The polar regions experience widespread transformations, such that efficient
methods are needed to monitor and understand Arctic landscape changes in
response to climate warming and low-frequency, high-magnitude hydrological
and geomorphological events. One example of such events, capable of causing
serious landscape changes, is glacier lake outburst floods. On 6 August 2017, a flood event related to glacial lake outburst affected the Zackenberg
River (NE Greenland). Here, we provided a very-high-resolution dataset
representing unique time series of data captured immediately before (5 August 2017), during (6 August 2017), and after (8 August 2017) the flood.
Our dataset covers a 2.1 km long distal section of the Zackenberg River. The
available files comprise (1) unprocessed images captured using an unmanned
aerial vehicle (UAV; 10.5281/zenodo.4495282, Tomczyk and Ewertowski, 2021a) and (2) results of structure-from-motion (SfM) processing (orthomosaics, digital elevation
models, and hillshade models in a raster format), uncertainty assessments
(precision maps), and effects of geomorphological mapping in vector formats
(10.5281/zenodo.4498296, Tomczyk and
Ewertowski, 2021b). Potential applications of the presented dataset include
(1) assessment and quantification of landscape changes as an immediate
result of a glacier lake outburst flood; (2) long-term monitoring of
high-Arctic river valley development (in conjunction with other datasets);
(3) establishing a baseline for quantification of geomorphological impacts
of future glacier lake outburst floods; (4) assessment of geohazards related
to bank erosion and debris flow development (hazards for research station
infrastructure – station buildings and bridge); (5) monitoring of
permafrost degradation; and (6) modelling flood impacts on river ecosystem,
transport capacity, and channel stability.
Introduction
Long-term evolution of river system is the effect of an interplay between
“normal” processes (i.e. low-magnitude, high-frequency geomorphological work) and
“extreme” processes (i.e. high-magnitude, low-frequency events) (see Death
et al., 2015; Garcia-Castellanos and O'Connor, 2018). One of the critical
issues in fluvial geomorphology is the quantification of geomorphological
effects caused by both groups of processes that affect river channel
morphology and functioning. The problem is that catastrophic events are hard
to predict, such that our ability to collect qualitative data about their
direct impact is limited, and yet this knowledge is crucial for river
monitoring and modelling (Tamminga et al., 2015a, b).
Among the most severe flood-related extreme events are glacier lake outburst
floods (GLOFs), usually related to a sudden release of water stored in
ice-dammed or moraine-dammed lakes and frequent in modern glacierised
mountain areas (Russell et al., 2007; Moore et al., 2009; Iribarren et
al., 2015; Harrison et al., 2018; Nie et al., 2018; Carrivick and Tweed,
2019). The direct cause of the water release is usually related to (1)
increase in water level in subglacial lakes, causing ice flotation and
breaching of the ice dam (Tweed and Russell, 1999; Roberts et al., 2003);
(2) breaching of a moraine dam (Watanabe and Rothacher, 1996; Reynolds,
1998; Westoby et al., 2014); and (3) increase in the amount of meltwater due to
the explosion of subglacial volcanoes (Carrivick et al., 2004; Russell et
al., 2010).
GLOFs can vary in size and frequency, and yet such flood events can
significantly impact river morphology, as they often far exceed the
potential maximum of meteorological floods (Desloges and Church, 1992;
Cook et al., 2018; Garcia-Castellanos and O'Connor, 2018). As such, the
documentation of the geomorphological records of such events is essential
for the prediction and management of future transformations in the context
of ongoing climate changes (Nardi and Rinaldi, 2015; Carrivick and Tweed,
2016) that can cause an intensification of these flood events (Reynolds,
1998; Harrison et al., 2006; Watanabe et al., 2009; Harrison et al., 2018).
GLOFs in Greenland were reported from several locations (see Carrivick
and Tweed, 2019, for more detailed review), including Lake Isvand (Weidick and Citterio, 2011), Russell Glacier (e.g. Russell, 2009; Russell et al., 2011; Carrivick et al., 2013, 2017; Hasholt et al., 2018), Kuannersuit Glacier (Yde et al., 2019), Lake Tininnilik (Furuya and Wahr, 2005), Lake Hullet (Dawson, 1983), Qorlortorssup Tasia (Mayer and Schuler, 2005),
Zackenberg River (Søndergaard et al., 2015; Kroon et al., 2017;
Ladegaard-Pedersen et al., 2017), and Catalina Lake (Grinsted et al., 2017). Estimated water volume losses varied from ∼5×106 to ∼6400×106 m3, while peak discharges
could reach up to ∼1430 m3 s-1 (Dawson, 1983;
Furuya and Wahr, 2005; Russell et al., 2011; Carrivick et al., 2013;
Søndergaard et al., 2015; Carrivick and Tweed, 2019). The frequency of
GLOFs in Greenland varies from annual to decadal (e.g. Zackenberg
River, Russell Glacier, Lake Tininnilik) to one-time events (e.g.
Kuannersuit Glacier) (Furuya and Wahr, 2005; Russell et al., 2011;
Carrivick and Tweed, 2019; Yde et al., 2019). The most significant
geomorphological and hydrological effects included the formation of bedrock
canyons and spillways, transport of large boulders, riverbank erosion,
development of coarse-sediment bars and deltas, outwash surfaces, and
ice-walled canyons (Russell, 2009; Carrivick et al., 2013; Carrivick and
Tweed, 2019; Yde et al., 2019). Despite numerous reports, so far, no
detailed topographical data of a river system exist, which could serve as a
baseline for long-term monitoring of landscape changes to understand,
quantify, and model changes resulting from GLOF in comparison to normal-frequency processes.
On 6 August 2017, a flood event related to a glacier lake outburst affected
the Zackenberg River (NE Greenland), leaving behind substantial
geomorphological impacts on the riverbanks and channel morphology (see Tomczyk et al., 2020). Here, we provided a very-high-resolution dataset representing time series of data captured
immediately before (5 August 2017), during (6 August 2017), and after
(8 August 2017) the flood. This unique set of data makes it possible to
study the immediate landscape response to the GLOF event and can be used as
a baseline for any long-term monitoring exercise. Our dataset covers
approximately a 2.1 km long distal section of the Zackenberg River.
Available files comprise (1) unprocessed images captured using an unmanned
aerial vehicle (UAV; 10.5281/zenodo.4495282, Tomczyk and Ewertowski, 2021a) and (2) results of structure-from-motion (SfM) processing (orthomosaics, digital elevation
models, and hillshade models in a raster format), uncertainty assessments
(precision maps), and effects of geomorphological mapping in vector format
(10.5281/zenodo.4498296, Tomczyk and Ewertowski, 2021b). The availability of unprocessed images means that the
potential user can derive their own photogrammetric products using more
advanced technologies (potentially available in the future) to ensure
coherence with future-collected monitoring data.
Potential applications of the presented dataset include (1) assessment and
quantification of landscape changes as an immediate result of glacier lake
outburst flood (Tomczyk and Ewertowski, 2020; Tomczyk et al., 2020); (2)
long-term monitoring of high-Arctic river valley development (in conjunction
with other datasets); (3) establishing a baseline for quantification of
geomorphological impacts of future glacier lake outburst floods; (4)
assessment of geohazards related to bank erosion and debris flow development
(hazards for research station infrastructure – station buildings and
bridge); (5) monitoring of permafrost degradation; and (6) modelling flood
impacts on river ecosystem, transport capacity, and channel stability.
Data acquisitionStudy area
The Zackenberg River is located in northeast Greenland (74∘30′ N,
20∘30′ W) (Fig. 1a, b). The river is approximately 36 km
long, and its catchment covers 514 km2, 20 % of which is
glacier-covered. Water sources include melting glaciers, snowmelt, thawing
of permafrost, and precipitation (Søndergaard et al., 2015; Kroon et
al., 2017; Christensen et al., 2021). Typical discharges during summer months
were from 20 to 50 m3 s-1 and usually lower at
the end of the melting season (September–October) (Søndergaard et al.,
2015; Ladegaard-Pedersen et al., 2017). One of the Zackenberg River's
characteristics is regular floods during summer related to sudden lake
drainage – probably due to rupture of the glacier dam (see Jensen et
al., 2013; Behm et al., 2017, 2020). Between 1996 and 2018, 14
extreme flood events with discharges of over 100 m3 s-1 were
recorded (Kroon et al., 2017; Tomczyk and Ewertowski, 2020), while two
additional ones were observed in the winter period (Kroon et al., 2017). Such events had an enormous impact on the riverscape geomorphology (Tomczyk and Ewertowski, 2020; Tomczyk et al., 2020), discharge and sediment transport (Hasholt et al., 2008; Søndergaard et al., 2015; Ladegaard-Pedersen et al., 2017), and delivery of nutrients and sediments
into the fiord and delta development (Bendixen et al., 2017; Kroon et
al., 2017). In this context, the given dataset aims to establish a baseline
for monitoring the consequences of future extreme floods by documenting the
state of the riverscape before, during, and after the 2017 glacier lake outburst flood.
Location of the study area (reprinted from Tomczyk et al., 2020, with permission from Elsevier, copyright
2020). Panel (d) shows survey area with extent of Figs. 2, 3, 6, and 7 indicated with boxes.
UAV surveys
According to the guidelines for using structure-from-motion (SfM)
photogrammetry in geomorphological research (see James et al., 2019), details about UAV surveys are presented in Sect. 2.2, and the
parameters used for SfM processing are detailed in Sect. 3. In that way,
other researchers can use the data to replicate our results; alternatively,
as new approaches become available, novel processing methods can be
utilised.
Rationale
There were three primary goals for conducting the UAV surveys: (1) to collect
data that would enable quantifying medium-term (i.e. temporal scale of
several years) changes in the river landscape – compared to the available
high-resolution 2014 data (COWI, 2015); (2) to document river state
and immediate landscape response during the 2017 flood; and (3) to establish
a baseline for the monitoring of geomorphological changes in response to
future glacier lake outburst floods, including potential geohazards to
research infrastructure (i.e. bridge and buildings of the research station).
To achieve these aims, it was necessary to collect data with high spatial
resolution, preferably better than 0.05 m ground sampling distance (GSD)
(Fig. 2), covering a 2.1 km long section of the river from the bridge to the
delta.
We used a lightweight, consumer-grade UAV – multirotor DJI Phantom 4 Pro.
The low weight (1.4 kg) combined with a small size (0.35 m diagonal)
ensures that the UAV could be easily transported in the field using a
backpack – this was essential, as mechanised transport is not allowed due
to fragility of the vegetation. The UAV was equipped with DJI 20MP, 1 in.
size CMOS RGB sensor and a global shutter – camera model FC6310 (Table 1).
There was a prime lens with 8.8 mm focal length (24 mm equivalent for 35 mm), aperture range from f/2.8 to f/11, and autofocus. A three-axis (pitch, roll, yaw) gimbal stabilised the camera, enabling it to take sharp pictures while
the craft was in motion. The UAV was equipped with a global navigation
satellite system (GNSS) receiver, capable of receiving signals from GPS and
GLONASS satellite positioning systems.
Outline of UAV surveys' parameters, processing errors, and final
products' characteristics following the guidelines suggested by
James et al. (2019).
Survey date 5 August 20176 August 20178 August 2017Camera modelFC6310 Sensor size (mm)13.2 × 4.62 Image size (pixels)5464 × 3640 Focal length (mm): nominal (35 mm equivalent)8.8 (24) Pixel size (µm)2.42 Camera shutter typeMechanical, global Coverage (km2)0.971.180.96Average flight height above ground level (m)7110987Number of images19728871929Ground sampling distance (cm px-1)1.792.782.21Number of tie points after filtration1 438 4531 158 3101 173 564Tie point root-mean-square reprojection error (px)0.290.440.28Average tie point multiplicity4.574.904.76Mean key point size (px)2.613.052.58Dense cloud point density (points m-2)778322512Number of control points615761Number of checkpoints392122Total (3D) RMSE (cm) on control points13.8812.0410.77Total (3D) RMSE (cm) on checkpoints15.3312.1613.30SD of total (3D) errors (cm) on checkpoints6.944.435.04Mean point coordinate precision (mm) [SD]:X3.8 [1.5]6.1 [3.1]4.3 [1.8]Y3.7 [1.4]5.6 [2.99]3.9 [1.5]Z10.7 [4.3]15.3 [7.9]11.9 [4.4]Survey design and execution
To collect the necessary data, we designed an initial survey plan comprised
of five lines approximately parallel to the main river channel's course
routed over the centre of the main channel and both banks. During the
surveys, this design was modified, as the river sections containing
meandering segments were too wide to be captured with five lines of images
with necessary overlap. Therefore, we turned to surveying N–S lines of the
images, covering both the river channel and its neighbourhood.
Individual flights were operated manually, using DJI GO 4 app for Android,
for in such high latitudes the on-board GNSS and magnetometer were potentially
prone to erroneous reading. Related unexpected behaviours (e.g. errors in
compass reading or loss of GNSS signal) were easier to tackle in the manual
than automated mode. We captured mostly nadir images with a high overlap
(>80 %). Additional oblique images were collected to cover the
steep, near-vertical riverbank sections so as to ensure their proper
representation in the model. Due to the length of the studied river section,
and to comply with the visual line of sight (VLOS) flight operations, three
take-off/landing sites were used each day. The weather condition for each
day was good (i.e. no precipitation nor strong winds), and illumination
conditions were sunny. The UAV surveys were performed at average nominal
altitudes (from 70 to 110 m above ground level) to achieve the desired GSD
(Table 1). In total, 1972 images were taken on 5 August 2017 (before-flood
dataset), 887 images on 6 August 2017 (during-flood dataset), and 1929
images on 8 August 2017 (after-flood dataset). As the river level was
fluctuating during the flood (6 August survey), we used a higher flight
altitude, which translated into a lower number of images captured on 6 August but enabled us to cover the area more quickly with approximately the
same water level during the survey. Therefore, it was a compromise between
photogrammetric quality (i.e. the image network geometry), desired GSD, and
rapidly changing flood conditions.
The unprocessed images captured during the surveys are available at
10.5281/zenodo.4495282 (Tomczyk and Ewertowski, 2021a). They can be used by interested parties to generate their
own photogrammetric products using different methods and/or software than
those described in Sect. 3.
Data processingStructure-from-motion processing
The UAV-captured images were processed using Agisoft Metashape Professional
Edition 1.5.2. The values used for processing settings in each step were the
following.
Camera settings. Camera type: frame; enable rolling shutter compensation:
unchecked (as the UAV was equipped with global shutter).
Image alignment and sparse point cloud generation. Accuracy: high;
generic preselection: yes; reference preselection: yes; key point limit:
100 000; tie point limit: 0 (i.e. unlimited).
Gradual selection and removal of the outliers and erroneous points.
Three-stage selection based on reconstruction uncertainty: 10; reprojection
error: 0.5; projection accuracy: 6.
Optimisation of the sparse point cloud. Parameters: f, B1, B2, cx, cy, K1, K2, P1, and P2.
Dense point cloud generation. Quality: high; depth filtering: aggressive.
DEM generation. Source data: dense cloud; interpolation: enabled.
The external orientation of the reconstructed scene was established using
coordinates of each camera position obtained from the on-board GNSS system.
To further constrain the geometry of the scene, additional control points (CPs)
were used. As we were not able to collect high-quality ground control
points (we did not have access to centimetre-accuracy survey equipment, and it was
not possible to cross the river during the flood, because of the high water
level), CPs were then generated post-survey using previous UAV dataset from
2014 (COWI, 2015). In total, 100 points were selected, located mostly
on stable, flat boulders, which were easy to identify in the images. CPs
were distributed on level terrain to minimise the impact of potential
permafrost creep. Distribution of CPs was along both sides of the river to
ensure that the distance between individual points is less than 100 m, which
was suggested as optimal by Tonkin and Midgley (2016). The projection
used was UTM 27N. The number of points used as control to optimise the
exterior orientation was 61 (5 August), 57 (6 August), and 61 (8 August). The
remaining points were used as independent checkpoints: 39 (5 August), 21 (6
August), and 22 (8 August). A smaller number of points used for data collected
on 6 and 8 August were related to differences in coverage.
SfM processing results
The produced tie points clouds consisted of between 1.2 million (6 and 8 August) and 1.4 million (5 August) filtered points, with low tie point
reprojection errors from 0.28 to 0.44 px, which was indicative of the
high quality of the image geometry network (Table 1). Dense cloud point
density varied from 322 points m-2 (6 August) to 778 points m-2 (5 August). These translated to orthomosaics with GSDs from 0.018 m (5 August)
to 0.028 m (6 August) and DEMs with GSDs from 0.036 to 0.056 m (Fig. 3).
The RMSEs for control points and checkpoints were between 0.12 and
0.15 m, which was expected, as the control points and checkpoints were transferred
from previously existing data. The coherence between models was also
estimated based on test areas selected in stable fragments of moraine and
palaeo-delta to ensure significant systematic differences in elevation
between datasets do not exist. The final products of SfM processing
(orthomosaic and DEMs) and their derivative (hillshade models) for each data
are available at 10.5281/zenodo.4498296 (Tomczyk and Ewertowski, 2021b).
Examples of the delivered dataset illustrating before and after the
flood situation: (a, e) digital elevation model; (b, f) hillshade model; (c, g) orthomosaics; (d, h) results of geomorphological mapping.
Mapping
The mapping process was based on the approach proposed by Chandler et al. (2018); i.e. identification and
interpretation of the geomorphological features were based on a combined
analysis of remote sensing products and their derivatives (orthomosaics,
DEMs, slope maps, hillshade models) as well as ground truthing. Final
shapefile datasets were vectorised on-screen in ArcMap 10.6 software. The
main geomorphological units (e.g. relict fluvial terraces, modern
floodplain, slopes) and areas affected by mass movements of various types
(e.g. debris flows, debris slumps) were mapped as polygons. Additional
layers of polylines included features such as scarps or thermal-contraction
cracks. River extent (i.e. area covered by water) is provided for each day
as a separate polygon layer. Geomorphological features are provided as a
separate file for before-the-flood (5 August 2017) and after-the-flood (8 August 2017) datasets. The mapping results in the form of vector files in the
SHP format (compatible with most GIS software) are available to download
from 10.5281/zenodo.4498296 (Tomczyk and Ewertowski, 2021b). Vector data combined with the
hillshade models were presented as a series of geomorphological maps (see Tomczyk and Ewertowski, 2020, for details).
Quality assessment and known limitations
The quality of the presented datasets was assessed in relation to the
outside world (i.e. external or absolute accuracy) and in relation to each
survey (internal precision). Quality assessment based on data presented in
Table 1 indicates a high quality of internal image network geometry,
illustrated by low sub-pixel values of tie point reprojection errors. The
external accuracy was estimated based on root-mean-square errors (RMSEs) and
standard deviations (SDs) of errors on checkpoints, which were between 0.12
and 0.15 m (Table 1). The maximum external error for two checkpoints was
-0.4 and 0.4 m. Although such values are higher than the GSD of all
datasets (between 0.018 and 0.028 m), such magnitude of errors was
considered acceptable for the quantification and mapping of landscape
changes, especially as between 5 and 8 August the resultant lateral
erosion of riverbanks from the flood reached almost 10 m in some sections (see Tomczyk et al., 2020, for details); therefore, the observed
changes were up 100 times larger than RMSE. If necessary, lower values of
absolute accuracy can be achieved in the future if additional ground control
points are surveyed using a centimetre-accuracy survey equipment. Moreover, if
better relative accuracy (i.e. survey-to-survey accuracy) is necessary in
the future monitoring applications, co-alignment of UAV time series can
provide better relative accuracy than the classic approach of individual SfM
processing of each survey using ground control points (GCPs) – as demonstrated in several studies (e.g. Feurer and Vinatier, 2018; Cook and Dietze, 2019; de Haas et al.,
2021). Therefore, we provided also unprocessed images so the potential user
can perform their own SfM processing.
Precision estimates for X, Y, and Z coordinates of tie points.
Location of the studied river section is presented in Fig. 1d.
The internal quality of the reconstructed scenes was based on tie point
precision. To estimate the spatial variability of the models'
photogrammetric and georeferencing uncertainties, the precision estimates
for sparse point clouds were generated in Agisoft Metashape and exported
using the Python script provided by James et al. (2020).
The precision analysis indicated that the vertical component was less
spatially consistent than the horizontal ones for all three surveys (Fig. 4). For the models' ground parts, the overall precision was limited by the
precision of control points, which is not surprising as they were derived
from the older, less detailed remote sensing dataset. The internal accuracy
of each survey was assessed based on the mean point precision estimates,
which varied from 4 to 6 mm for the horizontal component and from 11 to 15 mm for the vertical one (Table 1) – the weakest values were for the 6 August 2017 dataset, which was expected as the average flying altitude was
highest then. Precision maps are available to download from 10.5281/zenodo.4498296 (Tomczyk and
Ewertowski, 2021b). Z discrepancies on control points were calculated using
Doming Analysis software (v.1.0) (James et al., 2020). The
analysis indicated no doming distortion (Fig. 5), which is probably related
to the generally very high overlap of images and the inclusion of oblique
images of the steep riverbanks.
Spatial distribution of errors on control points and checkpoints: (a)Z error against radial distance from the tie point cloud centroid (i.e. from
the centre of the reconstructed scene). The distribution of errors along a
straight line (indicated here also as “modelled constant”) suggests that
no systematic errors such as doming or dishing were observed in the
reconstructed scenes (see James et al., 2020, for details
about interpretation); (b)Z error by colour in plan view (X and Y are
distanced from tie point centroid). Note that each row shows an individual
survey.
Examples of encountered problems: (a) undercut/overhanging river
sections; (b) rapidly moving water; (c) artefacts related to errors in
surface reconstruction.
Individual orthomosaics and DEMs were also inspected, resulting in the
discovery of the following problems, which ought to be taken into
account in any future analysis.
In general, the interpretation of riverbank conditions can be influenced by
vegetation cover and/or bank undercutting (Niedzielski et al., 2016;
Hemmelder et al., 2018). While vegetation cover is usually not a problem in
the case of Arctic rivers, other obstacles (e.g. shadows, infrastructure)
might prevent the direct measurements of the bank's heights. In the case of
the presented dataset, some sections of riverbanks were steep,
near-vertical, before the flood (Figs. 6a and 7). However, during the flood,
some of the sections were significantly undercut, forming deeply incised
niches (Fig. 7) – these overhanging banks obstructed the view of the bottom part of
some studied sections from the air. During the UAV campaigns, we took
oblique images to at least produce a proper representation of steep slopes;
however, it was not possible to take horizontal images due to the presence
of water. As a result, it was impossible to calculate the volume of
sediments eroded from the niches under these overhanging sections.
Structure-from-motion is based on reconstructing the image network geometry
based on characteristic points that appear in several images (Westoby et al., 2012). It therefore fails where
there are rapidly moving objects, which changed their position in time
between the images captured. The structure-from-motion photogrammetry can
reconstruct the location of points in dry areas and, in the case of
transparent water, also points located underwater (Carrivick and Smith, 2019). However, in our study, the high turbidity of water and sediment suspension prevented viewing of the
riverbed. As an Arctic river, the Zackenberg River has suspended sediment
concentrations within a range of 50 to 500 mg L-1 (Søndergaard et al., 2015), which can increase even up
to 4000 mg L-1 during glacial lake outburst floods, indicated by the
lack of transparency and the yellow or brown colours of water in the
orthomosaics. The turbidity of water is also very high (Ladegaard-Pedersen et al., 2017), as was also found in our
surveys. The fact that the water surface was full of ripples gave rise to
bidirectional reflectance problems. Therefore, it was not possible to
adequately resolve the surface of flowing water (Fig. 6b). To partly address this issue, the water bodies were masked from DEMs and hillshade models. They are, however, visible in orthomosaics, which enables the user to assess the character of water flow (Fig. 8).
Some fragments of the models revealed artefacts associated with mismatches
in point generation. These areas can generate erroneous elevation values,
which can be identified in the DEM and hillshade model as unexpectedly rough
surfaces in places where the ground level should be uniform (Fig. 6c). These areas were indicated with polygon files for easy identification in case of future analysis.
Examples of steep and undercut riverbanks.
Different character of water surfaces: (a) stagnant and slow
flowing water; (b) moderate flow rate; (c) rapid, turbulent water flow.
Examples of DEM of Differences demonstrating geomorphic change
detection for two debris flows located in the vicinity of the Zackenberg
Research Station.
Code and data availability
All described data are available in the Zenodo repository. The structure of the dataset is as follows.
Unprocessed UAV-captured images (∼46 GB) are available at
10.5281/zenodo.4495282 (Tomczyk and Ewertowski, 2021a). The images are zipped into three folders following naming convention: 2017_08_05_before_flood_unprocessed_UAV_images, 2017_08_06_during_flood_unprocessed_UAV_images, and 2017_08_08_after_flood_unprocessed_UAV_images. The images are in JPG format and contain embedded positions in geographic
coordinate system WGS84 obtained from the on-board GNSS receiver.
The results of photogrammetric processing (∼ 18 GB) are
available at 10.5281/zenodo.4498296 (Tomczyk and Ewertowski, 2021b) in the file Sfm_products.zip and are grouped into subfolders with the following names: dem (containing digital elevation models), orthomosaic (containing orthomosaics), and hs (containing hillshade models); all data are in GeoTIFF format in the UTM 27N projected coordinate system. Individual files are named as follows: yyyy_mm_dd_[filetype]_[status].tif, where
yyyy_mm_dd is a date, e.g.
2017_08_05;
[filetype] represents the possible values dem (= DEM), ortho (= orthomosaic), and hs (= hillshade); and
[status] represents the possible values before_flood,
during_flood, and after_flood.
The mapping results (25 MB) are in the same repository entry as SfM
processing results, i.e. at 10.5281/zenodo.4498296 (Tomczyk and
Ewertowski, 2021b) in the folder “mapping.zip”. Inside, there are four
subfolders:
General, which contains general vectors that did not change over the course of 3 d (e.g. station buildings, 4x4 trail);
River_extent, which contains polygons for river extent for 2014
(generated from older UAV data (COWI, 2015)) and for 5, 6, and 8 August 2017 – the 2017 data are named as yyyy_mm_dd_river;
Before_flood_geomorphology, which contains polygon
and lines illustrating geomorphological features before the flood, with
separate files providing extent of mass movements which can be potentially
hazardous, e.g. debris flows, debris falls, rockfalls, and slumps (names of
individual files are provided in Table 2); and
After_flood_geomorphology, which contains polygons
and lines illustrating geomorphological features after the flood, with
separate files providing extent of mass movements which can be potentially
hazardous, e.g. debris flows, debris falls, rockfalls, and slumps (names of
individual files are provided in Table 2).
All data are in SHP vector format in the UTM 27N projected coordinate
system.
Precision estimates for tie points and precision maps for X, Y, and Z
coordinates are in the same repository entry as SfM processing results, i.e. at 10.5281/zenodo.4498296 (Tomczyk and
Ewertowski, 2021a) in the folder “uncertainty_assessment.zip”. Individual files are named as follows:
yyyy_mm_dd_[before_flood/during_flood/after_flood]_points_precision.txt, which contain precision estimates for each tie point; and
yyyy_mm_dd_[before_flood/during_flood/after_flood]_[X/Y/Z]_precision.tif, which contain precision estimates for each coordinate as raster file.
Structure-from-motion processing was performed in the proprietary software
Agisoft Metashape (https://www.agisoft.com/, Agisoft, 2021). Mapping was
performed in ArcMap (https://www.esri.com/en-us/arcgis/about-arcgis/overview, Esri, 2021). Python script
exporting precision estimates from Agisoft Metashape and Doming Analysis
software (v.1.0) (James et al., 2020) are available to
download from https://www.lancaster.ac.uk/staff/jamesm/software/sfm_georef.htm.
List of filenames for corresponding dates and content.
FilenameContent descriptionGeneral files folder 2017_4x4_track.shpTrack accessible to station vehicle2017_bridge.shpLocation of the pedestrian bridge across the Zackenberg River2017_thermal_contraction_cracks.shpThermal-contraction cracksRiver_extent folder 2010_river_mask.shpExtent of the river vectorised from 2014 data (COWI, 2015)2017_08_05_before_flood_land_mask.shpExtent of the land area in before-flood orthomosaic2017_08_05_before_flood_river_mask.shpArea covered by water in before-flood orthomosaic2017_08_06_during_flood_land_mask.shpExtent of the land area in during-flood orthomosaic2017_08_06_during_flood_river_mask.shpArea covered by water in during-flood orthomosaic2017_08_08_after_flood_land_mask.shpExtent of the land area in after-flood orthomosaic2017_08_08_after_flood_river_mask.shpArea covered by water in after-flood orthomosaicGeomorphological features 5 August 2017 (before flood)8 August 2017 (after flood)Content description2017_08_05_before_flood_mass_movement_lines.shp2017_08_08_after_flood_mass_movement_lines.shpLinear elements of mass-movement-related features (active fluvial scarps, stable fluvial scarps, old failure scarp)2017_08_05_before_flood_mm_debris_fall.shp2017_08_08_after_flood_ mm_debris_fall.shpLandforms related to debris fall activity2017_08_05_before_flood_mm_debris_flow.shp2017_08_08_after_flood_mm_debris_flow.shpLandforms related to debris flow activity–2017_08_08_after_flood_ mm_rockfall.shpLandforms related to debris rockfall activity2017_08_05_before_flood_mm_slump.shp2017_08_08_after_flood_mm_slump.shpLandforms related to debris slump activity2017_08_05_before_flood_morphology_polygons.shp2017_08_08_after_flood_morphology_polygons.shpMorphological units stored as polygons (e.g. modern floodplain, alluvial fan, relict fluvial terrace, flat area, gentle bank, steep bank)2017_08_05_before_flood_surface_runoff_traces.shp2017_08_08_after_flood_surface_runoff_traces.shpTraces of surface runoffConclusions
The ability to detect changes in the geomorphology of the riverbed and
riparian areas remains a crucial issue in monitoring and modelling the
geomorphic effects of flood events. Using a UAV survey for rapid assessment,
as in the case of the studied 2017 flood, can be more beneficial than other
methods (like high-resolution satellite imagery, terrestrial laser scanning) (cf. Carrivick et al., 2016; Smith et al., 2016), as it allows for
covering the substantial length of the river with high-resolution data. Such
data are intended to be a baseline for future monitoring projects. Potential
applications of the presented dataset include the following.
Establishing a long-term monitoring of high-Arctic river valley development in a permafrost terrain. Climate warming in the Arctic is more intense
than in other regions (see Moritz et al., 2002; Walsh et al., 2011;
Duarte et al., 2012), with the thawing of permafrost in Greenland being one of the effects (Elberling et al., 2013; Anderson et al., 2017). In such a dynamic environment, riverscapes are also likely to transform rapidly (Chassiot et al., 2020). As our data cover the river section located close to the Zackenberg Research Station, it facilitates logistics and can potentially enable developing long-term remote sensing data series illustrating the dynamic response of the riverscape to ongoing climate change, which is essential from the standpoint of long-term landscape evolution.
Quantification, monitoring, and modelling of geomorphological impacts of glacier lake outburst flood. The presented dataset was meant to quantify changes related to the 2017 GLOF (see Tomczyk and Ewertowski, 2020; Tomczyk et al., 2020); however, these studies only described the immediate impacts of a single flood event. An example of geomorphic change detection is presented in Fig. 9, demonstrating the acceleration of debris flows resulting from sediment entrainment at the base of the river banks by floodwater. Overall, the observed changes were spatially variable – erosion dominated along steep banks as expected; however, understanding of differences in erosion rates between sites requires further studies, which will consider differences in lithology as well as modelling of water flow to investigate potential erosion forces in relation to channel characteristics.
The first GLOF at Zackenberg was observed in 1996, and since then floods
occurred every year or at 2-year intervals (Kroon et al., 2017; Tomczyk and Ewertowski, 2020). The lake, which is the source of GLOF, is located
more than 3 km from the current ice margin, so we expect a similar or higher
frequency of floods as more water will be melting from glaciers and stored in the
lake. Thus, future monitoring is needed to investigate whether the GLOFs
will be observed more frequently but with lower discharge magnitude or less often but with higher discharge.
Process-based modelling studies. As the high-magnitude, low-frequency
events are typically rare and difficult to predict, our understanding of the
quantitative aspect of geomorphological changes related to them remains
limited compared to the normal processes (Tamminga et al., 2015a). These
arise particularly from difficulties in collecting high-resolution data
before and after these innately unpredictable and rare flood events.
However, investigation into the geomorphological response of river
morphology to extreme events is key to understanding the evolution of
river morphology and crucial from the standpoint of river modelling
and monitoring (Tamminga et al., 2015a, b). Moreover,
the relationship between the magnitude of the flood and geomorphological
effects is not fully understood. For example, in the case of Zackenberg
River, immediate (2 d) lateral erosion compared to 3-year erosion was
spatially very diversified. In some sections, immediate lateral erosion
after the 2017 flood reached up to 10 m, whereas the same section was stable
between 2014 and 2017, even though higher peak discharges characterised 2015
and 2016 GLOFs compared to 2017 GLOF (Tomczyk et al., 2020). Further process-based
studies are necessary to observe and model links between the magnitude of a
flood and the severity of erosion. It is especially important in periglacial
landscapes where lateral bank erosion can be responsible for delivering a
large quantity of organic matter and widespread changes in ecosystems,
especially combined with other weather extreme events (see Christensen et al., 2021). Using the provided dataset as a baseline for the monitoring of
future changes, it should be possible to quantify the difference between
geomorphological effects of normal (i.e. high-frequency, low-magnitude)
processes on the one hand and extreme (i.e. low-frequency, high-magnitude)
events on the other. Also, by linking the intensity of a geomorphological
response to hydrological data about flood characteristics, it should be
possible to improve modelling routines (see Carrivick, 2007a, b;
Carrivick et al., 2011; Guan et al., 2015; Staines and Carrivick, 2015).
Geohazard assessment. The Zackenberg Research Station premises are
located close to the riverbank, which is regularly affected by floods. The
development of debris flows, which has started to threaten the station's
infrastructure, is one outcome of the removal of sediments from the channel
by flood. Another example of geohazards is the washing out of the foundation of the
bridge located up the valley. These hazards require regular monitoring to
prevent damage to the infrastructure, and the presented database can be used
to assess current hazards and establish a baseline for future monitoring.
Author contributions
AMT and MWE collected data during the field campaign and performed the
photogrammetric processing and uncertainty analysis. AMT mapped the
geomorphology and wrote the paper with input from MWE.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are very grateful for the support from INTERACT network, which allowed us
to visit Zackenberg Research Station in 2017.
The realisation of the fieldwork would not have been possible without
logistic support provided by the crew of the Zackenberg Research Station.
Financial support
This research has been supported by the Horizon 2020 project INTERACT (grant no. 730938, project number 119 (ArcticFan)).
Review statement
This paper was edited by Alexander Gelfan and reviewed by Dmitry Petrakov and three anonymous referees.
ReferencesAgisoft: Homepage, available at: https://www.agisoft.com/, last access: 9 November 2021.Anderson, N. J., Saros, J. E., Bullard, J. E., Cahoon, S. M. P., McGowan,
S., Bagshaw, E. A., Barry, C. D., Bindler, R., Burpee, B. T., Carrivick, J.
L., Fowler, R. A., Fox, A. D., Fritz, S. C., Giles, M. E., Hamerlik, L.,
Ingeman-Nielsen, T., Law, A. C., Mernild, S. H., Northington, R. M., Osburn,
C. L., Pla-Rabès, S., Post, E., Telling, J., Stroud, D. A., Whiteford,
E. J., Yallop, M. L., and Yde, J. C.: The Arctic in the Twenty-First
Century: Changing Biogeochemical Linkages across a Paraglacial Landscape of
Greenland, Bioscience, 67, 118–133, 10.1093/biosci/biw158, 2017.Behm, M., Walter, J. I., Binder, D., and Mertl, S.: Seismic Monitoring and
Characterization of the 2012 Outburst Flood of the Ice-Dammed Lake A.P. Olsen
(NE Greenland), AGU Fall Meeting 2017, New Orleans, 11–15 December 2017, C41D-0432, available at: https://ui.adsabs.harvard.edu/abs/2017AGUFM.C41D1260B (last access: 9 November 2021), 2017.Behm, M., Walter, J. I., Binder, D., Cheng, F., Citterio, M., Kulessa, B.,
Langley, K., Limpach, P., Mertl, S., Schöner, W., Tamstorf, M., and
Weyss, G.: Seismic characterization of a rapidly-rising jökulhlaup cycle
at the A.P. Olsen Ice Cap, NE-Greenland, J. Glaciol., 66, 329–347,
10.1017/jog.2020.9, 2020.Bendixen, M., Lønsmann Iversen, L., Anker Bjørk, A., Elberling, B.,
Westergaard-Nielsen, A., Overeem, I., Barnhart, K. R., Abbas Khan, S., Box,
J. E., Abermann, J., Langley, K., and Kroon, A.: Delta progradation in
Greenland driven by increasing glacial mass loss, Nature, 550, 101–104,
10.1038/nature23873, 2017.Carrivick, J. L.: Modelling coupled hydraulics and sediment transport of a
high-magnitude flood and associated landscape change, Ann. Glaciol., 45,
143–154, 10.3189/172756407782282480, 2007a.Carrivick, J. L.: Hydrodynamics and geomorphic work of jökulhlaups
(glacial outburst floods) from Kverkfjöll volcano, Iceland, Hydrol.
Process., 21, 725–740, 10.1002/hyp.6248, 2007b.Carrivick, J. L. and Smith, M. W.: Fluvial and aquatic applications of
Structure from Motion photogrammetry and unmanned aerial vehicle/drone
technology, WIREs Water, 6, e1328, 10.1002/wat2.1328, 2019.Carrivick, J. L. and Tweed, F. S.: A global assessment of the societal
impacts of glacier outburst floods, Global Planet. Change, 144, 1–16,
10.1016/j.gloplacha.2016.07.001, 2016.Carrivick, J. L. and Tweed, F. S.: A review of glacier outburst floods in
Iceland and Greenland with a megafloods perspective, Earth-Sci. Rev., 196,
102876, 10.1016/j.earscirev.2019.102876,
2019.Carrivick, J. L., Russell, A. J., and Tweed, F. S.: Geomorphological
evidence for jökulhlaups from Kverkfjöll volcano, Iceland,
Geomorphology, 63, 81–102, 10.1016/j.geomorph.2004.03.006, 2004.Carrivick, J. L., Jones, R., and Keevil, G.: Experimental insights on
geomorphological processes within dam break outburst floods, J. Hydrol., 408,
153–163, 10.1016/j.jhydrol.2011.07.037,
2011.Carrivick, J. L., Turner, A. G. D., Russell, A. J., Ingeman-Nielsen, T., and
Yde, J. C.: Outburst flood evolution at Russell Glacier, western Greenland:
effects of a bedrock channel cascade with intermediary lakes, Quaternary Sci.
Rev., 67, 39–58, 10.1016/j.quascirev.2013.01.023, 2013.
Carrivick, J. L., Smith, M. W., and Quincey, D. J.: Structure from Motion in
the Geosciences, Analytical Methods in Earth and Environmental Science,
Wiley-Blackwell, Oxford, UK, 208 pp., 2016.Carrivick, J. L., Tweed, F. S., Ng, F., Quincey, D. J., Mallalieu, J.,
Ingeman-Nielsen, T., Mikkelsen, A. B., Palmer, S. J., Yde, J. C., Homer, R.,
Russell, A. J., and Hubbard, A.: Ice-Dammed Lake Drainage Evolution at
Russell Glacier, West Greenland, Front. Earth Sci., 5, 100,
10.3389/feart.2017.00100, 2017.Chandler, B. M. P., Lovell, H., Boston, C. M., Lukas, S., Barr, I. D.,
Benediktsson, Í. Ö., Benn, D. I., Clark, C. D., Darvill, C. M.,
Evans, D. J. A., Ewertowski, M. W., Loibl, D., Margold, M., Otto, J.-C.,
Roberts, D. H., Stokes, C. R., Storrar, R. D., and Stroeven, A. P.: Glacial
geomorphological mapping: A review of approaches and frameworks for best
practice, Earth-Sci. Rev., 185, 806–846, 10.1016/j.earscirev.2018.07.015,
2018.Chassiot, L., Lajeunesse, P., and Bernier, J.-F.: Riverbank erosion in cold
environments: Review and outlook, Earth-Sci. Rev., 207, 103231, 10.1016/j.earscirev.2020.103231, 2020.Christensen, T. R., Lund, M., Skov, K., Abermann, J., López-Blanco, E.,
Scheller, J., Scheel, M., Jackowicz-Korczynski, M., Langley, K., Murphy, M.
J., and Mastepanov, M.: Multiple Ecosystem Effects of Extreme Weather Events
in the Arctic, Ecosystems, 24, 122–136, 10.1007/s10021-020-00507-6,
2021.Cook, K. L. and Dietze, M.: Short Communication: A simple workflow for robust low-cost UAV-derived change detection without ground control points, Earth Surf. Dynam., 7, 1009–1017, 10.5194/esurf-7-1009-2019, 2019.Cook, K. L., Andermann, C., Gimbert, F., Adhikari, B. R., and Hovius, N.:
Glacial lake outburst floods as drivers of fluvial erosion in the Himalaya,
Science, 362, 53–57, 10.1126/science.aat4981, 2018.COWI: Mapping Greenland's Zackenberg Research Station, available at: https://www.sensefly.com/app/uploads/2017/11/eBee_saves_day_mapping_greenlands_zackenberg_research_station.pdf (last access: 9 November 2021), 2015.
Dawson, A. G.: Glacier-dammed lake investigations in the Hullet Lake area,
South Greenland, in: Medd. Grønl. Geosci., 11, The Commission for Scientific Research in Greenland, Denmark, Copenhagen, 24 pp., ISBN 8763511584, 1983.Death, R. G., Fuller, I. C., and Macklin, M. G.: Resetting the river
template: the potential for climate-related extreme floods to transform
river geomorphology and ecology, Freshwater Biol., 60, 2477–2496, 10.1111/fwb.12639, 2015.de Haas, T., Nijland, W., McArdell, B. W., and Kalthof, M. W. M. L.: Case
Report: Optimization of Topographic Change Detection With UAV
Structure-From-Motion Photogrammetry Through Survey Co-Alignment, Frontiers
in Remote Sensing, 2, 626810, 10.3389/frsen.2021.626810, 2021.Desloges, J. R. and Church, M.: Geomorphic implications of glacier outburst
flooding: Noeick River valley, British Columbia, Can. J. Earth
Sci., 29, 551–564, 10.1139/e92-048, 1992.Duarte, C. M., Lenton, T. M., Wadhams, P., and Wassmann, P.: Abrupt climate
change in the Arctic, Nat. Clim. Change, 2, 60–62,
10.1038/nclimate1386, 2012.Elberling, B., Michelsen, A., Schädel, C., Schuur, E. A. G.,
Christiansen, H. H., Berg, L., Tamstorf, M. P., and Sigsgaard, C.: Long-term
CO2 production following permafrost thaw, Nat. Clim. Change, 3, 890–894,
10.1038/nclimate1955, 2013.Esri: ArcGis, Esri [software], available at: https://www.esri.com/en-us/arcgis/about-arcgis/overview, last access: 9 November 2021.Feurer, D. and Vinatier, F.: Joining multi-epoch archival aerial images in
a single SfM block allows 3-D change detection with almost exclusively image
information, ISPRS J. Photogramm., 146,
495–506, 10.1016/j.isprsjprs.2018.10.016,
2018.Furuya, M. and Wahr, J. M.: Water level changes at an ice-dammed lake in
west Greenland inferred from InSAR data, Geophys. Res. Lett., 32, L14501,
10.1029/2005GL023458, 2005.Garcia-Castellanos, D. and O'Connor, J. E.: Outburst floods provide
erodability estimates consistent with long-term landscape evolution,
Scientific Reports, 8, 10573, 10.1038/s41598-018-28981-y, 2018.Grinsted, A., Hvidberg, C. S., Campos, N., and Dahl-Jensen, D.: Periodic
outburst floods from an ice-dammed lake in East Greenland, Scientific
Reports, 7, 9966, 10.1038/s41598-017-07960-9, 2017.Guan, M., Wright, N. G., Sleigh, P. A., and Carrivick, J. L.: Assessment of
hydro-morphodynamic modelling and geomorphological impacts of a
sediment-charged jökulhlaup, at Sólheimajökull, Iceland, J.
Hydrol., 530, 336–349, 10.1016/j.jhydrol.2015.09.062, 2015.Harrison, S., Glasser, N., Winchester, V., Haresign, E., Warren, C., and
Jansson, K.: A glacial lake outburst flood associated with recent mountain
glacier retreat, Patagonian Andes, Holocene, 16, 611–620,
10.1191/0959683606hl957rr, 2006.Harrison, S., Kargel, J. S., Huggel, C., Reynolds, J., Shugar, D. H., Betts, R. A., Emmer, A., Glasser, N., Haritashya, U. K., Klimeš, J., Reinhardt, L., Schaub, Y., Wiltshire, A., Regmi, D., and Vilímek, V.: Climate change and the global pattern of moraine-dammed glacial lake outburst floods, The Cryosphere, 12, 1195–1209, 10.5194/tc-12-1195-2018, 2018.Hasholt, B., Mernild, S. H., Sigsgaard, C., Elberling, B., Petersen, D.,
Jakobsen, B. H., Hansen, B. U., Hinkler, J., and Søgaard, H.: Hydrology
and Transport of Sediment and Solutes at Zackenberg, Adv.
Ecol. Res., 40, 197–221, 10.1016/S0065-2504(07)00009-8 2008.Hasholt, B., van As, D., Mikkelsen, A. B., Mernild, S. H., and Yde, J. C.:
Observed sediment and solute transport from the Kangerlussuaq sector of the
Greenland Ice Sheet (2006–2016), Arct. Antarct. Alp. Res.,
50, S100009, 10.1080/15230430.2018.1433789, 2018.Hemmelder, S., Marra, W., Markies, H., and De Jong, S. M.: Monitoring river
morphology & bank erosion using UAV imagery – A case study of the river
Buëch, Hautes-Alpes, France, Int. J. Appl. Earth
Obs., 73, 428–437, 10.1016/j.jag.2018.07.016, 2018.Iribarren, P., Mackintosh, A., and Norton, K. P.: Hazardous processes and
events from glacier and permafrost areas: lessons from the Chilean and
Argentinean Andes, Earth Surf. Proc. Land., 40, 2–21, 10.1002/esp.3524,
2015.James, M. R., Chandler, J. H., Eltner, A., Fraser, C., Miller, P. E., Mills,
J. P., Noble, T., Robson, S., and Lane, S. N.: Guidelines on the use of
structure-from-motion photogrammetry in geomorphic research, Earth Surf. Proc.
Land., 44, 2081–2084, 10.1002/esp.4637, 2019.James, M. R., Antoniazza, G., Robson, S., and Lane, S. N.: Mitigating
systematic error in topographic models for geomorphic change detection:
accuracy, precision and considerations beyond off-nadir imagery, Earth Surf.
Proc. Land., 45, 2251–2271, 10.1002/esp.4878,
2020 (data available at: https://www.lancaster.ac.uk/staff/jamesm/software/sfm_georef.htm, last access: 9 November 2021).
Jensen, L. M., Rasch, M., and Schmidt, N. M. (Eds.): Zackenberg Ecological Research
Operations, 18th Annual Report, 2012, Aarhus University, DCE – Danish
Centre for Environment and Energy, Roskilde, Denmark, 122, 2013.Kroon, A., Abermann, J., Bendixen, M., Lund, M., Sigsgaard, C., Skov, K.,
and Hansen, B. U.: Deltas, freshwater discharge, and waves along the Young
Sound, NE Greenland, Ambio, 46, 132–145, 10.1007/s13280-016-0869-3,
2017.Ladegaard-Pedersen, P., Sigsgaard, C., Kroon, A., Abermann, J., Skov, K.,
and Elberling, B.: Suspended sediment in a high-Arctic river: An appraisal
of flux estimation methods, Sci. Total Environ., 580, 582–592, 10.1016/j.scitotenv.2016.12.006, 2017.Mayer, C. and Schuler, T. V.: Breaching of an ice dam at Qorlortossup
tasia, south Greenland, Ann. Glaciol., 42, 297–302,
10.3189/172756405781812989, 2005.Moore, R. D., Fleming, S. W., Menounos, B., Wheate, R., Fountain, A., Stahl,
K., Holm, K., and Jakob, M.: Glacier change in western North America:
influences on hydrology, geomorphic hazards and water quality, Hydrol.
Process., 23, 42–61, 10.1002/Hyp.7162, 2009.Moritz, R. E., Bitz, C. M., and Steig, E. J.: Dynamics of Recent Climate
Change in the Arctic, Science, 297, 1497–1502, 10.1126/science.1076522, 2002.Nardi, L. and Rinaldi, M.: Spatio-temporal patterns of channel changes in
response to a major flood event: the case of the Magra River
(central–northern Italy), Earth Surf. Proc. Land., 40, 326–339,
10.1002/esp.3636, 2015.Nie, Y., Liu, Q., Wang, J., Zhang, Y., Sheng, Y., and Liu, S.: An inventory
of historical glacial lake outburst floods in the Himalayas based on remote
sensing observations and geomorphological analysis, Geomorphology, 308,
91–106, 10.1016/j.geomorph.2018.02.002,
2018.Niedzielski, T., Witek, M., and Spallek, W.: Observing river stages using unmanned aerial vehicles, Hydrol. Earth Syst. Sci., 20, 3193–3205, 10.5194/hess-20-3193-2016, 2016.
Planet Team: Planet Application Program Interface: In Space for Life on
Earth, San Francisco, CA, 2017.Reynolds, J. M.: High-altitude glacial lake hazard assessment and
mitigation: a Himalayan perspective, Geol. Soc. Eng. Geol. Sp., 15, 25–34,
10.1144/GSL.ENG.1998.015.01.03, 1998.Roberts, M. J., Tweed, F. S., Russell, A. J., Knudsen, Ó., and Harris,
T. D.: Hydrologic and geomorphic effects of temporary ice-dammed lake
formation during jökulhlaups, Earth Surf. Proc. Land., 28, 723–737,
10.1002/esp.476, 2003.Russell, A. J.: Jökulhlaup (ice-dammed lake outburst flood) impact
within a valley-confined sandur subject to backwater conditions,
Kangerlussuaq, West Greenland, Sediment. Geol., 215, 33–49, 10.1016/j.sedgeo.2008.06.011, 2009.Russell, A. J., Gregory, A. R., Large, A. R. G., Fleisher, P. J., and
Harris, T. D.: Tunnel channel formation during the November 1996 jokulhlaup,
Skeioararjokull, Iceland, Ann. Glaciol., 45, 95–103,
10.3189/172756407782282552, 2007.Russell, A. J., Tweed, F. S., Roberts, M. J., Harris, T. D., Gudmundsson, M.
T., Knudsen, Ó., and Marren, P. M.: An unusual jökulhlaup resulting
from subglacial volcanism, Sólheimajökull, Iceland, Quaternary Sci.
Rev., 29, 1363–1381, 10.1016/j.quascirev.2010.02.023, 2010.Russell, A. J., Carrivick, J. L., Ingeman-Nielsen, T., Yde, J. C., and
Williams, M.: A new cycle of jökulhlaups at Russell Glacier,
Kangerlussuaq, West Greenland, J. Glaciol., 57, 238–246,
10.3189/002214311796405997, 2011.Smith, M. W., Carrivick, J. L., and Quincey, D. J.: Structure from motion
photogrammetry in physical geography, Prog. Phys. Geog., 40, 247–275, 10.1177/0309133315615805, 2016.Søndergaard, J., Tamstorf, M., Elberling, B., Larsen, M. M., Mylius, M.
R., Lund, M., Abermann, J., and Rigét, F.: Mercury exports from a
High-Arctic river basin in Northeast Greenland (74∘ N) largely
controlled by glacial lake outburst floods, Sci. Total Environ., 514, 83–91,
10.1016/j.scitotenv.2015.01.097, 2015.Staines, K. E. H. and Carrivick, J. L.: Geomorphological impact and
morphodynamic effects on flow conveyance of the 1999 jökulhlaup at
Sólheimajökull, Iceland, Earth Surf. Proc. Land., 40, 1401–1416,
10.1002/esp.3750, 2015.Tamminga, A., Hugenholtz, C., Eaton, B., and Lapointe, M.: Hyperspatial
remote sensing of channel reach morphology and hydraulic fish habitat using
an unmanned aerial vehicle (UAV): A first assessment in the context of river
research and management, River Res. Appl., 31, 379–391, 10.1002/rra.2743, 2015a.Tamminga, A. D., Eaton, B. C., and Hugenholtz, C. H.: UAS-based remote
sensing of fluvial change following an extreme flood event, Earth Surf. Proc.
Land., 40, 1464–1476, 10.1002/esp.3728, 2015b.Tomczyk, A. M. and Ewertowski, M. W.: UAV-based remote sensing of immediate
changes in geomorphology following a glacial lake outburst flood at the
Zackenberg river, northeast Greenland, J. Maps, 16, 86–100,
10.1080/17445647.2020.1749146, 2020.Tomczyk, A. M. and Ewertowski, M. W.: Before-, during-, and after-flood
UAV-generated images of the distal part of Zackenberg river, northeast
Greenland (August 2017), Zenodo [data set], 10.5281/zenodo.4495282, 2021a.Tomczyk, A. M. and Ewertowski, M. W.: Before-, during-, and after-flood
UAV-generated digital elevation models, orthomosaics, and GIS datasets of
the distal part of Zackenberg river, northeast Greenland (August 2017), Zenodo [data set], 10.5281/zenodo.4498296, 2021b.Tomczyk, A. M., Ewertowski, M. W., and Carrivick, J. L.: Geomorphological
impacts of a glacier lake outburst flood in the high arctic Zackenberg
River, NE Greenland, J. Hydrol., 591, 125300,
10.1016/j.jhydrol.2020.125300, 2020.Tonkin, T. and Midgley, N.: Ground-Control Networks for Image Based Surface
Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived
Imagery and Structure-from-Motion Photogrammetry, Remote Sensing, 8, 786,
10.3390/rs8090786, 2016.Tweed, F. S. and Russell, A. J.: Controls on the formation and sudden
drainage of glacier-impounded lakes: implications for jökulhlaup
characteristics, Prog. Phys. Geog., 23, 79–110, 10.1177/030913339902300104, 1999.Walsh, J. E., Overland, J. E., Groisman, P. Y., and Rudolf, B.: Ongoing
Climate Change in the Arctic, Ambio, 40, 6–16,
10.1007/s13280-011-0211-z, 2011.Watanabe, T. and Rothacher, D.: The 1994 Lugge Tsho Glacial Lake Outburst
Flood, Bhutan Himalaya, Mt. Res. Dev., 16, 77–81, 10.2307/3673897, 1996.Watanabe, T., Lamsal, D., and Ives, J. D.: Evaluating the growth
characteristics of a glacial lake and its degree of danger of outburst
flooding: Imja Glacier, Khumbu Himal, Nepal, Norsk Geogr. Tidsskr., 63,
255–267, 10.1080/00291950903368367, 2009.Weidick, A. and Citterio, M.: The ice-dammed lake Isvand, West Greenland,
has lost its water, J. Glaciol., 57, 186–188, 10.3189/002214311795306600,
2011.Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and
Reynolds, J. M.: `Structure-from-Motion' photogrammetry: A low-cost,
effective tool for geoscience applications, Geomorphology, 179, 300–314,
10.1016/j.geomorph.2012.08.021, 2012.Westoby, M. J., Glasser, N. F., Brasington, J., Hambrey, M. J., Quincey, D.,
and Reynolds, J. M.: Modelling outburst floods from moraine-dammed glacial
lakes, Earth-Sci. Rev., 134, 137–159, 10.1016/j.earscirev.2014.03.009, 2014.Yde, J. C., Žárský, J. D., Kohler, T. J., Knudsen, N. T.,
Gillespie, M. K., and Stibal, M.: Kuannersuit Glacier revisited:
Constraining ice dynamics, landform formations and glaciomorphological
changes in the early quiescent phase following the 1995–98 surge event,
Geomorphology, 330, 89–99, 10.1016/j.geomorph.2019.01.012, 2019.