Mapping of permafrost landscape dynamics

Land surface disturbances are an omnipresent feature of permafrost landscapes and they can be an indicator of permafrost degradation. Increasing average and maximum air temperatures in the Arctic are projected to put further pressure on the stability of permafrost. Lake changes,

6 - Discussion/Synthesis

118

retrogressive, thaw slumps and fire are some of the most important pulse disturbances in permafrost regions (Grosse, et al., 2011), which can be readily analyzed with remote sensing techniques.

6.2.1 Lake dynamics

I analyzed the distribution and dynamics of lakes over several large regions in Siberia and Alaska with a more local to-regional scale focus in the second research paper (Chapter 3) and a broader spatial scale in our third research paper (Chapter 4). The approach, based on a combination of the previously developed trend analysis (Chapter 2), machine learning and object-based analysis is novel and able to detect individual lakes larger than 1 ha and their changes with subpixel accuracy from 1999 through 2014 (Chapter 3). The focus on statistics of individual lake changes on Landsat-like spatial scale (30m) and large extent (>2.3 million km²) is novel and applied in only few studies (Chapter 4). Global products of dynamic lake extent based on Landsat data have been published recently (Pekel, Cottam, Gorelick, &

Belward, 2016; Donchyts, et al., 2016), but they locally have large inaccuracies, e.g. strong bias of wetting, in Arctic environments due to ice and snow cover and other data challenges.

There are many local scale analyses in usually lake rich-regions (Jones B. M., et al., 2011;

Riordan, Verbyla, & McGuire, 2006; Tarasenko, 2013; Lantz & Turner, 2015; Jones, et al., 2017), which capture local dynamics, but cannot take regional- or continental-scale landscape heterogeneity into account.

Lakes are a highly frequent feature in the northern high latitudes, where they have the highest abundance globally (Lehner & Döll, 2004; Pekel, Cottam, Gorelick, & Belward, 2016). Most of the lakes in permafrost regions are thermokarst lakes that actively degrade surrounding and underlying frozen ground (Grosse, Jones, & Arp, 2013). Within the analyzed regions their spatial distribution follows different patterns, which are dependent on several parameters, such as geology and geomorphology, permafrost extent and ground-ice content or glacial history (Kokelj & Jorgenson, 2013; Olefeldt, et al., 2016). Thermokarst lakes are most abundant in lowlands of sedimentary surface geology, such as coastal plains, river deltas or interior basins, where the land surface is sufficiently flat to develop lakes due to suppressed or absent water runoff. These general patterns became apparent within the lake analysis in this project, where very high lake densities (>20%) were predominantly located in ice-rich lowland permafrost. However, ice-poor sediments, which are less affected by thermokarst (Farquharson, Mann, Grosse, Jones, & Romanovsky, 2016) can still have very high lake

119

compared to the typical thermokarst cycle with multiple generations in ice-rich sediments (Jorgenson & Shur, 2007).

Lakes in permafrost have a wide range of dynamics from stagnation to quick growth or rapid drainage. Overall lake area loss outweighed lake area gain with strong spatial heterogeneity (Chapter 4). The magnitude and direction of lake changes and dynamics is dependent on a complex set of cryo-geological, geomorphological, climatic and spatial influencing factors. In discontinuous permafrost and in the transition zone from discontinuous to continuous permafrost frequent and widespread lake drainage has been observed in thermokarst lakes (Chapters 3 and 4). Degradation and destabilization of permafrost has been identified or proposed as a potentially important factor along the margins of continuous permafrost (Riordan, Verbyla, & McGuire, 2006; Jones B. M., et al., 2011; Smith L. C., Sheng, MacDonald, & Hinzman, 2005). Vertical connectivity to groundwater due to talik penetration (Yoshikawa & Hinzman, 2003) as well as lateral exchange following lake shore erosion, breaching, and overflow due to near-surface permafrost degradation (Jones B. M., et al., 2011) or tapping by rivers (Hinkel, et al., 2007), are among the most likely scenarios, which would explain the accelerated lake area loss (Grosse, Jones, & Arp, 2013). Within this transition zone, lake expansion occurs simultaneously with drainage in Alaska, whereas the transition zone in western Siberia (southern Yamal Peninsula) is only affected by drainage without significant lake expansion (Chapter 4). The difference in expansion rates indicates diverse underlying mechanisms of lake area loss in different regions, which could be a great scientific topic to target in further, local scale studies.

I found that other regions have a large diversity from net growth (e.g. Central Yakutia, Kobuk Delta or northern Kolyma Lowland) to loss (Northern Seward Peninsula, Yukon Flats, Yamal peninsula, northern Quebec). The spatial pattern is very diverse and does not follow simple relationships that could be linked to one single environmental factor. The studies in this thesis showed very localized regional dynamics, which related to geological and geomorphological heterogeneity. The strongest changes were recorded in Central Yakutia, within very ice-rich yedoma ice-complex sediments, where lake area expanded by nearly 50% (Chapter 3) within a very short period, from 2006 to 2008 caused by strong precipitation events and local agricultural practices (Boike, et al., 2016; Ulrich, et al., 2017). In contrast, large net lake area

6 - Discussion/Synthesis

120

loss occurred in several clusters on the Yamal Peninsula in western Siberia, also in ice-rich sediments.

Ground ice is generally claimed as an important driver of thermokarst initiation and lake dynamics (Grosse, Jones, & Arp, 2013; Olefeldt, et al., 2016). Lake change analysis results and the correlation to ground-ice distribution maps (Chapter 4) could not generally back up this proposed effect on a pan-arctic scale. However, local-scale analysis (Chapter 3), where the spatial diversity of large scale patterns (e.g. climate) are minimized and more accurate auxiliary site-specific datasets are available, revealed intensified lake dynamics in ice-rich sediments. With the spatial expansion of the analysis, more large-scale environmental factors (e.g. climate) potentially come into effect in addition to local-scale influencing factors.

6.2.2 Wildfire

Wildfires are caused by the availability of dry climatic conditions, sufficient fuel/biomass, and ignition mechanisms (Johnson E. A., 1996; Veraverbeke, et al., 2017). These conditions prevail in highly continental climates in eastern-central Siberia (8.15%) and a wide swath from interior Alaska (8.89%) to north-western Canada. More humid conditions, as in boreal western Siberia and eastern Canada, suppressed wildfires, where they affected a smaller area (2.43%; 5.06%) (Chapter 4). Most of the fires are limited to forested regions of the boreal zone, where many studies on wildfire have focused their efforts (Kasischke & Turetsky, 2006;

Stocks, et al., 1998; Yoshikawa, Bolton, Romanovsky, Fukuda, & Hinzman, 2002).

Due to the decreased amounts of dry biomass and less favorable climatic conditions tundra wildfires occurred very rarely and were restricted to Alaska. With the trend analysis and machine learning classification several tundra fires were detected affecting a total area of 4600 km² (out of 732,000 km² of tundra in the analyzed study area) (Chapter 4) including the Kougarok fire in western Alaska (Liljedahl A. , Hinzman, Busey, & Yoshikawa, 2007) and the severe Anaktuvuk fire in northern Alaska (Jones B. M., et al., 2015). With current technology tundra fires can be detected and monitored, e.g. with coarse resolution MODIS fire products (Giglio, Csiszar, & Justice, 2006), but past fire events may not have been recognized properly even in official fire databases (Jones, et al., 2013), which leaves a high uncertainty in estimates of tundra fire abundances. The multi-spectral trend analysis developed in this thesis (Chapter 2) allows detecting old burn scars from before the actual observation period, due to the trajectory of vegetation succession and the potential occurrence of thermokarst. However,

121

(Hu, et al., 2015) and the method developed here could be used to monitor and quantify these developments.

The influence of fire on permafrost is dependent on fire extent, burn intensity and local permafrost and ground conditions (Liljedahl A. , Hinzman, Busey, & Yoshikawa, 2007). With current monitoring techniques, fire extent in can be quantified with sufficient accuracy. Burn intensity in contrast is not sufficiently monitored over large spatial scales, which leaves large uncertainties about the consequences of wildfire on the ground thermal regime and subsurface carbon stocks and fluxes in permafrost.

6.2.3 Retrogressive Thaw Slumps

The occurrence of retrogressive thaw slumps (RTS) is bound to a narrow margin of environmental conditions. They are primarily limited to regions with very ice-rich permafrost conditions, topographic gradients and sufficiently cold conditions in the past to preserve the ground-ice (Chapter 4) (Kokelj S. V., Lantz, Tunnicliffe, Segal, & Lacelle, 2017). Ground ice in these regions may have different origins, including buried glacial ice or syngenetic ice wedge ice. In large regions along the margins of the former Laurentide ice sheet in northern and north-western Canada large amounts of massive buried glacial ice remains in the ground and is prone to melting upon disturbance (Kokelj S. V., Lantz, Tunnicliffe, Segal, & Lacelle, 2017).

The spatial analyses over several large regions from this thesis support prior findings (Kokelj S. V., Lantz, Tunnicliffe, Segal, & Lacelle, 2017) that RTS are generally located in ice-rich conditions and sloped terrain, where disturbances of the ground thermal regime may cause the ground ice to degrade or collapse. RTS typically occur in clusters, where favorable conditions for their initiation occur. These clusters, some more, some less extensively studied, were found in the vicinity of formerly glaciated mountain ranges, e.g. the northern Alaska Brooks Range (Balser, Jones, & Gens, 2014), or the Verkhoyansk mountain range in eastern Siberia, along former ice-sheet margins in northwestern Canada, e.g. Herschel Island and the Yukon Coast (Lantuit, et al., 2012; Obu, et al., 2017) or in very ice-rich yedoma ice-complex deposits in the Lena delta region (Lantuit, et al., 2011) and northern Alaska. The occurrence of a single RTS in a region, such as the large Selawik slump in western Alaska, is rare and an outlier from the typical clustered pattern.

6 - Discussion/Synthesis

122

The relation of RTS clusters to former glacial ice margins has great potential for applying this method for the reconstruction of past ice sheet and glacier extents in northern Siberia and northern Canada. Several clusters of RTS I identified in the vicinity of the Verkhoyansk mountain range (Chapter 4) are an indicator of former glaciations extent for a region where such information is very sparse so far and datasets of LGM extent (Ehlers & Gibbard, 2003) are potentially inaccurate.

Retrogressive thaw slumps and other thermo-erosion features such as active layer detachment slides or thermo-erosion gullies usually cover small spatial footprints ranging from few m² up to nearly one km² (Balser, Jones, & Gens, 2014; Kokelj S. V., Lantz, Tunnicliffe, Segal, &

Lacelle, 2017; Murton, et al., 2017). The majority of small features have an aerial extent below the detection limit of Landsat data, but larger features are observable. RTS-abundant regions can be outlined based on the developed analysis methods within the framework of this PhD thesis (Chapter 4). Analysis with high-resolution imagery and field work could provide detailed information and monitoring capabilities on appropriate spatial scales. As RTS are three-dimensional landscape features, high-resolution digital elevation models (DEM) derived from space-borne, air-borne or field-based instruments (LiDAR, RADAR, Stereo photogrammetry), will provide improved understanding and quantification of RTS on bio-geochemical cycles.