km2.

Figure 7 shows cumulative frequency distributions ofglacier areas separated by first-order region. In seven regionsthe 50th percentile of the distribution (the ‘median area’) issmaller than 2 km2. The median area ranges upward to181 km2 in Arctic Canada North, 351 km2 in Iceland and706 km2 in Antarctica. In every region the mean area iscon siderably less than the median area, reflecting theprominence of smaller glaciers in the size distribution ofglacier numbers.

With the possible exceptions of Central Europe and theAntarctic and Subantarctic, the regional size distributions ofglacier numbers (Fig. 8) exhibit a common pattern in whichnumbers increase steeply from the smallest-size classtowards a maximum, typically between 0.25 and 1 km2.Beyond this inflection, numbers decrease at a rate that varieslittle from region to region, with a tendency to steepentowards the largest-size class.

The inflection is due, to an unknown extent, to under-recording of glacierets and other very small glaciers,whether because of the imposition of a minimum-area

threshold, or coarse DEM resolution, or for other reasons.Over-recording, by systematic misidentification of small (asopposed to large) snowpatches, is also possible. If weneglect this possibility because we are unable to quantify it,a lower bound on the underestimate due to under-recordingis obtained by assuming that the RGI always distinguishescorrectly between glacierets and snowpatches: no glacieretsare omitted, and the underestimate is zero. An upper boundcan be obtained by assuming that inverse power-law scaling,as suggested by the form of the size–frequency curves atlarger sizes (Fig. 8), continues in reality down to the smallestsizes. For each region (inset of Fig. 8) we extrapolate the lineconnecting the most numerous class and its next largerneighbour, and sum the quantity s� [nx(s) – nRGI(s)] from theWGI minimum, smin = 0.01 km

2, to sx, the size of the mostnumerous class. Here nx(s) is the extrapolated estimate of the‘real’ number and nRGI(s) is the number of RGI glaciers ofsize s. The result is 10 180 km2 if we sum the 19 extrapo-lates, and 7819 km2 if we sum the regional glacier numbersand repeat the calculation for the entire world. Thus theunderestimate of global total area due to omission ofglacierets appears to lie between zero and an upper boundof 1.1–1.4%. The upper-bound calculations also imply alarge increase in the total number of glaciers over thatsuggested in Section 4.1, to 462 000 for the region-by-regioncalculation and to 435 000 for the global calculation.

4.3. Glacier hypsometryAmong others (Huss and Farinotti, 2012; Marzeion andothers, 2012), Radić and others (2014) have generated thehypsometry (area–altitude distribution) of each glacier from

Fig. 6. Frequency distributions of glacier areas (histogram; left axis)and standard errors (connected dots; right axis). The continuous lineshows the cumulative frequency distribution of areas (left axis, to bemultiplied by 10).

Fig. 7. Cumulative frequency distributions of glacier areas for theRGI regions. Coloured numerals: region numbers (see Table 2).Open circles: percentiles of the mean glacier areas. Regionnumbers: 01. Alaska; 02. Western Canada and US; 03. ArcticCanada North; 04. Arctic Canada South; 05. Greenland Periphery;06. Iceland; 07. Svalbard; 08. Scandinavia; 09. Russian Arctic;10. North Asia; 11. Central Europe; 12. Caucasus and Middle East;13. Central Asia; 14. South Asia West; 15. South Asia East; 16. LowLatitudes; 17. Southern Andes; 18. New Zealand; 19. Antarctic andSubantarctic.

Pfeffer and others: The Randolph Glacier Inventory 545

the RGI outlines and regional-scale DEMs. Figure 9a showsthe total hypsometry of each region. Most of the world’sglacier area is below 2000m, with comparatively littlebetween 2500m and 3500m, where only Central Europeand the Caucasus and Middle East have hypsometric modes.The mid- and low-latitude glaciers of Central Asia, South AsiaEast, South Asia West and the Low Latitudes have most oftheir area above 4000m. The spikes in the curves for ArcticCanada North, Arctic Canada South and Antarctica are DEMartefacts arising from poor interpolation from contour maps.

In Figure 9b, each glacier’s elevation range is normalizedfrom 0 to 100 (i.e. its minimum elevation is set to 0 and itsmaximum to 100) and its total area is normalized to beequal to 100. Then all glaciers in each region are averagedwithout weighting, resulting in a typical, size-independenthypsometric curve for the glaciers of the region. Thesenormalized, regionally averaged hypsometries exhibit threecharacteristic patterns. In most regions, most glaciers havemost of their area in the middle of their elevation ranges,with less on steep high-elevation slopes and in narrow low-elevation tongues. In the Antarctic and Subantarctic, thepredominance of tidewater glaciers skews the curve to lowernormalized elevations. In Arctic Canada North, ArcticCanada South, the Russian Arctic and Greenland, the typicalhypsometry is skewed to higher normalized elevations byice caps on high-elevation plateaus with relatively restrictedlow-elevation tongues.

4.4. Glacier volume and massWorking with WGI-XF (Cogley, 2009a), Radić and Hock(2010) produced an upscaled estimate, 600� 70mmSLE(sea-level equivalent), of global glacier volume includingglaciers around the ice sheets. This estimate was obtained byvolume–area scaling (Bahr and others, 1997). One estimaterelying on RGI 1.0, and three on RGI 2.0, have appeared

recently (Table 4). Radić and others (2014) reduced theearlier Radić–Hock estimate to 522mmSLE. Most of thereduction was ascribed to the availability of the RGI, whichmade upscaling unnecessary. An alternative assumptionabout the prevalence of ice caps, used by Radić and others(2014) as part of their assessment of uncertainty, made theglobal total lower still, at 405mmSLE. Marzeion and others(2012), working with RGI 1.0, modelled regional icevolumes by volume–area scaling, but because they excludedglaciers in Antarctica the global total suggested in Table 4 fortheir work, 468mmSLE, is a rough estimate and is notindependent of the three other estimates. Huss and Farinotti(2012) estimated a total of 423� 57mmSLE, based on asimple model of the glacier dynamics implied by thedistribution of glacier surface elevations. Grinsted (2013),relying on a multivariate extension of volume–area scalingand on a different minimization criterion during calibrationof the volume–area relationship, estimated a lesser total of350� 70mmSLE.

The range of the estimates of total mass in Table 4 is 28%of their average (412mmSLE). Explaining this spread isbeyond the present scope, but some of the dispersion can beattributed to the rapid evolution of the RGI. For example,glaciers should not be aggregated for volume–area scaling,so glacier complexes, which were more numerous in RGI1.0 and 2.0, dilute the accuracy of three of the estimates.Moreover, except in the Antarctic and Subantarctic, evenRGI 3.2 offers no way to distinguish mountain and valleyglaciers from ice caps. Volume–area scaling exponents forice caps, whose thickness is large by comparison with therelief of their beds, are known to differ from those ofmountain and valley glaciers (Bahr and others, 1997). Thestudies differed in their handling of this difference of scalingbehaviour. The work of Huss and Farinotti may be lessaffected than the other three, although they noted that in

Fig. 8. Size distributions of the number of glaciers for the RGI regions. Inset: an illustration of the argument for an upper bound on the RGIarea (represented by the shaded region) that is missing due to the omission of glacierets.

Pfeffer and others: The Randolph Glacier Inventory546

Arctic Canada South, where separate sets of outlines wereavailable for glaciers and glacier complexes, the totalvolume obtained by modelling the glaciers was 7% lessthan that obtained by modelling the complexes.

Bahr and Radić (2012) show that, for some purposes,glaciers in the smallest size classes of a given sample cancontain a significant fraction of the total volume of thesample. They suggest that, at the global scale, an accuracy involume of �1% requires inclusion of all glaciers larger than1 km2. At regional scales the threshold for a given desiredaccuracy depends on the size distribution. Where the largestglacier is of the order of 100 km2, total regional volume canonly be estimated to �10% or better if the minimum-areathreshold is 0.01 km2 or smaller. In some regions, therefore,more accurate estimates of volume will require morecomplete inventories of the smallest glaciers.

4.5. Glacier climatologyFigure 10, less detailed versions of which have appeared inCogley (2012) and Huss and Farinotti (2012), illustrates howa complete inventory can add value to information fromother sources. The figure summarizes a wealth of glacio-climatic information, showing for example that at any

latitude glaciers with lower mid-range altitudes tend to bewarmer or more ‘maritime’. Maritime glaciers descend tolower altitudes than their more ‘continental’ counterparts,because more summer heat is required to remove the massgained during their snowy winters. Conversely, continentalglaciers tend to be dry and to persist only at higher, colderaltitudes. Assuming that the mid-range altitude approxi-mates the equilibrium-line altitude (which is not correct fortidewater glaciers), Figure 10 shows that the equilibrium lineor snowline is not an isotherm: its temperature variesglobally and at some single latitudes over a range of �208C.

Mernild and others (2013) extend the analysis shown inFigure 10. They use RGI glacier outlines to enable themodelling of Northern Hemisphere glacier freshwaterbalances driven by reanalysis data for annual time spans.

4.6. Evolution of glacier mass balanceGardner and others (2013) used RGI 3.0 in the estimation ofglobal average mass balance for 2003–09, a period forwhich estimates by interpolation of in situ measurements,satellite gravimetry and satellite laser altimetry are concur-rently available. Results from in situ and remote-sensingmethods differed significantly, but the RGI eliminated what

Fig. 9. Area–elevation distributions of the RGI regions. (a) Distribution of regional glacierized area with elevation. (b) Distribution ofnormalized glacierized area with normalized elevation, with the idealized approximations of Raper and Braithwaite (2006) drawn as dottedlines (triangle for mountain glaciers, representative curved line for ice caps); the normalizations are explained in the text. Arctic CanadaNorth and South, Greenland and the Russian Arctic (thick solid curves), and the Antarctic and Subantarctic (thick dashed curve), arediscussed in the text.

Pfeffer and others: The Randolph Glacier Inventory 547

was formerly the primary source of such differences, namelythe previously differing estimates of regional glacier extent.

Giesen and Oerlemans (2013) used RGI 1.0 to scalesingle-glacier mass-balance projections up to global scaleover the 21st century, while Hirabayashi and others (2013)used information derived from RGI 1.0 to simulate world-wide climatic mass balance from 1948 to 2099. Marzeionand others (2012) and Radić and others (2014) havepublished more detailed mass-balance modelling studiesapplied to each single RGI glacier, from versions 1.0 and 2.0respectively. In each case the role of the RGI outline was tomake possible the initialization of glacier area and hencevolume, and the extraction of topography from a DEM. Bothstudies relied on the RGI regions as a standardized way topresent geographical variations. Both were obliged to dotheir own subdivision of glacier complexes, and to generatetopographic information by overlaying RGI outlines onDEMs. Marzeion and others, lacking dates for the outlines ofRGI 1.0, had to supply them by intelligent guesswork. Radićand others accepted RGI 2.0, which has better but stillincomplete date coverage, as a representation of ‘present-day’ glaciers. No global compilation of rates of area changeis available, and it is therefore not yet possible to interpolatemeasured areas, even when their dates are known, to anyparticular reference date.

5. SUMMARY AND OUTLOOKThe Randolph Glacier Inventory, a cooperative effort of thecommunity of glaciologists, is a globally complete compil-ation of digital vector outlines of the world’s glaciers otherthan the ice sheets. It contains outlines of nearly 200 000glaciers. Allowing for currently omitted glacierets the total

number of glaciers could be greater, easily exceeding400000, but the missing glacierets represent 1.4% or lessof the recorded glacierized area, and possibly much less.The total glacierized area is estimated as 726 800�34000 km2, although a number of questions remain abouthow best to calculate the uncertainty of this total. The areaof glaciers on islands surrounding the Antarctic mainland is132 900� 2520 km2 and the area of Greenland glaciersother than the ice sheet is 89 700� 4490 km2; the remainingglacierized regions contribute 504 200� 27 000 km2 tothe total.

The RGI is a much richer product than the national listsenvisaged half a century ago (Section 1.1), but is never-theless a work in progress. Outstanding tasks include:

Improving the quality of outlines where recent regionalinventories are likely to be more accurate. For example,recent work at the International Centre for IntegratedMountain Development, Kathmandu, Nepal (Bajra-charya and Shrestha, 2011), and in China, is known tohave better georeferencing and accuracy than RGI 3.2.

Providing outlines for �3000 glaciers, mostly in NorthAsia, for which only location and area are known atpresent.

Shortening the time span of coverage. Of the outlineswith dates, �8% pre-date 1999.

Replacing date ranges with dates and supplying datesthat are missing. Sometimes multiple images for aparticular glacier make a range necessary, but manysources provide information in a form not readilyconverted to explicit dates.

Table 4. Glacier masses (Gt)* estimated with the RGI

Regional glacier mass

Region Radić and others (2014){{ Marzeion and others (2012)§ Huss and Farinotti (2012){ Grinsted (2013){

01 Alaska 16 736 28021�2465 18379� 1341 16 16802 Western Canada and US 1148 1124�72 906�72 94203 Arctic Canada North 31 244 37555�4858 30958� 4241 22 36604 Arctic Canada South 6295 7540�544 8845� 1015 551005 Greenland Periphery 12 229 10005�1595 17146� 2392 17 03806 Iceland 2390 4640� 1595 3988�326 315407 Svalbard and Jan Mayen 6119 8011�580 8700�834 482108 Scandinavia 182 218 217 29009 Russian Arctic 11 016 21315�3625 15152� 1994 12 25210 North Asia 247 218�36 109 18111 Central Europe 125 109 109 10912 Caucasus and Middle East 61 72 72 7213 Central Asia 5465 5655�109 4531�435 859114 South Asia West 3413 3444�218 2900�254 344415 South Asia East 1623 1378�36 1196�109 148616 Low Latitudes 208 218 145 10917 Southern Andes 4606 4640�145 6018�471 424118 New Zealand 71 72 72 10919 Antarctic and Subantarctic 43 772 – 33749� 7576 27 224Total (Gt) 146949 (169 185)§ 153192�20699 126875� 25375Total (mmSLE) 405 (468) § 423�57 350� 70

*1Gt is 1.0/362.5mmSLE, 1.0 km3w.e. and (assuming a density of 900 kgm–3 for the glacier) 1.11. . . km3 ice equivalent.{Using RGI 2.0.{Their quantity V*.§Using RGI 1.0. Modelled for a nominal date of 2009, and adjusted here by taking the area of the ocean to be 362.5�106 km2. Ice in region 19 was notmodelled and the entries in the Total rows have been augmented by the average of the three region-19 estimates.

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Distinguishing between land-terminating and lake-terminating glaciers. So far, lake-terminating glaciershave been identified as such in only three regions.

Adding outlines of the debris-covered parts of glaciers.

Adding information about topography and hypsometry.Given the importance of early availability, it was decidednot to try to include this information in the RGI. Severalinvestigators have already exploited the RGI to retrievedetail from DEMs, but a standardized product (Paul andothers, 2009b) of acknowledged uniform quality wouldreduce uncertainty arising from the use of differenttopographic sources, and would allow ice caps to beidentified by examination of longitudinal profiles ofsurface elevation.

Documenting more completely the data sources and themethods used to delineate glaciers.

Merging the RGI into GLIMS. This is an organizationalmatter requiring forethought.

The RGI was not designed for the measurement of rates ofglacier area change, for which the greatest possibleaccuracies in dating, delineation and georeferencing areessential. Many RGI outlines pass this test, but in generalcompleteness of coverage had higher priority. Rather, thestrength of the RGI lies in the capacity it offers for handlingmany glaciers at once (e.g. for estimating glacier volumesand rates of elevation change at regional and global scalesand for simulating cryospheric responses to climatic forcing).

Finally, much remains to be done in the investigation ofuncertainty. Our error model (Eqn (1)) incorporates someunderstanding of the sources of uncertainty, but does notaddress the non-normality of the errors arising from thediversity of information sources and methods of analysis.Further, the problem of uncertainty in glacier volume, at allscales from the single glacier to the world, is intractable.Measurements of volume are few, and understanding of therelationships between volume and the observable quantitiesis limited. Nevertheless uncertainty in glacier volume is aproblem of wider significance than uncertainty in glacierarea, and deserves continued attention.

Fig. 10. Zonal averages of the mean temperature of the warmest month at the glacier mid-range altitude (the average of the minimum andmaximum glacier altitude). RGI glaciers with minimum altitudes of zero, assumed to be tidewater glaciers, were discarded. The mid-rangealtitude of each remaining glacier was estimated by overlaying its outline on a suitable DEM (see Radić and others, 2014, for details). Thealtitudes were averaged firstly into cells of size 50m� 0.58� 0.58 and then over longitude. Temperatures are warmest-month free-airtemperatures interpolated from the US National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research(NCAR) Reanalysis (Kalnay and others, 1996).

Pfeffer and others: The Randolph Glacier Inventory 549

ACKNOWLEDGEMENTSWe thank D. Bahr for helpful discussions, and an anony-mous reviewer for a constructive review. Support forplanning of the RGI, and for meetings in Winter Park,Colorado, and Randolph, New Hampshire, USA, wasprovided by the International Association for CryosphericSciences and the International Arctic Science Committee.K.M. Cuffey kindly provided the venue and helped toarrange a meeting in Berkeley, California, USA. We aregrateful for support from NASA’s Cryospheric ResearchBranch (grants NNX11AF41G to C.K., NNX13AK37G toA.A. and J.R., NNX11AO23G to A.B.); the US GeologicalSurvey’s Alaska Climate Science Center and the US NationalPark Service (grant H9911080028 to A.A. and J.R.); theEuropean Space Agency (Glaciers_cci project 4000101778/10/I-AM to T.B. and F.P.); the ice2sea programme (EuropeanUnion 7th Framework Programme grant No. 226375 to P.R.;ice2sea contribution No. 100); the German ResearchFoundation (DFG, grant BO 3199/2-1 to T.B.); the USNational Science Foundation (grants ANT1043649 and EAR0943742 to R.H.); the Austrian Science Fund (FWF; grantsI900-N21 and P25362-N26 to G.K.); and the NaturalSciences and Engineering Research Council of Canada(Discovery Grant) and Environment Canada (to M.S.).

REFERENCESADD Consortium (2000) Antarctic Digital Database, Version 3.0,

Database, Manual and Bibliography. Version 4.1, digital data.Scientific Committee on Antarctic Research, Cambridge

Arendt A and 77 others (2013) Randolph Glacier Inventory [v3.2]: adataset of global glacier outlines. Global Land Ice Measurementsfrom Space, Boulder, CO. Digital media: http://www.glims.org/RGI/randolph.html

Bahr DB and Radić V (2012) Significant contribution to total massfrom very small glaciers. Cryosphere, 6(4), 763–770 (doi:10.5194/tc-6-763-2012)

Bahr DB, Meier MF and Peckham SD (1997) The physical basis ofglacier volume–area scaling. J. Geophys. Res., 102(B9),20 355–20 362 (doi: 10.1029/97JB01696)

Bahr DB, Dyurgerov M and Meier MF (2009) Sea-level rise fromglaciers and ice caps: a lower bound. Geophys. Res. Lett., 36(3),L03501 (doi: 10.1029/2008GL036309)

Bajracharya SR and Shrestha B eds (2011) The status of glaciers inthe Hindu Kush–Himalayan region. International Centre forIntegrated Mountain Development, Kathmandu

Bedford D and Haggerty C (1996) New digitized glacier inventoryfor the former Soviet Union and China. Earth Syst. Monitor, 6(3),8–10

Berthier E, Schiefer E, Clarke GKC, Menounos B and Rémy F (2010)Contribution of Alaskan glaciers to sea-level rise derived fromsatellite imagery. Nature Geosci., 3(2), 92–95 (doi: 10.1038/ngeo737)

Bhambri R and Bolch T (2009) Glacier mapping: a review withspecial reference to the Indian Himalayas. Progr. Phys. Geogr.,33(5), 672–704 (doi: 10.1177/0309133309348112)

Bliss A, Hock R and Cogley JG (2013) A new inventory of mountainglaciers and ice caps for the Antarctic periphery. Ann. Glaciol.,54(63 Pt 2), 191–199 (doi: 10.3189/2013AoG63A377)

Bolch T, Menounos B andWheate R (2010) Landsat-based inventoryof glaciers in western Canada, 1985–2005. Remote Sens.Environ., 114(1), 127–137 (doi: 10.1016/j.rse.2009.08.015)

Burgess EW, Forster RR and Larsen CF (2013) Flow velocities ofAlaskan glaciers. Nature Commun., 4, 2146 (doi: 10.1038/ncomms3146)

Casey A (2003) Papers and recommendations: Snow Watch2002 Workshop and Workshop on Assessing Global Glacier

Recession. (Glaciological Data GD-32) National Snow andIce Data Center and World Data Center A for Glaciology,Boulder, CO

Cogley JG (2003) GGHYDRO: Global Hydrographic Data, Release2.3. (Trent Technical Note 2003-1) Department of Geography,Trent University, Peterborough, Ont. http://people.trentu.ca/~gcogley/glaciology/gghrls231.pdf

Cogley JG (2009a) A more complete version of the World GlacierInventory. Ann. Glaciol., 50(53), 32–38 (doi: 10.3189/172756410790595859)

Cogley JG (2009b) Geodetic and direct mass-balance measure-ments: comparison and joint analysis. Ann. Glaciol., 50(50),96–100 (doi: 10.3189/172756409787769744)

Cogley JG (2012) The future of the world’s glaciers. In Henderson-Sellers A and McGuffie K eds. The future of the world’s climate.Elsevier, Waltham, MA, 197–222

Danko DM (1992) The Digital Chart of the World project.Photogramm. Eng. Remote Sens., 58(8), 1125–1128

Dyurgerov MB and Meier MF (2005) Glaciers and the changingEarth system: a 2004 snapshot. (INSTAAR Occasional Paper 58)Institute of Arctic and Alpine Research, University of Colorado,Boulder, CO

Farinotti D, Huss M, Bauder A, Funk M and Truffer M (2009) Amethod to estimate ice volume and ice-thickness distribution ofalpine glaciers. J. Glaciol., 55(191), 422–430 (doi: 10.3189/002214309788816759)

Frey H and Paul F (2012) On the suitability of the SRTM DEM andASTER GDEM for the compilation of topographic parameters inglacier inventories. Int. J. Appl. Earth Obs. Geoinf., 18, 480–490(doi: 10.1016/j.jag.2011.09.020)

Gardelle J, Berthier E, Arnaud Y and Kääb A (2013) Region-wideglacier mass balances over the Pamir–Karakoram–Himalayaduring 1999–2011. Cryosphere, 7(4), 1263–1286 (doi: 10.5194/tc-7-1263-2013)

Gardner AS and 8 others (2011) Sharply increased mass loss fromglaciers and ice caps in the Canadian Arctic Archipelago.Nature, 473(7347), 357–360 (doi: 10.1038/nature10089)

Gardner AS and 15 others (2013) A reconciled estimate of glaciercontributions to sea level rise: 2003 to 2009. Science,340(6134), 852–857 (doi: 10.1126/science.1234532)

Giesen RH and Oerlemans J (2013) Climate-model induceddifferences in the 21st century global and regional glaciercontributions to sea-level rise. Climate Dyn., 41(11–12),3283–3300 (doi: 10.1007/s00382-013-1743-7)

Gregory JM and Oerlemans J (1998) Simulated future sea-level risedue to glacier melt based on regionally and seasonally resolvedtemperature changes. Nature, 391(6666), 474–476 (doi:10.1038/35119)

Grinsted A (2013) An estimate of global glacier volume.Cryosphere, 7(1), 141–151 (doi: 10.5194/tc-7-141-2013)

Hirabayashi Y, Zang Y, Watanabe S, Koirala S and Kanae S (2013)Projection of glacier mass changes under a high-emissionclimate scenario using the global glacier model HYOGA2.Hydrol. Res. Lett., 7(1), 6–11 (doi: 10.3178/hrl.7.6)

Hock R, De Woul M, Radić V and Dyurgerov M (2009) Mountainglaciers and ice caps around Antarctica make a large sea-levelrise contribution. Geophys. Res. Lett., 36(7), L07501 (doi:10.1029/2008GL037020)

Howat IM, Negrete A and Smith BE (2014) The Grenland IceMapping Project (GIMP) land classification and surface elevationdatasets. Cryos. Discuss., 8(1), 1–26 (doi: 10.5194/tcd-8-1-2014)

Huss M and Farinotti D (2012) Distributed ice thickness and volumeof all glaciers around the globe. J. Geophys. Res., 117(F4),F04010 (doi: 10.1029/2012JF002523)

Kalnay E and 21 others (1996) The NCEP/NCAR 40-year reanalysisproject. Bull. Am. Meteorol. Soc., 77(3), 437–471 (doi: 10.1175/1520-0477(1996)0772.0.CO;2)

Kargel JS, Leonard GJ, Bishop MP, Kääb A and Raup B eds. (in press)Global Land Ice Measurements from Space. Springer Praxis,Chichester

Pfeffer and others: The Randolph Glacier Inventory550

Kienholz C, Hock R and Arendt A (2013) A new semi-automaticapproach for dividing glacier complexes into individual glaciers.J. Glaciol., 59(217), 925–937 (doi: 10.3189/2013JoG12J138)

Le Bris R, Paul F, Frey H and Bolch T (2011) A new satellite-derivedglacier inventory for western Alaska. Ann. Glaciol., 52(59),135–143 (doi: 10.3189/172756411799096303)

Linsbauer A, Paul F and Haeberli W (2012) Modeling glacierthickness distribution and bed topography over entire mountainranges with GlabTop: application of a fast and robust approach.J. Geophys. Res., 117(F3), F03007 (doi: 10.1029/2011JF002313)

Markham BL, Storey JC, Williams DL and Irons JR (2004) Landsatsensor performance: history and current status. IEEE Trans.Geosci. Remote Sens., 42(12), 2691–2694 (doi: 10.1109/TGRS.2004.840720)

Marzeion B, Jarosch AH and Hofer M (2012) Past and future sea-level change from the surface mass balance of glaciers.Cryosphere, 6(6), 1295–1322 (doi: 10.5194/tc-6-1295-2012)

Meier MF and Bahr DB (1996) Counting glaciers: use of scalingmethods to estimate the number and size distribution of glaciersof the world. CRREL Spec. Rep. 96-27, 89–94

Mernild SH, Liston GE and Hiemstra CA (2013) Individual andregional glacier and ice cap surface mass balance and runoffmodeling for the Northern Hemisphere. Geophys. Res. Abstr.,15, EGU2013-02900

Nuth C and 7 others (2013) Decadal changes from a multi-temporalglacier inventory of Svalbard. Cryos. Discuss., 7(3), 2489–2532(doi: 10.5194/tcd-7-2489-2013)

Ohmura A (2004) Cryosphere during the twentieth century. InSparling JY and Hawkesworth CJ eds. The state of the planet:frontiers and challenges in geophysics. American GeophysicalUnion, Washington DC, 239–257

Ohmura A (2009) Completing the World Glacier Inventory. Ann.Glaciol., 50(53), 144–148 (doi: 10.3189/172756410790595840)

Paul F, Kääb A, Rott H, Shepherd A, Strozzi T and Volden E (2009a)GlobGlacier: a new ESA project to map the world’s glaciers andice caps from space. EARSeL eProc., 8(1), 11–25

Paul F and 18 others (2009b) Recommendations for the compilationof glacier inventory data from digital sources. Ann. Glaciol.,50(53), 119–126 (doi: 10.3189/172756410790595778)

Paul F, Bolch T, Kääb A, Nagler T, Shepherd A and Strozzi T (2012)Satellite-based glacier monitoring in the ESA project Glaciers_c-ci. In Proceedings of the International Geoscience and RemoteSensing Symposium (IGARSS), 23–27 July 2012, Munich,Germany. Institute of Electrical and Electronics Engineers,Piscataway, NJ, 3222–3225

Paul F and 18 others (2013) On the accuracy of glacier outlinesderived from remote-sensing data. Ann. Glaciol., 54(63 Pt 1),171–182 (doi: 10.3189/2013AoG63A296)

Radić V and Hock R (2010) Regional and global volumes ofglaciers derived from statistical upscaling of glacier inventorydata. J. Geophys. Res., 115(F1), F01010 (doi: 10.1029/2009JF001373)

Radić V, Bliss A, Beedlow AC, Hock R, Miles E and Cogley JG(2014) Regional and global projections of twenty-first centuryglacier mass changes in response to climate scenarios fromglobal climate models. Climate Dyn., 42(1–2), 37–58 (doi:10.1007/s00382-013-1719-7)

Raper SCB and Braithwaite RJ (2005) The potential for sea level rise:new estimates from glacier and ice cap area and volumedistributions. Geophys. Res. Lett., 32(5), L05502 (doi: 10.1029/2004GL021981)

Raper SCB and Braithwaite RJ (2006) Low sea level rise projectionsfrom mountain glaciers and icecaps under global warming.Nature, 439(7074), 311–313 (doi: 10.1038/nature04448)

Rastner P, Bolch T, Mölg N, Machguth H, Le Bris R and Paul F(2012) The first complete inventory of the local glaciers and icecaps on Greenland. Cryosphere, 6(6), 1483–1495 (doi: 10.5194/tc-6-1483-2012)

Raup B and Khalsa SJS (2007) GLIMS analysis tutorial. http://www.glims.org/MapsAndDocs/assets/GLIMS_Analysis_Tutorial_a4.pdf

Raup B and 11 others (2007) Remote sensing and GIS technology inthe Global Land Ice Measurements from Space (GLIMS)Project. Comput. Geosci., 33(1), 104–125 (doi: 10.1016/j.cageo.2006.05.015)

Raup B, Arendt A, Armstrong R, Barrett A, Khalsa SJS andRacoviteanu A (2013) GLIMS and the RGI: relationships presentand future. Geophys. Res. Abstr., 15, EGU2013-11831

Schiefer E, Menounos B and Wheate R (2008) An inventoryand morphometric analysis of British Columbia glaciers,Canada. J. Glaciol., 54(186), 551–560 (doi: 10.3189/002214308785836995)

Sedov RV (1997) Ledniki Chukotki [Glaciers of Chukotka].Mater. Glyatsiol. Issled. 82, 213–217 [in Russian with Englishsummary]

World Glacier Monitoring Service (WGMS) (1989) World glacierinventory: status 1988, ed. Haeberli W, Bösch H, Scherler K,Østrem G, and Wallén CC. IAHS(ICSI)–UNEP–UNESCO, WorldGlacier Monitoring Service, Zürich

World Meteorological Organization (WMO) (2004) Implemen-tation plan for the Global Observing System for climate insupport of the UNFCCC. (WMO/TD No. 1219) World Meteoro-logical Organization, Geneva

Wulder MA, Masek JG, Cohen WB, Loveland TR and Woodcock CE(2012) Opening the archive: how free data has enabled thescience and monitoring promise of Landsat. Remote Sens.Environ., 122, 2–10 (doi: 10.1016/j.rse.2012.01.010)

APPENDIXMembers of the Randolph ConsortiumL.M. Andreassen,1 S. Bajracharya,2 N.E. Barrand,3 M.J. Beedle,4 E. Berthier,5 R. Bhambri,6 I. Brown,7 D.O. Burgess,8 E.W.Burgess,9 F. Cawkwell,10 T. Chinn,11 L. Copland,12 N.J. Cullen,13 B. Davies,14 H. De Angelis,7 A.G. Fountain,15 H. Frey,16

B.A. Giffen,17 N.F. Glasser,14 S.D. Gurney,18 W. Hagg,19 D.K. Hall,20 U.K. Haritashya,21 G. Hartmann,22 S. Herreid,9 I.Howat,23 H. Jiskoot,24 T.E. Khromova,25 A. Klein,26 J. Kohler,27 M. König,27 D. Kriegel,28 S. Kutuzov,25 I. Lavrentiev,25 R. LeBris,16 X. Li,29 W.F. Manley,30 C. Mayer,31 B. Menounos,32 A. Mercer,7 P. Mool,2 A. Negrete,23 G. Nosenko,25 C. Nuth,33 A.Osmonov,34 R. Pettersson,35 A. Racoviteanu,36 R. Ranzi,37 M.A. Sarıkaya,38 C. Schneider,39 O. Sigurðsson,40 P. Sirguey,13

C.R. Stokes,41 R. Wheate,32 G.J. Wolken,42 L.Z. Wu,29 F.R. Wyatt43

1Norwegian Water Resources and Energy Directorate, Oslo, Norway2International Centre for Integrated Mountain Development, Kathmandu, Nepal

3University of Birmingham, Birmingham, UK4University of Northern British Columbia, Terrace, British Columbia, Canada

5Laboratoire d’Etudes en Géophysique et Océanographie Spatiales, Toulouse, France6Wadia Institute of Himalayan Geology, Dehradun, India

7Stockholm University, Stockholm, Sweden8Geological Survey of Canada, Ottawa, Ontario, Canada

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9University of Alaska Fairbanks, Fairbanks, AK, USA10University College Cork, Cork, Ireland

11National Institute of Water and Atmospheric Research, Dunedin, New Zealand12University of Ottawa, Ottawa, Ontario, Canada13University of Otago, Dunedin, New Zealand14University of Aberystwyth, Aberystwyth, UK15Portland State University, Portland, OR, USA

16University of Zürich, Zürich, Switzerland17National Park Service, Anchorage, AK, USA

18University of Reading, Reading, UK19Ludwig Maximilians University, Munich, Germany20Goddard Space Flight Center, Greenbelt, MD, USA

21University of Dayton, Dayton, OH, USA22Alberta Geological Survey, Edmonton, Alberta, Canada

23The Ohio State University, Columbus, OH, USA24University of Lethbridge, Lethbridge, Alberta, Canada

25Institute of Geography, Russian Academy of Sciences, Moscow, Russia26Texas A&M University, College Station, TX, USA

27Norwegian Polar Institute, Tromsø, Norway28German Research Centre for Geosciences, Potsdam, Germany

29Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou, China30University of Colorado, Boulder, CO, USA

31Commission for Geodesy and Glaciology, Bavarian Academy of Sciences, Munich, Germany32University of Northern British Columbia, Prince George, British Columbia, Canada

33University of Oslo, Oslo, Norway34Central Asian Institute of Applied Geosciences, Bishkek, Kyrgyzstan

35University of Uppsala, Uppsala, Sweden36Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble, France

37University of Brescia, Brescia, Italy38Fatih University, Istanbul, Turkey

39RWTH Aachen University, Aachen, Germany40Icelandic Meteorological Office, Reykjavı́k, Iceland

41Durham University, Durham, UK42Alaska Division of Geological and Geophysical Surveys, Fairbanks, AK, USA

43University of Alberta, Edmonton, Alberta, Canada

MS received 6 September 2013 and accepted in revised form 26 January 2014

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