![]() ![]() ![]() (2016) estimates that ponds and lakes north of 50°N alone emit the same amount. Accounting additionally for ebullition Wik et al. Holgerson and Raymond (2016) estimate global waterbody methane emissions of 12 Tg CH 4 -C yr - 1 through diffusion only. Previous upscaling efforts of methane emissions from lakes and ponds differ considerably. A good grasp on the main mechanisms responsible for spatial variability is essential to improve upscaling and modeling of water methane emissions. In this study, we focus on identifying drivers of local methane concentration variability. The variability of methane emissions and concentrations is also higher in ponds than it is in lakes ( Juutinen et al., 2009 Laurion et al., 2010 Sepulveda-Jauregui et al., 2015 Holgerson and Raymond, 2016) and uncertainty remains as to what causes the variability. Thus, they are important contributors to the methane budget of the Arctic. Notably, ponds are the most common waterbody type in the Arctic ( Muster et al., 2017) and emit more methane (CH 4) per area than larger lakes ( Juutinen et al., 2009 Downing, 2010 Holgerson and Raymond, 2016 Wik et al., 2016). 10 4 m 2 ( Ramsar Convention Secretariat, 2016).Here, we follow the Ramsar classification scheme, scheme, which sets a comparably high limit of 8 Ponds are small waterbodies that are often defined by their area with varying thresholds. 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. Overall, our findings underpin the strong variability of methane concentrations in ponds. 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. The plants provide labile substrate to the methane-producing microbes. 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. In polygonal-center ponds, high methane surface concentrations are mostly determined by a small water depth. This causes surface concentrations to mainly depend on wind speed and on the amount of methane that has accumulated in the hypolimnion. We find that ice-wedge ponds feature a strong stratification due to consistently low bottom temperatures. All ponds are supersaturated in methane, but ice-wedge ponds exhibit the highest surface water 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. Additionally, we collected information on the key properties of the ponds to identify drivers of surface water methane concentrations. We collected water samples at different locations and depths in each pond and determined methane concentrations using gas chromatography. We studied 41 polygonal-tundra ponds in the Lena River Delta, northeast Siberia. 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 need to better understand this variability to improve upscaling estimates of freshwater methane emissions. However, methane concentrations in and methane emissions from ponds show more spatial variability than larger waterbodies. 10 4m 2, contribute out of proportion to the aquatic methane budget compared to the total area they cover and compared to other waterbodies.Thus, ponds, defined here as having an area smaller than 8 Waterbody methane emissions per area are negatively correlated with the size of the emitting waterbody. 5Institute of Soil Science, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany.4The Trofimuk Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.3Department of Geology and Geophysics, Novosibirsk State University, Novosibirsk, Russia.2International Max Planck Research School on Earth System Modeling, Hamburg, Germany.1Department of the Land in the Earth System, Max Planck Institute for Meteorology, Hamburg, Germany.Zoé Rehder 1,2 *, Anna Zaplavnova 3,4 and Lars Kutzbach 5 ![]()
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