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Waterbody methane emissions per area are negatively correlated with the size of the emitting waterbody. Thus, ponds, defined here as having an area smaller than 8 · 10 4 m 2 , contribute out of proportion to the aquatic methane budget compared to the total area they cover and compared to other waterbodies. However, methane concentrations in and methane emissions from ponds show more spatial variability than larger waterbodies. We need to better understand this variability to improve upscaling estimates of freshwater methane emissions. In this regard, the Arctic permafrost landscape is an important region, which, besides carbon-rich soils, features a high pond density and is exposed to above-average climatic warming. We studied 41 polygonal-tundra ponds in the Lena River Delta, northeast Siberia. We collected water samples at different locations and depths in each pond and determined methane concentrations using gas chromatography. Additionally, we collected information on the key properties of the ponds to identify drivers of surface water methane concentrations. The ponds can be categorized into three geomorphological types with distinct differences in drivers of methane concentrations: polygonal-center ponds, ice-wedge ponds and larger merged polygonal ponds. All ponds are supersaturated in methane, but ice-wedge ponds exhibit the highest surface water concentrations. We find that ice-wedge ponds feature a strong stratification due to consistently low bottom temperatures. This causes surface concentrations to mainly depend on wind speed and on the amount of methane that has accumulated in the hypolimnion. In polygonal-center ponds, high methane surface concentrations are mostly determined by a small water depth. Apart from the influence of water depth on mixing speed, water depth controls the overgrown fraction, the fraction of the pond covered by vascular plants. The plants provide labile substrate to the methane-producing microbes. This link can also be seen in merged polygonal ponds, which furthermore show the strongest dependence on area as well as an anticorrelation to energy input indicating that stratification influences the surface water methane concentrations in larger ponds. Overall, our findings underpin the strong variability of methane concentrations in ponds. No single driver could explain a significant part of the variability over all pond types suggesting that more complex upscaling methods such as process-based modeling are needed.

Identifying Drivers Behind Spatial Variability of Methane Concentrations in East Siberian Ponds