Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the Yukon-Kuskokwim (YK) Delta. The YK Delta contains extensive surface waters (∼33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO₂ and CH₄ emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO₂ and CH₄ are also some of the most uncertain. We measured dissolved CH₄ and CO₂ concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. We used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. We develop a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O₂, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CH₄ and CO₂ concentrations across deltaic waterbodies. CO₂ concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH₄ concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. Our results illustrate the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies.

This landcover classification was created for the purposes of determining watershed landcover as potential drivers of downstream waterbody CH₄ and CO₂ concentrations. The region of interest is a watershed in the central portion of the Yukon-Kuskokwim Delta, Alaska, where field observations were based. The landcover map has been clipped to the watershed extent, and included as a shapefile.

We created a 10-m resolution landcover map for the region of interest to determine the presence and abundance of various terrestrial, wetland, surface waterbodies, and disturbed areas in sample watersheds. We used an unsupervised k-means algorithm (Google Earth Engine, “wekaKMeans”) with the surface reflectance raw bands, derived bands (NDWI, NDVI), slope, and elevation as inputs for the classification. The Alaska Interagency Coordination Center historical wildfire database was used for wildfire delineations. Wildfires in the region of interest included fire scars from the 1970s, 1990s, and early 2000s, collectively designated as “old fires,” and fire scars from the large area burned in 2015. First, the region of interest was divided into unburned, old fire scars, and 2015 fire scars, and the classification algorithm was run separately for each. We used an initial number of classes “k” higher than the number of known landcover types in order to capture the variability in the driving layers, then later grouped similar classes produced by the k-means algorithm.

Landcover accuracy was assessed using 350 randomly stratified points from the region of interest. The classifications at these points were compared to higher resolution (Worldview-2) imagery using Google Earth Engine and reclassified using expert assessment. We used a confusion matrix to assess the balanced accuracy of each classification, which ranged from 0.75 to 0.99 (Figure S2 in Supporting Information S1 from Ludwig et al. 2022 (the article associated with this dataset)).

The data type for the landcover map is integer, with 0 = no data (includes areas outside the research watershed and a small area where and old fire scar reburned in 2015 that could not be mapped),

1= lichen dominant tundra on peat plateaus, some graminoids and prostrate dwarf shrubs

2= degrading permafrost on peat plateaus (either sparse but productive wetland graminoid, exposed mud, or shallow water, depending on antecedent rainfall)

3= vegetated wetland (peat fens, often with small channels and adjacent sphagnum bog or mosses underlying graminoids), lower in elevation relative to peat plateaus

4= shrub tundra, often the 'banks' or edges of peat plateaus

5= tundra at the edges of degrading permafrost on peat plateaus, often wetter and more liekly to be dominated by sphagnum species and sphagnum fuscum

6= vegetated wetland (fens, but darker green, possibly tall shrubs. This was lumped with category 3 for most analyses), lower in elevation relative to peat plateaus.

7= waterbody edges; including unvegetated shoreline and areas with emergent vegetation and surface water

8= sedge tundra on peat plateaus (often the near the edges of plateaus or more sloped areas of plateaus)

9= low severity 2015 fire scar (peat plateaus). Includes small pockets of unburned within the fire scar.

10= 2015 fire scar tundra at plateau edges (similar to category 8 in unburned)

11= 2015 fire scar peat plateau

12= 2015 fire scar vegetated wetland (peat fens)

13= 2015 fire scar degrading permafrost (similar to category 2 unburned)

14= Old fire scar peat plateau edges (similar to category 8 unburned)

15= Old fire scar vegetated wetland (peat fens)

16= Old fire scar water edge (similar to category 7 unburned)

17= Old fire scar degrading permafrost (similar to category 2 unburned)

18= Old fire scar tundra peat plateau

19= Old fire scar tundra peat plateau, but more abundance of shrubs and graminoids

20= Waterbodies (includes those in unburned and all fire scars)