Scaling waterbody carbon dioxide and methane fluxes in the arctic using an integrated terrestrial-aquatic approach

In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO2 and CH4 fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO2 emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km(2)) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001-0.1 km(2)) contributed the majority of carbon emissions from waterbodies. Waterbody CH4 emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO2 and CH4 emissions by 26%-79% and 8%-53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic.

Automated summary

In the Arctic, rapid thaw of permafrost is affecting the carbon cycle and hydrology in waterbodies. Accurate assessment and scaling of carbon emissions in Arctic ecosystems is crucial. New high-resolution remote sensing datasets allow for better understanding of the physical characteristics of Arctic landscapes. Machine learning models greatly improve the accuracy of scaling waterbody CO2 and CH4 fluxes, with waterbody size and contour being strong predictors for CO2 emissions and wetland landcover and related drivers, watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter being predictors for CH4 emissions. Traditional scaling methods without considering landscape characteristics overestimated CO2 and CH4 emissions compared to the machine learning approach.