Controls on rapid supraglacial lake drainage in West Greenland: an Exploratory Data Analysis approach

ABSTRACTThe controls on rapid surface lake drainage on the Greenland ice sheet (GrIS) remain uncertain, making it challenging to incorporate lake drainage into models of GrIS hydrology, and so to determine the ice-dynamic impact of meltwater reaching the ice-sheet bed. Here, we first use a lake area and volume tracking algorithm to identify rapidly draining lakes within West Greenland during summer 2014. Second, we derive hydrological, morphological, glaciological and surface-mass-balance data for various factors that may influence rapid lake drainage. Third, these factors are used within Exploratory Data Analysis to examine existing hypotheses for rapid lake drainage. This involves testing for statistical differences between the rapidly and non-rapidly draining lake types, as well as examining associations between lake size and the potential controlling factors. This study shows that the two lake types are statistically indistinguishable for almost all factors investigated, except lake area. Thus, we are unable to recommend an empirically supported, deterministic alternative to the fracture area threshold parameter for modelling rapid lake drainage within existing surface-hydrology models of the GrIS. However, if improved remotely sensed datasets (e.g. ice-velocity maps, climate model outputs) were included in future research, it may be possible to detect the causes of rapid drainage.

Download Full-text

Winter drainage of surface lakes on the Greenland Ice Sheet from Sentinel-1 SAR imagery

The Cryosphere ◽

10.5194/tc-15-1587-2021 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1587-1606

Author(s):

Corinne L. Benedek ◽

Ian C. Willis

Keyword(s):

Ice Sheet ◽

Greenland Ice Sheet ◽

Shape From Shading ◽

Lake Area ◽

Ice Flow ◽

Surface Mass ◽

Flow Acceleration ◽

Optical Imagery ◽

Sar Imagery ◽

Lake Drainage

Abstract. Surface lakes on the Greenland Ice Sheet play a key role in its surface mass balance, hydrology and biogeochemistry. They often drain rapidly in the summer via hydrofracture, which delivers lake water to the ice sheet base over timescales of hours to days and then can allow meltwater to reach the base for the rest of the summer. Rapid lake drainage, therefore, influences subglacial drainage evolution; water pressures; ice flow; biogeochemical activity; and ultimately the delivery of water, sediments and nutrients to the ocean. It has generally been assumed that rapid lake drainage events are confined to the summer, as this is typically when observations are made using satellite optical imagery. Here we develop a method to quantify backscatter changes in satellite radar imagery, which we use to document the drainage of six different lakes during three winters (2014/15, 2015/16 and 2016/17) in fast-flowing parts of the Greenland Ice Sheet. Analysis of optical imagery from before and after the three winters supports the radar-based evidence for winter lake drainage events and also provides estimates of lake drainage volumes, which range between 0.000046 ± 0.000017 and 0.0200 ± 0.002817 km3. For three of the events, optical imagery allows repeat photoclinometry (shape from shading) calculations to be made showing mean vertical collapse of the lake surfaces ranging between 1.21 ± 1.61 and 7.25 ± 1.61 m and drainage volumes of 0.002 ± 0.002968 to 0.044 ± 0.009858 km3. For one of these three, time-stamped ArcticDEM strips allow for DEM differencing, which demonstrates a mean collapse depth of 2.17 ± 0.28 m across the lake area. The findings show that lake drainage can occur in the winter in the absence of active surface melt and notable ice flow acceleration, which may have important implications for subglacial hydrology and biogeochemical processes.

Download Full-text

Simulating the growth of supraglacial lakes at the western margin of the Greenland ice sheet

The Cryosphere ◽

10.5194/tc-6-1077-2012 ◽

2012 ◽

Vol 6 (5) ◽

pp. 1077-1086 ◽

Cited By ~ 27

Author(s):

A. A. Leeson ◽

A. Shepherd ◽

S. Palmer ◽

A. Sundal ◽

X. Fettweis

Keyword(s):

Climate Model ◽

Pearson Correlation ◽

Regional Climate ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Lake Area ◽

Western Margin ◽

Supraglacial Lakes ◽

Using Data ◽

Elevation Model

Abstract. We present a new method of modelling the growth of supraglacial lakes at the western margin of the Greenland ice sheet, based on routing runoff estimated by a regional climate model across a digital elevation model (DEM) of the ice sheet surface. Using data acquired during the 2003 melt season, we demonstrate that the model is 19 times more likely to correctly predict the presence (or absence) of lakes than it is to make incorrect predictions, within an elevation range of 1100 to 1700 metres above sea level (m a.s.l.), when compared with MODIS satellite imagery. Of the 66% of observed lake locations which the model correctly reproduces, the simulated lake onset day is found to be correlated with that observed with a Pearson correlation coefficient of 0.76. Our model accurately simulates maximum cumulative lake area with only a 1.5% overestimate. However, because our model does not simulate processes leading to lake stagnation or decay, such as refreezing or drainage, at present we do not simulate absolute daily lake area. We find that the maximum potential lake-covered ice sheet area is limited by topography to 6.4%. We estimate that this corresponds to a volume of 1.49 km3, 12% of the runoff produced in 2003. This can be taken as an upper bound given uncertainty in the DEM. This study has proved a good first step towards capturing the variability of supraglacial lake evolution with a numerical model. These initial results are promising and suggest that the model is a useful tool for use in analysing the behaviour of supraglacial lakes on the Greenland ice sheet in the present day and potentially beyond.

Download Full-text

An Integrated View of Greenland Ice Sheet Mass Changes Based on Models and Satellite Observations

10.20944/preprints201810.0337.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ruth Mottram ◽

Sebastian B. Simonsen ◽

Synne Høyer Svendsen ◽

Valentina R. Barletta ◽

Louise Sandberg Sørensen ◽

...

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Climate Model ◽

Regional Climate ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Future Climate Change ◽

Future Research ◽

Surface Mass ◽

Mass Budget

The Greenland ice sheet is a major contributor to sea level rise, adding an estimated 0.47 +/− 0.23 mm/yr to global mean sea level between 1991 and 2015 (van den Broeke et al., 2016). Making sea level rise projections for the future and understanding the processes controlling current observed rates of sea level rise are crucially dependent on understanding the present-day state of the ice sheet. Here, we provide an overview of the current state of the mass budget of Greenland based on satellite gravimetry and remote sensing observations of surface elevation change, ice sheet velocity and calving front positions. We also combine these essential climate variables with a regional climate model (RCM) output from an ice sheet model (ISM) to gain insight into poorly understood ice sheet dynamical and surface mass processes. On average from 1992 to 2017 the ice sheet in some locations has lost up −2.65 m/yr in elevation based on ESA Radar altimetry analysis. Calving fronts have retreated all around Greenland since the 1990s and in only two out of 28 study locations have they remained stable. The locations of grounding lines at 5 key glaciers with floating ice tongues have remained stable over the observation period. However a detailed case study at Petermann glacier with an ice fracture model shows the sensitivity of these floating ice shelves to future climate change. GRACE gravimetric mass balance (GMB) data allows us to tie together disparate lines of evidence showing that Greenland has lost about 265 +/− 25 Gt/yr of ice over the period 2002 to 2015. RCM and ISM simulations show that surface mass processes dominate the overall Greenland ice sheet mass budget except for areas of fast ice sheet flow but marked differences between models and between models and observations indicate that not all processes are captured accurately, indicating areas of greater uncertainty and directions of future research for future sea level rise projections.

Download Full-text

Crowd Capital in Governance Contexts

10.31235/osf.io/d9jua ◽

2017 ◽

Author(s):

John Prpić

Keyword(s):

Data Analysis ◽

Data Collection ◽

Private Sector ◽

Exploratory Data Analysis ◽

Research Literature ◽

Future Research ◽

Capital Theory ◽

University Of Oxford ◽

Exploratory Data ◽

Specific Factors

To begin to understand the implications of the implementation of IT-mediated Crowds for Politics and Policy purposes, this research builds the first-known dataset of IT-mediated Crowd applications currently in use in the governance context. Using Crowd Capital theory and governance theory as frameworks to organize our data collection, we undertake an exploratory data analysis of some fundamental factors defining this emerging field. Specific factors outlined and discussed include the type of actors implementing IT mediated Crowds in the governance context, the global geographic distribution of the applications, and the nature of the Crowd derived resources being generated for governance purposes. The findings from our dataset of 209 on-going endeavours indicates that a wide-diversity of actors are engaging IT-mediated Crowds in the governance context, both jointly and severally, that these endeavours can be found to exist on all continents, and that said actors are generating Crowd-derived resources in at least ten distinct governance sectors. We discuss the ramifications of these and our other findings in comparison to the research literature on the private-sector use of IT-mediated Crowds, while highlighting some unique future research opportunities stemming from our work.Prpić, J., & Shukla, P. (2014). Crowd Capital in Governance Contexts. Oxford Internet Institute, University of Oxford - IPP 2014 - Crowdsourcing for Politics and Policy.

Download Full-text

John Tukey, Exploratory Data Analysis, and Its Possibilities for Participatory Action Research

PsycEXTRA Dataset ◽

10.1037/e567862014-001 ◽

2014 ◽

Author(s):

Brett Stoudt

Keyword(s):

Data Analysis ◽

Action Research ◽

Participatory Action Research ◽

Exploratory Data Analysis ◽

Participatory Action ◽

Exploratory Data

Download Full-text

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

Download Full-text

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

10.2196/preprints.24226 ◽

2020 ◽

Cited By ~ 2

Author(s):

Jayesh S

Keyword(s):

Data Analysis ◽

Exploratory Data Analysis ◽

Case Fatality ◽

Public Health Emergency ◽

Virus Spread ◽

Analysis Model ◽

Case Fatality Ratio ◽

First Case ◽

Exploratory Data ◽

The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

Download Full-text

Follow The Clicks: Learning and Anticipating Mouse Interactions During Exploratory Data Analysis

Computer Graphics Forum ◽

10.1111/cgf.13670 ◽

2019 ◽

Vol 38 (3) ◽

pp. 41-52 ◽

Cited By ~ 4

Author(s):

Alvitta Ottley ◽

Roman Garnett ◽

Ran Wan

Keyword(s):

Data Analysis ◽

Exploratory Data Analysis ◽

Exploratory Data

Download Full-text

Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽

10.3390/molecules26051393 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1393

Author(s):

Ralitsa Robeva ◽

Miroslava Nedyalkova ◽

Georgi Kirilov ◽

Atanaska Elenkova ◽

Sabina Zacharieva ◽

...

Keyword(s):

Type 2 Diabetes ◽

Metabolic Syndrome ◽

Data Analysis ◽

Stress Response ◽

Exploratory Data Analysis ◽

Principal Component ◽

Hierarchical Cluster ◽

Obese Women ◽

Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

Download Full-text