Quantitative remote sensing study indicates doubling of coastal erosion rate in past 50 yr along a segment of the Arctic coast of Alaska

Geology ◽  
2007 ◽  
Vol 35 (7) ◽  
pp. 583 ◽  
Author(s):  
J.C. Mars ◽  
D.W. Houseknecht
Keyword(s):  
Remote Sensing ◽  
Coastal Erosion ◽  
Erosion Rate ◽  
The Arctic ◽  
Quantitative Remote Sensing ◽  
Arctic Coast

Sensitivity of organic carbon fluxes from Arctic coastal erosion to climate change

2021 ◽  
Author(s):  
David Marcolino Nielsen ◽  
Patrick Pieper ◽  
Victor Brovkin ◽  
Paul Overduin ◽  
Tatiana Ilyina ◽  
...  
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Sea Ice ◽  
Coastal Erosion ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Carbon Release ◽  
Arctic Coast

<p>When unprotected by sea-ice and exposed to the warm air and ocean waves, the Arctic coast erodes and releases organic carbon from permafrost to the surrounding ocean and atmosphere. This release is estimated to deliver similar amounts of organic carbon to the Arctic Ocean as all Arctic rivers combined, at the present-day climate. Depending on the degradation pathway of the eroded material, the erosion of the Arctic coast could represent a positive feedback loop in the climate system, to an extent still unknown. In addition, the organic carbon flux from Arctic coastal erosion is expected to increase in the future, mainly due to surface warming and sea-ice loss. In this work, we aim at addressing the following questions: How is Arctic coastal erosion projected to change in the future? How sensitive is Arctic coastal erosion to climate change?</p><p>To address these questions, we use a 10-member ensemble of climate change simulations performed with the Max Planck Institute Earth System Model (MPI-ESM) for the Coupled Model Intercomparison Project phase 6 (CMIP6) to make projections of coastal erosion at a pan-Arctic scale. We use a semi-empirical approach to model Arctic coastal erosion, assuming a linear contribution of its thermal and mechanical drivers. The pan-Arctic carbon release due to coastal erosion is projected to increase from 6.9 ± 5.4 TgC/year (mean estimate ± two standard deviations from the distribution of uncertainties) during the historical period (mean over 1850 -1950) to between 13.1 ± 6.7 TgC/year and 17.2 ± 8.2 TgC/year in the period 2081-2100 following an intermediate (SSP2.4-5) and a high-end (SSP5.8-5) climate change scenario, respectively. The sensitivity of the organic carbon release from Arctic coastal erosion to climate warming is estimated to range from 1.52 TgC/year/K to 2.79 TgC/year/K depending on the scenario. Our results present the first projections of Arctic coastal erosion, combining observations and Earth system model (ESM) simulations. This allows us to make first-order estimates of sensitivity and feedback magnitudes between Arctic coastal erosion and climate change, which can lay out pathways for future coupled ESM simulations.</p><p> </p>

scholarly journals Projected increase of Arctic coastal erosion and its sensitivity to warming in the 21st Century

2021 ◽  
Author(s):  
David Nielsen ◽  
Patrick Pieper ◽  
Armineh Barkhordarian ◽  
Paul Overduin ◽  
Tatiana Ilyina ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Coastal Erosion ◽  
Erosion Rate ◽  
Carbon Sink ◽  
The Arctic ◽  
Coastal Conservation ◽  
Organic Carbon Flux ◽  
High Emission ◽  
Future Work ◽  
The Impact

Abstract Arctic coastal erosion damages infrastructure, threatens coastal communities, and releases organic carbon from permafrost. However, the magnitude, timing and sensitivity of coastal erosion increase to global warming remain unknown. Here, we project the Arctic-mean erosion rate to roughly double by 2100 and very likely exceed its historical range of variability by mid-21st century. The sensitivity of erosion to warming also doubles, reaching 0.4-0.5 m year-1 oC-1 and 2.3-2.8 TgC year-1 oC-1 by the end of the century under moderate and high-emission scenarios. Our first 21st-century pan-Arctic coastal erosion rate projections should inform policy makers on coastal conservation and socioeconomic planning. Our organic carbon flux projections also lay out the path for future work to investigate the impact of Arctic coastal erosion on the changing Arctic Ocean, on its role as a global carbon sink, and on the permafrost-carbon feedback.

scholarly journals USING REMOTE SENSING DATA FOR CALCULATING THE COASTAL EROSION IN SOUTHERN THAILAND

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 51-56 ◽  
Author(s):  
W. Pantanahiran
Keyword(s):  
Remote Sensing ◽  
Coastal Erosion ◽  
Erosion Rate ◽  
Remote Sensing Data ◽  
Aerial Photographs ◽  
Observation Area ◽  
Southern Thailand ◽  
Overlay Technique ◽  
Coastal Changes ◽  
Long Term Observation

<p><strong>Abstract.</strong> There has been long-term observation of coastal erosion in Koh Kho Khao , Ban Nam Khem, Phang Nga province, in Thailand, which was affected by a tsunami on December 26, 2004. The disaster, as is well known, caused the loss of lives and property. This area is recognized as one of the best tourist areas in Thailand. The objective of the research was to identify the coastal changes to the island, Koh Kho Khao. The Geographic Information System and Remote Sensing were used. Five- time periods were used, in which aerial photographs and satellite images were taken, with the aerial photographs taken in February, 2002. IKONOS images were taken on December 29, 2004, and Quick Bird images were dated the 23rd of February, 2009. Worldview-2 images were dated the 6th of December, 2012, while the Pleiades images were dated January 8, 2016. The coastlines were compared using the overlay technique. Coastal erosion and coastal deposition during consecutive years were calculated. The results showed that the tsunami in 2004 caused coastal erosion in the area, as coastal changes during those years were found. Additionally, natural adaptation was found after 14 years at the middle and upper parts of the island. Severe coastal erosion of the lower part of the island has been continuously found, with an erosion rate between 2002 and 2004 (2-year period), 2004 and 2009 (5-year period), 2009 and 2012 (3-year period), and 2012 and 2016 (4-year period) of 22.44, 9.96, 19.63, and 12.34 meters per year respectively. In addition the erosion rate between 2002 and 2016 (14-year period) was 100.97 meters per year. It was also found that the seawall was the main factor in the coastal erosion in the lower part of the island because it was recognized that the coastline was sharply cut along the seawall. It is recommended that the lower part of the island be declared a special observation area in order to prevent further coastal erosion.</p>

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

The Last Interglaciation in Northeast Siberia

1995 ◽  
Vol 43 (2) ◽  
pp. 147-158 ◽  
Author(s):  
Anatoly V. Lozhkin ◽  
Patricia M. Anderson
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
The Arctic ◽  
The North ◽  
River Terraces ◽  
Atmospheric General Circulation ◽  
Winter Temperatures ◽  
Atmospheric General Circulation Models ◽  
Arctic Coast ◽  
Organic Deposits

AbstractAlluvial, fluvial, and organic deposits of the last interglaciation are exposed along numerous river terraces in northeast Siberia. Although chronological control is often poor, the paleobotanical data suggest range extensions of up to 1000 km for the primary tree species. These data also indicate that boreal communities of the last interglaciation were similar to modern ones in composition, but their distributions were displaced significantly to the north-northwest. Inferences about climate of this period suggest that mean July temperatures were warmer by 4 to 8°C, and seasonal precipitation was slightly greater. Mean January temperatures may have been severely cooler than today (up to 12°C) along the Arctic coast, but similar or slightly warmer than present in other areas. The direction and magnitude of change in July temperatures agree with Atmospheric General Circulation Models, but the 126,000-year-B.P. model results also suggest trends opposite to the paleobotanical data, with simulated cooler winter temperatures and drier conditions than present during the climatic optimum.

scholarly journals Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

2009 ◽  
Vol 1 (3) ◽  
pp. 577-605 ◽  
Author(s):  
Eija Honkavaara ◽  
Roman Arbiol ◽  
Lauri Markelin ◽  
Lucas Martinez ◽  
Michael Cramer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Quantitative Remote Sensing

Reconstructing the initial shape of volcanic islands to quantify long-term coastal erosion

2021 ◽  
Author(s):  
Rémi Bossis ◽  
Vincent Regard ◽  
Sébastien Carretier
Keyword(s):  
Coastal Erosion ◽  
Erosion Rate ◽  
Developed Countries ◽  
Global Scale ◽  
Radial Symmetry ◽  
Wave Direction ◽  
Initial Shape ◽  
Vertical Movements ◽  
Volcanic Islands

&lt;p&gt;The global solid flux from continent to ocean is usually reduced to the input of sediments from rivers, and is estimated at approximately 20 Gt/year. Another input of sediments to ocean is coastal erosion, but this flux is difficult to estimate on a global scale and it is often neglected, perhaps wrongly according to regional studies [1,2]. Most studies attempting to quantify coastal erosion have focused on the coasts of developed countries and are limited to the timescale of decades or less [3]. The difficulty in quantifying long-term coastal erosion is that there are still many uncertainties about the factors controlling coastal erosion on this time scale, and it would be necessary to know the initial geometry of coastlines to calculate an eroded volume.&lt;/p&gt;&lt;p&gt;Volcanic islands, as geomorphological objects, seem to be very good objects of study to remedy these limitations. Indeed, many young volcanic islands are made of only one central edifice with a strong radial symmetry despite its degradation by erosion [4,5]. By knowing the age of an island and by comparing reconstructed shape with current shape, we can calculate a total eroded volume and an integrated average coastal erosion rate on the age of the island. Moreover, due to their geographical, petrological and tectonic diversity, volcanic islands allow to compare the influence of different factors on long-term coastal erosion, such as climate, wave direction and height, rock resistance or vertical movements. Thus, we will be able to prioritize them to propose coastal erosion laws that would applicable to all rocky coasts.&lt;/p&gt;&lt;p&gt;Here we built on previous works that have used aerial geospatial databases to reconstruct the initial shape of these islands [6,7] but we improve this approach by using offshore topographic data to determine the maximum and initial extension of their coasts. From both onshore and offshore topographies, we determine a long-term mean coastal erosion rate and we quantify precisely its uncertainty. Using the example of Corvo Island, in the Azores archipelago, we show how our approach allows us to obtain first estimates of long-term coastal erosion rate around this island.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;[1] Landemaine V. (2016). Ph.D. thesis, University of Rouen.&lt;/p&gt;&lt;p&gt;[2] Rachold V., Grigoriev M.N., Are F.E., Solomon S., Reimnitz E., Kassens H., Antonow M. (2000). International Journal of Earth Sciences, 89(3), 450-460.&lt;/p&gt;&lt;p&gt;[3] Pr&amp;#233;maillon M. (2018). Ph.D. thesis, University of Toulouse.&lt;/p&gt;&lt;p&gt;[4] Kar&amp;#225;tson D., Favalli M., Tarquini S., Fornaciai A., W&amp;#246;rner G. (2010). Journal of Volcanology and Geothermal Research, 193, 171-181.&lt;/p&gt;&lt;p&gt;[5] Favalli M., Kar&amp;#225;tson D., Yepes J., NannipierI L. (2014). Geomorphology, 221, 139-149.&lt;/p&gt;&lt;p&gt;[6] Lahitte P., Samper A., Quidelleur X. (2012). Geomorphology, 136, 148-164.&lt;/p&gt;&lt;p&gt;[7] Kar&amp;#225;tson D., Yepes J., Favalli M., Rodr&amp;#237;guez-Peces M.J., Fornaciai A. (2016). Geomorphology, 253, 123-134.&lt;/p&gt;

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