2019
DOI: 10.5194/essd-2019-213
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2

Abstract: Abstract. The on-going glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring or modeling purposes (e.g. determination of run-off, mass balance, or future glacier extent) and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. Fortunately, already the first S2 images acquired in August 2015 … Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

2
47
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
8
1

Relationship

3
6

Authors

Journals

citations
Cited by 17 publications
(49 citation statements)
references
References 32 publications
2
47
0
Order By: Relevance
“…The dataset of initial glacier ice thickness, available for the year 2003, determines the starting point of our simulations. We performed a validation simulation for the 2003-2015 period by running our model through this period and comparing the simulated glacier surface area of each of the 32 glaciers with MB to observations from the 2015 glacier inventory 16,52 . Then, we ran multiple simulations for this same period by altering the initial ice thickness by ±30% and the glacier geometry update parametrizations by ±10%, according to the estimated uncertainties of each of the two methods 31 .…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The dataset of initial glacier ice thickness, available for the year 2003, determines the starting point of our simulations. We performed a validation simulation for the 2003-2015 period by running our model through this period and comparing the simulated glacier surface area of each of the 32 glaciers with MB to observations from the 2015 glacier inventory 16,52 . Then, we ran multiple simulations for this same period by altering the initial ice thickness by ±30% and the glacier geometry update parametrizations by ±10%, according to the estimated uncertainties of each of the two methods 31 .…”
Section: Methodsmentioning
confidence: 99%
“…The French Alps, located in the westernmost part of the European Alps, experience some of the strongest glacier retreat in the world [15][16][17] . Long-term historical interactions between French society and glaciers have developed a dependency of society on them for water resources, agriculture, tourism 18 -particularly the ski business 19 -and hydropower generation.…”
mentioning
confidence: 99%
“…Complete and accurate glacier inventories also provide the information required for various hydrological and climate modelling applications (Vaughan et al, 2013) as well as change assessment. Accordingly, a frequent update of glacier inventories is required to reduce uncertainties in subsequent calculations (Paul et al, 2020). Updated glacier inventories are also critical to outline environmental policies for glacier protection and monitoring programmes, as well as for developing mitigation and adaptation strate-L. G. Tielidze et al: The new Caucasus glacier inventory gies in response to the impact of climate changes on future glacier development (Pfeffer et al, 2014;Huss et al, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…First of all, the ground control points (GCPs) may not be stable due to the difficulty of establishing permanent targets in the glacier areas, or to the limited GSM coverage in order to exploit real-time GNSS positioning services. In addition, the quality of the individual photogrammetric blocks may be uneven, due to the different drone adopted, the meteorological conditions and the flight plan (O'Connor et al, 2017;Pepe et al, 2018). The goal of this work is to assess the accuracy of Dense Point Clouds (DPCs) and Digital Surface Models (DSMs) obtained from photogrammetric blocks captured by UAVs with the aim of computing volume changes from multi-temporal acquisitions.…”
Section: Introductionmentioning
confidence: 99%