The Arctic is rapidly changing. Outside the Arctic, large-sample catchment
databases have transformed catchment science from focusing on local case
studies to more systematic studies of watershed functioning. Here we present
an integrated pan-ARctic CAtchments summary DatabasE (ARCADE) of
> 40 000 catchments that drain into the Arctic Ocean and range in
size from 1 to 3.1 × 106 km2.
These watersheds, delineated at a 90 m resolution, are provided with 103
geospatial, environmental, climatic, and physiographic catchment properties.
ARCADE is the first aggregated database of pan-Arctic river catchments that
also includes numerous small watersheds at a high resolution. These small
catchments are experiencing the greatest climatic warming while also storing
large quantities of soil carbon in landscapes that are especially prone to
degradation of permafrost (i.e., ice wedge polygon terrain) and associated
hydrological regime shifts. ARCADE is a key step toward monitoring the
pan-Arctic across scales and is publicly available: 10.34894/U9HSPV (Speetjens et al., 2022).
Introduction
Earth's rapidly changing climate is particularly evident in the Arctic.
Decreasing sea ice extent has amplified Arctic warming, which has led to an
increase in mean land-surface air temperature of 3.1 ∘C (3
times the global average of ∼ 1 ∘C) over the period
1979–2019 (Lenssen et al., 2019; AMAP, 2021; GISTEMP Team, 2021). Under
all IPCC (Intergovernmental Panel on Climate Change) climate scenarios, the Arctic will be substantially different by the
mid-century (e.g., less snow and sea ice, degraded permafrost, and altered
ecosystems) (Overland et al., 2019). The Arctic is important in regulating
the global climate system (IPCC, 2021; Meredith et al., 2019) and global biogeochemical
cycles (Parmentier et al., 2017). Ongoing changes in the Arctic and their
consequential impacts are both local (e.g., ecosystem changes, changing food
web interactions, and potential loss of biodiversity) (Vincent, 2019) and
global (e.g., changing atmospheric circulation, ocean acidification, and an
altered carbon cycle) (Box et al., 2019; Yamanouchi and Takata, 2020), which
raises the urgency to understand this intricate system better.
In the Arctic, marine and terrestrial systems are tightly coupled. More than
10 % of global river discharge flows into the Arctic Ocean (AO), which
only contains about 1 % of the global ocean volume (Aagaard and Carmack,
1989; McClelland et al., 2012). In addition, river discharge transports
sediment, (organic) carbon, nutrients, and contaminants (Terhaar et al.,
2021) into the AO. Arctic rivers integrate over local to regional scales and
are therefore useful for studying the impacts of environmental and climatic
change at various scales (Holmes et al., 2012).
Permanently frozen soils (permafrost) that are rich in organic carbon (OC)
(Hugelius et al., 2014; Mishra et al., 2021) underlie about 60 %–80 % of
the AO watershed (Zhang et al., 2000, 2005; Obu et al., 2019). Permafrost
conditions have long stabilized the subsurface, but ground temperatures are
now warming across the Northern Hemisphere (Biskaborn et al., 2019).
Permafrost degradation occurs slowly through deepening of the active layer
(the layer that thaws during summer and refreezes during winter) (Ran et
al., 2022) or more quickly through abrupt thaw of permafrost with high
ground ice contents. Both types of thaw expose soil OC to degradation, which
transforms it into greenhouse gases. Thus, the thawing of permafrost can
accelerate global warming but also impacts hydrological, biogeochemical, and
ecological processes in Arctic ecosystems, with complex consequences for
lateral transport of terrestrial material to downstream freshwater and
marine systems (Vonk and Gustafsson, 2013).
Investigations of Arctic change (e.g., Schuur et al., 2015; Walvoord and
Kurylyk, 2016; Liljedahl et al., 2016; Lafrenière and Lamoureux, 2019;
Bruhwiler et al., 2021) critically rely on data. The “Arctic Great Rivers
Observatory” initiative, which has run since 2003, is a unique dataset
covering the six largest Arctic rivers (McClelland et al., 2008,
http://www.arcticgreatrivers.org, last access: 5 August 2022). While data on these large river systems can
provide important insights into Arctic change (e.g., Wild et al., 2019;
Terhaar et al., 2021; Behnke et al., 2021), they do not reveal the changes
that occur at finer scales. Revealing such insights requires data from
smaller pan-Arctic watersheds.
Small and medium-sized watersheds drain roughly a third of the circumpolar
landmass (Holmes et al., 2012). In contrast to the watersheds of the six
largest Arctic rivers (Ob', Yenisey, Lena, Kolyma, Mackenzie, Yukon), the
smaller watersheds are almost exclusively underlain by continuous permafrost
(Holmes et al., 2012) and are often directly located at the coast. This
makes these small watersheds fundamentally different from “The Big Six”
because large rivers drain to a few coastal locations (Mann et al., 2022),
while the cumulative inputs of small watersheds are spread over a much
larger coastal area. In addition, given their size and proximity to the AO,
the changes in these watersheds could be more rapidly transferred and
substantial to the Arctic coastal ecosystem.
Outside of the Arctic, the emergence of large-sample catchment databases
(e.g., Hartmann et al., 2014; Newman et al., 2015; Alvarez-Garreton et
al., 2018), which combine data from many watersheds, have transformed the
field from placing emphasis on local case studies towards more systematic
insights into drivers of watershed functioning. For example, large-sample
watershed studies allow one to reveal regional differences (and similarities) in
hydrological response, make space-for-time transformations, and
systematically test hypotheses. This has proven critical in, for example,
understanding the impacts of climate change (e.g., Berghuijs et al., 2014)
and testing modeling implications (e.g., Knoben et al., 2020). Such
developments have not yet been possible in the Arctic, as large-sample
databases of smaller watersheds are not yet available.
Here, we present an integrated pan-ARctic CAtchments summary DatabasE
(ARCADE) of > 40 000 catchments, including small and medium-sized
watersheds, draining into the Arctic Ocean. These watersheds, delineated at
a high resolution (90 m), are coupled with comprehensive information from
various geospatial, environmental, climatic, and physiographic datasets with
pan-Arctic coverage. This publication aims to provide a high-resolution
geographical register, relevant to those studying environmental and climatic
changes in relation to Arctic catchment hydrology and biogeochemistry.
MethodsSpatial extent and projection
The ARCADE database encompasses all major and minor drainage basins that are
considered part of the pan-Arctic watershed, with their outlets draining into the Arctic Ocean
and surrounding seas. More specifically, this includes all watersheds with a
Strahler order of 5 (i.e. at least five hierarchical branching orders) or
larger that drain into the Arctic Ocean, as well as basins that drain into the
Bering Sea and north of the Yukon River outlet, with inclusion of the Yukon
River. This follows the pan-Arctic watershed definition as defined by
McGuire et al. (2009), with an area of 20.4 × 106 km2,
including the Canadian Archipelago, Greenland, and Hudson Bay (Fig. 1). The
data presented here have been transformed and re-projected to the WGS84/NSIDC
EASE-Grid 2.0 North (EPSG:6931) projection, an equal-area projection system
designed for gridding and small-scale digital mapping for environmental
sciences in the Northern Hemisphere (Brodzik et al., 2014).
Circumpolar map of all ARCADE watersheds, 1 km2 and larger,
Strahler order 5 and higher, at 90 m resolution with insets of the
Southern Beaufort Sea region (upper left) and the Laptev Sea coast,
including the New Siberian Islands (upper right). (Background map:
International Bathymetric Chart of the Arctic Ocean V4.0 (IBCAO) (Jakobsson
et al., 2020)).
Watershed delineationDigital elevation model (DEM)
Terrain parameters such as altitude, slope, aspect, topographic position
index, and slope length and steepness factor (LS-factor) (Renard et al.,
2017) were derived and calculated from Copernicus DEM GLO-90, a high-quality
global 90 m resolution digital elevation model provided by the European
Space Agency (ESA, 2021). The Copernicus DEM was accessed on 10 September 2021. For computational practicality, we chose the 90 m resolution product
rather than the 30 m resolution product. The latter could be used for future
version updates of the ARCADE database. However, we deem the 90 m resolution
sufficiently detailed for our purposes (gaining insights into drainage areas
on a pan-Arctic scale). We constrain the number of catchments in the
database by using Strahler order 5 as the minimum outlet order and 1 km2 catchment area as a lower threshold value (see next paragraph). A
higher-resolution DEM would not necessarily make for a better delineation.
Moreover, most of the datasets used to link to the catchment areas have resolutions
lower than 90 m. We are aware that, at any given resolution, the
relative error regarding catchment delineation increases when looking at
smaller watersheds. Yet, at our chosen resolution, we conclude there to be a
reasonable tradeoff between efficiency and error.
Hydrological DEM conditioning and watershed extraction
The DEM was hydrologically conditioned (a.k.a. pit filling) before deriving
flow direction, flow accumulation, Strahler order, watershed delineations,
and topographic wetness index. This was done using the “r.hydrodem” module (Lindsay and
Creed, 2005) in GRASS GIS (Neteler et al., 2012).
We delineated the watersheds at 90 m resolution for subdivisions of the
pan-Arctic landmass using the hydrologically conditioned DEM. This
subdivision was necessary because processing the DEM in one piece was
computationally too intensive. Delineation was done using SAGA GIS (Conrad
et al., 2015) using the module “Channel Network and Drainage Basins”. A lower threshold of Strahler order 5 was
chosen to constrain watershed generation, i.e., only watersheds of streams
with Strahler order 5 or higher at the outlet were delineated. This
threshold was necessary to limit the number of watersheds in the final
product and to ensure that only watersheds with actual streams were
included. Another consideration was that, as the watershed area approaches
the DEM's source resolution, the relative accuracy decreases. Subdivisions
of the pan-Arctic watersheds were combined into one dataset of all
watersheds that drain into the AO (i.e., upstream areas of outlets at the
AO). A known limitation of DEM-derived watershed delineation is that the
algorithm struggles to find the channels and ridges in flat terrain. Since
we are mostly interested in the drainage area rather than channel location,
errors in channels were tolerated more than errors in catchment boundaries.
Small, flat catchments (area ≲ 10 km2 and slope
≲ 0.1∘, mainly in fluvial deltas) are
most prone to error, which is why we advise users to be critical when using
these delineations for local purposes. Another limitation and source of
uncertainty lies with watershed delineation on the Greenland ice sheet.
Here, we simply proceeded delineating catchments using the
surface and ice topography as captured in the DEM.
Environmental data
All variables are described in file S1 in 10.34894/U9HSPV (Speetjens et al., 2022). Elaborated
explanations are provided below.
Climatological data
Climatological data were extracted from the ERA5-Land monthly averaged –
ECMWF climate reanalysis dataset (Muñoz-Sabater et al., 2021) using
Google Earth Engine (“Planetary-scale geospatial analysis for everyone”; Gorelick et al., 2017). This dataset has a spatial resolution of 11 132 m and consists of 50 bands containing climatological variables related
to temperature, precipitation, evaporation, heat fluxes, wind, and
vegetation. Minimum, maximum, mean, standard deviation, and median annual
values of a subset of these variables (a complete overview of all variables
is available in file S1) were calculated for each watershed from 1 January 1990 to
31 December 2019. In the case of pixels falling partly within the geometry of a
watershed, the value is weighted by the fraction of each pixel that falls
within the geometry. Precipitation, evaporation, and runoff totals were
accumulated and averaged over the 30-year period (i.e., the mean annual
total of each of these variables was calculated). For snow statistics, we
calculated the 30-year average maximum snow depth (m), snow cover (%),
snowmelt (m d-1), and snowfall (m d-1) based on the month with the
highest value of each year. In the case of snowmelt, this is an indicator of
the intensity of snowmelt during the melting season.
We also tested for trends using Sen's slope estimator for the same period.
Sen (1968) calculates the slope as
Qi=xj-xij-ii=1,2,3,…,N,
where xj and xi are records at time j and i (j>i). With n data
records in a time series, the number of slope estimates equals N=n(n-1)/2.
Qi then follows by calculating the median of all the slope estimators. We
chose to calculate these statistics on monthly data for temperature
variables, while for snow-related variables, we only selected the winter
months (November–April), and for evaporation-related variables, we only selected the
summer months (June–September).
Physiographic dataCatchment properties
Basic catchment properties include minimum, maximum, mean, standard
deviation, and median of elevation (meters), slope (degrees), and aspect
(degrees). Furthermore, we included centroid latitude (degrees), Gravelius
index (watershed perimeter divided by the perimeter of a circle that has the
same area; unitless), watershed perimeter (kilometers), and watershed area
(square kilometers).
Soil properties
SoilGrids is a globally consistent dataset that contains soil properties
(soil organic carbon, SOC, content – dg kg-1; organic carbon density – dg dm-3; nitrogen content – cg kg-1; coarse fragments volumetric
content – per 10 000; sand, silt, and clay content – g kg-1; soil bulk
density – cg cm-3 – for six depth intervals: 0–5, 5–15, 15–30,
40–60, 60–100, 100–200 cm; and organic carbon stock, OCS – t ha-1 – for the upper 30 cm of the soil) and classes (the most the likely
soil class according to the World Reference Base (WRB) classification system, IUSS Working Group WRB, 2015) at 250 m resolution (Poggio et al.,
2021). The ARCADE database aggregates soil property data from SoilGrids into
watershed minimum, maximum, mean, and standard deviation. OCS was also
summarized into total watershed OCS (Gt) in the upper 30 cm of the soil.
Soil class data from SoilGrids were summarized by calculating the fractional
coverage of each class for each watershed. All watershed statistics were
calculated using the “image.reduceRegion()” function in Google Earth
Engine (Gorelick et al., 2017). We note that estimates of soil properties,
especially for deeper soils, are often uncertain due to data scarcity in the
permafrost region. We refer to Poggio et al. (2021) for more detailed
discussions of uncertainties in the soil property projections.
Land cover class fractional coverage
Watershed land cover fractional coverage was obtained from ESA WorldCover
10m v100 (Zanaga et al., 2021). This classifies the land surface at 10 m
resolution into 11 classes: trees, shrubland, grassland, cropland, built-up
areas, barren or sparse vegetation, snow and ice, open water, herbaceous
wetland, mangroves, and moss and lichen.
Landform class fractional coverage
Another useful characterization parameter for watersheds is the fractional
coverage of landforms. We chose to use a landform classification scheme
proposed by Theobald et al. (2015). Their classification scheme maps
ecologically relevant landforms (see tables included in
dataset file S1 in 10.34894/U9HSPV, Speetjens et al., 2022), which we deem of particular interest in characterizing a
catchment, for instance to indicate sensitivity to the occurrence of abrupt
permafrost thaw.
Burned area fraction coverage
The burned-area fraction for each watershed over the period 2012–2022 was
calculated from MODIS FireCCI5, a monthly global 250 m spatial
resolution burn scar classification product (Padilla Parelada, 2018). We
selected and summarized recent (< 10 years) annual fire scars, as they
are most likely to have an ongoing and lasting effect on watershed
biogeochemistry.
Permafrost extent
Permafrost fraction pixel cover was taken from the permafrost extent by Obu
et al. (2019) and converted into watershed area fractional coverage per
permafrost coverage type. The used product has a spatial resolution of 1 km and a temporal range from 2000–2016. Continuous permafrost is
classified as a pixel area coverage of 90 %–100 %, discontinuous permafrost
as 50 %–90 %, sporadic permafrost as 10 %–50 %, and isolated patches of
permafrost as 0 %–10 %.
Active-layer thickness
Recently published high-resolution estimates of active-layer thickness (ALT)
(Ran et al., 2022) were summarized for each watershed. The source dataset
has a 1 km resolution for the period of 2000–2016. The authors
generated the data by combining large amounts of field data and
multisource geospatial remote sensing data into a statistical learning
model. It has bias = 2.71 ± 16.46 cm and RMSE = 86.93 ± 19.61 cm for ALT.
The distribution of watershed areas in the pan-Arctic watersheds
database and the range of the four groups that are classified based on watershed
area. “BS” stands for “Big Seven”, “MN” for “Middle Nine”, “PAT” for
“Pan-Arctic Thousands”, and PAS for “Pan-Arctic Small watersheds”. Note that
the x axis has a logarithmic scale. The colors represent the
mean catchment slope of the bin.
Glacial fractional coverage
Glacial coverage was calculated by combining two datasets: Global Land Ice
Measurements from Space (GLIMS), from which we used the latest available
snapshot as of 14 September 2021 for the glacial extent (Kargel et al.,
2014), and the Greenland ice and ocean mask from the Greenland Mapping
Project (GIMP), which contains a 15 m resolution land ice mask for the
Greenland ice sheet (Howat et al., 2014). We resampled the combined datasets
to a 250 m resolution grid to calculate fractional glacial coverage for
each watershed.
Surface water fractional coverage
A high-resolution water mask, JRC Global Surface Water Mapping Layers, v1.3
(30 m) (Pekel et al., 2016), was used to calculate fractional watershed
area coverage. The conditions for the presence of water were determined by
the occurrence of water in each cell for at least 50 % of the time between
1984 and 2020.
Vegetation index
The summarized statistics of the normalized difference vegetation index
(NDVI) and the Sen slope of NDVI were calculated using MOD13A1.006 Terra
Vegetation Indices 16-Day Global 500m (Didan, 2015).
This dataset is MODIS derived and has a 500 m resolution. We used the
annual maximum NDVI of each year from 2000 to 2021.
Topographic wetness index
As an indicator of terrain wetness, we used SAGA wetness index (Böhner
and Selige, 2006), a modified topographic wetness index that is based on
Moore et al. (1993). The indicator uses topography to differentiate
catchments dominated by wetland terrain versus more well-drained terrain.
LS-factor
Slope length and steepness factor (LS-factor) is a factor used in the
Universal Soil Loss Equation (USLE) (Renard et al., 2017) that serves as a
predictor of soil loss ratio as a function of slope length and steepness.
The LS-factor was calculated using the SAGA GIS tool module “LS-factor”, which uses
specific catchment area (SCA) as a substitute of slope length (Böhner
and Selige, 2006).
Tasseled-cap trend index of visible spectra
As an indicator for changes in wetness (TCW), greenness (TCG), and brightness
(TCB) (indicative of bare soil), we included tasseled-cap indices derived
from Landsat visible-spectra images, as provided by Nitze et al. (2018). The
minima, maxima, and average of these pixel-based slopes were calculated for
each watershed.
Results and discussionDatabase inventory
The database consists of 47 054 watersheds ranging in size from 1
to 3.1 × 106 km2 (Ob' watershed). We will refer to four groups of
watersheds based on size (Fig. 1, Tables 1–5) because our work focuses on
inventorying watersheds of all sizes and highlights the contrasts between
the larger, well-studied rivers and smaller rivers. The first group consists
of “The Big Six” (Ob', Yenisey, Lena, Mackenzie, Yukon, Lena Rivers) and one
major watershed draining into the Hudson Bay (Nelson River). Therefore, the
“The Big Six” becomes “The Big Seven”, abbreviated as BS. Then, “The Middle
Nine” (MN) consists of the Severnaya Dvina, Indigirka, Pechora, Olenek,
Thelon, Yana, Khatanga, Pyasina, and Taz Rivers. We then split the remaining
watersheds into areas greater than 1000 km2 (yet smaller than the MN),
which we named “The Pan-Arctic Thousands” (PAT), and watersheds smaller than
1000 km2, which we named the “The Pan-Arctic Small watersheds” (PAS).
Summary statistics of the pan-Arctic watersheds database focused on
permafrost. Note that this summary excludes watersheds that are fully
covered by glaciers or ice sheets.
* Abbreviations stand for the grouped watersheds by area: Big Seven
(BS), Middle Nine (MN), Pan-Arctic Thousands (PAT), Pan-Arctic Small
watersheds (PAS); “count” represents the number of catchments covered by the
subsequent data columns; a.m.s.l. stands for “above mean sea level”; ALT stands
for “active-layer thickness”; MAAT stands for “mean annual air
temperature”.
The BS account for 50 % of the total AO watershed area, while watersheds
under 1000 km2 (PAS) account for only 9 % of the entire area.
However, these small watersheds are much more abundant, and their landmass
is more directly connected to the Arctic Ocean than the BS. Since large
parts of the BS watersheds reach into low latitudes (∼ 60 %
of their watershed areas are located south of 60∘ north, or 93 %
south of the Arctic Circle), the mean annual air temperature in these
watersheds is higher compared to the rest of the pan-Arctic watershed (Table 1), influencing mean permafrost coverage, active layer thickness (ALT), and
occurrence of ice wedge polygon terrain (Table 3). These
permafrost-related watershed properties are susceptible to change under
climate-warming trends and play a central role in Arctic watershed
hydrology. Our database shows that the MN, PAT, and PAS watersheds have been
warming much faster than the BS (Table 7), highlighting the need for more
research on these smaller northern watersheds.
Watershed topographic properties summarized by group
(classification based on area) and relevant (sub)continent.
* Abbreviations stand for the grouped watersheds by area: Big Seven
(BS), Middle Nine (MN), Pan-Arctic Thousands (PAT), Pan-Arctic Small
watersheds (PAS); “count” represents the number of catchments covered by the
subsequent data columns; TWI stands for “topographic wetness index” and is
based on the SAGA Wetness Index Tool; LS stands for “slope steepness and
length factor”.
Watershed permafrost properties summarized by group (based on area)
and relevant (sub)continent.
* Abbreviations stand for the grouped watersheds by area: Big Seven
(BS), Middle Nine (MN), Pan-Arctic Thousands (PAT), Pan-Arctic Small
watersheds (PAS); “count” represents the number of catchments covered by the
subsequent data columns; IWP stands for “ice wedge polygon”;
OCS0–30 cm stands for “organic carbon stock” in the
upper 0–30 cm of the soil; ALT stands for “active-layer thickness”.
Data coverage
The hydrological functioning of small catchments in the Arctic remains
uncertain. Therefore, our database provides a set of catchment properties to
help address these uncertainties. Basic topographical catchment metrics such
as area, elevation, catchment slope, mean aspect, LS-factor, and TWI are
available for all recorded catchments. Due to their resolution and extent,
some of the other aggregated datasets have lower coverage. Most notably,
ERA5-Land data has ∼ 87 % spatial coverage in the database.
Most omitted watersheds are small coastal watersheds that were less than
50 % covered by a cell of the ERA5-Land dataset. The same holds for the
ALT and SoilGrids data, which cover about ∼ 82 % and
∼ 92 % of all watersheds, respectively. For all aggregated
data sources, > 80 % of watersheds are covered.
Data quality assessment and limitations
The ARCADE database is the first published 90 m resolution dataset of
watersheds draining into the Arctic Ocean. A few unavoidable errors occurred
during the watershed delineation. Errors most commonly arise in flat terrain
where flow-routing algorithms struggle to determine the flow direction,
which troubles the watershed border definition. To deal with this, we used
an internal SAGA function to artificially maintain a minimal channel slope
by slightly altering the DEM. This minimal slope function effect is visually
detectable in small deltas and floodplains where watershed borders sometimes
appear to be less accurate than in steep, well-defined terrain. Additionally, this
flow path uncertainty in flat terrain caused some errors in approximating
the locations of coastal outlets. Given the high DEM resolution, these
errors are generally in the order of meters rather than kilometers. This
could be improved in future versions by “burning” outlets and channels, such as those derived from satellite imagery, into the DEM.
Our cut-off value in defining a river catchment (outlet Strahler order 5;
minimum area of 1 km2) leads to the omission of areas that lie within
the pan-Arctic drainage basin but are outside our database's scope (i.e., so-called wolf-tooth patches, remaining coastal areas in between catchments).
However, we estimate the summarized area to be less than 1 % of the total
pan-Arctic watershed area. The strength of this database lies in the large
spatial extent, its novelty, and the range of spatially explicit variables
coupled to the delineated catchments. We therefore advise that this
database be used to target specific (groups of) catchments and to make comparisons
among those to gain insight into spatial patterns and for the localization of
target areas for further research.
Siberian coastal watersheds with ice wedge polygon (IWP) terrain
(% watershed coverage) (a), soil organic carbon stock (OCS) in metric
tonnes per hectare (b), and the mean watershed temperature trend taken over
the period 1990–2019 (c) (map source: ARCADE database (Speetjens et al.,
2022)).
Correlations of binned data of selected catchment properties from
our database. We calculated Spearman's rho on the binned data. Most notably,
we observe that small watersheds have experienced the greatest warming
while having the highest mean carbon stocks and the highest fraction of IWP
terrain. Similarly, the data show that high OC stocks are found where most
warming has occurred.
Pan-Arctic watersheds properties
ARCADE provides 103 variables with catchment properties divided over 353
columns (including statistics), showcasing a wide variability and
spatial resemblances of catchments in the pan-Arctic drainage basin.
Additionally, we provide summaries of the most important properties for the
BS, MN, PAT, and PAS, both as a whole and on a regional basis (i.e., North
America, Greenland, and Eurasia).
Physiographic features
Basic catchment-scale topographical information can be used to categorize
watershed types and to estimate their runoff, sediment transport regimes, and
biogeochemical constituents. As an example, Connoly et al. (2018) found
strong negative correlations between catchment slope, DOM, and NO3-
concentrations in Arctic watersheds. According to the data presented here,
PAS watersheds have, on average, the highest mean catchment slope. This is
partially because Greenlandic small coastal watersheds are mountainous
(Tables 3 and 4). Eurasia and North America's proportions of PAT, on the
other hand, consist of relatively low elevation, flat terrain (mean slope
Eurasia: ∼ 3.1 ± 1.54∘; mean slope North America:
∼ 2.4 ± 1.53∘). The PAS watersheds are
underlain mainly by continuous permafrost and feature wetland-type land cover
(Eurasia: ∼ 27 % wetland; North America: ∼ 14 % wetland) as opposed to BS (Eurasia 4 % wetland; North America 1 %
wetland), with a high area fraction of surface water (Eurasia:
∼ 6 % water; North America: ∼ 8 % water).
Because of their permafrost coverage (mostly continuous), PAS watersheds are
more likely to feature IWP terrain (37 % IWP terrain in PAS as opposed to
1 % IWP terrain in BS). Another noteworthy property of PAS watersheds is
that, on average, they feature higher OC stocks (Eurasia: ∼ 88 t ha-1; Greenland: ∼ 87 t ha-1; North America:
∼ 71 t ha-1) than more commonly studied catchments (BS:
∼ 64.5 t ha-1; MN: ∼ 69.5 t ha-1).
Additionally, Greenland stands out in most aspects, with a relatively high
mean catchment slope (∼ 7.5∘), elevation (532 m a.m.s.l.), and glacial coverage (77 %) (Tables 4 and 5). This distinction
in basic characteristics most likely distinguishes the lateral flux
characteristics of Greenlandic watersheds from the rest of PAS. Greenland
also includes several (149 out of 929) PAT catchments which are largely
(> 80 % of their area) covered by the Greenland ice sheet.
Since principles of watershed hydrology do not apply to ice sheets or
glaciers, we advise users of this database to take note of the presence of
these “ice sheet watersheds” in the database. A solution to circumvent these
watersheds is filtering by fractional ice coverage to a value aligned with the
study goals.
Watershed land cover type and properties summarized by group
(classification based on area) and relevant (sub)continent.
* Abbreviations stand for the grouped watersheds by area: Big Seven
(BS), Middle Nine (MN), Pan-Arctic Thousands (PAT), Pan-Arctic Small
watersheds (PAS); “count” represents the number of catchments covered by the
subsequent data columns
Watershed climatological properties summarized by group
(classification based on area) and relevant (sub)continent.
* Abbreviations stand for the grouped watersheds by area:
Big Seven (BS), Middle Nine (MN), Pan-Arctic Thousands (PAT), Pan-Arctic
Small watersheds (PAS); “count” represents the number of catchments covered by
the subsequent data columns; T stands for temperature, ET stands for total
evapotranspiration, P stands for precipitation, and Q stands for runoff.
ΔT mean and ΔP mean are
calculated from the Sen slope of the monthly mean temperature and
precipitation over the period 1999–2019 (mo-1)
multiplied by the number of months.
Climatological properties
Since MN, PAT, and PAS are, on average, located in higher latitudes, these
watersheds are colder than the BS (BS: -2.9 ∘C; MN: -10.5 ∘C; PAT: -9.1 ∘C; PAS: -10.4 ∘C) (Table 3).
While for the BS the Eurasian watersheds are the coldest, the opposite is
true for PAS (PAS of Eurasia: -4.7 ∘C; North America:
-11.0 ∘C. This is partially because the Gulf Stream warms smaller
coastal watersheds of western Eurasia, but there might also already be some
effect of temperature increase which has been greatest in the Eurasian PAS
(+3.4 ∘C; Table 5). Annual precipitation, mean annual runoff,
and the mean increase in precipitation over the past 30 years are highest in
PAS and PAT (i.e., smaller watersheds) (Table 7).
Pan-Arctic trends in data
We provide this database as a basis to explore the vast number of watersheds
outside the BS and MN that have previously been lumped into a single
“unknown”. As a result, they have been underappreciated in terms of their
contribution to the pan-Arctic lateral flux budget and their potential
sensitivity to climate change as opposed to their bigger siblings. While
continuing the scientific focus on large catchment studies (BS) in the
Arctic remains vital, we suggest, in parallel, to strongly increase the focus on pan-Arctic small catchments situated entirely at high latitudes. These
catchments are experiencing the greatest climatic warming while also storing
large quantities of soil carbon in landscapes that are especially prone to
degradation of permafrost (i.e., IWP terrain) and associated hydrological-regime shifts. Using our database, these and many other variables are now
quantified and made spatially explicit (Figs. 3, 4).
Data availability
The ARCADE database is publicly available via DataverseNL:
10.34894/U9HSPV (Speetjens et al., 2022) under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
Outlook and future development
ARCADE is the first aggregated database of pan-Arctic river catchments that
includes small watersheds at a high resolution. The publication of this
database is a necessary step toward more integrated monitoring of the
pan-Arctic watershed. An important addition in the following version will be
discharge data and derived seasonality (and changes therein) from the RADR
database (Feng et al., 2021), which recently greatly advanced understanding
of discharge in smaller Arctic rivers. Another important future addition
will be the delineations of subbasins and data on river biogeochemistry that
is available, albeit non-uniformly and largely unaggregated throughout
literature. When numerous valuable datasets from various scientific
disciplines are merged, it will be possible to better understand the
Arctic's changing hydrology and biogeochemistry. This allows the scientific
community to form new hypotheses that direct scientific efforts to specific
regions and processes that may have remained under the radar.
Author contributions
NJS coordinated the ARCADE database creation and structure; constructed the
data-processing pipeline of the database; collected, aggregated, and
inserted all data records; and wrote the first version of the paper. HL
and JEV were involved in the initial conceptualization of the database. JEV,
WRB, AP, HG, TG, and PAP provided insights from their respective fields of
expertise to guide the following database conceptualization steps and
during various iterations of its creation. All authors contributed to the final draft of this paper through their various fields of expertise. JEV and HL
played key roles in providing funding for the project.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors thank all those who have made this work possible. We thank Justine Ramage for hosting working sessions at Stockholm University. Ingmar Nitze is thanked for providing helpful advice for using Google Earth Engine. We thank Caroline Coch for her role as a sparring partner at the very beginning of this project.
Financial support
This research has been supported by the Horizon 2020 program (Nunataryuk (grant no. 773421)), and additional financial support was received from ERC (THAWSOME (grant no. 676982).
Review statement
This paper was edited by Lukas Gudmundsson and reviewed by Lucas Menzel and one anonymous referee.
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