Worldwide version-controlled database of glacier thickness observations
Although worldwide inventories of glacier area have been coordinated internationally for several decades, a similar effort for glacier ice thicknesses was only initiated in 2013. Here, we present the third version of the Glacier Thickness Database (GlaThiDa v3), which includes 3 854 279 thickness measurements distributed over roughly 3000 glaciers worldwide. Overall, 14 % of global glacier area is now within 1 km of a thickness measurement (located on the same glacier) – a significant improvement over GlaThiDa v2, which covered only 6 % of global glacier area and only 1100 glaciers. Improvements in measurement coverage increase the robustness of numerical interpolations and model extrapolations, resulting in better estimates of regional to global glacier volumes and their potential contributions to sea-level rise.
In this paper, we summarize the sources and compilation of glacier thickness data and the spatial and temporal coverage of the resulting database. In addition, we detail our use of open-source metadata formats and software tools to describe the data, validate the data format and content against this metadata description, and track changes to the data following modern data management best practices. Archived versions of GlaThiDa are available from the World Glacier Monitoring Service (e.g., v3.1.0, from which this paper was generated: https://doi.org/10.5904/wgms-glathida-2020-10; GlaThiDa Consortium, 2020), while the development version is available on GitLab (https://gitlab.com/wgms/glathida, last access: 9 November 2020).
A central challenge of glaciology is assessing the distribution and total ice volume of the world's glaciers. Increasingly detailed and globally complete inventories of the world's glaciers (WGMS and NSIDC, 2012; GLIMS and NSIDC, 2018; RGI Consortium, 2017) have been compiled with great effort over the last few decades. However, these inventories have been limited to glacier extent and surface elevation. The Glacier Thickness Database (GlaThiDa), launched by the World Glacier Monitoring Service (WGMS, https://wgms.ch, last access: 9 November 2020) and supported by the International Association of Cryospheric Sciences (IACS) Working Group on Glacier Ice Thickness Estimation (https://cryosphericsciences.org/activities/ice-thickness, last access: 9 November 2020), complements these existing efforts by compiling and publishing a freely accessible database of glacier thickness observations (Gärtner-Roer et al., 2014).
Knowing the thickness of glacier ice is critical for predicting the rate and timing of glacier retreat and disappearance, the subsequent effects on local and regional hydrologic cycles and global sea level, and the associated environmental and social impacts. As the only worldwide repository of its kind, GlaThiDa plays an important role in local, regional, and global studies of glaciers, glacier ice volumes, and their potential sea-level rise contributions (e.g., Thorlaksson, 2017; Farinotti et al., 2017, 2019; Meyer et al., 2018; Fischer, 2018; Ayala et al., 2019; Werder et al., 2020). The Ice Thickness Models Intercomparison eXperiment (ITMIX; Farinotti et al., 2017) only used GlaThiDa v1 (WGMS, 2014) to calibrate one of the participating models, but it helped garner support and data for GlaThiDa v2 (WGMS, 2016). GlaThiDa v2 was subsequently used to calibrate all participating models and evaluate model performance for an ensemble-based estimate of the thicknesses of all glaciers on Earth (Farinotti et al., 2019).
GlaThiDa v3 represents a major step forward for the database. We have
more than doubled the spatial coverage and more than quadrupled the
number of observations relative to v2, released in 2016, adding 3 million thickness measurements either submitted by researchers
(46 % of new measurements) or imported from the IceBridge
Data Portal (54 %, https://nsidc.org/data/icebridge, last access: 28 August 2019).
In addition to summarizing the spatial and temporal coverage of the
database, we present a case study on how simple open-source metadata
formats and software tools can be used to implement modern data
management practices. In the sections that follow, we describe a
development environment for data – based on universal text-based file
formats and the distributed version control system
(Chacon and Straub, 2014) – that maximizes data access and interoperability,
automatically tracks and archives every change made to the dataset,
continuously validates the structure and contents of the data, and
facilitates dialogue with (and bug reports by) data users. A monospace
font is used throughout the manuscript for software packages
git), files (e.g.,
T), database table fields
POINT_LAT), and code samples (e.g., Fig. 3).
2.1 Data sources
2.1.1 Data compilation
Since the release of GlaThiDa v1 in 2014 (Gärtner-Roer et al., 2014), which focused on gathering glacier mean and maximum thickness estimates from published literature, a large number of thickness measurements have been submitted by members of the research community in response to two calls for data, one for version 2 in 2016 and another for version 3 in 2018. In all, researchers from institutions in Europe, North and South America, Oceania, and Asia have contributed data from Antarctica, Africa (Kenya, Tanzania), Asia (China, Georgia, India, Kazakhstan, Kyrgyzstan, Mongolia, Nepal, Russia, Tajikistan), Europe (Austria, Germany, Greenland, Svalbard, Iceland, Italy, Norway, Sweden, Switzerland), Oceania (New Zealand), North America (Canada, United States), and South America (Bolivia, Chile, Colombia, Peru).
Alongside these data submissions, airborne glacier thickness profiles collected by National Aeronautics and Space Administration (NASA) Operation IceBridge were retrieved from the corresponding National Snow and Ice Data Center (NSIDC) data portals. Since these campaigns were primarily focused on the Greenland and Antarctic ice sheets, only measurements within Randolph Glacier Inventory (RGI 6.0) glacier outlines (RGI Consortium, 2017) were included in GlaThiDa. These replaced the IceBridge data, located within Randolph Glacier Inventory (RGI 3.2) glacier outlines (RGI Consortium, 2013), included in GlaThiDa v1 in 2014.
2.1.2 Measurement methods
The surveys in GlaThiDa span the history of glacier ice thickness measurement and thus include several different survey methods, summarized in Table 1 and illustrated in Fig. 1. The most direct methods involve excavating or drilling through the ice. Although these typically produce a precise measurement, they do so only for a single point and at a great expense of time and money; correspondingly, these account for only 0.35 % of surveys in GlaThiDa (here, a “survey” roughly represents one measurement campaign on one glacier). The drilling surveys added in v3 (compiled by Fürst et al., 2018a, Table S3) were carried out in Svalbard in the 1970s through 1990s to map the thermal structure of glaciers (e.g., Jania et al., 1996) or to extract paleoclimate records (e.g., Kotlyakov et al., 2004). No drilling surveys were added in v2.
More often, ice thickness is measured indirectly using geophysical methods. For example, seismic soundings employ the propagation properties of elastic waves to determine the structure of the subsurface (described in Susstrunk, 1951). Although common in the 1950s through 1980s, they are expensive and time-consuming to collect; correspondingly, these account for only 0.84 % of surveys in GlaThiDa. No seismic surveys were added in v3; of those already in the database, the majority were carried out in the Austrian Alps up until the 1970s (reviewed in Aric and Brückl, 2001). Only one seismic survey – of Tasman Glacier, New Zealand, from 1971 (Anderton, 1975) – was added in v2.
The most common geophysical method is radar (i.e., radio detection and ranging, also known as “radio-echo sounding” or “ground-penetrating radar”), which is based on the transmission, reflection, and subsequent detection of radio waves (reviewed in Schroeder et al., 2020). Radar measurements can be collected quickly, from the ice surface or from airborne platforms, and account for 98.44 % of surveys in GlaThiDa. Of the radar surveys added in v3, a majority are provided by NASA Operation IceBridge (Koenig et al., 2010), which sponsored a series of airborne radar platforms: the Multichannel Coherent Radar Depth Sounder (MCoRDS; Paden et al., 2011, 2018; Shi et al., 2010), High-Capability Airborne Radar Sounder (HiCARS; Blankenship et al., 2017a, b), Pathfinder Advanced Radar Ice Sounder (PARIS; Raney, 2010), and Warm Ice Sounding Explorer (WISE; Rignot et al., 2013a, b). Other large additions in v3 include terrestrial and aerial radar surveys in Svalbard (compiled by Fürst et al., 2018a, Table S2) from the early 1980s (e.g., Dowdeswell et al., 1984, 1986) to the present day (e.g., Martín-Español et al., 2013; Navarro et al., 2014; Lindbäck et al., 2018), as well as helicopter-borne radar surveys in the Swiss Alps (Rutishauser et al., 2016). Large additions in v2 include extensive terrestrial radar surveys of glaciers in the Italian and Austrian Alps (Fischer et al., 2015a, b).
Geophysical techniques less commonly used for measuring glacier ice thickness include geoelectric (e.g., electrical resistivity tomography) and electromagnetic (e.g., magnetotellurics, controlled-source induction) methods which invert variations in electrical resistivity with depth to map the subsurface. The only examples of these in the database (0.04 %), added in v1, are helicopter electromagnetic surveys of two Cascade Range volcanoes (Finn et al., 2012). The methods of the remaining surveys (0.33 %) are unknown because the original source is either not known or cannot be found. All studies included in GlaThiDa are acknowledged in the database; the studies cited above are only provided as examples.
2.2 Data package structure
Packaging of data is as important as the data themselves. This includes the physical representation of the data within files, the design of the metadata that describes the data, and the distribution of the data package to prospective users. Without proper packaging, data are much less likely to achieve their full potential. The approach described in the following sections implements (and extends) the FAIR guiding principles: scientific data should be Findable, Accessible, Interoperable and Reusable for both machines and people (Wilkinson et al., 2016). Our implementation, built on simple text files, was designed to meet the following criteria:
compatible with line-based version control systems like
gitto automatically track and store changes (Blischak et al., 2016), facilitate collaboration between multiple authors (Ram, 2013), and continuously release new versions as the dataset evolves over time (Rauber et al., 2015);
described according to existing metadata standards to facilitate data interpretation, validation, and future contributions (Fowler et al., 2017), as well as reuse by software applications like the Global Terrestrial Network for Glaciers (GTN-G) data browser (http://www.glims.org/maps/gtng, last access: 9 November 2020).
2.2.1 Data (
The data are structured as three relational database tables ordered in
increasing level of detail (Fig. 2). The
first, overview table (
T) contains information on the location,
identity, and area of the surveyed glacier; the survey method used; and
details about the authors and sources of the data. Glacier mean and
maximum thickness, estimated from point measurements, are also included
when available from data providers. The second table (
contains any mean and maximum thicknesses, estimated from point
measurements, for surface elevation bands. Although rare, some ice
thickness surveys are only available as surface elevation band
estimates, their point measurements having been lost or never published.
The third table (
TTT) contains point thickness measurements.
All tables include a survey identifier (
GlaThiDa_ID, unique in
T) that links entries between the tables, as well as a country
POLITICAL_UNIT) and glacier name
GLACIER_NAME), which are replicated in
TTT as a convenience to users. Structural changes since
GlaThiDa v1, described in the changelog (Sect. 2.2.4),
have been limited to adding fields (e.g.,
PROFILE_ID in v3 to
group point measurements by survey profile) and renaming fields
ELEVATION_DATE in v3 to clarify
that provided surface elevations need not be from a digital elevation
Following FAIR principles, the three tables are stored as CSV (comma-separated values) files, a universally supported text format for representing tabular data. To maximize machine readability, the files do not contain any nondata content other than a single header line with field names. Data documentation is performed by a separate metadata file, described below.
2.2.2 Metadata (
Each table field is described in turn, including its name and
description, the data type it represents (string, integer, or floating
point number), and any constraints on the values it can take
(e.g., whether a value is required, falls within a numeric range, or
matches a search pattern). In the example in Fig. 4, the description informs users that the field
values are stored in the data files with “up to seven decimal places”,
while the pattern
(a regular expression conforming, as required by the Frictionless Data specification, to the XML Schema syntax; Biron and Malhotra, 2004)
makes possible an automated test that this is indeed the case.
Finally, relations within and between tables are defined following
relational database nomenclature. A unique key is a field (or set of
fields) whose values must be unique for each row in the table. Unique
keys can be stored in other tables (where they are called “foreign
keys”) to link the tables together. In the example in Fig. 5, each row of table
measurements) is uniquely identified by the combination of a survey
GlaThiDa_ID), profile identifier
PROFILE_ID), and point identifier (
of these point measurements is linked to the corresponding row in table
T (survey overviews) by the value of its survey identifier
GlaThiDa_ID), along with the replicated fields for country
POLITICAL_UNIT) and glacier name
2.2.3 Documentation (
The data package is fully described in
the JSON format may not be familiar or welcoming to some users.
Therefore, we automatically generate a more human-readable version from
the contents of
datapackage.json. The resulting
README.md is a text file structured with Markdown, a
widely supported markup language (Gruber, 2004). As a result, it is
both easy to read as plain text and readily converted to other formats
such as HTML (Hypertext Markup Language) or PDF (Portable Document
Format). The choice of file formats increases user access while their
shared JSON origin eliminates the risks and overhead associated with
manually maintaining multiple files.
2.2.4 History (
All notable changes made to the data or metadata are recorded in a
chronological list formatted in Markdown,
includes the update or removal of existing data records, additions of
new data records, and changes to the file structure or data schema. The
goal is to provide a variety of user groups with important information
about the history of the dataset. Future maintainers can review past
changes, developers can evaluate whether and how to update their
processing chain based on structural changes, and users can discover
what data have been added or updated since the last version.
2.3 Product development cycle
The Glacier Thickness Database (GlaThiDa) is a community effort that grows as more data are collected. For an evolving dataset like ours, the ability to revise and review collaboratively – to track changes and share those changes with others – is of great benefit to the communities that contribute to, maintain, and use the data. The development environment should therefore support the following activities:
receive, review, and discuss issues with the dataset from and with the community;
automatically track all changes made to the dataset by a distributed team of contributors;
continuously validate the dataset as changes are made;
To achieve the goals listed above, we have adopted tools, widely used
for open-source software development, for open-data development. In our
case, the dataset is stored as a file repository managed by the
distributed version control system
git and hosted on GitLab
(https://gitlab.com, last access: 9 November 2020), an open-source equivalent of GitHub
(https://github.com, last access: 9 November 2020), the popular online platform for
collaborative software development. The underlying
tracks changes (or “commits”), while GitLab provides interactive tools
for garnering and managing input from the community. “Issues” – which
can be posted by anyone with a free account – track bug reports,
feature requests, and other community dialogue. “Releases” tag a
snapshot of the dataset, at any stage in the development cycle, as a
numbered version. These snapshots can then be assigned a DOI and placed
in a scientific data repository for citing and safekeeping.
Version control systems like
git are line based; that is, they
track changes to text files on a line-by-line basis. Storing all data
and metadata as text files, rather than binary files, allows us to
automatically track all changes to the dataset. When fixes are made to
existing data records, the change consists of the updated lines. When
new records are added, the change consists of the appended lines. In
this way, we avoid making a new copy of a file each time a change is
made. Only versions published for download by users are compressed to a
binary format to reduce bandwidth.
The project follows the Semantic Versioning Specification
(Preston-Werner, 2013) for software, adapted for data. Given a
major version is
incremented for new data, the
minor version is incremented for
changes to existing data, and the
patch version is incremented
for changes to metadata only.
Note that our versioning scheme does not communicate compatibility with
downstream software dependencies, which is the primary purpose of
semantic versioning. A proposed software-oriented alternative
(Pollock and Walsh, 2017) is to increment the
major version for
incompatible changes (e.g., field removed, field constraint made more
minor version for backwards-compatible
changes (e.g., data added, field constraint made less restrictive), and
patch version for backwards-compatible fixes (e.g., fix data
errors, update field description). However, we believe our data-oriented
versioning is better aligned with our users and contributors, who are
primarily concerned with the addition of new data following each call
2.3.3 Schema validation
A major benefit of describing the data with machine-readable metadata
datapackage.json) is the ability to automatically
validate the data against this description. This includes relations
between tables, uniqueness within tables, and whether field values match
field types and constraints: for example, whether dates match the
expected format (
YYYYMMDD) and latitude and longitude are
numbers within the allowed limits ( and ).
Furthermore, by using a standard format for the metadata, we can
automatically validate the metadata itself against this standard.
That said, all metadata standards have their limits; they cannot express
all the possible constraints we may wish to impose on our data. In
addition to tests of single fields, validation of GlaThiDa includes
tests across multiple fields – for example, that country codes
POLITICAL_UNIT) and point measurements
TTT.POINT_LON) are spatially
consistent with the provided coordinates for the glacier center point
T.LON), or that survey method details
T.SURVEY_METHOD_DETAILS) are provided if the survey method
T.SURVEY_METHOD) is “other”. The latter test, implemented
in Python, is shown in Fig. 6 as an example.
Continuous integration (CI) is a standard software development practice wherein changes to the code are verified by automated tests to detect and fix issues as quickly as possible (Fowler, 2006). In our case, we use CI pipelines integrated into GitLab to automatically validate data and metadata whenever a change is made to the repository, catching issues early in the development cycle and, crucially, before the next release.
3.1 Spatial and temporal coverage
GlaThiDa v3 is the most comprehensive public database of glacier thickness measurements to date. We have added 3 million new thickness measurements relative to GlaThiDa v2, released in 2016 (Table 2). The new data, submitted by researchers or imported from the IceBridge data portal (https://nsidc.org/data/icebridge, last access: 28 August 2019), include glaciers in Antarctica, Alaska, Canada, China, Greenland, Kazakhstan, Norway, Svalbard, Switzerland, and Tanzania.
3.1.1 Spatial coverage
To evaluate the spatial coverage of GlaThiDa with respect to the world's roughly 217 000 glaciers (RGI Consortium, 2017), we assigned each survey to glaciers in the Randolph Glacier Inventory (RGI 6.0) by intersecting point measurements and nominal glacier centerpoints with RGI glacier outlines. The result is that 3054 RGI glaciers have thickness measurements in GlaThiDa (a large increase from 1133 RGI glaciers in version 2). Out of 5141 glacier surveys, only 11 (0.2 %) do not fall within an RGI glacier outline. Of these, most are for glaciers not included in RGI 6.0 – specifically, very small glaciers in the European Alps (Blauschnee and Glacier de Tsarmine, Switzerland; Schwarzmilzferner, Austria) and glaciers that may be considered part of the Antarctic Ice Sheet (Lambert Glacier, Starbuck Glacier, and Scharffenbergbotnen). The remaining do not intersect an RGI outline because either the corresponding RGI outline is incorrect (Nördlicher Schneeferner, Germany) or the glacier has retreated since the survey was conducted (Columbia Glacier, Alaska).
Figure 7 shows the coverage of intersected RGI glaciers on a world map. Table 3 lists the number and total area of intersected RGI glaciers by glacier region (GTN-G, 2017). While the proportion of intersected glaciers in a region is at most 14 % (region 7: Svalbard and Jan Mayen), the proportional area of intersected glaciers is much higher, up to 77 % (again, for Svalbard and Jan Mayen) – a result of larger glaciers being preferentially selected for measurement. The coverage in Svalbard is so high in v3 thanks to a recent regional compilation of available measurements (Martín-Español et al., 2015; Fürst et al., 2018b). The regions with the next best area coverage are those with substantial contributions from NASA Operation IceBridge: Arctic Canada, Antarctica, and Greenland. Despite these advances, large gaps persist in GlaThiDa, especially throughout Asia, the Russian Arctic, and the Andes. Poor coverage in these regions necessarily limits the quality of local and global glacier volume assessments and predictions of future change. Future efforts should be aimed at increasing spatial coverage and regional representation, both by performing new measurements and by conducting literature surveys and calls for data in underrepresented languages and regions of the world.
Overall, RGI glaciers with at least one thickness measurement account for 40 % (299 141 km2) of the global RGI area of 746 092 km2 (RGI Consortium, 2017). A better measure of data coverage is the area of a glacier that is within a certain distance of a thickness point measurement located on the same glacier. Globally, 36 % of the area of all surveyed glaciers (102 030 km2), and 14 % of global glacier area, is within 1 km of a measurement. Although this represents a significant improvement over GlaThiDa v2 (6 %), this nevertheless means that the thickness of the vast majority of global glacier area must still be estimated through extrapolation, scaling methods (reviewed in Bahr et al., 2015), or models (reviewed in Farinotti et al., 2017).
The spatial coverage of point thickness measurements varies greatly by glacier. While 14 % of glaciers in GlaThiDa with point measurements have more than 100 point measurements km−2 on average, 50 % have fewer than 18 points (Fig. 8). Although measurements are often sparse with respect to total glacier area, measurements tend to be well distributed across glacier surface elevations, since measurements are often collected along longitudinal profiles. Dividing each glacier into 100 m elevation bands (calculated from the surface elevations of point measurements in GlaThiDa and the minimum and maximum glacier surface elevations in RGI), 50 % of glaciers with point measurements have measurements in at least half of their elevation bands and 11 % have measurements in all of their elevation bands. Glaciers with few measurements but well distributed along their length can still be very useful for validating modeled ice thicknesses (Castellani, 2019), which are often computed along longitudinal ice-flow lines (reviewed in Farinotti et al., 2017).
3.1.2 Temporal coverage
The glacier thickness surveys in GlaThiDa span the years 1935–2018 (Table 1). This wide range of survey dates enables glaciers with repeat surveys (such as the example in Fig. 9) to be compared over time. However, it also complicates regional and global studies, which must account for thickness measurements spanning multiple years. Ideally, modeled ice thicknesses are evaluated against measured ice thicknesses coincident in time with the measured glacier outlines, surface elevations, and other time-varying parameters (e.g., surface mass balance, rates of ice thickness change, and surface velocities) used to initialize the model (Farinotti et al., 2017). For analysis spanning many glaciers (or all of the world's glaciers), it is not possible to ensure that all these data are coincident in time. For example, the median survey year for RGI glacier outlines is 2002, a decade earlier than the 2012 median for GlaThiDa surveys (Fig. 10), and the offset between surveys in GlaThiDa and their spatially coincident RGI outlines is 11–17 years (interquartile range). As for surface elevations, the majority (88 %) of point thickness measurements in GlaThiDa include corresponding surface elevations, 85 % of which were measured the same year as the ice thickness. When available, temporally coincident ice thickness and surface elevation measurements can be used to calculate the elevation of the glacier bed (independent of the survey date over decades to centuries) and thus the ice thickness relative to a glacier surface surveyed at any other time.
3.1.3 Future additions
Our intention is for GlaThiDa to continue to grow and improve as errors are found and fixed, new measurements are made, and more data are found or submitted. Several datasets already published in open-data portals (e.g., https://data.npolar.no, last access: 25 March 2020; https://pangaea.de, last access: 25 March 2020; https://arcticdata.io, last access: 25 March 2020; https://nsidc.org, last access: 25 March 2020, and https://www.usap-dc.org, last access: 25 March 2020) are slated for inclusion in a future version. However, these account for only a small number of the glacier thickness measurements still missing in GlaThiDa. For example, 460 glacier surveys pulled from the literature for v1 (Gärtner-Roer et al., 2014) are still missing the original point measurements from which the reported glacier-wide estimates were derived. An assessment of the Arctic data in GlaThiDa v2 by the Integrated Arctic Observation System (INTAROS, 2018) identified missing observations and concluded that pressure must continue to be placed on research groups to submit their data to the wider community. Ideally, all ice thickness surveys would be published in open-data portals and then added to GlaThiDa for complete coverage in a standard format.
To streamline data aggregation going forward, GlaThiDa may need to be
restructured such that data are primarily organized by campaign or
dataset rather than by glacier. The data tables were originally
designed to accommodate mean glacier thicknesses pulled from the
literature (Gärtner-Roer et al., 2014). Thus, each survey (i.e., each entry
T, and thus each
GlaThiDa_ID) is expected to
contain measurements gathered on one visit to one glacier – even though
each associated point (i.e., each entry in
TTT) is also encoded
with temporal and spatial coordinates. This data model complicates the
addition of large campaigns and introduces confusing redundancy to the
database. For example, the six datasets from Operation IceBridge had to
be split across 4124 glacier surveys by date and by
intersection with RGI glacier outlines.
Operation IceBridge, the source of 61 % of the thickness point measurements in GlaThiDa, is ending operation in 2020. The airborne mission was designed to avoid a gap in measurements between the ICESat satellite (2003–2009) and its successor ICESat-2, which was launched in 2018. However, the ICESat satellites only measure surface elevation and not ice thickness, therefore ending a decade-long ice thickness campaign. In the absence of a successor to Operation IceBridge, future updates to GlaThiDa may not include as many new measurements as the latest version. However, since RGI 6.0 does not include glaciers on the Antarctic Peninsula mainland (Huber et al., 2017) or in the McMurdo Dry Valleys (Frank Paul, personal communication, 2020), IceBridge data for those glaciers were not included in GlaThiDa v3; these remaining IceBridge data will be added in a future version.
3.2 Thickness uncertainties
The uncertainty of a glacier thickness measurement varies widely with the method used, the characteristics of the site, and the interpretation of the raw data (reviewed in Gärtner-Roer et al., 2014, Sect. 3.2). For example, sources of error for radar measurements (reviewed in Lapazaran et al., 2016a, Sect. 3.1) include the radio-wave velocity, the timing of the reflection, and migration (inverting for the reflection surface immediately below, rather than to the side), which can fail in or near steep terrain (Welch et al., 1998). Errors in the measurement position (e.g., due to the accuracy, placement, and movement of the GPS receiver) also translate to thickness errors proportional to the local thickness gradient (reviewed in Lapazaran et al., 2016a, Sect. 3.2). This is a larger issue for older data, especially those transformed from poorly defined coordinate systems or digitized from printed maps (e.g., Andreassen et al., 2015).
The uncertainty of a spatially averaged thickness further varies with the adequacy of the interpolation (and extrapolation), the assumed glacier boundary and, most importantly, the spatial coverage of the measurements (reviewed in Lapazaran et al., 2016b). Thickness measurements are typically acquired along sparse profiles, with coverage biased towards gentler terrain. Rarely, if ever, do they approximate a dense grid blanketing the whole glacier. From the law of error propagation, we would expect measurement errors to be smaller for spatial means. In practice, however, the opposite is more commonly the case, since measurement errors are often partly spatially dependent (Martín-Español et al., 2016), rather than truly random, and point coverage is often far from ideal.
A fraction of glacier thicknesses in GlaThiDa were published with
uncertainty estimates: 26 % of mean glacier thicknesses in
T (drawn from about 35 studies, based on the listed
references), 19 % of mean elevation band thicknesses in
TT (drawn from 4 studies), and 40 % of point
thicknesses in table
TTT (drawn from 51 studies). By computing
can compare the distribution of uncertainties by thickness type. We
find that the reported uncertainties are significantly lower for point
thicknesses than for the spatial means – an interquartile range of
3.1 %–5.5 % of the measured value for points versus
9.9 %–22.8 % and 20 %–50 % for glacier and
elevation band means, respectively. The uncertainties reported by these
studies may or may not be realistic. Nevertheless, the relatively high
uncertainties reported for spatial means clearly indicate that, for
these studies, interpolation errors outweighed any benefit gained from
averaging the spatially independent errors in the point measurements.
In practice, the statistical definition of the uncertainties reported in GlaThiDa is likely to vary considerably. For example, many studies do not specify whether or not a reported error is the standard deviation. Others may not necessarily provide a full error estimate but rather the “resolution of the measurements”, as in the case of some IceBridge datasets (MCoRDS, HiCARS, and PARIS). As pointed out by Martín-Español et al. (2016), based in part on two studies included in GlaThiDa (Pettersson et al., 2011; Saintenoy et al., 2013), errors for spatial means (e) in the literature can vary by orders of magnitude between two extreme assumptions: (underestimate) local errors (ei) are spatially independent, such that (where is the mean of the local errors and n is their number), or (overestimate) local errors are linearly dependent, such that (i.e., the mean of the local errors is taken as the error of the mean). As a consequence, we intend to tighten reporting requirements and flag statistically nonconforming uncertainties in future versions of GlaThiDa.
3.3 Data management
3.3.1 Error detection
As recorded in the changelog, we fixed a large number of errors
introduced in previous versions and in the initial compilation of the
current version. Many were trivial to fix once discovered; others
required reviewing published literature and datasets, checking original
submissions, and, if necessary, corresponding with the data provider.
Most errors were detected by the suite of automated validation tests
described in Sect. 2.3.3. Checks of field-level
constraints identified missing values in required fields, duplicate
values in unique fields, out-of-range values in numeric fields, invalid
characters in text fields, invalid values in enumerable fields, and
future or nonexistent dates in date fields. Checks of table-level
constraints identified duplicate values in unique keys (e.g., duplicate
combinations of survey and point identifiers in
missing values in foreign keys (e.g., survey identifiers in
TTT missing in
T). More complex tests identified
missing values that were required by logical implication (e.g., thickness
missing when thickness uncertainty provided), values that were invalid
by logical implication (e.g., glacier identifier not present in the
glacier database to which it refers), and values that were invalid by
spatial implication (e.g., glacier coordinates outside assigned country
or far from associated point measurements). Additional tests will
necessarily need to be added proactively as the data evolve and
retroactively as unforeseen errors are introduced and later discovered.
3.3.2 Data storage and version control
Every change, addition, and subtraction made to a file in the data
package is tracked by
git. As the number of changes grows over
git repository will inevitably grow in size.
Cloud-storage hosts place storage limits on free accounts: GitLab.com
limits repositories to 10 GB compressed; GitHub.com limits
repositories to 100 GB and each file to 100 MB uncompressed.
To keep repositories small in the presence of large files,
git-lfs (Git Large File Storage) was developed to track files
in the repository while storing them externally (Carlson and Schneider, 2019).
However, whether the file is binary or text, a new copy of the file is
made for each change – no matter how small the change. By storing our
data as text files in the repository, changes are stored incrementally,
which imparts significant storage benefits. At the time of writing, the
repository is only 47.9 MB compressed despite 121 changes,
including 8 versions of
TTT.csv (294 MB
uncompressed, 42.3 MB compressed). Reaching the
10 GB storage limit on GitLab.com would require adding (or
changing) roughly 1 billion point measurements. If this limit were
ever reached, it could be lifted by migrating to a self-hosted GitLab
Nevertheless, line-based version control systems like
not optimized for tracking changes to tabular data. A change to a single
cell is recorded as a change to the entire row, and swapping the order
of two columns is recorded as a change of every row in the table
(Fitzpatrick, 2013). Changes to tabular data can be described more
compactly (and legibly) using specialized syntax
(e.g., Tabular Diff Specification; Fitzpatrick, 2014), but these
impart no storage benefit unless the underlying version control systems
were rewritten to use them to store changes internally. Alternatively,
many changes can be described as operations rather than as changes to
file content, such as a log of Structured Query Language (SQL) commands
to a relational database or the change history tracked by OpenRefine
(Hirst, 2013). However, these would require specific software and a
strict (and nonstandard) workflow to make changes to the data.
GlaThiDa is maintained as a
git repository hosted at
https://gitlab.com/wgms/glathida (last access: 9 November 2020). Bug reports, data submissions,
and other issues should be posted to the issue tracker at
https://gitlab.com/wgms/glathida/-/issues (last access: 9 November 2020). Published versions of
GlaThiDa – those with an assigned DOI (digital object identifier) –
are hosted by the World Glacier Monitoring Service (WGMS) in Zurich,
Switzerland (e.g., v3.1.0, from which this paper was generated:
GlaThiDa Consortium, 2020). GlaThiDa is licensed under Creative
Commons Attribution 4.0 International (CC-BY-4.0:
https://creativecommons.org/licenses/by/4.0, last access: 9 November 2020).
The Glacier Thickness Database (GlaThiDa) has been established as the international data repository for glacier ice thickness observations. Version 3 contains standardized data for roughly 3000 glaciers worldwide, collected from in situ and airborne measurements. Overall, 14 % of global glacier area is now within 1 km of a thickness measurement (located on the same glacier), although large regional gaps persist, especially in Asia, the Russian Arctic, and the Andes. Thanks to simple metadata formats and a development environment based on open-source software, GlaThiDa fulfills the FAIR principles (Findable, Accessible, Interoperable and Reusable for both machines and people) and surpasses them with automatic version control, continuous validation, and an interface for community dialogue. Hosted by the World Glacier Monitoring Service (WGMS), GlaThiDa will continue to serve the glaciological community as a trustworthy dataset.
The additional contributors listed below helped compile earlier versions of GlaThiDa, performed measurements, processed data, and/or submitted data to GlaThiDa. They are listed below in alphabetical order by last name. This list does not include the authors of published literature and datasets which were added to GlaThiDa by the authors of GlaThiDa. Affiliations were recorded at the time of contribution and may not be current.
Jakob Abermann (Asiaq Greenland Survey, Greenland), Songtao Ai (Wuhan University, China), Brian Anderson (Victoria University of Wellington: Antarctic Research Centre, New Zealand), Serguei M. Arkhipov (Russian Academy of Sciences: Institute of Geography, Russia), Izumi Asaji (Hokkaido University, Japan), Andreas Bauder (Swiss Federal Institute of Technology (ETH) Zürich: Laboratory of Hydraulics, Hydrology and Glaciology (VAW), Switzerland), Jostein Bakke (University of Bergen: Department of Earth Sciences, Norway), Toby J. Benham (Scott Polar Research Institute, United Kingdom), Douglas I. Benn (University of Saint Andrews, United Kingdom), Daniel Binder (Central Institute for Meteorology and Geodynamics (ZAMG), Austria), Elisa Bjerre (Technical University of Denmark: Arctic Technology Centre, Denmark), Helgi Björnsson (University of Iceland, Iceland), Norbert Blindow (Institute for Geophysics, University of Münster, Germany), Pascal Bohleber (Austrian Academy of Sciences (ÖAW): Institute for Interdisciplinary Mountain Research (IGF), Austria), Eliane Brändle (University of Fribourg, Switzerland), Gino Casassa (University of Magallanes: GAIA Antarctic Research Center (CIGA), Chile), Jorge Luis Ceballos (Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), Colombia), Julian A. Dowdeswell (Scott Polar Research Institute, United Kingdom), Felipe Andres Echeverry Acosta (Hallgeir Elvehøy – Norwegian Water Resources and Energy Directorate (NVE), Norway), Rune Engeset (Norwegian Water Resources and Energy Directorate (NVE), Norway), Andrea Fischer (Institute of Interdisciplinary Mountain Research (IGF), Austria), Mauro Fischer (University of Fribourg, Switzerland), Gwenn E. Flowers (Simon Fraser University: Department of Earth Sciences, Canada), Erlend Førre (University of Bergen: Department of Earth Sciences, Norway), Yoshiyuki Fujii (National Institute of Polar Research, Japan), Mariusz Grabiec (University of Silesia in Katowice, Poland), Jon Ove Hagen (University of Oslo, Norway), Svein-Erik Hamran (University of Oslo, Norway), Lea Hartl (Institute of Interdisciplinary Mountain Research (IGF), Austria), Robert Hawley (Dartmouth College, United States), Kay Helfricht (Institute of Interdisciplinary Mountain Research (IGF), Austria), Elisabeth Isaksson (Norwegian Polar Institute, Norway), Jacek Jania (University of Silesia in Katowice, Poland), Robert W. Jacobel (Saint Olaf College: Physics Department, United States), Michael Kennett (Norwegian Water Resources and Energy Directorate (NVE), Norway), Bjarne Kjøllmoen (Norwegian Water Resources and Energy Directorate (NVE), Norway), Thomas Knecht (University of Zürich, Switzerland), Jack Kohler (Norwegian Polar Institute, Norway), Vladimir Kotlyakov (Russian Academy of Sciences: Institute of Geography, Russia), Steen Savstrup Kristensen (Technical University of Denmark: Department of Space Research and Space Technology (DTU Space), Denmark), Stanislav Kutuzov (University of Reading, United Kingdom), Javier Lapazaran (Universidad Politécnica de Madrid, Spain), Tron Laumann (Norwegian Water Resources and Energy Directorate (NVE), Norway), Ivan Lavrentiev (Russian Academy of Sciences: Institute of Geography, Russia), Katrin Lindbäck (Norwegian Polar Institute, Norway), Peter Lisager (Asiaq Greenland Survey, Greenland), Francisco Machío (Universidad Internacional de La Rioja (UNIR), Spain), Gerhard Markl (Institute of Interdisciplinary Mountain Research (IGF), Austria), Enrico Mattea (University of Fribourg: Department of Geography, Switzerland), Kjetil Melvold (Norwegian Water Resources and Energy Directorate (NVE), Norway), Laurent Mingo (Blue System Integration Ltd., Canada), Christian Mitterer (Institute of Interdisciplinary Mountain Research (IGF), Austria), Andri Moll (University of Zürich, Switzerland), Ian Owens (University of Canterbury: Department of Geography, New Zealand), Finnur Pálsson (University of Iceland, Iceland), Rickard Pettersson (Uppsala University, Sweden), Rainer Prinz (University of Graz: Department of Geography and Regional Science, Austria), Jaan-Mati Punning (Estonian Academy of Sciences (USSR Academy of Sciences-Estonia): Institute of Geology, Estonia), Antoine Rabatel (University Grenoble Alpes, France), Ian Raphael (Dartmouth College, United States), David Rippin (University of York, United Kingdom), Andrés Rivera (Center for Scientific Studies (CECs), Chile), José Luis Rodríguez Lagos (Center for Scientific Studies (CECs), Chile), John Sanders (University of California, Berkeley: Department of Earth and Planetary Science, United States), Albane Saintenoy (University of Paris-Sud, France), Arne C. Sætrang (Norwegian Polar Institute, Norway), Marius Schaefer (Austral University of Chile: Institute of Physical and Mathematical Sciences (ICFM), Chile), Stefan Scheiblauer (Environmental Earth Observation Information Technology (ENVEO IT GmbH), Austria), Thomas V. Schuler (University of Oslo, Norway), Heïdi Sevestre (University of Saint Andrews, United Kingdom), Bernd Seiser (Institute of Interdisciplinary Mountain Research (IGF), Austria), Ingvild Sørdal (University of Oslo: Department of Geosciences, Norway), Jakob Steiner (University of Utrecht: Faculty of Geosciences, the Netherlands), Peter Alexander Stentoft (Technical University of Denmark: Arctic Technology Centre (ARTEK), Denmark), Martin Stocker-Waldhuber (Technical University of Denmark: Arctic Technology Centre (ARTEK), Denmark), Bernd Seiser (Institute of Interdisciplinary Mountain Research (IGF), Austria), Shin Sugiyama (Hokkaido University: Institute of Low Temperature Science, Japan), Rein Vaikmäe (Tallinn University of Technology, Estonia), Evgeny Vasilenko (Academy of Sciences of Uzbekistan, Uzbekistan), Nat J. Wilson (Simon Fraser University: Department of Earth Sciences, Canada), Victor S. Zagorodnov (Russian Academy of Sciences: Institute of Geography, Russia), and Rodrigo Zamora (Center for Scientific Studies (CECs), Chile).
MZ conceived the project. EW designed and implemented the data package and development environment, with input from MZ. EW prepared the manuscript, with feedback from MZ, FN, MH, JJF, IGR, JL, HM, KN, LMA, DF, and HL. MZ, FN, MH, JJF, IGR, JL, HM, and KN helped compile earlier versions of GlaThiDa. LMA, DF, and HL cochaired the International Association of Cryospheric Sciences (IACS) Working Group on Glacier Ice Thickness Estimation. Additional contributors are named in the Team list.
The authors declare that they have no conflict of interest.
This work is a contribution to the International Association of Cryospheric Sciences (IACS) Working Group on Glacier Ice Thickness Estimation (https://cryosphericsciences.org/activities/ice-thickness, last access: 9 November 2020). We are grateful to NASA Operation IceBridge and the National Snow and Ice Data Center for making their data publicly available online. We thank the countless people who contributed to the planning, execution, and processing of glacier thickness measurements included in GlaThiDa who are not named in the Team list. Institutions that funded the acquisition, processing, and archiving of thickness measurements included in GlaThiDa are acknowledged in the database.
This research has been supported by the International Association of Cryospheric Sciences and by the Federal Office of Meteorology and Climatology (MeteoSwiss) within the framework of the Global Climate Observing System (GCOS) in Switzerland.
This paper was edited by Prasad Gogineni and reviewed by Bruce Raup, Aparna Shukla, and one anonymous referee.
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